diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml index 9faaaa1c20ad3..6973e64f742e6 100644 --- a/.github/workflows/api_doc_build.yml +++ b/.github/workflows/api_doc_build.yml @@ -73,6 +73,10 @@ jobs: with: repository: langchain-ai/langchain-unstructured path: langchain-unstructured + - uses: actions/checkout@v4 + with: + repository: langchain-ai/langchain-databricks + path: langchain-databricks - name: Set Git config @@ -97,7 +101,8 @@ jobs: langchain/libs/standard-tests \ langchain/libs/experimental \ langchain/libs/partners/milvus \ - langchain/libs/partners/unstructured + langchain/libs/partners/unstructured \ + langchain/libs/databricks mv langchain-google/libs/genai langchain/libs/partners/google-genai mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai mv langchain-google/libs/community langchain/libs/partners/google-community @@ -113,6 +118,7 @@ jobs: mv langchain-experimental/libs/experimental langchain/libs/experimental mv langchain-milvus/libs/milvus langchain/libs/partners/milvus mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured + mv langchain-databricks/libs/databricks langchain/libs/partners/databricks - name: Rm old html run: diff --git a/docs/docs/concepts.mdx b/docs/docs/concepts.mdx index 2f48f14297656..136433240b364 100644 --- a/docs/docs/concepts.mdx +++ b/docs/docs/concepts.mdx @@ -713,7 +713,7 @@ Callback handlers can either be `sync` or `async`: * Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface. * Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface. -During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered. +During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html)) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered. #### Passing callbacks diff --git a/docs/docs/how_to/custom_tools.ipynb b/docs/docs/how_to/custom_tools.ipynb index 3b9c5b1118df5..49d54908ff238 100644 --- a/docs/docs/how_to/custom_tools.ipynb +++ b/docs/docs/how_to/custom_tools.ipynb @@ -22,7 +22,7 @@ "2. LangChain [Runnables](/docs/concepts#runnable-interface);\n", "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n", "\n", - "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method.\n", + "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n", "\n", "In this guide we provide an overview of these methods.\n", "\n", diff --git a/docs/docs/integrations/chat/oci_data_science.ipynb b/docs/docs/integrations/chat/oci_data_science.ipynb new file mode 100644 index 0000000000000..fdc1d8cda4463 --- /dev/null +++ b/docs/docs/integrations/chat/oci_data_science.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "---\n", + "sidebar_label: ChatOCIModelDeployment\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ChatOCIModelDeployment\n", + "\n", + "This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html).\n", + "\n", + "[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. You can use [AI Quick Actions](https://blogs.oracle.com/ai-and-datascience/post/ai-quick-actions-in-oci-data-science) to easily deploy LLMs on [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). You may choose to deploy the model with popular inference frameworks such as vLLM or TGI. By default, the model deployment endpoint mimics the OpenAI API protocol.\n", + "\n", + "> For the latest updates, examples and experimental features, please see [ADS LangChain Integration](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/large_language_model/langchain_models.html).\n", + "\n", + "## Overview\n", + "### Integration details\n", + "\n", + "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n", + "| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n", + "| [ChatOCIModelDeployment](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n", + "\n", + "### Model features\n", + "\n", + "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n", + "| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n", + "| depends | depends | depends | depends | depends | depends | ✅ | ✅ | ✅ | ✅ | \n", + "\n", + "Some model features, including tool calling, structured output, JSON mode and multi-modal inputs, are depending on deployed model.\n", + "\n", + "\n", + "## Setup\n", + "\n", + "To use ChatOCIModelDeployment you'll need to deploy a chat model with chat completion endpoint and install the `langchain-community`, `langchain-openai` and `oracle-ads` integration packages.\n", + "\n", + "You can easily deploy foundation models using the [AI Quick Actions](https://github.com/oracle-samples/oci-data-science-ai-samples/blob/main/ai-quick-actions/model-deployment-tips.md) on OCI Data Science Model deployment. For additional deployment examples, please visit the [Oracle GitHub samples repository](https://github.com/oracle-samples/oci-data-science-ai-samples/tree/main/ai-quick-actions).\n", + "\n", + "### Policies\n", + "Make sure to have the required [policies](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint) to access the OCI Data Science Model Deployment endpoint.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Credentials\n", + "\n", + "You can set authentication through Oracle ADS. When you are working in OCI Data Science Notebook Session, you can leverage resource principal to access other OCI resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ads\n", + "\n", + "# Set authentication through ads\n", + "# Use resource principal are operating within a\n", + "# OCI service that has resource principal based\n", + "# authentication configured\n", + "ads.set_auth(\"resource_principal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can configure the credentials using the following environment variables. For example, to use API key with specific profile:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Set authentication through environment variables\n", + "# Use API Key setup when you are working from a local\n", + "# workstation or on platform which does not support\n", + "# resource principals.\n", + "os.environ[\"OCI_IAM_TYPE\"] = \"api_key\"\n", + "os.environ[\"OCI_CONFIG_PROFILE\"] = \"default\"\n", + "os.environ[\"OCI_CONFIG_LOCATION\"] = \"~/.oci\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check out [Oracle ADS docs](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) to see more options." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation\n", + "\n", + "The LangChain OCIModelDeployment integration lives in the `langchain-community` package. The following command will install `langchain-community` and the required dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-community langchain-openai oracle-ads" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instantiation\n", + "\n", + "You may instantiate the model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.\n", + "\n", + "* Using `ChatOCIModelDeployment` when you need a generic entry point for deploying models. You can pass model parameters through `model_kwargs` during the instantiation of this class. This allows for flexibility and ease of configuration without needing to rely on framework-specific details." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_models import ChatOCIModelDeployment\n", + "\n", + "# Create an instance of OCI Model Deployment Endpoint\n", + "# Replace the endpoint uri with your own\n", + "# Using generic class as entry point, you will be able\n", + "# to pass model parameters through model_kwargs during\n", + "# instantiation.\n", + "chat = ChatOCIModelDeployment(\n", + " endpoint=\"https://modeldeployment..oci.customer-oci.com//predict\",\n", + " streaming=True,\n", + " max_retries=1,\n", + " model_kwargs={\n", + " \"temperature\": 0.2,\n", + " \"max_tokens\": 512,\n", + " }, # other model params...\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Using framework specific class like `ChatOCIModelDeploymentVLLM`: This is suitable when you are working with a specific framework (e.g. `vLLM`) and need to pass model parameters directly through the constructor, streamlining the setup process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_models import ChatOCIModelDeploymentVLLM\n", + "\n", + "# Create an instance of OCI Model Deployment Endpoint\n", + "# Replace the endpoint uri with your own\n", + "# Using framework specific class as entry point, you will\n", + "# be able to pass model parameters in constructor.\n", + "chat = ChatOCIModelDeploymentVLLM(\n", + " endpoint=\"https://modeldeployment..oci.customer-oci.com//predict\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Invocation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content=\"J'adore programmer.\", response_metadata={'token_usage': {'prompt_tokens': 44, 'total_tokens': 52, 'completion_tokens': 8}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-ca145168-efa9-414c-9dd1-21d10766fdd3-0')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages = [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n", + " ),\n", + " (\"human\", \"I love programming.\"),\n", + "]\n", + "\n", + "ai_msg = chat.invoke(messages)\n", + "ai_msg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "J'adore programmer.\n" + ] + } + ], + "source": [ + "print(ai_msg.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chaining" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Ich liebe Programmierung.', response_metadata={'token_usage': {'prompt_tokens': 38, 'total_tokens': 48, 'completion_tokens': 10}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-5dd936b0-b97e-490e-9869-2ad3dd524234-0')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n", + " ),\n", + " (\"human\", \"{input}\"),\n", + " ]\n", + ")\n", + "\n", + "chain = prompt | chat\n", + "chain.invoke(\n", + " {\n", + " \"input_language\": \"English\",\n", + " \"output_language\": \"German\",\n", + " \"input\": \"I love programming.\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Asynchronous calls" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='我喜欢编程', response_metadata={'token_usage': {'prompt_tokens': 37, 'total_tokens': 50, 'completion_tokens': 13}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-a2dc9393-f269-41a4-b908-b1d8a92cf827-0')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_community.chat_models import ChatOCIModelDeployment\n", + "\n", + "system = \"You are a helpful translator that translates {input_language} to {output_language}.\"\n", + "human = \"{text}\"\n", + "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", + "\n", + "chat = ChatOCIModelDeployment(\n", + " endpoint=\"https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict\"\n", + ")\n", + "chain = prompt | chat\n", + "\n", + "await chain.ainvoke(\n", + " {\n", + " \"input_language\": \"English\",\n", + " \"output_language\": \"Chinese\",\n", + " \"text\": \"I love programming\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming calls" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "1. California\n", + "2. Texas\n", + "3. Florida\n", + "4. New York\n", + "5. Illinois" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "\n", + "from langchain_community.chat_models import ChatOCIModelDeployment\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [(\"human\", \"List out the 5 states in the United State.\")]\n", + ")\n", + "\n", + "chat = ChatOCIModelDeployment(\n", + " endpoint=\"https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict\"\n", + ")\n", + "\n", + "chain = prompt | chat\n", + "\n", + "for chunk in chain.stream({}):\n", + " sys.stdout.write(chunk.content)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Structured output" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'setup': 'Why did the cat get stuck in the tree?',\n", + " 'punchline': 'Because it was chasing its tail!'}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_community.chat_models import ChatOCIModelDeployment\n", + "from pydantic import BaseModel\n", + "\n", + "\n", + "class Joke(BaseModel):\n", + " \"\"\"A setup to a joke and the punchline.\"\"\"\n", + "\n", + " setup: str\n", + " punchline: str\n", + "\n", + "\n", + "chat = ChatOCIModelDeployment(\n", + " endpoint=\"https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict\",\n", + ")\n", + "structured_llm = chat.with_structured_output(Joke, method=\"json_mode\")\n", + "output = structured_llm.invoke(\n", + " \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n", + ")\n", + "\n", + "output.dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "For comprehensive details on all features and configurations, please refer to the API reference documentation for each class:\n", + "\n", + "* [ChatOCIModelDeployment](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeployment.html)\n", + "* [ChatOCIModelDeploymentVLLM](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeploymentVLLM.html)\n", + "* [ChatOCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeploymentTGI.html)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langchain", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/integrations/chat/sambastudio.ipynb b/docs/docs/integrations/chat/sambastudio.ipynb index 9301d68fe33df..3c7d029ab80ce 100644 --- a/docs/docs/integrations/chat/sambastudio.ipynb +++ b/docs/docs/integrations/chat/sambastudio.ipynb @@ -19,7 +19,7 @@ "source": [ "# ChatSambaStudio\n", "\n", - "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n", + "This will help you getting started with SambaStudio [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n", "\n", "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n", "\n", diff --git a/docs/docs/integrations/document_loaders/firecrawl.ipynb b/docs/docs/integrations/document_loaders/firecrawl.ipynb index 4abf280da8f92..a6ec4ff418b01 100644 --- a/docs/docs/integrations/document_loaders/firecrawl.ipynb +++ b/docs/docs/integrations/document_loaders/firecrawl.ipynb @@ -26,33 +26,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setup\n", - "\n", - "### Credentials \n", - "\n", - "You will need to get your own API key. Go to [this page](https://firecrawl.dev) to learn more." + "## Setup" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": null, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "import getpass\n", - "import os\n", - "\n", - "if \"FIRECRAWL_API_KEY\" not in os.environ:\n", - " os.environ[\"FIRECRAWL_API_KEY\"] = getpass.getpass(\"Enter your Firecrawl API key: \")" + "pip install firecrawl-py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Installation\n", - "\n", - "You will need to install both the `langchain_community` and `firecrawl-py` pacakges:" + "## Usage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will need to get your own API key. See https://firecrawl.dev" ] }, { @@ -61,151 +60,161 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install -qU firecrawl-py==0.0.20 langchain_community" + "from langchain_community.document_loaders.firecrawl import FireCrawlLoader" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Document(metadata={'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}, page_content='Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\\n Join the waitlist to turn any website into an API with AI\\n\\n\\n\\n[🔥 Firecrawl](/)\\n\\n* [Playground](/playground)\\n \\n* [Docs](https://docs.firecrawl.dev)\\n \\n* [Pricing](/pricing)\\n \\n* [Blog](/blog)\\n \\n* Beta Features\\n\\n[Log In](/signin)\\n[Log In](/signin)\\n[Sign Up](/signin/signup)\\n 8.9k\\n\\n[💥 Get 2 months free with yearly plan](/pricing)\\n\\nTurn websites into \\n_LLM-ready_ data\\n=====================================\\n\\nPower your AI apps with clean data crawled from any website. It\\'s also open-source.\\n\\nStart for free (500 credits)Start for free[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nA product by\\n\\n[![Mendable Logo](https://www.firecrawl.dev/images/mendable_logo_transparent.png)Mendable](https://mendable.ai)\\n\\n![Example Webpage](https://www.firecrawl.dev/multiple-websites.png)\\n\\nCrawl, Scrape, Clean\\n--------------------\\n\\nWe crawl all accessible subpages and give you clean markdown for each. No sitemap required.\\n\\n \\n [\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/\",\\\\\\n \"markdown\": \"## Welcome to Firecrawl\\\\\\n Firecrawl is a web scraper that allows you to extract the content of a webpage.\"\\\\\\n },\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/features\",\\\\\\n \"markdown\": \"## Features\\\\\\n Discover how Firecrawl\\'s cutting-edge features can \\\\\\n transform your data operations.\"\\\\\\n },\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/pricing\",\\\\\\n \"markdown\": \"## Pricing Plans\\\\\\n Choose the perfect plan that fits your needs.\"\\\\\\n },\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/about\",\\\\\\n \"markdown\": \"## About Us\\\\\\n Learn more about Firecrawl\\'s mission and the \\\\\\n team behind our innovative platform.\"\\\\\\n }\\\\\\n ]\\n \\n\\nNote: The markdown has been edited for display purposes.\\n\\nTrusted by Top Companies\\n------------------------\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/zapier.png)](https://www.zapier.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/gamma.svg)](https://gamma.app)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nvidia-com.png)](https://www.nvidia.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/teller-io.svg)](https://www.teller.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/stackai.svg)](https://www.stack-ai.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/palladiumdigital-co-uk.svg)](https://www.palladiumdigital.co.uk)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/worldwide-casting-com.svg)](https://www.worldwide-casting.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/open-gov-sg.png)](https://www.open.gov.sg)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/bain-com.svg)](https://www.bain.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/demand-io.svg)](https://www.demand.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nocodegarden-io.png)](https://www.nocodegarden.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/cyberagent-co-jp.svg)](https://www.cyberagent.co.jp)\\n\\nIntegrate today\\n---------------\\n\\nEnhance your applications with top-tier web scraping and crawling capabilities.\\n\\n#### Scrape\\n\\nExtract markdown or structured data from websites quickly and efficiently.\\n\\n#### Crawling\\n\\nNavigate and retrieve data from all accessible subpages, even without a sitemap.\\n\\nNode.js\\n\\nPython\\n\\ncURL\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\n9\\n\\n10\\n\\n// npm install @mendable/firecrawl-js \\n \\nimport FirecrawlApp from \\'@mendable/firecrawl-js\\'; \\n \\nconst app \\\\= new FirecrawlApp({ apiKey: \"fc-YOUR\\\\_API\\\\_KEY\" }); \\n \\n// Scrape a website: \\nconst scrapeResult \\\\= await app.scrapeUrl(\\'firecrawl.dev\\'); \\n \\nconsole.log(scrapeResult.data.markdown)\\n\\n#### Use well-known tools\\n\\nAlready fully integrated with the greatest existing tools and workflows.\\n\\n[![LlamaIndex](https://www.firecrawl.dev/logos/llamaindex.svg)](https://docs.llamaindex.ai/en/stable/examples/data_connectors/WebPageDemo/#using-firecrawl-reader/)\\n[![Langchain](https://www.firecrawl.dev/integrations/langchain.png)](https://python.langchain.com/docs/integrations/document_loaders/firecrawl/)\\n[![Dify](https://www.firecrawl.dev/logos/dify.png)](https://dify.ai/blog/dify-ai-blog-integrated-with-firecrawl/)\\n[![Dify](https://www.firecrawl.dev/integrations/langflow_2.png)](https://www.langflow.org/)\\n[![Flowise](https://www.firecrawl.dev/integrations/flowise.png)](https://flowiseai.com/)\\n[![CrewAI](https://www.firecrawl.dev/integrations/crewai.png)](https://crewai.com/)\\n\\n#### Start for free, scale easily\\n\\nKick off your journey for free and scale seamlessly as your project expands.\\n\\n[Try it out](/signin/signup)\\n\\n#### Open-source\\n\\nDeveloped transparently and collaboratively. Join our community of contributors.\\n\\n[Check out our repo](https://github.com/mendableai/firecrawl)\\n\\nWe handle the hard stuff\\n------------------------\\n\\nRotating proxies, caching, rate limits, js-blocked content and more\\n\\n#### Crawling\\n\\nFirecrawl crawls all accessible subpages, even without a sitemap.\\n\\n#### Dynamic content\\n\\nFirecrawl gathers data even if a website uses javascript to render content.\\n\\n#### To Markdown\\n\\nFirecrawl returns clean, well formatted markdown - ready for use in LLM applications\\n\\n#### Crawling Orchestration\\n\\nFirecrawl orchestrates the crawling process in parallel for the fastest results.\\n\\n#### Caching\\n\\nFirecrawl caches content, so you don\\'t have to wait for a full scrape unless new content exists.\\n\\n#### Built for AI\\n\\nBuilt by LLM engineers, for LLM engineers. Giving you clean data the way you want it.\\n\\nOur wall of love\\n\\nDon\\'t take our word for it\\n--------------------------\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\nFlexible Pricing\\n----------------\\n\\nStart for free, then scale as you grow\\n\\nYearly (17% off)Yearly (2 months free)Monthly\\n\\nFree Plan\\n---------\\n\\n500 credits\\n\\n$0/month\\n\\n* Scrape 500 pages\\n* 5 /scrape per min\\n* 1 /crawl per min\\n\\nGet Started\\n\\nHobby\\n-----\\n\\n3,000 credits\\n\\n$16/month\\n\\n* Scrape 3,000 pages\\n* 10 /scrape per min\\n* 3 /crawl per min\\n\\nSubscribe\\n\\nStandardMost Popular\\n--------------------\\n\\n100,000 credits\\n\\n$83/month\\n\\n* Scrape 100,000 pages\\n* 50 /scrape per min\\n* 10 /crawl per min\\n\\nSubscribe\\n\\nGrowth\\n------\\n\\n500,000 credits\\n\\n$333/month\\n\\n* Scrape 500,000 pages\\n* 500 /scrape per min\\n* 50 /crawl per min\\n* Priority Support\\n\\nSubscribe\\n\\nEnterprise Plan\\n---------------\\n\\nUnlimited credits. Custom RPMs.\\n\\nTalk to us\\n\\n* Top priority support\\n* Feature Acceleration\\n* SLAs\\n* Account Manager\\n* Custom rate limits volume\\n* Custom concurrency limits\\n* Beta features access\\n* CEO\\'s number\\n\\n\\\\* a /scrape refers to the [scrape](https://docs.firecrawl.dev/api-reference/endpoint/scrape)\\n API endpoint.\\n\\n\\\\* a /crawl refers to the [crawl](https://docs.firecrawl.dev/api-reference/endpoint/crawl)\\n API endpoint.\\n\\nScrape Credits\\n--------------\\n\\nScrape credits are consumed for each API request, varying by endpoint and feature.\\n\\n| Features | Credits per page |\\n| --- | --- |\\n| Scrape(/scrape) | 1 |\\n| Crawl(/crawl) | 1 |\\n| Search(/search) | 1 |\\n| Scrape + LLM extraction (/scrape) | 50 |\\n\\n[🔥](/)\\n\\nReady to _Build?_\\n-----------------\\n\\nStart scraping web data for your AI apps today. \\nNo credit card needed.\\n\\n[Get Started](/signin)\\n\\n[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nFAQ\\n---\\n\\nFrequently asked questions about Firecrawl\\n\\n#### General\\n\\nWhat is Firecrawl?\\n\\nFirecrawl turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Ideal for AI companies looking to empower their LLM applications with web data.\\n\\nWhat sites work?\\n\\nFirecrawl is best suited for business websites, docs and help centers. We currently don\\'t support social media platforms.\\n\\nWho can benefit from using Firecrawl?\\n\\nFirecrawl is tailored for LLM engineers, data scientists, AI researchers, and developers looking to harness web data for training machine learning models, market research, content aggregation, and more. It simplifies the data preparation process, allowing professionals to focus on insights and model development.\\n\\nIs Firecrawl open-source?\\n\\nYes, it is. You can check out the repository on GitHub. Keep in mind that this repository is currently in its early stages of development. We are in the process of merging custom modules into this mono repository.\\n\\n#### Scraping & Crawling\\n\\nHow does Firecrawl handle dynamic content on websites?\\n\\nUnlike traditional web scrapers, Firecrawl is equipped to handle dynamic content rendered with JavaScript. It ensures comprehensive data collection from all accessible subpages, making it a reliable tool for scraping websites that rely heavily on JS for content delivery.\\n\\nWhy is it not crawling all the pages?\\n\\nThere are a few reasons why Firecrawl may not be able to crawl all the pages of a website. Some common reasons include rate limiting, and anti-scraping mechanisms, disallowing the crawler from accessing certain pages. If you\\'re experiencing issues with the crawler, please reach out to our support team at hello@firecrawl.com.\\n\\nCan Firecrawl crawl websites without a sitemap?\\n\\nYes, Firecrawl can access and crawl all accessible subpages of a website, even in the absence of a sitemap. This feature enables users to gather data from a wide array of web sources with minimal setup.\\n\\nWhat formats can Firecrawl convert web data into?\\n\\nFirecrawl specializes in converting web data into clean, well-formatted markdown. This format is particularly suited for LLM applications, offering a structured yet flexible way to represent web content.\\n\\nHow does Firecrawl ensure the cleanliness of the data?\\n\\nFirecrawl employs advanced algorithms to clean and structure the scraped data, removing unnecessary elements and formatting the content into readable markdown. This process ensures that the data is ready for use in LLM applications without further preprocessing.\\n\\nIs Firecrawl suitable for large-scale data scraping projects?\\n\\nAbsolutely. Firecrawl offers various pricing plans, including a Scale plan that supports scraping of millions of pages. With features like caching and scheduled syncs, it\\'s designed to efficiently handle large-scale data scraping and continuous updates, making it ideal for enterprises and large projects.\\n\\nDoes it respect robots.txt?\\n\\nYes, Firecrawl crawler respects the rules set in a website\\'s robots.txt file. If you notice any issues with the way Firecrawl interacts with your website, you can adjust the robots.txt file to control the crawler\\'s behavior. Firecrawl user agent name is \\'FirecrawlAgent\\'. If you notice any behavior that is not expected, please let us know at hello@firecrawl.com.\\n\\nWhat measures does Firecrawl take to handle web scraping challenges like rate limits and caching?\\n\\nFirecrawl is built to navigate common web scraping challenges, including reverse proxies, rate limits, and caching. It smartly manages requests and employs caching techniques to minimize bandwidth usage and avoid triggering anti-scraping mechanisms, ensuring reliable data collection.\\n\\nDoes Firecrawl handle captcha or authentication?\\n\\nFirecrawl avoids captcha by using stealth proxyies. When it encounters captcha, it attempts to solve it automatically, but this is not always possible. We are working to add support for more captcha solving methods. Firecrawl can handle authentication by providing auth headers to the API.\\n\\n#### API Related\\n\\nWhere can I find my API key?\\n\\nClick on the dashboard button on the top navigation menu when logged in and you will find your API key in the main screen and under API Keys.\\n\\n#### Billing\\n\\nIs Firecrawl free?\\n\\nFirecrawl is free for the first 500 scraped pages (500 free credits). After that, you can upgrade to our Standard or Scale plans for more credits.\\n\\nIs there a pay per use plan instead of monthly?\\n\\nNo we do not currently offer a pay per use plan, instead you can upgrade to our Standard or Growth plans for more credits and higher rate limits.\\n\\nHow many credit does scraping, crawling, and extraction cost?\\n\\nScraping costs 1 credit per page. Crawling costs 1 credit per page.\\n\\nDo you charge for failed requests (scrape, crawl, extract)?\\n\\nWe do not charge for any failed requests (scrape, crawl, extract). Please contact support at help@firecrawl.dev if you have any questions.\\n\\nWhat payment methods do you accept?\\n\\nWe accept payments through Stripe which accepts most major credit cards, debit cards, and PayPal.\\n\\n[🔥](/)\\n\\n© A product by Mendable.ai - All rights reserved.\\n\\n[StatusStatus](https://firecrawl.betteruptime.com)\\n[Terms of ServiceTerms of Service](/terms-of-service)\\n[Privacy PolicyPrivacy Policy](/privacy-policy)\\n\\n[Twitter](https://twitter.com/mendableai)\\n[GitHub](https://github.com/mendableai)\\n[Discord](https://discord.gg/gSmWdAkdwd)\\n\\n###### Helpful Links\\n\\n* [Status](https://firecrawl.betteruptime.com/)\\n \\n* [Pricing](/pricing)\\n \\n* [Blog](https://www.firecrawl.dev/blog)\\n \\n* [Docs](https://docs.firecrawl.dev)\\n \\n\\nBacked by![Y Combinator Logo](https://www.firecrawl.dev/images/yc.svg)\\n\\n![SOC 2 Type II](https://www.firecrawl.dev/soc2type2badge.png)\\n\\n###### Resources\\n\\n* [Community](#0)\\n \\n* [Terms of service](#0)\\n \\n* [Collaboration features](#0)\\n \\n\\n###### Legals\\n\\n* [Refund policy](#0)\\n \\n* [Terms & Conditions](#0)\\n \\n* [Privacy policy](#0)\\n \\n* [Brand Kit](#0)')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Initialization\n", - "\n", - "### Modes\n", + "loader = FireCrawlLoader(\n", + " api_key=\"YOUR_API_KEY\", url=\"https://firecrawl.dev\", mode=\"scrape\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pages = []\n", + "for doc in loader.lazy_load():\n", + " pages.append(doc)\n", + " if len(pages) >= 10:\n", + " # do some paged operation, e.g.\n", + " # index.upsert(page)\n", "\n", - "- `scrape`: Scrape single url and return the markdown.\n", - "- `crawl`: Crawl the url and all accessible sub pages and return the markdown for each one." + " pages = []" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from langchain_community.document_loaders import FireCrawlLoader\n", + "pages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modes\n", "\n", - "loader = FireCrawlLoader(url=\"https://firecrawl.dev\", mode=\"crawl\")" + "- `scrape`: Scrape single url and return the markdown.\n", + "- `crawl`: Crawl the url and all accessible sub pages and return the markdown for each one.\n", + "- `map`: Maps the URL and returns a list of semantically related pages." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load" + "### Crawl\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Document(metadata={'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}, page_content='Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\\n Join the waitlist to turn any website into an API with AI\\n\\n\\n\\n[🔥 Firecrawl](/)\\n\\n* [Playground](/playground)\\n \\n* [Docs](https://docs.firecrawl.dev)\\n \\n* [Pricing](/pricing)\\n \\n* [Blog](/blog)\\n \\n* Beta Features\\n\\n[Log In](/signin)\\n[Log In](/signin)\\n[Sign Up](/signin/signup)\\n 8.9k\\n\\n[💥 Get 2 months free with yearly plan](/pricing)\\n\\nTurn websites into \\n_LLM-ready_ data\\n=====================================\\n\\nPower your AI apps with clean data crawled from any website. It\\'s also open-source.\\n\\nStart for free (500 credits)Start for free[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nA product by\\n\\n[![Mendable Logo](https://www.firecrawl.dev/images/mendable_logo_transparent.png)Mendable](https://mendable.ai)\\n\\n![Example Webpage](https://www.firecrawl.dev/multiple-websites.png)\\n\\nCrawl, Scrape, Clean\\n--------------------\\n\\nWe crawl all accessible subpages and give you clean markdown for each. No sitemap required.\\n\\n \\n [\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/\",\\\\\\n \"markdown\": \"## Welcome to Firecrawl\\\\\\n Firecrawl is a web scraper that allows you to extract the content of a webpage.\"\\\\\\n },\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/features\",\\\\\\n \"markdown\": \"## Features\\\\\\n Discover how Firecrawl\\'s cutting-edge features can \\\\\\n transform your data operations.\"\\\\\\n },\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/pricing\",\\\\\\n \"markdown\": \"## Pricing Plans\\\\\\n Choose the perfect plan that fits your needs.\"\\\\\\n },\\\\\\n {\\\\\\n \"url\": \"https://www.firecrawl.dev/about\",\\\\\\n \"markdown\": \"## About Us\\\\\\n Learn more about Firecrawl\\'s mission and the \\\\\\n team behind our innovative platform.\"\\\\\\n }\\\\\\n ]\\n \\n\\nNote: The markdown has been edited for display purposes.\\n\\nTrusted by Top Companies\\n------------------------\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/zapier.png)](https://www.zapier.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/gamma.svg)](https://gamma.app)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nvidia-com.png)](https://www.nvidia.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/teller-io.svg)](https://www.teller.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/stackai.svg)](https://www.stack-ai.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/palladiumdigital-co-uk.svg)](https://www.palladiumdigital.co.uk)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/worldwide-casting-com.svg)](https://www.worldwide-casting.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/open-gov-sg.png)](https://www.open.gov.sg)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/bain-com.svg)](https://www.bain.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/demand-io.svg)](https://www.demand.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nocodegarden-io.png)](https://www.nocodegarden.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/cyberagent-co-jp.svg)](https://www.cyberagent.co.jp)\\n\\nIntegrate today\\n---------------\\n\\nEnhance your applications with top-tier web scraping and crawling capabilities.\\n\\n#### Scrape\\n\\nExtract markdown or structured data from websites quickly and efficiently.\\n\\n#### Crawling\\n\\nNavigate and retrieve data from all accessible subpages, even without a sitemap.\\n\\nNode.js\\n\\nPython\\n\\ncURL\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\n9\\n\\n10\\n\\n// npm install @mendable/firecrawl-js \\n \\nimport FirecrawlApp from \\'@mendable/firecrawl-js\\'; \\n \\nconst app \\\\= new FirecrawlApp({ apiKey: \"fc-YOUR\\\\_API\\\\_KEY\" }); \\n \\n// Scrape a website: \\nconst scrapeResult \\\\= await app.scrapeUrl(\\'firecrawl.dev\\'); \\n \\nconsole.log(scrapeResult.data.markdown)\\n\\n#### Use well-known tools\\n\\nAlready fully integrated with the greatest existing tools and workflows.\\n\\n[![LlamaIndex](https://www.firecrawl.dev/logos/llamaindex.svg)](https://docs.llamaindex.ai/en/stable/examples/data_connectors/WebPageDemo/#using-firecrawl-reader/)\\n[![Langchain](https://www.firecrawl.dev/integrations/langchain.png)](https://python.langchain.com/docs/integrations/document_loaders/firecrawl/)\\n[![Dify](https://www.firecrawl.dev/logos/dify.png)](https://dify.ai/blog/dify-ai-blog-integrated-with-firecrawl/)\\n[![Dify](https://www.firecrawl.dev/integrations/langflow_2.png)](https://www.langflow.org/)\\n[![Flowise](https://www.firecrawl.dev/integrations/flowise.png)](https://flowiseai.com/)\\n[![CrewAI](https://www.firecrawl.dev/integrations/crewai.png)](https://crewai.com/)\\n\\n#### Start for free, scale easily\\n\\nKick off your journey for free and scale seamlessly as your project expands.\\n\\n[Try it out](/signin/signup)\\n\\n#### Open-source\\n\\nDeveloped transparently and collaboratively. Join our community of contributors.\\n\\n[Check out our repo](https://github.com/mendableai/firecrawl)\\n\\nWe handle the hard stuff\\n------------------------\\n\\nRotating proxies, caching, rate limits, js-blocked content and more\\n\\n#### Crawling\\n\\nFirecrawl crawls all accessible subpages, even without a sitemap.\\n\\n#### Dynamic content\\n\\nFirecrawl gathers data even if a website uses javascript to render content.\\n\\n#### To Markdown\\n\\nFirecrawl returns clean, well formatted markdown - ready for use in LLM applications\\n\\n#### Crawling Orchestration\\n\\nFirecrawl orchestrates the crawling process in parallel for the fastest results.\\n\\n#### Caching\\n\\nFirecrawl caches content, so you don\\'t have to wait for a full scrape unless new content exists.\\n\\n#### Built for AI\\n\\nBuilt by LLM engineers, for LLM engineers. Giving you clean data the way you want it.\\n\\nOur wall of love\\n\\nDon\\'t take our word for it\\n--------------------------\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\nFlexible Pricing\\n----------------\\n\\nStart for free, then scale as you grow\\n\\nYearly (17% off)Yearly (2 months free)Monthly\\n\\nFree Plan\\n---------\\n\\n500 credits\\n\\n$0/month\\n\\n* Scrape 500 pages\\n* 5 /scrape per min\\n* 1 /crawl per min\\n\\nGet Started\\n\\nHobby\\n-----\\n\\n3,000 credits\\n\\n$16/month\\n\\n* Scrape 3,000 pages\\n* 10 /scrape per min\\n* 3 /crawl per min\\n\\nSubscribe\\n\\nStandardMost Popular\\n--------------------\\n\\n100,000 credits\\n\\n$83/month\\n\\n* Scrape 100,000 pages\\n* 50 /scrape per min\\n* 10 /crawl per min\\n\\nSubscribe\\n\\nGrowth\\n------\\n\\n500,000 credits\\n\\n$333/month\\n\\n* Scrape 500,000 pages\\n* 500 /scrape per min\\n* 50 /crawl per min\\n* Priority Support\\n\\nSubscribe\\n\\nEnterprise Plan\\n---------------\\n\\nUnlimited credits. Custom RPMs.\\n\\nTalk to us\\n\\n* Top priority support\\n* Feature Acceleration\\n* SLAs\\n* Account Manager\\n* Custom rate limits volume\\n* Custom concurrency limits\\n* Beta features access\\n* CEO\\'s number\\n\\n\\\\* a /scrape refers to the [scrape](https://docs.firecrawl.dev/api-reference/endpoint/scrape)\\n API endpoint.\\n\\n\\\\* a /crawl refers to the [crawl](https://docs.firecrawl.dev/api-reference/endpoint/crawl)\\n API endpoint.\\n\\nScrape Credits\\n--------------\\n\\nScrape credits are consumed for each API request, varying by endpoint and feature.\\n\\n| Features | Credits per page |\\n| --- | --- |\\n| Scrape(/scrape) | 1 |\\n| Crawl(/crawl) | 1 |\\n| Search(/search) | 1 |\\n| Scrape + LLM extraction (/scrape) | 50 |\\n\\n[🔥](/)\\n\\nReady to _Build?_\\n-----------------\\n\\nStart scraping web data for your AI apps today. \\nNo credit card needed.\\n\\n[Get Started](/signin)\\n\\n[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nFAQ\\n---\\n\\nFrequently asked questions about Firecrawl\\n\\n#### General\\n\\nWhat is Firecrawl?\\n\\nFirecrawl turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Ideal for AI companies looking to empower their LLM applications with web data.\\n\\nWhat sites work?\\n\\nFirecrawl is best suited for business websites, docs and help centers. We currently don\\'t support social media platforms.\\n\\nWho can benefit from using Firecrawl?\\n\\nFirecrawl is tailored for LLM engineers, data scientists, AI researchers, and developers looking to harness web data for training machine learning models, market research, content aggregation, and more. It simplifies the data preparation process, allowing professionals to focus on insights and model development.\\n\\nIs Firecrawl open-source?\\n\\nYes, it is. You can check out the repository on GitHub. Keep in mind that this repository is currently in its early stages of development. We are in the process of merging custom modules into this mono repository.\\n\\n#### Scraping & Crawling\\n\\nHow does Firecrawl handle dynamic content on websites?\\n\\nUnlike traditional web scrapers, Firecrawl is equipped to handle dynamic content rendered with JavaScript. It ensures comprehensive data collection from all accessible subpages, making it a reliable tool for scraping websites that rely heavily on JS for content delivery.\\n\\nWhy is it not crawling all the pages?\\n\\nThere are a few reasons why Firecrawl may not be able to crawl all the pages of a website. Some common reasons include rate limiting, and anti-scraping mechanisms, disallowing the crawler from accessing certain pages. If you\\'re experiencing issues with the crawler, please reach out to our support team at hello@firecrawl.com.\\n\\nCan Firecrawl crawl websites without a sitemap?\\n\\nYes, Firecrawl can access and crawl all accessible subpages of a website, even in the absence of a sitemap. This feature enables users to gather data from a wide array of web sources with minimal setup.\\n\\nWhat formats can Firecrawl convert web data into?\\n\\nFirecrawl specializes in converting web data into clean, well-formatted markdown. This format is particularly suited for LLM applications, offering a structured yet flexible way to represent web content.\\n\\nHow does Firecrawl ensure the cleanliness of the data?\\n\\nFirecrawl employs advanced algorithms to clean and structure the scraped data, removing unnecessary elements and formatting the content into readable markdown. This process ensures that the data is ready for use in LLM applications without further preprocessing.\\n\\nIs Firecrawl suitable for large-scale data scraping projects?\\n\\nAbsolutely. Firecrawl offers various pricing plans, including a Scale plan that supports scraping of millions of pages. With features like caching and scheduled syncs, it\\'s designed to efficiently handle large-scale data scraping and continuous updates, making it ideal for enterprises and large projects.\\n\\nDoes it respect robots.txt?\\n\\nYes, Firecrawl crawler respects the rules set in a website\\'s robots.txt file. If you notice any issues with the way Firecrawl interacts with your website, you can adjust the robots.txt file to control the crawler\\'s behavior. Firecrawl user agent name is \\'FirecrawlAgent\\'. If you notice any behavior that is not expected, please let us know at hello@firecrawl.com.\\n\\nWhat measures does Firecrawl take to handle web scraping challenges like rate limits and caching?\\n\\nFirecrawl is built to navigate common web scraping challenges, including reverse proxies, rate limits, and caching. It smartly manages requests and employs caching techniques to minimize bandwidth usage and avoid triggering anti-scraping mechanisms, ensuring reliable data collection.\\n\\nDoes Firecrawl handle captcha or authentication?\\n\\nFirecrawl avoids captcha by using stealth proxyies. When it encounters captcha, it attempts to solve it automatically, but this is not always possible. We are working to add support for more captcha solving methods. Firecrawl can handle authentication by providing auth headers to the API.\\n\\n#### API Related\\n\\nWhere can I find my API key?\\n\\nClick on the dashboard button on the top navigation menu when logged in and you will find your API key in the main screen and under API Keys.\\n\\n#### Billing\\n\\nIs Firecrawl free?\\n\\nFirecrawl is free for the first 500 scraped pages (500 free credits). After that, you can upgrade to our Standard or Scale plans for more credits.\\n\\nIs there a pay per use plan instead of monthly?\\n\\nNo we do not currently offer a pay per use plan, instead you can upgrade to our Standard or Growth plans for more credits and higher rate limits.\\n\\nHow many credit does scraping, crawling, and extraction cost?\\n\\nScraping costs 1 credit per page. Crawling costs 1 credit per page.\\n\\nDo you charge for failed requests (scrape, crawl, extract)?\\n\\nWe do not charge for any failed requests (scrape, crawl, extract). Please contact support at help@firecrawl.dev if you have any questions.\\n\\nWhat payment methods do you accept?\\n\\nWe accept payments through Stripe which accepts most major credit cards, debit cards, and PayPal.\\n\\n[🔥](/)\\n\\n© A product by Mendable.ai - All rights reserved.\\n\\n[StatusStatus](https://firecrawl.betteruptime.com)\\n[Terms of ServiceTerms of Service](/terms-of-service)\\n[Privacy PolicyPrivacy Policy](/privacy-policy)\\n\\n[Twitter](https://twitter.com/mendableai)\\n[GitHub](https://github.com/mendableai)\\n[Discord](https://discord.gg/gSmWdAkdwd)\\n\\n###### Helpful Links\\n\\n* [Status](https://firecrawl.betteruptime.com/)\\n \\n* [Pricing](/pricing)\\n \\n* [Blog](https://www.firecrawl.dev/blog)\\n \\n* [Docs](https://docs.firecrawl.dev)\\n \\n\\nBacked by![Y Combinator Logo](https://www.firecrawl.dev/images/yc.svg)\\n\\n![SOC 2 Type II](https://www.firecrawl.dev/soc2type2badge.png)\\n\\n###### Resources\\n\\n* [Community](#0)\\n \\n* [Terms of service](#0)\\n \\n* [Collaboration features](#0)\\n \\n\\n###### Legals\\n\\n* [Refund policy](#0)\\n \\n* [Terms & Conditions](#0)\\n \\n* [Privacy policy](#0)\\n \\n* [Brand Kit](#0)')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "docs = loader.load()\n", - "\n", - "docs[0]" + "loader = FireCrawlLoader(\n", + " api_key=\"YOUR_API_KEY\",\n", + " url=\"https://firecrawl.dev\",\n", + " mode=\"crawl\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data = loader.load()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}\n" - ] - } - ], + "outputs": [], "source": [ - "print(docs[0].metadata)" + "print(pages[0].page_content[:100])\n", + "print(pages[0].metadata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Lazy Load\n", + "#### Crawl Options\n", "\n", - "You can use lazy loading to minimize memory requirements." + "You can also pass `params` to the loader. This is a dictionary of options to pass to the crawler. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Map" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "pages = []\n", - "for doc in loader.lazy_load():\n", - " pages.append(doc)\n", - " if len(pages) >= 10:\n", - " # do some paged operation, e.g.\n", - " # index.upsert(page)\n", - "\n", - " pages = []" + "loader = FireCrawlLoader(api_key=\"YOUR_API_KEY\", url=\"firecrawl.dev\", mode=\"map\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "len(pages)" + "docs = loader.load()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\n", - " Join the waitlist to turn any web\n", - "{'ogUrl': 'https://www.firecrawl.dev/blog/introducing-fire-engine-for-firecrawl', 'title': 'Introducing Fire Engine for Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/images/blog/fire-engine-launch.png', 'ogTitle': 'Introducing Fire Engine for Firecrawl', 'sitemap': {'lastmod': '2024-08-06T00:00:00.000Z', 'changefreq': 'weekly'}, 'keywords': 'firecrawl,fireengine,web crawling,dashboard,web scraping,LLM,data extraction', 'sourceURL': 'https://www.firecrawl.dev/blog/introducing-fire-engine-for-firecrawl', 'ogSiteName': 'Firecrawl', 'description': 'The most scalable, reliable, and fast way to get web data for Firecrawl.', 'ogDescription': 'The most scalable, reliable, and fast way to get web data for Firecrawl.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}\n" - ] - } - ], + "outputs": [], "source": [ - "print(pages[0].page_content[:100])\n", - "print(pages[0].metadata)" + "docs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Crawler Options\n", + "#### Map Options\n", "\n", - "You can also pass `params` to the loader. This is a dictionary of options to pass to the crawler. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information.\n", - "\n" + "You can also pass `params` to the loader. This is a dictionary of options to pass to the loader. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information." ] }, { @@ -220,7 +229,7 @@ ], "metadata": { "kernelspec": { - "display_name": "langchain", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/docs/docs/integrations/llms/oci_model_deployment_endpoint.ipynb b/docs/docs/integrations/llms/oci_model_deployment_endpoint.ipynb index b7be0db497050..0110a9d575646 100644 --- a/docs/docs/integrations/llms/oci_model_deployment_endpoint.ipynb +++ b/docs/docs/integrations/llms/oci_model_deployment_endpoint.ipynb @@ -8,9 +8,11 @@ "\n", "[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure.\n", "\n", + "> For the latest updates, examples and experimental features, please see [ADS LangChain Integration](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/large_language_model/langchain_models.html).\n", + "\n", "This notebooks goes over how to use an LLM hosted on a [OCI Data Science Model Deployment](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm).\n", "\n", - "To authenticate, [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) has been used to automatically load credentials for invoking endpoint." + "For authentication, the [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) library is used to automatically load credentials required for invoking the endpoint." ] }, { @@ -29,29 +31,52 @@ "## Prerequisite\n", "\n", "### Deploy model\n", - "Check [Oracle GitHub samples repository](https://github.com/oracle-samples/oci-data-science-ai-samples/tree/main/model-deployment/containers/llama2) on how to deploy your llm on OCI Data Science Model deployment.\n", + "You can easily deploy, fine-tune, and evaluate foundation models using the [AI Quick Actions](https://docs.oracle.com/en-us/iaas/data-science/using/ai-quick-actions.htm) on OCI Data Science Model deployment. For additional deployment examples, please visit the [Oracle GitHub samples repository](https://github.com/oracle-samples/oci-data-science-ai-samples/blob/main/ai-quick-actions/llama3-with-smc.md). \n", "\n", "### Policies\n", "Make sure to have the required [policies](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint) to access the OCI Data Science Model Deployment endpoint.\n", "\n", "## Set up\n", "\n", - "### vLLM\n", - "After having deployed model, you have to set up following required parameters of the `OCIModelDeploymentVLLM` call:\n", - "\n", - "- **`endpoint`**: The model HTTP endpoint from the deployed model, e.g. `https:///predict`. \n", - "- **`model`**: The location of the model.\n", + "After having deployed model, you have to set up following required parameters of the call:\n", "\n", - "### Text generation inference (TGI)\n", - "You have to set up following required parameters of the `OCIModelDeploymentTGI` call:\n", + "- **`endpoint`**: The model HTTP endpoint from the deployed model, e.g. `https://modeldeployment..oci.customer-oci.com//predict`. \n", "\n", - "- **`endpoint`**: The model HTTP endpoint from the deployed model, e.g. `https:///predict`. \n", "\n", "### Authentication\n", "\n", "You can set authentication through either ads or environment variables. When you are working in OCI Data Science Notebook Session, you can leverage resource principal to access other OCI resources. Check out [here](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) to see more options. \n", "\n", - "## Example" + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ads\n", + "from langchain_community.llms import OCIModelDeploymentLLM\n", + "\n", + "# Set authentication through ads\n", + "# Use resource principal are operating within a\n", + "# OCI service that has resource principal based\n", + "# authentication configured\n", + "ads.set_auth(\"resource_principal\")\n", + "\n", + "# Create an instance of OCI Model Deployment Endpoint\n", + "# Replace the endpoint uri and model name with your own\n", + "# Using generic class as entry point, you will be able\n", + "# to pass model parameters through model_kwargs during\n", + "# instantiation.\n", + "llm = OCIModelDeploymentLLM(\n", + " endpoint=\"https://modeldeployment..oci.customer-oci.com//predict\",\n", + " model=\"odsc-llm\",\n", + ")\n", + "\n", + "# Run the LLM\n", + "llm.invoke(\"Who is the first president of United States?\")" ] }, { @@ -71,7 +96,11 @@ "\n", "# Create an instance of OCI Model Deployment Endpoint\n", "# Replace the endpoint uri and model name with your own\n", - "llm = OCIModelDeploymentVLLM(endpoint=\"https:///predict\", model=\"model_name\")\n", + "# Using framework specific class as entry point, you will\n", + "# be able to pass model parameters in constructor.\n", + "llm = OCIModelDeploymentVLLM(\n", + " endpoint=\"https://modeldeployment..oci.customer-oci.com//predict\",\n", + ")\n", "\n", "# Run the LLM\n", "llm.invoke(\"Who is the first president of United States?\")" @@ -97,14 +126,64 @@ "\n", "# Set endpoint through environment variables\n", "# Replace the endpoint uri with your own\n", - "os.environ[\"OCI_LLM_ENDPOINT\"] = \"https:///predict\"\n", + "os.environ[\"OCI_LLM_ENDPOINT\"] = (\n", + " \"https://modeldeployment..oci.customer-oci.com//predict\"\n", + ")\n", "\n", "# Create an instance of OCI Model Deployment Endpoint\n", + "# Using framework specific class as entry point, you will\n", + "# be able to pass model parameters in constructor.\n", "llm = OCIModelDeploymentTGI()\n", "\n", "# Run the LLM\n", "llm.invoke(\"Who is the first president of United States?\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Asynchronous calls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "await llm.ainvoke(\"Tell me a joke.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming calls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for chunk in llm.stream(\"Tell me a joke.\"):\n", + " print(chunk, end=\"\", flush=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "For comprehensive details on all features and configurations, please refer to the API reference documentation for each class:\n", + "\n", + "* [OCIModelDeploymentLLM](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentLLM.html)\n", + "* [OCIModelDeploymentVLLM](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentVLLM.html)\n", + "* [OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html)" + ] } ], "metadata": { diff --git a/docs/docs/integrations/providers/konlpy.mdx b/docs/docs/integrations/providers/konlpy.mdx new file mode 100644 index 0000000000000..d4d925144e34c --- /dev/null +++ b/docs/docs/integrations/providers/konlpy.mdx @@ -0,0 +1,21 @@ +# KoNLPY + +>[KoNLPy](https://konlpy.org/) is a Python package for natural language processing (NLP) +> of the Korean language. + + +## Installation and Setup + +You need to install the `konlpy` python package. + +```bash +pip install konlpy +``` + +## Text splitter + +See a [usage example](/docs/how_to/split_by_token/#konlpy). + +```python +from langchain_text_splitters import KonlpyTextSplitter +``` diff --git a/docs/docs/integrations/providers/kuzu.mdx b/docs/docs/integrations/providers/kuzu.mdx new file mode 100644 index 0000000000000..286b38e0d81cc --- /dev/null +++ b/docs/docs/integrations/providers/kuzu.mdx @@ -0,0 +1,32 @@ +# Kùzu + +>[Kùzu](https://kuzudb.com/) is a company based in Waterloo, Ontario, Canada. +> It provides a highly scalable, extremely fast, easy-to-use [embeddable graph database](https://github.com/kuzudb/kuzu). + + + +## Installation and Setup + +You need to install the `kuzu` python package. + +```bash +pip install kuzu +``` + +## Graph database + +See a [usage example](/docs/integrations/graphs/kuzu_db). + +```python +from langchain_community.graphs import KuzuGraph +``` + +## Chain + +See a [usage example](/docs/integrations/graphs/kuzu_db/#creating-kuzuqachain). + +```python +from langchain.chains import KuzuQAChain +``` + + diff --git a/docs/docs/integrations/providers/llama_index.mdx b/docs/docs/integrations/providers/llama_index.mdx new file mode 100644 index 0000000000000..4ba7ac0eebae6 --- /dev/null +++ b/docs/docs/integrations/providers/llama_index.mdx @@ -0,0 +1,32 @@ +# LlamaIndex + +>[LlamaIndex](https://www.llamaindex.ai/) is the leading data framework for building LLM applications + + +## Installation and Setup + +You need to install the `llama-index` python package. + +```bash +pip install llama-index +``` + +See the [installation instructions](https://docs.llamaindex.ai/en/stable/getting_started/installation/). + +## Retrievers + +### LlamaIndexRetriever + +>It is used for the question-answering with sources over an LlamaIndex data structure. + +```python +from langchain_community.retrievers.llama_index import LlamaIndexRetriever +``` + +### LlamaIndexGraphRetriever + +>It is used for question-answering with sources over an LlamaIndex graph data structure. + +```python +from langchain_community.retrievers.llama_index import LlamaIndexGraphRetriever +``` diff --git a/docs/docs/integrations/providers/llamaedge.mdx b/docs/docs/integrations/providers/llamaedge.mdx new file mode 100644 index 0000000000000..64fbb50d389db --- /dev/null +++ b/docs/docs/integrations/providers/llamaedge.mdx @@ -0,0 +1,24 @@ +# LlamaEdge + +>[LlamaEdge](https://llamaedge.com/docs/intro/) is the easiest & fastest way to run customized +> and fine-tuned LLMs locally or on the edge. +> +>* Lightweight inference apps. `LlamaEdge` is in MBs instead of GBs +>* Native and GPU accelerated performance +>* Supports many GPU and hardware accelerators +>* Supports many optimized inference libraries +>* Wide selection of AI / LLM models + + + +## Installation and Setup + +See the [installation instructions](https://llamaedge.com/docs/user-guide/quick-start-command). + +## Chat models + +See a [usage example](/docs/integrations/chat/llama_edge). + +```python +from langchain_community.chat_models.llama_edge import LlamaEdgeChatService +``` diff --git a/docs/docs/integrations/providers/llamafile.mdx b/docs/docs/integrations/providers/llamafile.mdx new file mode 100644 index 0000000000000..d0d0f268d100b --- /dev/null +++ b/docs/docs/integrations/providers/llamafile.mdx @@ -0,0 +1,31 @@ +# llamafile + +>[llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs +> with a single file. + +>`llamafile` makes open LLMs much more accessible to both developers and end users. +> `llamafile` is doing that by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with +> [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses +> all the complexity of LLMs down to a single-file executable (called a "llamafile") +> that runs locally on most computers, with no installation. + + +## Installation and Setup + +See the [installation instructions](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#quickstart). + +## LLMs + +See a [usage example](/docs/integrations/llms/llamafile). + +```python +from langchain_community.llms.llamafile import Llamafile +``` + +## Embedding models + +See a [usage example](/docs/integrations/text_embedding/llamafile). + +```python +from langchain_community.embeddings import LlamafileEmbeddings +``` diff --git a/docs/docs/integrations/providers/oci.mdx b/docs/docs/integrations/providers/oci.mdx index 5037fb86f192f..58c167995bfbf 100644 --- a/docs/docs/integrations/providers/oci.mdx +++ b/docs/docs/integrations/providers/oci.mdx @@ -32,20 +32,18 @@ from langchain_community.embeddings import OCIGenAIEmbeddings > as an OCI Model Deployment Endpoint using the > [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). -If you deployed a LLM with the VLLM or TGI framework, you can use the -`OCIModelDeploymentVLLM` or `OCIModelDeploymentTGI` classes to interact with it. - To use, you should have the latest `oracle-ads` python SDK installed. ```bash pip install -U oracle-ads ``` -See [usage examples](/docs/integrations/llms/oci_model_deployment_endpoint). +See [chat](/docs/integrations/chat/oci_data_science) and [complete](/docs/integrations/llms/oci_model_deployment_endpoint) usage examples. + ```python -from langchain_community.llms import OCIModelDeploymentVLLM +from langchain_community.chat_models import ChatOCIModelDeployment -from langchain_community.llms import OCIModelDeploymentTGI +from langchain_community.llms import OCIModelDeploymentLLM ``` diff --git a/docs/docs/integrations/tools/databricks.ipynb b/docs/docs/integrations/tools/databricks.ipynb index 823ab803e1fa2..bb44a716f587e 100644 --- a/docs/docs/integrations/tools/databricks.ipynb +++ b/docs/docs/integrations/tools/databricks.ipynb @@ -74,6 +74,24 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"UC_TOOL_CLIENT_EXECUTION_TIMEOUT\"] = \"200\"" + ] + }, { "cell_type": "code", "execution_count": 4, diff --git a/docs/docs/tutorials/chatbot.ipynb b/docs/docs/tutorials/chatbot.ipynb index ec24551903eea..9a64507f5464c 100644 --- a/docs/docs/tutorials/chatbot.ipynb +++ b/docs/docs/tutorials/chatbot.ipynb @@ -438,7 +438,7 @@ "app = workflow.compile(checkpointer=MemorySaver())\n", "\n", "# Async invocation:\n", - "output = await app.ainvoke({\"messages\": input_messages}, config):\n", + "output = await app.ainvoke({\"messages\": input_messages}, config)\n", "output[\"messages\"][-1].pretty_print()\n", "```\n", "\n", diff --git a/docs/package.json b/docs/package.json index ec1391dafcc60..b82fda1c4c0b3 100644 --- a/docs/package.json +++ b/docs/package.json @@ -26,13 +26,14 @@ "@docusaurus/preset-classic": "3.5.2", "@docusaurus/remark-plugin-npm2yarn": "^3.5.2", "@docusaurus/theme-mermaid": "3.5.2", - "prism-react-renderer": "^2.1.0", + "@giscus/react": "^3.0.0", "@mdx-js/react": "^3", "@supabase/supabase-js": "^2.39.7", "clsx": "^1.2.1", "cookie": "^0.6.0", "isomorphic-dompurify": "^2.7.0", "json-loader": "^0.5.7", + "prism-react-renderer": "^2.1.0", "process": "^0.11.10", "react": "^18", "react-dom": "^18", diff --git a/docs/src/theme/DocItem/Paginator/index.js b/docs/src/theme/DocItem/Paginator/index.js index fddb1d42b2941..d9be074997f72 100644 --- a/docs/src/theme/DocItem/Paginator/index.js +++ b/docs/src/theme/DocItem/Paginator/index.js @@ -1,11 +1,25 @@ import React from 'react'; import Paginator from '@theme-original/DocItem/Paginator'; import Feedback from "@theme/Feedback"; +import Giscus from "@giscus/react"; export default function PaginatorWrapper(props) { return ( <> + ); diff --git a/docs/src/theme/Feedback.js b/docs/src/theme/Feedback.js index 1546bdd5c8d1d..157e4e6427a58 100644 --- a/docs/src/theme/Feedback.js +++ b/docs/src/theme/Feedback.js @@ -220,10 +220,6 @@ export default function Feedback() { onMouseUp: (e) => (e.currentTarget.style.backgroundColor = "#f0f0f0"), }; - const newGithubIssueURL = pathname - ? `https://github.com/langchain-ai/langchain/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CIssue+related+to+${pathname}%3E&url=https://python.langchain.com${pathname}` - : "https://github.com/langchain-ai/langchain/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E"; - return (
@@ -296,14 +292,6 @@ export default function Feedback() {
)} -
-

- You can also leave detailed feedback{" "} - - on GitHub - - . -

); } diff --git a/docs/yarn.lock b/docs/yarn.lock index b0167e3928c3a..94436d8e05823 100644 --- a/docs/yarn.lock +++ b/docs/yarn.lock @@ -1709,6 +1709,13 @@ resolved "https://registry.yarnpkg.com/@eslint/js/-/js-8.57.1.tgz#de633db3ec2ef6a3c89e2f19038063e8a122e2c2" integrity sha512-d9zaMRSTIKDLhctzH12MtXvJKSSUhaHcjV+2Z+GK+EEY7XKpP5yR4x+N3TAcHTcu963nIr+TMcCb4DBCYX1z6Q== +"@giscus/react@^3.0.0": + version "3.0.0" + resolved "https://registry.yarnpkg.com/@giscus/react/-/react-3.0.0.tgz#fdadce2c7e4023eb4fdbcc219cdd97f6d7aa17f0" + integrity sha512-hgCjLpg3Wgh8VbTF5p8ZLcIHI74wvDk1VIFv12+eKhenNVUDjgwNg2B1aq/3puyHOad47u/ZSyqiMtohjy/OOA== + dependencies: + giscus "^1.5.0" + "@hapi/hoek@^9.0.0", "@hapi/hoek@^9.3.0": version "9.3.0" resolved "https://registry.yarnpkg.com/@hapi/hoek/-/hoek-9.3.0.tgz#8368869dcb735be2e7f5cb7647de78e167a251fb" @@ -1835,6 +1842,18 @@ resolved "https://registry.yarnpkg.com/@leichtgewicht/ip-codec/-/ip-codec-2.0.5.tgz#4fc56c15c580b9adb7dc3c333a134e540b44bfb1" integrity sha512-Vo+PSpZG2/fmgmiNzYK9qWRh8h/CHrwD0mo1h1DzL4yzHNSfWYujGTYsWGreD000gcgmZ7K4Ys6Tx9TxtsKdDw== +"@lit-labs/ssr-dom-shim@^1.2.0": + version "1.2.1" + resolved "https://registry.yarnpkg.com/@lit-labs/ssr-dom-shim/-/ssr-dom-shim-1.2.1.tgz#2f3a8f1d688935c704dbc89132394a41029acbb8" + integrity sha512-wx4aBmgeGvFmOKucFKY+8VFJSYZxs9poN3SDNQFF6lT6NrQUnHiPB2PWz2sc4ieEcAaYYzN+1uWahEeTq2aRIQ== + +"@lit/reactive-element@^2.0.4": + version "2.0.4" + resolved "https://registry.yarnpkg.com/@lit/reactive-element/-/reactive-element-2.0.4.tgz#8f2ed950a848016383894a26180ff06c56ae001b" + integrity sha512-GFn91inaUa2oHLak8awSIigYz0cU0Payr1rcFsrkf5OJ5eSPxElyZfKh0f2p9FsTiZWXQdWGJeXZICEfXXYSXQ== + dependencies: + "@lit-labs/ssr-dom-shim" "^1.2.0" + "@mdx-js/mdx@^3.0.0": version "3.0.1" resolved "https://registry.yarnpkg.com/@mdx-js/mdx/-/mdx-3.0.1.tgz#617bd2629ae561fdca1bb88e3badd947f5a82191" @@ -2569,7 +2588,7 @@ dependencies: "@types/node" "*" -"@types/trusted-types@*": +"@types/trusted-types@*", "@types/trusted-types@^2.0.2": version "2.0.7" resolved "https://registry.yarnpkg.com/@types/trusted-types/-/trusted-types-2.0.7.tgz#baccb07a970b91707df3a3e8ba6896c57ead2d11" integrity sha512-ScaPdn1dQczgbl0QFTeTOmVHFULt394XJgOQNoyVhZ6r2vLnMLJfBPd53SB52T/3G36VI1/g2MZaX0cwDuXsfw== @@ -5549,6 +5568,13 @@ get-symbol-description@^1.0.2: es-errors "^1.3.0" get-intrinsic "^1.2.4" +giscus@^1.5.0: + version "1.5.0" + resolved "https://registry.yarnpkg.com/giscus/-/giscus-1.5.0.tgz#8299fa056b2ed31ec8b05d4645871e016982b4b2" + integrity sha512-t3LL0qbSO3JXq3uyQeKpF5CegstGfKX/0gI6eDe1cmnI7D56R7j52yLdzw4pdKrg3VnufwCgCM3FDz7G1Qr6lg== + dependencies: + lit "^3.1.2" + github-slugger@^1.5.0: version "1.5.0" resolved "https://registry.yarnpkg.com/github-slugger/-/github-slugger-1.5.0.tgz#17891bbc73232051474d68bd867a34625c955f7d" @@ -6882,6 +6908,31 @@ lines-and-columns@^1.1.6: resolved "https://registry.yarnpkg.com/lines-and-columns/-/lines-and-columns-1.2.4.tgz#eca284f75d2965079309dc0ad9255abb2ebc1632" integrity sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg== +lit-element@^4.1.0: + version "4.1.1" + resolved "https://registry.yarnpkg.com/lit-element/-/lit-element-4.1.1.tgz#07905992815076e388cf6f1faffc7d6866c82007" + integrity sha512-HO9Tkkh34QkTeUmEdNYhMT8hzLid7YlMlATSi1q4q17HE5d9mrrEHJ/o8O2D0cMi182zK1F3v7x0PWFjrhXFew== + dependencies: + "@lit-labs/ssr-dom-shim" "^1.2.0" + "@lit/reactive-element" "^2.0.4" + lit-html "^3.2.0" + +lit-html@^3.2.0: + version "3.2.1" + resolved "https://registry.yarnpkg.com/lit-html/-/lit-html-3.2.1.tgz#8fc49e3531ee5947e4d93e8a5aa642ab1649833b" + integrity sha512-qI/3lziaPMSKsrwlxH/xMgikhQ0EGOX2ICU73Bi/YHFvz2j/yMCIrw4+puF2IpQ4+upd3EWbvnHM9+PnJn48YA== + dependencies: + "@types/trusted-types" "^2.0.2" + +lit@^3.1.2: + version "3.2.1" + resolved "https://registry.yarnpkg.com/lit/-/lit-3.2.1.tgz#d6dd15eac20db3a098e81e2c85f70a751ff55592" + integrity sha512-1BBa1E/z0O9ye5fZprPtdqnc0BFzxIxTTOO/tQFmyC/hj1O3jL4TfmLBw0WEwjAokdLwpclkvGgDJwTIh0/22w== + dependencies: + "@lit/reactive-element" "^2.0.4" + lit-element "^4.1.0" + lit-html "^3.2.0" + loader-runner@^4.2.0: version "4.3.0" resolved "https://registry.yarnpkg.com/loader-runner/-/loader-runner-4.3.0.tgz#c1b4a163b99f614830353b16755e7149ac2314e1" diff --git a/libs/community/extended_testing_deps.txt b/libs/community/extended_testing_deps.txt index 3cdf3d52c632a..b2548b2219394 100644 --- a/libs/community/extended_testing_deps.txt +++ b/libs/community/extended_testing_deps.txt @@ -38,6 +38,7 @@ jinja2>=3,<4 jq>=1.4.1,<2 jsonschema>1 keybert>=0.8.5 +langchain_openai>=0.2.1 litellm>=1.30,<=1.39.5 lxml>=4.9.3,<6.0 markdownify>=0.11.6,<0.12 diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner.py b/libs/community/langchain_community/agent_toolkits/openapi/planner.py index fbd3fc36b43aa..eb2c0efe4c606 100644 --- a/libs/community/langchain_community/agent_toolkits/openapi/planner.py +++ b/libs/community/langchain_community/agent_toolkits/openapi/planner.py @@ -356,7 +356,7 @@ def _create_and_run_api_controller_agent(plan_str: str) -> str: for endpoint_name in endpoint_names: found_match = False for name, _, docs in api_spec.endpoints: - regex_name = re.compile(re.sub("\{.*?\}", ".*", name)) + regex_name = re.compile(re.sub("\\{.*?\\}", ".*", name)) if regex_name.match(endpoint_name): found_match = True docs_str += f"== Docs for {endpoint_name} == \n{yaml.dump(docs)}\n" diff --git a/libs/community/langchain_community/chat_models/__init__.py b/libs/community/langchain_community/chat_models/__init__.py index db5076375288f..88df9d4330fe2 100644 --- a/libs/community/langchain_community/chat_models/__init__.py +++ b/libs/community/langchain_community/chat_models/__init__.py @@ -125,6 +125,11 @@ from langchain_community.chat_models.moonshot import ( MoonshotChat, ) + from langchain_community.chat_models.oci_data_science import ( + ChatOCIModelDeployment, + ChatOCIModelDeploymentTGI, + ChatOCIModelDeploymentVLLM, + ) from langchain_community.chat_models.oci_generative_ai import ( ChatOCIGenAI, # noqa: F401 ) @@ -211,6 +216,9 @@ "ChatMlflow", "ChatNebula", "ChatOCIGenAI", + "ChatOCIModelDeployment", + "ChatOCIModelDeploymentVLLM", + "ChatOCIModelDeploymentTGI", "ChatOllama", "ChatOpenAI", "ChatPerplexity", @@ -272,6 +280,9 @@ "ChatNebula": "langchain_community.chat_models.symblai_nebula", "ChatOctoAI": "langchain_community.chat_models.octoai", "ChatOCIGenAI": "langchain_community.chat_models.oci_generative_ai", + "ChatOCIModelDeployment": "langchain_community.chat_models.oci_data_science", + "ChatOCIModelDeploymentVLLM": "langchain_community.chat_models.oci_data_science", + "ChatOCIModelDeploymentTGI": "langchain_community.chat_models.oci_data_science", "ChatOllama": "langchain_community.chat_models.ollama", "ChatOpenAI": "langchain_community.chat_models.openai", "ChatPerplexity": "langchain_community.chat_models.perplexity", diff --git a/libs/community/langchain_community/chat_models/oci_data_science.py b/libs/community/langchain_community/chat_models/oci_data_science.py new file mode 100644 index 0000000000000..cdb181df897b3 --- /dev/null +++ b/libs/community/langchain_community/chat_models/oci_data_science.py @@ -0,0 +1,998 @@ +# Copyright (c) 2024, Oracle and/or its affiliates. + +"""Chat model for OCI data science model deployment endpoint.""" + +import importlib +import json +import logging +from operator import itemgetter +from typing import ( + Any, + AsyncIterator, + Callable, + Dict, + Iterator, + List, + Literal, + Optional, + Sequence, + Type, + Union, +) + +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models import LanguageModelInput +from langchain_core.language_models.chat_models import ( + BaseChatModel, + agenerate_from_stream, + generate_from_stream, +) +from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk +from langchain_core.output_parsers import ( + JsonOutputParser, + PydanticOutputParser, +) +from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough +from langchain_core.tools import BaseTool +from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import BaseModel, Field, model_validator + +from langchain_community.llms.oci_data_science_model_deployment_endpoint import ( + DEFAULT_MODEL_NAME, + BaseOCIModelDeployment, +) + +logger = logging.getLogger(__name__) + + +def _is_pydantic_class(obj: Any) -> bool: + return isinstance(obj, type) and issubclass(obj, BaseModel) + + +class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment): + """OCI Data Science Model Deployment chat model integration. + + Setup: + Install ``oracle-ads`` and ``langchain-openai``. + + .. code-block:: bash + + pip install -U oracle-ads langchain-openai + + Use `ads.set_auth()` to configure authentication. + For example, to use OCI resource_principal for authentication: + + .. code-block:: python + + import ads + ads.set_auth("resource_principal") + + For more details on authentication, see: + https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html + + Make sure to have the required policies to access the OCI Data + Science Model Deployment endpoint. See: + https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm + + + Key init args - completion params: + endpoint: str + The OCI model deployment endpoint. + temperature: float + Sampling temperature. + max_tokens: Optional[int] + Max number of tokens to generate. + + Key init args — client params: + auth: dict + ADS auth dictionary for OCI authentication. + + Instantiate: + .. code-block:: python + + from langchain_community.chat_models import ChatOCIModelDeployment + + chat = ChatOCIModelDeployment( + endpoint="https://modeldeployment..oci.customer-oci.com//predict", + model="odsc-llm", + streaming=True, + max_retries=3, + model_kwargs={ + "max_token": 512, + "temperature": 0.2, + # other model parameters ... + }, + ) + + Invocation: + .. code-block:: python + + messages = [ + ("system", "Translate the user sentence to French."), + ("human", "Hello World!"), + ] + chat.invoke(messages) + + .. code-block:: python + + AIMessage( + content='Bonjour le monde!', + response_metadata={ + 'token_usage': { + 'prompt_tokens': 40, + 'total_tokens': 50, + 'completion_tokens': 10 + }, + 'model_name': 'odsc-llm', + 'system_fingerprint': '', + 'finish_reason': 'stop' + }, + id='run-cbed62da-e1b3-4abd-9df3-ec89d69ca012-0' + ) + + Streaming: + .. code-block:: python + + for chunk in chat.stream(messages): + print(chunk) + + .. code-block:: python + + content='' id='run-02c6-c43f-42de' + content='\n' id='run-02c6-c43f-42de' + content='B' id='run-02c6-c43f-42de' + content='on' id='run-02c6-c43f-42de' + content='j' id='run-02c6-c43f-42de' + content='our' id='run-02c6-c43f-42de' + content=' le' id='run-02c6-c43f-42de' + content=' monde' id='run-02c6-c43f-42de' + content='!' id='run-02c6-c43f-42de' + content='' response_metadata={'finish_reason': 'stop'} id='run-02c6-c43f-42de' + + Async: + .. code-block:: python + + await chat.ainvoke(messages) + + # stream: + # async for chunk in (await chat.astream(messages)) + + .. code-block:: python + + AIMessage( + content='Bonjour le monde!', + response_metadata={'finish_reason': 'stop'}, + id='run-8657a105-96b7-4bb6-b98e-b69ca420e5d1-0' + ) + + Structured output: + .. code-block:: python + + from typing import Optional + from pydantic import BaseModel, Field + + class Joke(BaseModel): + setup: str = Field(description="The setup of the joke") + punchline: str = Field(description="The punchline to the joke") + + structured_llm = chat.with_structured_output(Joke, method="json_mode") + structured_llm.invoke( + "Tell me a joke about cats, " + "respond in JSON with `setup` and `punchline` keys" + ) + + .. code-block:: python + + Joke( + setup='Why did the cat get stuck in the tree?', + punchline='Because it was chasing its tail!' + ) + + See ``ChatOCIModelDeployment.with_structured_output()`` for more. + + Customized Usage: + You can inherit from base class and overwrite the `_process_response`, + `_process_stream_response`, `_construct_json_body` for customized usage. + + .. code-block:: python + + class MyChatModel(ChatOCIModelDeployment): + def _process_stream_response(self, response_json: dict) -> ChatGenerationChunk: + print("My customized streaming result handler.") + return GenerationChunk(...) + + def _process_response(self, response_json:dict) -> ChatResult: + print("My customized output handler.") + return ChatResult(...) + + def _construct_json_body(self, messages: list, params: dict) -> dict: + print("My customized payload handler.") + return { + "messages": messages, + **params, + } + + chat = MyChatModel( + endpoint=f"https://modeldeployment..oci.customer-oci.com/{ocid}/predict", + model="odsc-llm", + } + + chat.invoke("tell me a joke") + + Response metadata + .. code-block:: python + + ai_msg = chat.invoke(messages) + ai_msg.response_metadata + + .. code-block:: python + + { + 'token_usage': { + 'prompt_tokens': 40, + 'total_tokens': 50, + 'completion_tokens': 10 + }, + 'model_name': 'odsc-llm', + 'system_fingerprint': '', + 'finish_reason': 'stop' + } + + """ # noqa: E501 + + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass to the model.""" + + model: str = DEFAULT_MODEL_NAME + """The name of the model.""" + + stop: Optional[List[str]] = None + """Stop words to use when generating. Model output is cut off + at the first occurrence of any of these substrings.""" + + @model_validator(mode="before") + @classmethod + def validate_openai(cls, values: Any) -> Any: + """Checks if langchain_openai is installed.""" + if not importlib.util.find_spec("langchain_openai"): + raise ImportError( + "Could not import langchain_openai package. " + "Please install it with `pip install langchain_openai`." + ) + return values + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "oci_model_depolyment_chat_endpoint" + + @property + def _identifying_params(self) -> Dict[str, Any]: + """Get the identifying parameters.""" + _model_kwargs = self.model_kwargs or {} + return { + **{"endpoint": self.endpoint, "model_kwargs": _model_kwargs}, + **self._default_params, + } + + @property + def _default_params(self) -> Dict[str, Any]: + """Get the default parameters.""" + return { + "model": self.model, + "stop": self.stop, + "stream": self.streaming, + } + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + """Call out to an OCI Model Deployment Online endpoint. + + Args: + messages: The messages in the conversation with the chat model. + stop: Optional list of stop words to use when generating. + + Returns: + LangChain ChatResult + + Raises: + RuntimeError: + Raise when invoking endpoint fails. + + Example: + + .. code-block:: python + + messages = [ + ( + "system", + "You are a helpful assistant that translates English to French. Translate the user sentence.", + ), + ("human", "Hello World!"), + ] + + response = chat.invoke(messages) + """ # noqa: E501 + if self.streaming: + stream_iter = self._stream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return generate_from_stream(stream_iter) + + requests_kwargs = kwargs.pop("requests_kwargs", {}) + params = self._invocation_params(stop, **kwargs) + body = self._construct_json_body(messages, params) + res = self.completion_with_retry( + data=body, run_manager=run_manager, **requests_kwargs + ) + return self._process_response(res.json()) + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + """Stream OCI Data Science Model Deployment endpoint on given messages. + + Args: + messages (List[BaseMessage]): + The messagaes to pass into the model. + stop (List[str], Optional): + List of stop words to use when generating. + kwargs: + requests_kwargs: + Additional ``**kwargs`` to pass to requests.post + + Returns: + An iterator of ChatGenerationChunk. + + Raises: + RuntimeError: + Raise when invoking endpoint fails. + + Example: + + .. code-block:: python + + messages = [ + ( + "system", + "You are a helpful assistant that translates English to French. Translate the user sentence.", + ), + ("human", "Hello World!"), + ] + + chunk_iter = chat.stream(messages) + + """ # noqa: E501 + requests_kwargs = kwargs.pop("requests_kwargs", {}) + self.streaming = True + params = self._invocation_params(stop, **kwargs) + body = self._construct_json_body(messages, params) # request json body + + response = self.completion_with_retry( + data=body, run_manager=run_manager, stream=True, **requests_kwargs + ) + default_chunk_class = AIMessageChunk + for line in self._parse_stream(response.iter_lines()): + chunk = self._handle_sse_line(line, default_chunk_class) + if run_manager: + run_manager.on_llm_new_token(chunk.text, chunk=chunk) + yield chunk + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + """Asynchronously call out to OCI Data Science Model Deployment + endpoint on given messages. + + Args: + messages (List[BaseMessage]): + The messagaes to pass into the model. + stop (List[str], Optional): + List of stop words to use when generating. + kwargs: + requests_kwargs: + Additional ``**kwargs`` to pass to requests.post + + Returns: + LangChain ChatResult. + + Raises: + ValueError: + Raise when invoking endpoint fails. + + Example: + + .. code-block:: python + + messages = [ + ( + "system", + "You are a helpful assistant that translates English to French. Translate the user sentence.", + ), + ("human", "I love programming."), + ] + + resp = await chat.ainvoke(messages) + + """ # noqa: E501 + if self.streaming: + stream_iter = self._astream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return await agenerate_from_stream(stream_iter) + + requests_kwargs = kwargs.pop("requests_kwargs", {}) + params = self._invocation_params(stop, **kwargs) + body = self._construct_json_body(messages, params) + response = await self.acompletion_with_retry( + data=body, + run_manager=run_manager, + **requests_kwargs, + ) + return self._process_response(response) + + async def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + """Asynchronously streaming OCI Data Science Model Deployment + endpoint on given messages. + + Args: + messages (List[BaseMessage]): + The messagaes to pass into the model. + stop (List[str], Optional): + List of stop words to use when generating. + kwargs: + requests_kwargs: + Additional ``**kwargs`` to pass to requests.post + + Returns: + An Asynciterator of ChatGenerationChunk. + + Raises: + ValueError: + Raise when invoking endpoint fails. + + Example: + + .. code-block:: python + + messages = [ + ( + "system", + "You are a helpful assistant that translates English to French. Translate the user sentence.", + ), + ("human", "I love programming."), + ] + + chunk_iter = await chat.astream(messages) + + """ # noqa: E501 + requests_kwargs = kwargs.pop("requests_kwargs", {}) + self.streaming = True + params = self._invocation_params(stop, **kwargs) + body = self._construct_json_body(messages, params) # request json body + + default_chunk_class = AIMessageChunk + async for line in await self.acompletion_with_retry( + data=body, run_manager=run_manager, stream=True, **requests_kwargs + ): + chunk = self._handle_sse_line(line, default_chunk_class) + if run_manager: + await run_manager.on_llm_new_token(chunk.text, chunk=chunk) + yield chunk + + def with_structured_output( + self, + schema: Optional[Union[Dict, Type[BaseModel]]] = None, + *, + method: Literal["json_mode"] = "json_mode", + include_raw: bool = False, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]: + """Model wrapper that returns outputs formatted to match the given schema. + + Args: + schema: The output schema as a dict or a Pydantic class. If a Pydantic class + then the model output will be an object of that class. If a dict then + the model output will be a dict. With a Pydantic class the returned + attributes will be validated, whereas with a dict they will not be. If + `method` is "function_calling" and `schema` is a dict, then the dict + must match the OpenAI function-calling spec. + method: The method for steering model generation, currently only support + for "json_mode". If "json_mode" then JSON mode will be used. Note that + if using "json_mode" then you must include instructions for formatting + the output into the desired schema into the model call. + include_raw: If False then only the parsed structured output is returned. If + an error occurs during model output parsing it will be raised. If True + then both the raw model response (a BaseMessage) and the parsed model + response will be returned. If an error occurs during output parsing it + will be caught and returned as well. The final output is always a dict + with keys "raw", "parsed", and "parsing_error". + + Returns: + A Runnable that takes any ChatModel input and returns as output: + + If include_raw is True then a dict with keys: + raw: BaseMessage + parsed: Optional[_DictOrPydantic] + parsing_error: Optional[BaseException] + + If include_raw is False then just _DictOrPydantic is returned, + where _DictOrPydantic depends on the schema: + + If schema is a Pydantic class then _DictOrPydantic is the Pydantic + class. + + If schema is a dict then _DictOrPydantic is a dict. + + """ # noqa: E501 + if kwargs: + raise ValueError(f"Received unsupported arguments {kwargs}") + is_pydantic_schema = _is_pydantic_class(schema) + if method == "json_mode": + llm = self.bind(response_format={"type": "json_object"}) + output_parser = ( + PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] + if is_pydantic_schema + else JsonOutputParser() + ) + else: + raise ValueError( + f"Unrecognized method argument. Expected `json_mode`." + f"Received: `{method}`." + ) + + if include_raw: + parser_assign = RunnablePassthrough.assign( + parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None + ) + parser_none = RunnablePassthrough.assign(parsed=lambda _: None) + parser_with_fallback = parser_assign.with_fallbacks( + [parser_none], exception_key="parsing_error" + ) + return RunnableMap(raw=llm) | parser_with_fallback + else: + return llm | output_parser + + def _invocation_params(self, stop: Optional[List[str]], **kwargs: Any) -> dict: + """Combines the invocation parameters with default parameters.""" + params = self._default_params + _model_kwargs = self.model_kwargs or {} + params["stop"] = stop or params.get("stop", []) + return {**params, **_model_kwargs, **kwargs} + + def _handle_sse_line( + self, line: str, default_chunk_cls: Type[BaseMessageChunk] = AIMessageChunk + ) -> ChatGenerationChunk: + """Handle a single Server-Sent Events (SSE) line and process it into + a chat generation chunk. + + Args: + line (str): A single line from the SSE stream in string format. + default_chunk_cls (AIMessageChunk): The default class for message + chunks to be used during the processing of the stream response. + + Returns: + ChatGenerationChunk: The processed chat generation chunk. If an error + occurs, an empty `ChatGenerationChunk` is returned. + """ + try: + obj = json.loads(line) + return self._process_stream_response(obj, default_chunk_cls) + except Exception as e: + logger.debug(f"Error occurs when processing line={line}: {str(e)}") + return ChatGenerationChunk(message=AIMessageChunk(content="")) + + def _construct_json_body(self, messages: list, params: dict) -> dict: + """Constructs the request body as a dictionary (JSON). + + Args: + messages (list): A list of message objects to be included in the + request body. + params (dict): A dictionary of additional parameters to be included + in the request body. + + Returns: + dict: A dictionary representing the JSON request body, including + converted messages and additional parameters. + + """ + from langchain_openai.chat_models.base import _convert_message_to_dict + + return { + "messages": [_convert_message_to_dict(m) for m in messages], + **params, + } + + def _process_stream_response( + self, + response_json: dict, + default_chunk_cls: Type[BaseMessageChunk] = AIMessageChunk, + ) -> ChatGenerationChunk: + """Formats streaming response in OpenAI spec. + + Args: + response_json (dict): The JSON response from the streaming endpoint. + default_chunk_cls (type, optional): The default class to use for + creating message chunks. Defaults to `AIMessageChunk`. + + Returns: + ChatGenerationChunk: An object containing the processed message + chunk and any relevant generation information such as finish + reason and usage. + + Raises: + ValueError: If the response JSON is not well-formed or does not + contain the expected structure. + """ + from langchain_openai.chat_models.base import _convert_delta_to_message_chunk + + try: + choice = response_json["choices"][0] + if not isinstance(choice, dict): + raise TypeError("Endpoint response is not well formed.") + except (KeyError, IndexError, TypeError) as e: + raise ValueError( + "Error while formatting response payload for chat model of type" + ) from e + + chunk = _convert_delta_to_message_chunk(choice["delta"], default_chunk_cls) + default_chunk_cls = chunk.__class__ + finish_reason = choice.get("finish_reason") + usage = choice.get("usage") + gen_info = {} + if finish_reason is not None: + gen_info.update({"finish_reason": finish_reason}) + if usage is not None: + gen_info.update({"usage": usage}) + + return ChatGenerationChunk( + message=chunk, generation_info=gen_info if gen_info else None + ) + + def _process_response(self, response_json: dict) -> ChatResult: + """Formats response in OpenAI spec. + + Args: + response_json (dict): The JSON response from the chat model endpoint. + + Returns: + ChatResult: An object containing the list of `ChatGeneration` objects + and additional LLM output information. + + Raises: + ValueError: If the response JSON is not well-formed or does not + contain the expected structure. + + """ + from langchain_openai.chat_models.base import _convert_dict_to_message + + generations = [] + try: + choices = response_json["choices"] + if not isinstance(choices, list): + raise TypeError("Endpoint response is not well formed.") + except (KeyError, TypeError) as e: + raise ValueError( + "Error while formatting response payload for chat model of type" + ) from e + + for choice in choices: + message = _convert_dict_to_message(choice["message"]) + generation_info = dict(finish_reason=choice.get("finish_reason")) + if "logprobs" in choice: + generation_info["logprobs"] = choice["logprobs"] + + gen = ChatGeneration( + message=message, + generation_info=generation_info, + ) + generations.append(gen) + + token_usage = response_json.get("usage", {}) + llm_output = { + "token_usage": token_usage, + "model_name": self.model, + "system_fingerprint": response_json.get("system_fingerprint", ""), + } + return ChatResult(generations=generations, llm_output=llm_output) + + def bind_tools( + self, + tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], + **kwargs: Any, + ) -> Runnable[LanguageModelInput, BaseMessage]: + formatted_tools = [convert_to_openai_tool(tool) for tool in tools] + return super().bind(tools=formatted_tools, **kwargs) + + +class ChatOCIModelDeploymentVLLM(ChatOCIModelDeployment): + """OCI large language chat models deployed with vLLM. + + To use, you must provide the model HTTP endpoint from your deployed + model, e.g. https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict. + + To authenticate, `oracle-ads` has been used to automatically load + credentials: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html + + Make sure to have the required policies to access the OCI Data + Science Model Deployment endpoint. See: + https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint + + Example: + + .. code-block:: python + + from langchain_community.chat_models import ChatOCIModelDeploymentVLLM + + chat = ChatOCIModelDeploymentVLLM( + endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict", + frequency_penalty=0.1, + max_tokens=512, + temperature=0.2, + top_p=1.0, + # other model parameters... + ) + + """ # noqa: E501 + + frequency_penalty: float = 0.0 + """Penalizes repeated tokens according to frequency. Between 0 and 1.""" + + logit_bias: Optional[Dict[str, float]] = None + """Adjust the probability of specific tokens being generated.""" + + max_tokens: Optional[int] = 256 + """The maximum number of tokens to generate in the completion.""" + + n: int = 1 + """Number of output sequences to return for the given prompt.""" + + presence_penalty: float = 0.0 + """Penalizes repeated tokens. Between 0 and 1.""" + + temperature: float = 0.2 + """What sampling temperature to use.""" + + top_p: float = 1.0 + """Total probability mass of tokens to consider at each step.""" + + best_of: Optional[int] = None + """Generates best_of completions server-side and returns the "best" + (the one with the highest log probability per token). + """ + + use_beam_search: Optional[bool] = False + """Whether to use beam search instead of sampling.""" + + top_k: Optional[int] = -1 + """Number of most likely tokens to consider at each step.""" + + min_p: Optional[float] = 0.0 + """Float that represents the minimum probability for a token to be considered. + Must be in [0,1]. 0 to disable this.""" + + repetition_penalty: Optional[float] = 1.0 + """Float that penalizes new tokens based on their frequency in the + generated text. Values > 1 encourage the model to use new tokens.""" + + length_penalty: Optional[float] = 1.0 + """Float that penalizes sequences based on their length. Used only + when `use_beam_search` is True.""" + + early_stopping: Optional[bool] = False + """Controls the stopping condition for beam search. It accepts the + following values: `True`, where the generation stops as soon as there + are `best_of` complete candidates; `False`, where a heuristic is applied + to the generation stops when it is very unlikely to find better candidates; + `never`, where the beam search procedure only stops where there cannot be + better candidates (canonical beam search algorithm).""" + + ignore_eos: Optional[bool] = False + """Whether to ignore the EOS token and continue generating tokens after + the EOS token is generated.""" + + min_tokens: Optional[int] = 0 + """Minimum number of tokens to generate per output sequence before + EOS or stop_token_ids can be generated""" + + stop_token_ids: Optional[List[int]] = None + """List of tokens that stop the generation when they are generated. + The returned output will contain the stop tokens unless the stop tokens + are special tokens.""" + + skip_special_tokens: Optional[bool] = True + """Whether to skip special tokens in the output. Defaults to True.""" + + spaces_between_special_tokens: Optional[bool] = True + """Whether to add spaces between special tokens in the output. + Defaults to True.""" + + tool_choice: Optional[str] = None + """Whether to use tool calling. + Defaults to None, tool calling is disabled. + Tool calling requires model support and the vLLM to be configured + with `--tool-call-parser`. + Set this to `auto` for the model to make tool calls automatically. + Set this to `required` to force the model to always call one or more tools. + """ + + chat_template: Optional[str] = None + """Use customized chat template. + Defaults to None. The chat template from the tokenizer will be used. + """ + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "oci_model_depolyment_chat_endpoint_vllm" + + @property + def _default_params(self) -> Dict[str, Any]: + """Get the default parameters.""" + params = { + "model": self.model, + "stop": self.stop, + "stream": self.streaming, + } + for attr_name in self._get_model_params(): + try: + value = getattr(self, attr_name) + if value is not None: + params.update({attr_name: value}) + except Exception: + pass + + return params + + def _get_model_params(self) -> List[str]: + """Gets the name of model parameters.""" + return [ + "best_of", + "early_stopping", + "frequency_penalty", + "ignore_eos", + "length_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "min_p", + "min_tokens", + "n", + "presence_penalty", + "repetition_penalty", + "skip_special_tokens", + "spaces_between_special_tokens", + "stop_token_ids", + "temperature", + "top_k", + "top_p", + "use_beam_search", + "tool_choice", + "chat_template", + ] + + +class ChatOCIModelDeploymentTGI(ChatOCIModelDeployment): + """OCI large language chat models deployed with Text Generation Inference. + + To use, you must provide the model HTTP endpoint from your deployed + model, e.g. https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict. + + To authenticate, `oracle-ads` has been used to automatically load + credentials: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html + + Make sure to have the required policies to access the OCI Data + Science Model Deployment endpoint. See: + https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint + + Example: + + .. code-block:: python + + from langchain_community.chat_models import ChatOCIModelDeploymentTGI + + chat = ChatOCIModelDeploymentTGI( + endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict", + max_token=512, + temperature=0.2, + frequency_penalty=0.1, + seed=42, + # other model parameters... + ) + + """ # noqa: E501 + + frequency_penalty: Optional[float] = None + """Penalizes repeated tokens according to frequency. Between 0 and 1.""" + + logit_bias: Optional[Dict[str, float]] = None + """Adjust the probability of specific tokens being generated.""" + + logprobs: Optional[bool] = None + """Whether to return log probabilities of the output tokens or not.""" + + max_tokens: int = 256 + """The maximum number of tokens to generate in the completion.""" + + n: int = 1 + """Number of output sequences to return for the given prompt.""" + + presence_penalty: Optional[float] = None + """Penalizes repeated tokens. Between 0 and 1.""" + + seed: Optional[int] = None + """To sample deterministically,""" + + temperature: float = 0.2 + """What sampling temperature to use.""" + + top_p: Optional[float] = None + """Total probability mass of tokens to consider at each step.""" + + top_logprobs: Optional[int] = None + """An integer between 0 and 5 specifying the number of most + likely tokens to return at each token position, each with an + associated log probability. logprobs must be set to true if + this parameter is used.""" + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "oci_model_depolyment_chat_endpoint_tgi" + + @property + def _default_params(self) -> Dict[str, Any]: + """Get the default parameters.""" + params = { + "model": self.model, + "stop": self.stop, + "stream": self.streaming, + } + for attr_name in self._get_model_params(): + try: + value = getattr(self, attr_name) + if value is not None: + params.update({attr_name: value}) + except Exception: + pass + + return params + + def _get_model_params(self) -> List[str]: + """Gets the name of model parameters.""" + return [ + "frequency_penalty", + "logit_bias", + "logprobs", + "max_tokens", + "n", + "presence_penalty", + "seed", + "temperature", + "top_k", + "top_p", + "top_logprobs", + ] diff --git a/libs/community/langchain_community/chat_models/sambanova.py b/libs/community/langchain_community/chat_models/sambanova.py index cd95f4beefa5f..62bc366486805 100644 --- a/libs/community/langchain_community/chat_models/sambanova.py +++ b/libs/community/langchain_community/chat_models/sambanova.py @@ -174,10 +174,10 @@ class ChatSambaNovaCloud(BaseChatModel): temperature: float = Field(default=0.7) """model temperature""" - top_p: Optional[float] = Field() + top_p: Optional[float] = Field(default=None) """model top p""" - top_k: Optional[int] = Field() + top_k: Optional[int] = Field(default=None) """model top k""" stream_options: dict = Field(default={"include_usage": True}) @@ -593,7 +593,7 @@ class ChatSambaStudio(BaseChatModel): streaming_url: str = Field(default="", exclude=True) """SambaStudio streaming Url""" - model: Optional[str] = Field() + model: Optional[str] = Field(default=None) """The name of the model or expert to use (for CoE endpoints)""" streaming: bool = Field(default=False) @@ -605,16 +605,16 @@ class ChatSambaStudio(BaseChatModel): temperature: Optional[float] = Field(default=0.7) """model temperature""" - top_p: Optional[float] = Field() + top_p: Optional[float] = Field(default=None) """model top p""" - top_k: Optional[int] = Field() + top_k: Optional[int] = Field(default=None) """model top k""" - do_sample: Optional[bool] = Field() + do_sample: Optional[bool] = Field(default=None) """whether to do sampling""" - process_prompt: Optional[bool] = Field() + process_prompt: Optional[bool] = Field(default=True) """whether process prompt (for CoE generic v1 and v2 endpoints)""" stream_options: dict = Field(default={"include_usage": True}) @@ -1012,6 +1012,16 @@ def _process_stream_response( "system_fingerprint": data["system_fingerprint"], "created": data["created"], } + if data.get("usage") is not None: + content = "" + id = data["id"] + metadata = { + "finish_reason": finish_reason, + "usage": data.get("usage"), + "model_name": data["model"], + "system_fingerprint": data["system_fingerprint"], + "created": data["created"], + } yield AIMessageChunk( content=content, id=id, diff --git a/libs/community/langchain_community/document_loaders/base_o365.py b/libs/community/langchain_community/document_loaders/base_o365.py index b0d44e772403a..44002842bf25d 100644 --- a/libs/community/langchain_community/document_loaders/base_o365.py +++ b/libs/community/langchain_community/document_loaders/base_o365.py @@ -118,8 +118,8 @@ def _load_from_folder(self, folder: Folder) -> Iterable[Blob]: metadata_dict[file.name] = { "source": file.web_url, "mime_type": file.mime_type, - "created": file.created, - "modified": file.modified, + "created": str(file.created), + "modified": str(file.modified), "created_by": str(file.created_by), "modified_by": str(file.modified_by), "description": file.description, diff --git a/libs/community/langchain_community/document_loaders/firecrawl.py b/libs/community/langchain_community/document_loaders/firecrawl.py index 467813419ae29..4423881dfef4c 100644 --- a/libs/community/langchain_community/document_loaders/firecrawl.py +++ b/libs/community/langchain_community/document_loaders/firecrawl.py @@ -1,3 +1,4 @@ +import warnings from typing import Iterator, Literal, Optional from langchain_core.document_loaders import BaseLoader @@ -48,7 +49,6 @@ class FireCrawlLoader(BaseLoader): Join the waitlist to turn any web {'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []} - Async load: .. code-block:: python @@ -64,13 +64,169 @@ class FireCrawlLoader(BaseLoader): """ # noqa: E501 + def legacy_crawler_options_adapter(self, params: dict) -> dict: + use_legacy_options = False + legacy_keys = [ + "includes", + "excludes", + "allowBackwardCrawling", + "allowExternalContentLinks", + "pageOptions", + ] + for key in legacy_keys: + if params.get(key): + use_legacy_options = True + break + + if use_legacy_options: + warnings.warn( + "Deprecated parameters detected. See Firecrawl v1 docs for updates.", + DeprecationWarning, + ) + if "includes" in params: + if params["includes"] is True: + params["includePaths"] = params["includes"] + del params["includes"] + + if "excludes" in params: + if params["excludes"] is True: + params["excludePaths"] = params["excludes"] + del params["excludes"] + + if "allowBackwardCrawling" in params: + if params["allowBackwardCrawling"] is True: + params["allowBackwardLinks"] = params["allowBackwardCrawling"] + del params["allowBackwardCrawling"] + + if "allowExternalContentLinks" in params: + if params["allowExternalContentLinks"] is True: + params["allowExternalLinks"] = params["allowExternalContentLinks"] + del params["allowExternalContentLinks"] + + if "pageOptions" in params: + if isinstance(params["pageOptions"], dict): + params["scrapeOptions"] = self.legacy_scrape_options_adapter( + params["pageOptions"] + ) + del params["pageOptions"] + + return params + + def legacy_scrape_options_adapter(self, params: dict) -> dict: + use_legacy_options = False + formats = ["markdown"] + + if "extractorOptions" in params: + if "mode" in params["extractorOptions"]: + if ( + params["extractorOptions"]["mode"] == "llm-extraction" + or params["extractorOptions"]["mode"] + == "llm-extraction-from-raw-html" + or params["extractorOptions"]["mode"] + == "llm-extraction-from-markdown" + ): + use_legacy_options = True + if "extractionPrompt" in params["extractorOptions"]: + if params["extractorOptions"]["extractionPrompt"]: + params["prompt"] = params["extractorOptions"][ + "extractionPrompt" + ] + else: + params["prompt"] = params["extractorOptions"].get( + "extractionPrompt", + "Extract page information based on the schema.", + ) + + if "extractionSchema" in params["extractorOptions"]: + if params["extractorOptions"]["extractionSchema"]: + params["schema"] = params["extractorOptions"][ + "extractionSchema" + ] + + if "userPrompt" in params["extractorOptions"]: + if params["extractorOptions"]["userPrompt"]: + params["prompt"] = params["extractorOptions"]["userPrompt"] + + del params["extractorOptions"] + + scrape_keys = [ + "includeMarkdown", + "includeHtml", + "includeRawHtml", + "includeExtract", + "includeLinks", + "screenshot", + "fullPageScreenshot", + "onlyIncludeTags", + "removeTags", + ] + for key in scrape_keys: + if params.get(key): + use_legacy_options = True + break + + if use_legacy_options: + warnings.warn( + "Deprecated parameters detected. See Firecrawl v1 docs for updates.", + DeprecationWarning, + ) + if "includeMarkdown" in params: + if params["includeMarkdown"] is False: + formats.remove("markdown") + del params["includeMarkdown"] + + if "includeHtml" in params: + if params["includeHtml"] is True: + formats.append("html") + del params["includeHtml"] + + if "includeRawHtml" in params: + if params["includeRawHtml"] is True: + formats.append("rawHtml") + del params["includeRawHtml"] + + if "includeExtract" in params: + if params["includeExtract"] is True: + formats.append("extract") + del params["includeExtract"] + + if "includeLinks" in params: + if params["includeLinks"] is True: + formats.append("links") + del params["includeLinks"] + + if "screenshot" in params: + if params["screenshot"] is True: + formats.append("screenshot") + del params["screenshot"] + + if "fullPageScreenshot" in params: + if params["fullPageScreenshot"] is True: + formats.append("screenshot@fullPage") + del params["fullPageScreenshot"] + + if "onlyIncludeTags" in params: + if params["onlyIncludeTags"] is True: + params["includeTags"] = params["onlyIncludeTags"] + del params["onlyIncludeTags"] + + if "removeTags" in params: + if params["removeTags"] is True: + params["excludeTags"] = params["removeTags"] + del params["removeTags"] + + if "formats" not in params: + params["formats"] = formats + + return params + def __init__( self, url: str, *, api_key: Optional[str] = None, api_url: Optional[str] = None, - mode: Literal["crawl", "scrape"] = "crawl", + mode: Literal["crawl", "scrape", "map"] = "crawl", params: Optional[dict] = None, ): """Initialize with API key and url. @@ -82,8 +238,9 @@ def __init__( api_url: The Firecrawl API URL. If not specified will be read from env var FIRECRAWL_API_URL or defaults to https://api.firecrawl.dev. mode: The mode to run the loader in. Default is "crawl". - Options include "scrape" (single url) and - "crawl" (all accessible sub pages). + Options include "scrape" (single url), + "crawl" (all accessible sub pages), + "map" (returns list of links that are semantically related). params: The parameters to pass to the Firecrawl API. Examples include crawlerOptions. For more details, visit: https://github.com/mendableai/firecrawl-py @@ -95,30 +252,58 @@ def __init__( raise ImportError( "`firecrawl` package not found, please run `pip install firecrawl-py`" ) - if mode not in ("crawl", "scrape"): + if mode not in ("crawl", "scrape", "search", "map"): raise ValueError( - f"Unrecognized mode '{mode}'. Expected one of 'crawl', 'scrape'." + f"Invalid mode '{mode}'. Allowed: 'crawl', 'scrape', 'search', 'map'." ) + + if not url: + raise ValueError("Url must be provided") + api_key = api_key or get_from_env("api_key", "FIRECRAWL_API_KEY") self.firecrawl = FirecrawlApp(api_key=api_key, api_url=api_url) self.url = url self.mode = mode - self.params = params + self.params = params or {} def lazy_load(self) -> Iterator[Document]: if self.mode == "scrape": - firecrawl_docs = [self.firecrawl.scrape_url(self.url, params=self.params)] + firecrawl_docs = [ + self.firecrawl.scrape_url( + self.url, params=self.legacy_scrape_options_adapter(self.params) + ) + ] elif self.mode == "crawl": - firecrawl_docs = self.firecrawl.crawl_url(self.url, params=self.params) + if not self.url: + raise ValueError("URL is required for crawl mode") + crawl_response = self.firecrawl.crawl_url( + self.url, params=self.legacy_crawler_options_adapter(self.params) + ) + firecrawl_docs = crawl_response.get("data", []) + elif self.mode == "map": + if not self.url: + raise ValueError("URL is required for map mode") + firecrawl_docs = self.firecrawl.map_url(self.url, params=self.params) + elif self.mode == "search": + raise ValueError( + "Search mode is not supported in this version, please downgrade." + ) else: raise ValueError( - f"Unrecognized mode '{self.mode}'. Expected one of 'crawl', 'scrape'." + f"Invalid mode '{self.mode}'. Allowed: 'crawl', 'scrape', 'map'." ) for doc in firecrawl_docs: - metadata = doc.get("metadata", {}) - if (self.params is not None) and self.params.get( - "extractorOptions", {} - ).get("mode") == "llm-extraction": - metadata["llm_extraction"] = doc.get("llm_extraction") - - yield Document(page_content=doc.get("markdown", ""), metadata=metadata) + if self.mode == "map": + page_content = doc + metadata = {} + else: + page_content = ( + doc.get("markdown") or doc.get("html") or doc.get("rawHtml", "") + ) + metadata = doc.get("metadata", {}) + if not page_content: + continue + yield Document( + page_content=page_content, + metadata=metadata, + ) diff --git a/libs/community/langchain_community/embeddings/text2vec.py b/libs/community/langchain_community/embeddings/text2vec.py index 9b0cf353ec006..2d55f5585b5ea 100644 --- a/libs/community/langchain_community/embeddings/text2vec.py +++ b/libs/community/langchain_community/embeddings/text2vec.py @@ -10,7 +10,7 @@ class Text2vecEmbeddings(Embeddings, BaseModel): """text2vec embedding models. Install text2vec first, run 'pip install -U text2vec'. - The gitbub repository for text2vec is : https://github.com/shibing624/text2vec + The github repository for text2vec is : https://github.com/shibing624/text2vec Example: .. code-block:: python diff --git a/libs/community/langchain_community/graphs/neo4j_graph.py b/libs/community/langchain_community/graphs/neo4j_graph.py index 901e9604f6242..baca74bb3e4ed 100644 --- a/libs/community/langchain_community/graphs/neo4j_graph.py +++ b/libs/community/langchain_community/graphs/neo4j_graph.py @@ -430,7 +430,7 @@ def query( try: data, _, _ = self._driver.execute_query( Query(text=query, timeout=self.timeout), - database=self._database, + database_=self._database, parameters_=params, ) json_data = [r.data() for r in data] @@ -457,7 +457,7 @@ def query( ): raise # fallback to allow implicit transactions - with self._driver.session() as session: + with self._driver.session(database=self._database) as session: data = session.run(Query(text=query, timeout=self.timeout), params) json_data = [r.data() for r in data] if self.sanitize: diff --git a/libs/community/langchain_community/llms/__init__.py b/libs/community/langchain_community/llms/__init__.py index bc1ada6982e74..1bf5e0a7ee76e 100644 --- a/libs/community/langchain_community/llms/__init__.py +++ b/libs/community/langchain_community/llms/__init__.py @@ -396,6 +396,14 @@ def _import_oci_md_vllm() -> Type[BaseLLM]: return OCIModelDeploymentVLLM +def _import_oci_md() -> Type[BaseLLM]: + from langchain_community.llms.oci_data_science_model_deployment_endpoint import ( + OCIModelDeploymentLLM, + ) + + return OCIModelDeploymentLLM + + def _import_oci_gen_ai() -> Type[BaseLLM]: from langchain_community.llms.oci_generative_ai import OCIGenAI @@ -773,6 +781,8 @@ def __getattr__(name: str) -> Any: return _import_oci_md_tgi() elif name == "OCIModelDeploymentVLLM": return _import_oci_md_vllm() + elif name == "OCIModelDeploymentLLM": + return _import_oci_md() elif name == "OCIGenAI": return _import_oci_gen_ai() elif name == "OctoAIEndpoint": @@ -928,6 +938,7 @@ def __getattr__(name: str) -> Any: "OCIGenAI", "OCIModelDeploymentTGI", "OCIModelDeploymentVLLM", + "OCIModelDeploymentLLM", "OctoAIEndpoint", "Ollama", "OpaquePrompts", @@ -1029,6 +1040,7 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: "nlpcloud": _import_nlpcloud, "oci_model_deployment_tgi_endpoint": _import_oci_md_tgi, "oci_model_deployment_vllm_endpoint": _import_oci_md_vllm, + "oci_model_deployment_endpoint": _import_oci_md, "oci_generative_ai": _import_oci_gen_ai, "octoai_endpoint": _import_octoai_endpoint, "ollama": _import_ollama, diff --git a/libs/community/langchain_community/llms/bedrock.py b/libs/community/langchain_community/llms/bedrock.py index a0de3fb81e297..4c5fbdc476d66 100644 --- a/libs/community/langchain_community/llms/bedrock.py +++ b/libs/community/langchain_community/llms/bedrock.py @@ -396,7 +396,7 @@ def validate_environment(cls, values: Dict) -> Dict: """Validate that AWS credentials to and python package exists in environment.""" # Skip creating new client if passed in constructor - if values["client"] is not None: + if values.get("client") is not None: return values try: diff --git a/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py b/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py index 04f551ce27c42..c998fbc0ec40f 100644 --- a/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py +++ b/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py @@ -1,19 +1,68 @@ -import logging -from typing import Any, Dict, List, Optional +# Copyright (c) 2023, 2024, Oracle and/or its affiliates. + +"""LLM for OCI data science model deployment endpoint.""" +import json +import logging +import traceback +from typing import ( + Any, + AsyncIterator, + Callable, + Dict, + Iterator, + List, + Literal, + Optional, + Union, +) + +import aiohttp import requests -from langchain_core.callbacks import CallbackManagerForLLMRun -from langchain_core.language_models.llms import LLM -from langchain_core.utils import get_from_dict_or_env, pre_init -from pydantic import Field +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator +from langchain_core.load.serializable import Serializable +from langchain_core.outputs import Generation, GenerationChunk, LLMResult +from langchain_core.utils import get_from_dict_or_env +from pydantic import Field, model_validator + +from langchain_community.utilities.requests import Requests logger = logging.getLogger(__name__) + DEFAULT_TIME_OUT = 300 DEFAULT_CONTENT_TYPE_JSON = "application/json" +DEFAULT_MODEL_NAME = "odsc-llm" + + +class TokenExpiredError(Exception): + """Raises when token expired.""" + + +class ServerError(Exception): + """Raises when encounter server error when making inference.""" + +def _create_retry_decorator( + llm: "BaseOCIModelDeployment", + *, + run_manager: Optional[ + Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] + ] = None, +) -> Callable[[Any], Any]: + """Create a retry decorator.""" + errors = [requests.exceptions.ConnectTimeout, TokenExpiredError] + decorator = create_base_retry_decorator( + error_types=errors, max_retries=llm.max_retries, run_manager=run_manager + ) + return decorator -class OCIModelDeploymentLLM(LLM): + +class BaseOCIModelDeployment(Serializable): """Base class for LLM deployed on OCI Data Science Model Deployment.""" auth: dict = Field(default_factory=dict, exclude=True) @@ -23,35 +72,21 @@ class OCIModelDeploymentLLM(LLM): or `ads.common.auth.resource_principal()`. If this is not provided then the `ads.common.default_signer()` will be used.""" - max_tokens: int = 256 - """Denotes the number of tokens to predict per generation.""" - - temperature: float = 0.2 - """A non-negative float that tunes the degree of randomness in generation.""" - - k: int = 0 - """Number of most likely tokens to consider at each step.""" - - p: float = 0.75 - """Total probability mass of tokens to consider at each step.""" - endpoint: str = "" """The uri of the endpoint from the deployed Model Deployment model.""" - best_of: int = 1 - """Generates best_of completions server-side and returns the "best" - (the one with the highest log probability per token). - """ + streaming: bool = False + """Whether to stream the results or not.""" - stop: Optional[List[str]] = None - """Stop words to use when generating. Model output is cut off - at the first occurrence of any of these substrings.""" + max_retries: int = 3 + """Maximum number of retries to make when generating.""" - @pre_init - def validate_environment( # pylint: disable=no-self-argument - cls, values: Dict - ) -> Dict: - """Validate that python package exists in environment.""" + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Dict: + """Checks if oracle-ads is installed and + get credentials/endpoint from environment. + """ try: import ads @@ -60,8 +95,10 @@ def validate_environment( # pylint: disable=no-self-argument "Could not import ads python package. " "Please install it with `pip install oracle_ads`." ) from ex + if not values.get("auth", None): values["auth"] = ads.common.auth.default_signer() + values["endpoint"] = get_from_dict_or_env( values, "endpoint", @@ -69,49 +106,465 @@ def validate_environment( # pylint: disable=no-self-argument ) return values + def _headers( + self, is_async: Optional[bool] = False, body: Optional[dict] = None + ) -> Dict: + """Construct and return the headers for a request. + + Args: + is_async (bool, optional): Indicates if the request is asynchronous. + Defaults to `False`. + body (optional): The request body to be included in the headers if + the request is asynchronous. + + Returns: + Dict: A dictionary containing the appropriate headers for the request. + """ + if is_async: + signer = self.auth["signer"] + _req = requests.Request("POST", self.endpoint, json=body) + req = _req.prepare() + req = signer(req) + headers = {} + for key, value in req.headers.items(): + headers[key] = value + + if self.streaming: + headers.update( + {"enable-streaming": "true", "Accept": "text/event-stream"} + ) + return headers + + return ( + { + "Content-Type": DEFAULT_CONTENT_TYPE_JSON, + "enable-streaming": "true", + "Accept": "text/event-stream", + } + if self.streaming + else { + "Content-Type": DEFAULT_CONTENT_TYPE_JSON, + } + ) + + def completion_with_retry( + self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any + ) -> Any: + """Use tenacity to retry the completion call.""" + retry_decorator = _create_retry_decorator(self, run_manager=run_manager) + + @retry_decorator + def _completion_with_retry(**kwargs: Any) -> Any: + try: + request_timeout = kwargs.pop("request_timeout", DEFAULT_TIME_OUT) + data = kwargs.pop("data") + stream = kwargs.pop("stream", self.streaming) + + request = Requests( + headers=self._headers(), auth=self.auth.get("signer") + ) + response = request.post( + url=self.endpoint, + data=data, + timeout=request_timeout, + stream=stream, + **kwargs, + ) + self._check_response(response) + return response + except TokenExpiredError as e: + raise e + except Exception as err: + traceback.print_exc() + logger.debug( + f"Requests payload: {data}. Requests arguments: " + f"url={self.endpoint},timeout={request_timeout},stream={stream}. " + f"Additional request kwargs={kwargs}." + ) + raise RuntimeError( + f"Error occurs by inference endpoint: {str(err)}" + ) from err + + return _completion_with_retry(**kwargs) + + async def acompletion_with_retry( + self, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Any: + """Use tenacity to retry the async completion call.""" + retry_decorator = _create_retry_decorator(self, run_manager=run_manager) + + @retry_decorator + async def _completion_with_retry(**kwargs: Any) -> Any: + try: + request_timeout = kwargs.pop("request_timeout", DEFAULT_TIME_OUT) + data = kwargs.pop("data") + stream = kwargs.pop("stream", self.streaming) + + request = Requests(headers=self._headers(is_async=True, body=data)) + if stream: + response = request.apost( + url=self.endpoint, + data=data, + timeout=request_timeout, + ) + return self._aiter_sse(response) + else: + async with request.apost( + url=self.endpoint, + data=data, + timeout=request_timeout, + ) as resp: + self._check_response(resp) + data = await resp.json() + return data + except TokenExpiredError as e: + raise e + except Exception as err: + traceback.print_exc() + logger.debug( + f"Requests payload: `{data}`. " + f"Stream mode={stream}. " + f"Requests kwargs: url={self.endpoint}, timeout={request_timeout}." + ) + raise RuntimeError( + f"Error occurs by inference endpoint: {str(err)}" + ) from err + + return await _completion_with_retry(**kwargs) + + def _check_response(self, response: Any) -> None: + """Handle server error by checking the response status. + + Args: + response: + The response object from either `requests` or `aiohttp` library. + + Raises: + TokenExpiredError: + If the response status code is 401 and the token refresh is successful. + ServerError: + If any other HTTP error occurs. + """ + try: + response.raise_for_status() + except requests.exceptions.HTTPError as http_err: + status_code = ( + response.status_code + if hasattr(response, "status_code") + else response.status + ) + if status_code == 401 and self._refresh_signer(): + raise TokenExpiredError() from http_err + + raise ServerError( + f"Server error: {str(http_err)}. \nMessage: {response.text}" + ) from http_err + + def _parse_stream(self, lines: Iterator[bytes]) -> Iterator[str]: + """Parse a stream of byte lines and yield parsed string lines. + + Args: + lines (Iterator[bytes]): + An iterator that yields lines in byte format. + + Yields: + Iterator[str]: + An iterator that yields parsed lines as strings. + """ + for line in lines: + _line = self._parse_stream_line(line) + if _line is not None: + yield _line + + async def _parse_stream_async( + self, + lines: aiohttp.StreamReader, + ) -> AsyncIterator[str]: + """ + Asynchronously parse a stream of byte lines and yield parsed string lines. + + Args: + lines (aiohttp.StreamReader): + An `aiohttp.StreamReader` object that yields lines in byte format. + + Yields: + AsyncIterator[str]: + An asynchronous iterator that yields parsed lines as strings. + """ + async for line in lines: + _line = self._parse_stream_line(line) + if _line is not None: + yield _line + + def _parse_stream_line(self, line: bytes) -> Optional[str]: + """Parse a single byte line and return a processed string line if valid. + + Args: + line (bytes): A single line in byte format. + + Returns: + Optional[str]: + The processed line as a string if valid, otherwise `None`. + """ + line = line.strip() + if not line: + return None + _line = line.decode("utf-8") + + if _line.lower().startswith("data:"): + _line = _line[5:].lstrip() + + if _line.startswith("[DONE]"): + return None + return _line + return None + + async def _aiter_sse( + self, + async_cntx_mgr: Any, + ) -> AsyncIterator[str]: + """Asynchronously iterate over server-sent events (SSE). + + Args: + async_cntx_mgr: An asynchronous context manager that yields a client + response object. + + Yields: + AsyncIterator[str]: An asynchronous iterator that yields parsed server-sent + event lines as json string. + """ + async with async_cntx_mgr as client_resp: + self._check_response(client_resp) + async for line in self._parse_stream_async(client_resp.content): + yield line + + def _refresh_signer(self) -> bool: + """Attempt to refresh the security token using the signer. + + Returns: + bool: `True` if the token was successfully refreshed, `False` otherwise. + """ + if self.auth.get("signer", None) and hasattr( + self.auth["signer"], "refresh_security_token" + ): + self.auth["signer"].refresh_security_token() + return True + return False + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this model can be serialized by LangChain.""" + return True + + +class OCIModelDeploymentLLM(BaseLLM, BaseOCIModelDeployment): + """LLM deployed on OCI Data Science Model Deployment. + + To use, you must provide the model HTTP endpoint from your deployed + model, e.g. https://modeldeployment..oci.customer-oci.com//predict. + + To authenticate, `oracle-ads` has been used to automatically load + credentials: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html + + Make sure to have the required policies to access the OCI Data + Science Model Deployment endpoint. See: + https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint + + Example: + + .. code-block:: python + + from langchain_community.llms import OCIModelDeploymentLLM + + llm = OCIModelDeploymentLLM( + endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com//predict", + model="odsc-llm", + streaming=True, + model_kwargs={"frequency_penalty": 1.0}, + ) + llm.invoke("tell me a joke.") + + Customized Usage: + + User can inherit from our base class and overrwrite the `_process_response`, `_process_stream_response`, + `_construct_json_body` for satisfying customized needed. + + .. code-block:: python + + from langchain_community.llms import OCIModelDeploymentLLM + + class MyCutomizedModel(OCIModelDeploymentLLM): + def _process_stream_response(self, response_json:dict) -> GenerationChunk: + print("My customized output stream handler.") + return GenerationChunk() + + def _process_response(self, response_json:dict) -> List[Generation]: + print("My customized output handler.") + return [Generation()] + + def _construct_json_body(self, prompt: str, param:dict) -> dict: + print("My customized input handler.") + return {} + + llm = MyCutomizedModel( + endpoint=f"https://modeldeployment.us-ashburn-1.oci.customer-oci.com/{ocid}/predict", + model="", + } + + llm.invoke("tell me a joke.") + + """ # noqa: E501 + + model: str = DEFAULT_MODEL_NAME + """The name of the model.""" + + max_tokens: int = 256 + """Denotes the number of tokens to predict per generation.""" + + temperature: float = 0.2 + """A non-negative float that tunes the degree of randomness in generation.""" + + k: int = -1 + """Number of most likely tokens to consider at each step.""" + + p: float = 0.75 + """Total probability mass of tokens to consider at each step.""" + + best_of: int = 1 + """Generates best_of completions server-side and returns the "best" + (the one with the highest log probability per token). + """ + + stop: Optional[List[str]] = None + """Stop words to use when generating. Model output is cut off + at the first occurrence of any of these substrings.""" + + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass to the model.""" + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "oci_model_deployment_endpoint" + @property def _default_params(self) -> Dict[str, Any]: - """Default parameters for the model.""" - raise NotImplementedError + """Get the default parameters.""" + return { + "best_of": self.best_of, + "max_tokens": self.max_tokens, + "model": self.model, + "stop": self.stop, + "stream": self.streaming, + "temperature": self.temperature, + "top_k": self.k, + "top_p": self.p, + } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" + _model_kwargs = self.model_kwargs or {} return { - **{"endpoint": self.endpoint}, + **{"endpoint": self.endpoint, "model_kwargs": _model_kwargs}, **self._default_params, } - def _construct_json_body(self, prompt: str, params: dict) -> dict: - """Constructs the request body as a dictionary (JSON).""" - raise NotImplementedError + def _generate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + """Call out to OCI Data Science Model Deployment endpoint with k unique prompts. - def _invocation_params(self, stop: Optional[List[str]], **kwargs: Any) -> dict: - """Combines the invocation parameters with default parameters.""" - params = self._default_params - if self.stop is not None and stop is not None: - raise ValueError("`stop` found in both the input and default params.") - elif self.stop is not None: - params["stop"] = self.stop - elif stop is not None: - params["stop"] = stop - else: - # Don't set "stop" in param as None. It should be a list. - params["stop"] = [] - - return {**params, **kwargs} - - def _process_response(self, response_json: dict) -> str: - raise NotImplementedError - - def _call( + Args: + prompts: The prompts to pass into the service. + stop: Optional list of stop words to use when generating. + + Returns: + The full LLM output. + + Example: + .. code-block:: python + + response = llm.invoke("Tell me a joke.") + response = llm.generate(["Tell me a joke."]) + """ + generations: List[List[Generation]] = [] + params = self._invocation_params(stop, **kwargs) + for prompt in prompts: + body = self._construct_json_body(prompt, params) + if self.streaming: + generation = GenerationChunk(text="") + for chunk in self._stream( + prompt, stop=stop, run_manager=run_manager, **kwargs + ): + generation += chunk + generations.append([generation]) + else: + res = self.completion_with_retry( + data=body, + run_manager=run_manager, + **kwargs, + ) + generations.append(self._process_response(res.json())) + return LLMResult(generations=generations) + + async def _agenerate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + """Call out to OCI Data Science Model Deployment endpoint async with k unique prompts. + + Args: + prompts: The prompts to pass into the service. + stop: Optional list of stop words to use when generating. + + Returns: + The full LLM output. + + Example: + .. code-block:: python + + response = await llm.ainvoke("Tell me a joke.") + response = await llm.agenerate(["Tell me a joke."]) + """ # noqa: E501 + generations: List[List[Generation]] = [] + params = self._invocation_params(stop, **kwargs) + for prompt in prompts: + body = self._construct_json_body(prompt, params) + if self.streaming: + generation = GenerationChunk(text="") + async for chunk in self._astream( + prompt, stop=stop, run_manager=run_manager, **kwargs + ): + generation += chunk + generations.append([generation]) + else: + res = await self.acompletion_with_retry( + data=body, + run_manager=run_manager, + **kwargs, + ) + generations.append(self._process_response(res)) + return LLMResult(generations=generations) + + def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, - ) -> str: - """Call out to OCI Data Science Model Deployment endpoint. + ) -> Iterator[GenerationChunk]: + """Stream OCI Data Science Model Deployment endpoint on given prompt. + Args: prompt (str): @@ -123,100 +576,162 @@ def _call( Additional ``**kwargs`` to pass to requests.post Returns: - The string generated by the model. + An iterator of GenerationChunks. + Example: + .. code-block:: python - response = oci_md("Tell me a joke.") + response = llm.stream("Tell me a joke.") """ requests_kwargs = kwargs.pop("requests_kwargs", {}) + self.streaming = True params = self._invocation_params(stop, **kwargs) body = self._construct_json_body(prompt, params) - logger.info(f"LLM API Request:\n{prompt}") - response = self._send_request( - data=body, endpoint=self.endpoint, **requests_kwargs + + response = self.completion_with_retry( + data=body, run_manager=run_manager, stream=True, **requests_kwargs ) - completion = self._process_response(response) - logger.info(f"LLM API Completion:\n{completion}") - return completion + for line in self._parse_stream(response.iter_lines()): + chunk = self._handle_sse_line(line) + if run_manager: + run_manager.on_llm_new_token(chunk.text, chunk=chunk) - def _send_request( + yield chunk + + async def _astream( self, - data: Any, - endpoint: str, - header: Optional[dict] = {}, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, - ) -> Dict: - """Sends request to the oci data science model deployment endpoint. + ) -> AsyncIterator[GenerationChunk]: + """Stream OCI Data Science Model Deployment endpoint async on given prompt. + Args: - data (Json serializable): - data need to be sent to the endpoint. - endpoint (str): - The model HTTP endpoint. - header (dict, optional): - A dictionary of HTTP headers to send to the specified url. - Defaults to {}. + prompt (str): + The prompt to pass into the model. + stop (List[str], Optional): + List of stop words to use when generating. kwargs: - Additional ``**kwargs`` to pass to requests.post. - Raises: - Exception: - Raise when invoking fails. + requests_kwargs: + Additional ``**kwargs`` to pass to requests.post Returns: - A JSON representation of a requests.Response object. + An iterator of GenerationChunks. + + + Example: + + .. code-block:: python + + async for chunk in llm.astream(("Tell me a joke."): + print(chunk, end="", flush=True) + """ - if not header: - header = {} - header["Content-Type"] = ( - header.pop("content_type", DEFAULT_CONTENT_TYPE_JSON) - or DEFAULT_CONTENT_TYPE_JSON - ) - request_kwargs = {"json": data} - request_kwargs["headers"] = header - timeout = kwargs.pop("timeout", DEFAULT_TIME_OUT) - - attempts = 0 - while attempts < 2: - request_kwargs["auth"] = self.auth.get("signer") - response = requests.post( - endpoint, timeout=timeout, **request_kwargs, **kwargs - ) - if response.status_code == 401: - self._refresh_signer() - attempts += 1 - continue - break + requests_kwargs = kwargs.pop("requests_kwargs", {}) + self.streaming = True + params = self._invocation_params(stop, **kwargs) + body = self._construct_json_body(prompt, params) + + async for line in await self.acompletion_with_retry( + data=body, run_manager=run_manager, stream=True, **requests_kwargs + ): + chunk = self._handle_sse_line(line) + if run_manager: + await run_manager.on_llm_new_token(chunk.text, chunk=chunk) + yield chunk + + def _construct_json_body(self, prompt: str, params: dict) -> dict: + """Constructs the request body as a dictionary (JSON).""" + return { + "prompt": prompt, + **params, + } + + def _invocation_params( + self, stop: Optional[List[str]] = None, **kwargs: Any + ) -> dict: + """Combines the invocation parameters with default parameters.""" + params = self._default_params + _model_kwargs = self.model_kwargs or {} + params["stop"] = stop or params.get("stop", []) + return {**params, **_model_kwargs, **kwargs} + def _process_stream_response(self, response_json: dict) -> GenerationChunk: + """Formats streaming response for OpenAI spec into GenerationChunk.""" try: - response.raise_for_status() - response_json = response.json() + choice = response_json["choices"][0] + if not isinstance(choice, dict): + raise TypeError("Endpoint response is not well formed.") + except (KeyError, IndexError, TypeError) as e: + raise ValueError("Error while formatting response payload.") from e - except Exception: - logger.error( - "DEBUG INFO: request_kwargs=%s, status_code=%s, content=%s", - request_kwargs, - response.status_code, - response.content, - ) - raise + return GenerationChunk(text=choice.get("text", "")) - return response_json + def _process_response(self, response_json: dict) -> List[Generation]: + """Formats response in OpenAI spec. - def _refresh_signer(self) -> None: - if self.auth.get("signer", None) and hasattr( - self.auth["signer"], "refresh_security_token" - ): - self.auth["signer"].refresh_security_token() + Args: + response_json (dict): The JSON response from the chat model endpoint. + + Returns: + ChatResult: An object containing the list of `ChatGeneration` objects + and additional LLM output information. + + Raises: + ValueError: If the response JSON is not well-formed or does not + contain the expected structure. + + """ + generations = [] + try: + choices = response_json["choices"] + if not isinstance(choices, list): + raise TypeError("Endpoint response is not well formed.") + except (KeyError, TypeError) as e: + raise ValueError("Error while formatting response payload.") from e + + for choice in choices: + gen = Generation( + text=choice.get("text"), + generation_info=self._generate_info(choice), + ) + generations.append(gen) + + return generations + + def _generate_info(self, choice: dict) -> Any: + """Extracts generation info from the response.""" + gen_info = {} + finish_reason = choice.get("finish_reason", None) + logprobs = choice.get("logprobs", None) + index = choice.get("index", None) + if finish_reason: + gen_info.update({"finish_reason": finish_reason}) + if logprobs is not None: + gen_info.update({"logprobs": logprobs}) + if index is not None: + gen_info.update({"index": index}) + + return gen_info or None + + def _handle_sse_line(self, line: str) -> GenerationChunk: + try: + obj = json.loads(line) + return self._process_stream_response(obj) + except Exception: + return GenerationChunk(text="") class OCIModelDeploymentTGI(OCIModelDeploymentLLM): """OCI Data Science Model Deployment TGI Endpoint. To use, you must provide the model HTTP endpoint from your deployed - model, e.g. https:///predict. + model, e.g. https://modeldeployment..oci.customer-oci.com//predict. To authenticate, `oracle-ads` has been used to automatically load credentials: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html @@ -228,12 +743,34 @@ class OCIModelDeploymentTGI(OCIModelDeploymentLLM): Example: .. code-block:: python - from langchain_community.llms import ModelDeploymentTGI + from langchain_community.llms import OCIModelDeploymentTGI - oci_md = ModelDeploymentTGI(endpoint="https:///predict") + llm = OCIModelDeploymentTGI( + endpoint="https://modeldeployment..oci.customer-oci.com//predict", + api="/v1/completions", + streaming=True, + temperature=0.2, + seed=42, + # other model parameters ... + ) """ + api: Literal["/generate", "/v1/completions"] = "/v1/completions" + """Api spec.""" + + frequency_penalty: float = 0.0 + """Penalizes repeated tokens according to frequency. Between 0 and 1.""" + + seed: Optional[int] = None + """Random sampling seed""" + + repetition_penalty: Optional[float] = None + """The parameter for repetition penalty. 1.0 means no penalty.""" + + suffix: Optional[str] = None + """The text to append to the prompt. """ + do_sample: bool = True """If set to True, this parameter enables decoding strategies such as multi-nominal sampling, beam-search multi-nominal sampling, Top-K @@ -255,34 +792,78 @@ def _llm_type(self) -> str: @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for invoking OCI model deployment TGI endpoint.""" + return ( + { + "model": self.model, # can be any + "frequency_penalty": self.frequency_penalty, + "max_tokens": self.max_tokens, + "repetition_penalty": self.repetition_penalty, + "temperature": self.temperature, + "top_p": self.p, + "seed": self.seed, + "stream": self.streaming, + "suffix": self.suffix, + "stop": self.stop, + } + if self.api == "/v1/completions" + else { + "best_of": self.best_of, + "max_new_tokens": self.max_tokens, + "temperature": self.temperature, + "top_k": ( + self.k if self.k > 0 else None + ), # `top_k` must be strictly positive' + "top_p": self.p, + "do_sample": self.do_sample, + "return_full_text": self.return_full_text, + "watermark": self.watermark, + "stop": self.stop, + } + ) + + @property + def _identifying_params(self) -> Dict[str, Any]: + """Get the identifying parameters.""" + _model_kwargs = self.model_kwargs or {} return { - "best_of": self.best_of, - "max_new_tokens": self.max_tokens, - "temperature": self.temperature, - "top_k": self.k - if self.k > 0 - else None, # `top_k` must be strictly positive' - "top_p": self.p, - "do_sample": self.do_sample, - "return_full_text": self.return_full_text, - "watermark": self.watermark, + **{ + "endpoint": self.endpoint, + "api": self.api, + "model_kwargs": _model_kwargs, + }, + **self._default_params, } def _construct_json_body(self, prompt: str, params: dict) -> dict: + """Construct request payload.""" + if self.api == "/v1/completions": + return super()._construct_json_body(prompt, params) + return { "inputs": prompt, "parameters": params, } - def _process_response(self, response_json: dict) -> str: - return str(response_json.get("generated_text", response_json)) + "\n" + def _process_response(self, response_json: dict) -> List[Generation]: + """Formats response.""" + if self.api == "/v1/completions": + return super()._process_response(response_json) + + try: + text = response_json["generated_text"] + except KeyError as e: + raise ValueError( + f"Error while formatting response payload.response_json={response_json}" + ) from e + + return [Generation(text=text)] class OCIModelDeploymentVLLM(OCIModelDeploymentLLM): """VLLM deployed on OCI Data Science Model Deployment To use, you must provide the model HTTP endpoint from your deployed - model, e.g. https:///predict. + model, e.g. https://modeldeployment..oci.customer-oci.com//predict. To authenticate, `oracle-ads` has been used to automatically load credentials: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html @@ -296,16 +877,19 @@ class OCIModelDeploymentVLLM(OCIModelDeploymentLLM): from langchain_community.llms import OCIModelDeploymentVLLM - oci_md = OCIModelDeploymentVLLM( - endpoint="https:///predict", - model="mymodel" + llm = OCIModelDeploymentVLLM( + endpoint="https://modeldeployment..oci.customer-oci.com//predict", + model="odsc-llm", + streaming=False, + temperature=0.2, + max_tokens=512, + n=3, + best_of=3, + # other model parameters ) """ - model: str - """The name of the model.""" - n: int = 1 """Number of output sequences to return for the given prompt.""" @@ -346,17 +930,9 @@ def _default_params(self) -> Dict[str, Any]: "n": self.n, "presence_penalty": self.presence_penalty, "stop": self.stop, + "stream": self.streaming, "temperature": self.temperature, "top_k": self.k, "top_p": self.p, "use_beam_search": self.use_beam_search, } - - def _construct_json_body(self, prompt: str, params: dict) -> dict: - return { - "prompt": prompt, - **params, - } - - def _process_response(self, response_json: dict) -> str: - return response_json["choices"][0]["text"] diff --git a/libs/community/langchain_community/llms/vllm.py b/libs/community/langchain_community/llms/vllm.py index b887a8b0ab246..dc8a7a76d24ed 100644 --- a/libs/community/langchain_community/llms/vllm.py +++ b/libs/community/langchain_community/llms/vllm.py @@ -123,14 +123,19 @@ def _generate( **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" - from vllm import SamplingParams # build sampling parameters params = {**self._default_params, **kwargs, "stop": stop} - sampling_params = SamplingParams(**params) + + # filter params for SamplingParams + known_keys = SamplingParams.__annotations__.keys() + sample_params = SamplingParams( + **{k: v for k, v in params.items() if k in known_keys} + ) + # call the model - outputs = self.client.generate(prompts, sampling_params) + outputs = self.client.generate(prompts, sample_params) generations = [] for output in outputs: diff --git a/libs/community/langchain_community/tools/databricks/_execution.py b/libs/community/langchain_community/tools/databricks/_execution.py index 48401d55fd842..62e8414fe49a3 100644 --- a/libs/community/langchain_community/tools/databricks/_execution.py +++ b/libs/community/langchain_community/tools/databricks/_execution.py @@ -1,5 +1,8 @@ import inspect import json +import logging +import os +import time from dataclasses import dataclass from io import StringIO from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional @@ -7,7 +10,7 @@ if TYPE_CHECKING: from databricks.sdk import WorkspaceClient from databricks.sdk.service.catalog import FunctionInfo - from databricks.sdk.service.sql import StatementParameterListItem + from databricks.sdk.service.sql import StatementParameterListItem, StatementState EXECUTE_FUNCTION_ARG_NAME = "__execution_args__" DEFAULT_EXECUTE_FUNCTION_ARGS = { @@ -15,6 +18,9 @@ "row_limit": 100, "byte_limit": 4096, } +UC_TOOL_CLIENT_EXECUTION_TIMEOUT = "UC_TOOL_CLIENT_EXECUTION_TIMEOUT" +DEFAULT_UC_TOOL_CLIENT_EXECUTION_TIMEOUT = "120" +_logger = logging.getLogger(__name__) def is_scalar(function: "FunctionInfo") -> bool: @@ -174,13 +180,42 @@ def execute_function( parameters=parametrized_statement.parameters, **execute_statement_args, # type: ignore ) - status = response.status - assert status is not None, f"Statement execution failed: {response}" - if status.state != StatementState.SUCCEEDED: - error = status.error + if response.status and job_pending(response.status.state) and response.statement_id: + statement_id = response.statement_id + wait_time = 0 + retry_cnt = 0 + client_execution_timeout = int( + os.environ.get( + UC_TOOL_CLIENT_EXECUTION_TIMEOUT, + DEFAULT_UC_TOOL_CLIENT_EXECUTION_TIMEOUT, + ) + ) + while wait_time < client_execution_timeout: + wait = min(2**retry_cnt, client_execution_timeout - wait_time) + _logger.debug( + f"Retrying {retry_cnt} time to get statement execution " + f"status after {wait} seconds." + ) + time.sleep(wait) + response = ws.statement_execution.get_statement(statement_id) # type: ignore + if response.status is None or not job_pending(response.status.state): + break + wait_time += wait + retry_cnt += 1 + if response.status and job_pending(response.status.state): + return FunctionExecutionResult( + error=f"Statement execution is still pending after {wait_time} " + "seconds. Please increase the wait_timeout argument for executing " + f"the function or increase {UC_TOOL_CLIENT_EXECUTION_TIMEOUT} " + "environment variable for increasing retrying time, default is " + f"{DEFAULT_UC_TOOL_CLIENT_EXECUTION_TIMEOUT} seconds." + ) + assert response.status is not None, f"Statement execution failed: {response}" + if response.status.state != StatementState.SUCCEEDED: + error = response.status.error assert ( error is not None - ), "Statement execution failed but no error message was provided." + ), f"Statement execution failed but no error message was provided: {response}" return FunctionExecutionResult(error=f"{error.error_code}: {error.message}") manifest = response.manifest assert manifest is not None @@ -211,3 +246,9 @@ def execute_function( return FunctionExecutionResult( format="CSV", value=csv_buffer.getvalue(), truncated=truncated ) + + +def job_pending(state: Optional["StatementState"]) -> bool: + from databricks.sdk.service.sql import StatementState + + return state in (StatementState.PENDING, StatementState.RUNNING) diff --git a/libs/community/langchain_community/utilities/arxiv.py b/libs/community/langchain_community/utilities/arxiv.py index a4c6cf5aceaad..5ed5c763611ad 100644 --- a/libs/community/langchain_community/utilities/arxiv.py +++ b/libs/community/langchain_community/utilities/arxiv.py @@ -94,6 +94,16 @@ def validate_environment(cls, values: Dict) -> Any: ) return values + def _fetch_results(self, query: str) -> Any: + """Helper function to fetch arxiv results based on query.""" + if self.is_arxiv_identifier(query): + return self.arxiv_search( + id_list=query.split(), max_results=self.top_k_results + ).results() + return self.arxiv_search( + query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results + ).results() + def get_summaries_as_docs(self, query: str) -> List[Document]: """ Performs an arxiv search and returns list of @@ -107,16 +117,11 @@ def get_summaries_as_docs(self, query: str) -> List[Document]: query: a plaintext search query """ try: - if self.is_arxiv_identifier(query): - results = self.arxiv_search( - id_list=query.split(), - max_results=self.top_k_results, - ).results() - else: - results = self.arxiv_search( # type: ignore - query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results - ).results() + results = self._fetch_results( + query + ) # Using helper function to fetch results except self.arxiv_exceptions as ex: + logger.error(f"Arxiv exception: {ex}") # Added error logging return [Document(page_content=f"Arxiv exception: {ex}")] docs = [ Document( @@ -146,16 +151,11 @@ def run(self, query: str) -> str: query: a plaintext search query """ try: - if self.is_arxiv_identifier(query): - results = self.arxiv_search( - id_list=query.split(), - max_results=self.top_k_results, - ).results() - else: - results = self.arxiv_search( # type: ignore - query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results - ).results() + results = self._fetch_results( + query + ) # Using helper function to fetch results except self.arxiv_exceptions as ex: + logger.error(f"Arxiv exception: {ex}") # Added error logging return f"Arxiv exception: {ex}" docs = [ f"Published: {result.updated.date()}\n" @@ -208,15 +208,9 @@ def lazy_load(self, query: str) -> Iterator[Document]: try: # Remove the ":" and "-" from the query, as they can cause search problems query = query.replace(":", "").replace("-", "") - if self.is_arxiv_identifier(query): - results = self.arxiv_search( - id_list=query[: self.ARXIV_MAX_QUERY_LENGTH].split(), - max_results=self.load_max_docs, - ).results() - else: - results = self.arxiv_search( # type: ignore - query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.load_max_docs - ).results() + results = self._fetch_results( + query + ) # Using helper function to fetch results except self.arxiv_exceptions as ex: logger.debug("Error on arxiv: %s", ex) return diff --git a/libs/community/langchain_community/vectorstores/cassandra.py b/libs/community/langchain_community/vectorstores/cassandra.py index 85f6460d0133e..3e9ea17cbb0ae 100644 --- a/libs/community/langchain_community/vectorstores/cassandra.py +++ b/libs/community/langchain_community/vectorstores/cassandra.py @@ -1,6 +1,7 @@ from __future__ import annotations import asyncio +import importlib.metadata import typing import uuid from typing import ( @@ -18,6 +19,7 @@ ) import numpy as np +from packaging.version import Version # this is a lancghain-core dependency if typing.TYPE_CHECKING: from cassandra.cluster import Session @@ -30,6 +32,7 @@ from langchain_community.vectorstores.utils import maximal_marginal_relevance CVST = TypeVar("CVST", bound="Cassandra") +MIN_CASSIO_VERSION = Version("0.1.10") class Cassandra(VectorStore): @@ -110,6 +113,15 @@ def __init__( "Could not import cassio python package. " "Please install it with `pip install cassio`." ) + cassio_version = Version(importlib.metadata.version("cassio")) + + if cassio_version is not None and cassio_version < MIN_CASSIO_VERSION: + msg = ( + "Cassio version not supported. Please upgrade cassio " + f"to version {MIN_CASSIO_VERSION} or higher." + ) + raise ImportError(msg) + if not table_name: raise ValueError("Missing required parameter 'table_name'.") self.embedding = embedding @@ -143,6 +155,9 @@ def __init__( **kwargs, ) + if self.session is None: + self.session = self.table.session + @property def embeddings(self) -> Embeddings: return self.embedding @@ -231,6 +246,70 @@ async def adelete( await self.adelete_by_document_id(document_id) return True + def delete_by_metadata_filter( + self, + filter: dict[str, Any], + *, + batch_size: int = 50, + ) -> int: + """Delete all documents matching a certain metadata filtering condition. + + This operation does not use the vector embeddings in any way, it simply + removes all documents whose metadata match the provided condition. + + Args: + filter: Filter on the metadata to apply. The filter cannot be empty. + batch_size: amount of deletions per each batch (until exhaustion of + the matching documents). + + Returns: + A number expressing the amount of deleted documents. + """ + if not filter: + msg = ( + "Method `delete_by_metadata_filter` does not accept an empty " + "filter. Use the `clear()` method if you really want to empty " + "the vector store." + ) + raise ValueError(msg) + + return self.table.find_and_delete_entries( + metadata=filter, + batch_size=batch_size, + ) + + async def adelete_by_metadata_filter( + self, + filter: dict[str, Any], + *, + batch_size: int = 50, + ) -> int: + """Delete all documents matching a certain metadata filtering condition. + + This operation does not use the vector embeddings in any way, it simply + removes all documents whose metadata match the provided condition. + + Args: + filter: Filter on the metadata to apply. The filter cannot be empty. + batch_size: amount of deletions per each batch (until exhaustion of + the matching documents). + + Returns: + A number expressing the amount of deleted documents. + """ + if not filter: + msg = ( + "Method `delete_by_metadata_filter` does not accept an empty " + "filter. Use the `clear()` method if you really want to empty " + "the vector store." + ) + raise ValueError(msg) + + return await self.table.afind_and_delete_entries( + metadata=filter, + batch_size=batch_size, + ) + def add_texts( self, texts: Iterable[str], @@ -333,6 +412,180 @@ async def send_concurrently( await asyncio.gather(*tasks) return ids + def replace_metadata( + self, + id_to_metadata: dict[str, dict], + *, + batch_size: int = 50, + ) -> None: + """Replace the metadata of documents. + + For each document to update, identified by its ID, the new metadata + dictionary completely replaces what is on the store. This includes + passing empty metadata `{}` to erase the currently-stored information. + + Args: + id_to_metadata: map from the Document IDs to modify to the + new metadata for updating. + Keys in this dictionary that do not correspond to an existing + document will not cause an error, rather will result in new + rows being written into the Cassandra table but without an + associated vector: hence unreachable through vector search. + batch_size: Number of concurrent requests to send to the server. + + Returns: + None if the writes succeed (otherwise an error is raised). + """ + ids_and_metadatas = list(id_to_metadata.items()) + for i in range(0, len(ids_and_metadatas), batch_size): + batch_i_m = ids_and_metadatas[i : i + batch_size] + futures = [ + self.table.put_async( + row_id=doc_id, + metadata=doc_md, + ) + for doc_id, doc_md in batch_i_m + ] + for future in futures: + future.result() + return + + async def areplace_metadata( + self, + id_to_metadata: dict[str, dict], + *, + concurrency: int = 50, + ) -> None: + """Replace the metadata of documents. + + For each document to update, identified by its ID, the new metadata + dictionary completely replaces what is on the store. This includes + passing empty metadata `{}` to erase the currently-stored information. + + Args: + id_to_metadata: map from the Document IDs to modify to the + new metadata for updating. + Keys in this dictionary that do not correspond to an existing + document will not cause an error, rather will result in new + rows being written into the Cassandra table but without an + associated vector: hence unreachable through vector search. + concurrency: Number of concurrent queries to the database. + Defaults to 50. + + Returns: + None if the writes succeed (otherwise an error is raised). + """ + ids_and_metadatas = list(id_to_metadata.items()) + + sem = asyncio.Semaphore(concurrency) + + async def send_concurrently(doc_id: str, doc_md: dict) -> None: + async with sem: + await self.table.aput( + row_id=doc_id, + metadata=doc_md, + ) + + for doc_id, doc_md in ids_and_metadatas: + tasks = [asyncio.create_task(send_concurrently(doc_id, doc_md))] + await asyncio.gather(*tasks) + + return + + @staticmethod + def _row_to_document(row: Dict[str, Any]) -> Document: + return Document( + id=row["row_id"], + page_content=row["body_blob"], + metadata=row["metadata"], + ) + + def get_by_document_id(self, document_id: str) -> Document | None: + """Get by document ID. + + Args: + document_id: the document ID to get. + """ + row = self.table.get(row_id=document_id) + if row is None: + return None + return self._row_to_document(row=row) + + async def aget_by_document_id(self, document_id: str) -> Document | None: + """Get by document ID. + + Args: + document_id: the document ID to get. + """ + row = await self.table.aget(row_id=document_id) + if row is None: + return None + return self._row_to_document(row=row) + + def metadata_search( + self, + metadata: dict[str, Any] = {}, # noqa: B006 + n: int = 5, + ) -> Iterable[Document]: + """Get documents via a metadata search. + + Args: + metadata: the metadata to query for. + """ + rows = self.table.find_entries(metadata=metadata, n=n) + return [self._row_to_document(row=row) for row in rows if row] + + async def ametadata_search( + self, + metadata: dict[str, Any] = {}, # noqa: B006 + n: int = 5, + ) -> Iterable[Document]: + """Get documents via a metadata search. + + Args: + metadata: the metadata to query for. + """ + rows = await self.table.afind_entries(metadata=metadata, n=n) + return [self._row_to_document(row=row) for row in rows] + + async def asimilarity_search_with_embedding_id_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, str]] = None, + body_search: Optional[Union[str, List[str]]] = None, + ) -> List[Tuple[Document, List[float], str]]: + """Return docs most similar to embedding vector. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Filter on the metadata to apply. + body_search: Document textual search terms to apply. + Only supported by Astra DB at the moment. + Returns: + List of (Document, embedding, id), the most similar to the query vector. + """ + kwargs: Dict[str, Any] = {} + if filter is not None: + kwargs["metadata"] = filter + if body_search is not None: + kwargs["body_search"] = body_search + + hits = await self.table.aann_search( + vector=embedding, + n=k, + **kwargs, + ) + return [ + ( + self._row_to_document(row=hit), + hit["vector"], + hit["row_id"], + ) + for hit in hits + ] + @staticmethod def _search_to_documents( hits: Iterable[Dict[str, Any]], @@ -341,10 +594,7 @@ def _search_to_documents( # (1=most relevant), as required by this class' contract. return [ ( - Document( - page_content=hit["body_blob"], - metadata=hit["metadata"], - ), + Cassandra._row_to_document(row=hit), 0.5 + 0.5 * hit["distance"], hit["row_id"], ) @@ -375,7 +625,6 @@ def similarity_search_with_score_id_by_vector( kwargs["metadata"] = filter if body_search is not None: kwargs["body_search"] = body_search - hits = self.table.metric_ann_search( vector=embedding, n=k, @@ -712,13 +961,7 @@ def _mmr_search_to_documents( for pf_index, pf_hit in enumerate(prefetch_hits) if pf_index in mmr_chosen_indices ] - return [ - Document( - page_content=hit["body_blob"], - metadata=hit["metadata"], - ) - for hit in mmr_hits - ] + return [Cassandra._row_to_document(row=hit) for hit in mmr_hits] def max_marginal_relevance_search_by_vector( self, diff --git a/libs/community/langchain_community/vectorstores/clickhouse.py b/libs/community/langchain_community/vectorstores/clickhouse.py index a285cb8445210..4246abfc0ddde 100644 --- a/libs/community/langchain_community/vectorstores/clickhouse.py +++ b/libs/community/langchain_community/vectorstores/clickhouse.py @@ -309,6 +309,14 @@ def __init__( **kwargs, ) # Enable JSON type + try: + self.client.command("SET allow_experimental_json_type=1") + except Exception as _: + logger.debug( + f"Clickhouse version={self.client.server_version} - " + "There is no allow_experimental_json_type parameter." + ) + self.client.command("SET allow_experimental_object_type=1") if self.config.index_type: # Enable index diff --git a/libs/community/langchain_community/vectorstores/infinispanvs.py b/libs/community/langchain_community/vectorstores/infinispanvs.py index a7c3cee745a9a..3f7c34158df15 100644 --- a/libs/community/langchain_community/vectorstores/infinispanvs.py +++ b/libs/community/langchain_community/vectorstores/infinispanvs.py @@ -5,9 +5,10 @@ import json import logging import uuid -from typing import Any, Iterable, List, Optional, Tuple, Type, cast +import warnings +from typing import Any, Iterable, List, Optional, Tuple, Type, Union, cast -import requests +from httpx import Response from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore @@ -49,7 +50,7 @@ class InfinispanVS(VectorStore): embedding=RGBEmbeddings(), output_fields: ["texture", "color"], lambda_key: lambda text,meta: str(meta["_key"]), - lambda_content: lambda item: item["color"]}) + lambda_content: lambda item: item["color"]) """ def __init__( @@ -58,13 +59,48 @@ def __init__( ids: Optional[List[str]] = None, **kwargs: Any, ): + """ + Parameters + ---------- + cache_name: str + Embeddings cache name. Default "vector" + entity_name: str + Protobuf entity name for the embeddings. Default "vector" + text_field: str + Protobuf field name for text. Default "text" + vector_field: str + Protobuf field name for vector. Default "vector" + lambda_content: lambda + Lambda returning the content part of an item. Default returns text_field + lambda_metadata: lambda + Lambda returning the metadata part of an item. Default returns items + fields excepts text_field, vector_field, _type + output_fields: List[str] + List of fields to be returned from item, if None return all fields. + Default None + kwargs: Any + Rest of arguments passed to Infinispan. See docs""" self.ispn = Infinispan(**kwargs) self._configuration = kwargs self._cache_name = str(self._configuration.get("cache_name", "vector")) self._entity_name = str(self._configuration.get("entity_name", "vector")) self._embedding = embedding - self._textfield = self._configuration.get("textfield", "text") - self._vectorfield = self._configuration.get("vectorfield", "vector") + self._textfield = self._configuration.get("textfield", "") + if self._textfield == "": + self._textfield = self._configuration.get("text_field", "text") + else: + warnings.warn( + "`textfield` is deprecated. Please use `text_field` " "param.", + DeprecationWarning, + ) + self._vectorfield = self._configuration.get("vectorfield", "") + if self._vectorfield == "": + self._vectorfield = self._configuration.get("vector_field", "vector") + else: + warnings.warn( + "`vectorfield` is deprecated. Please use `vector_field` " "param.", + DeprecationWarning, + ) self._to_content = self._configuration.get( "lambda_content", lambda item: self._default_content(item) ) @@ -121,7 +157,7 @@ def schema_builder(self, templ: dict, dimension: int) -> str: metadata_proto += "}\n" return metadata_proto - def schema_create(self, proto: str) -> requests.Response: + def schema_create(self, proto: str) -> Response: """Deploy the schema for the vector db Args: proto(str): protobuf schema @@ -130,14 +166,14 @@ def schema_create(self, proto: str) -> requests.Response: """ return self.ispn.schema_post(self._entity_name + ".proto", proto) - def schema_delete(self) -> requests.Response: + def schema_delete(self) -> Response: """Delete the schema for the vector db Returns: An http Response containing the result of the operation """ return self.ispn.schema_delete(self._entity_name + ".proto") - def cache_create(self, config: str = "") -> requests.Response: + def cache_create(self, config: str = "") -> Response: """Create the cache for the vector db Args: config(str): configuration of the cache. @@ -172,14 +208,14 @@ def cache_create(self, config: str = "") -> requests.Response: ) return self.ispn.cache_post(self._cache_name, config) - def cache_delete(self) -> requests.Response: + def cache_delete(self) -> Response: """Delete the cache for the vector db Returns: An http Response containing the result of the operation """ return self.ispn.cache_delete(self._cache_name) - def cache_clear(self) -> requests.Response: + def cache_clear(self) -> Response: """Clear the cache for the vector db Returns: An http Response containing the result of the operation @@ -193,14 +229,14 @@ def cache_exists(self) -> bool: """ return self.ispn.cache_exists(self._cache_name) - def cache_index_clear(self) -> requests.Response: + def cache_index_clear(self) -> Response: """Clear the index for the vector db Returns: An http Response containing the result of the operation """ return self.ispn.index_clear(self._cache_name) - def cache_index_reindex(self) -> requests.Response: + def cache_index_reindex(self) -> Response: """Rebuild the for the vector db Returns: An http Response containing the result of the operation @@ -325,12 +361,16 @@ def _query_result_to_docs( def configure(self, metadata: dict, dimension: int) -> None: schema = self.schema_builder(metadata, dimension) output = self.schema_create(schema) - assert output.ok, "Unable to create schema. Already exists? " + assert ( + output.status_code == self.ispn.Codes.OK + ), "Unable to create schema. Already exists? " "Consider using clear_old=True" assert json.loads(output.text)["error"] is None if not self.cache_exists(): output = self.cache_create() - assert output.ok, "Unable to create cache. Already exists? " + assert ( + output.status_code == self.ispn.Codes.OK + ), "Unable to create cache. Already exists? " "Consider using clear_old=True" # Ensure index is clean self.cache_index_clear() @@ -350,7 +390,24 @@ def from_texts( auto_config: Optional[bool] = True, **kwargs: Any, ) -> InfinispanVS: - """Return VectorStore initialized from texts and embeddings.""" + """Return VectorStore initialized from texts and embeddings. + + In addition to parameters described by the super method, this + implementation provides other configuration params if different + configuration from default is needed. + + Parameters + ---------- + ids : List[str] + Additional list of keys associated to the embedding. If not + provided UUIDs will be generated + clear_old : bool + Whether old data must be deleted. Default True + auto_config: bool + Whether to do a complete server setup (caches, + protobuf definition...). Default True + kwargs: Any + Rest of arguments passed to InfinispanVS. See docs""" infinispanvs = cls(embedding=embedding, ids=ids, **kwargs) if auto_config and len(metadatas or []) > 0: if clear_old: @@ -381,20 +438,83 @@ class Infinispan: https://github.com/rigazilla/infinispan-vector#run-infinispan """ - def __init__(self, **kwargs: Any): + def __init__( + self, + schema: str = "http", + user: str = "", + password: str = "", + hosts: List[str] = ["127.0.0.1:11222"], + cache_url: str = "/rest/v2/caches", + schema_url: str = "/rest/v2/schemas", + use_post_for_query: bool = True, + http2: bool = True, + verify: bool = True, + **kwargs: Any, + ): + """ + Parameters + ---------- + schema: str + Schema for HTTP request: "http" or "https". Default "http" + user, password: str + User and password if auth is required. Default None + hosts: List[str] + List of server addresses. Default ["127.0.0.1:11222"] + cache_url: str + URL endpoint for cache API. Default "/rest/v2/caches" + schema_url: str + URL endpoint for schema API. Default "/rest/v2/schemas" + use_post_for_query: bool + Whether POST method should be used for query. Default True + http2: bool + Whether HTTP/2 protocol should be used. `pip install "httpx[http2]"` is + needed for HTTP/2. Default True + verify: bool + Whether TLS certificate must be verified. Default True + """ + + try: + import httpx + except ImportError: + raise ImportError( + "Could not import httpx python package. " + "Please install it with `pip install httpx`" + 'or `pip install "httpx[http2]"` if you need HTTP/2.' + ) + + self.Codes = httpx.codes + self._configuration = kwargs - self._schema = str(self._configuration.get("schema", "http")) - self._host = str(self._configuration.get("hosts", ["127.0.0.1:11222"])[0]) + self._schema = schema + self._user = user + self._password = password + self._host = hosts[0] self._default_node = self._schema + "://" + self._host - self._cache_url = str(self._configuration.get("cache_url", "/rest/v2/caches")) - self._schema_url = str(self._configuration.get("cache_url", "/rest/v2/schemas")) - self._use_post_for_query = str( - self._configuration.get("use_post_for_query", True) - ) + self._cache_url = cache_url + self._schema_url = schema_url + self._use_post_for_query = use_post_for_query + self._http2 = http2 + if self._user and self._password: + if self._schema == "http": + auth: Union[Tuple[str, str], httpx.DigestAuth] = httpx.DigestAuth( + username=self._user, password=self._password + ) + else: + auth = (self._user, self._password) + self._h2c = httpx.Client( + http2=self._http2, + http1=not self._http2, + auth=auth, + verify=verify, + ) + else: + self._h2c = httpx.Client( + http2=self._http2, + http1=not self._http2, + verify=verify, + ) - def req_query( - self, query: str, cache_name: str, local: bool = False - ) -> requests.Response: + def req_query(self, query: str, cache_name: str, local: bool = False) -> Response: """Request a query Args: query(str): query requested @@ -409,7 +529,7 @@ def req_query( def _query_post( self, query_str: str, cache_name: str, local: bool = False - ) -> requests.Response: + ) -> Response: api_url = ( self._default_node + self._cache_url @@ -420,9 +540,9 @@ def _query_post( ) data = {"query": query_str} data_json = json.dumps(data) - response = requests.post( + response = self._h2c.post( api_url, - data_json, + content=data_json, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT, ) @@ -430,7 +550,7 @@ def _query_post( def _query_get( self, query_str: str, cache_name: str, local: bool = False - ) -> requests.Response: + ) -> Response: api_url = ( self._default_node + self._cache_url @@ -441,10 +561,10 @@ def _query_get( + "&local=" + str(local) ) - response = requests.get(api_url, timeout=REST_TIMEOUT) + response = self._h2c.get(api_url, timeout=REST_TIMEOUT) return response - def post(self, key: str, data: str, cache_name: str) -> requests.Response: + def post(self, key: str, data: str, cache_name: str) -> Response: """Post an entry Args: key(str): key of the entry @@ -454,15 +574,15 @@ def post(self, key: str, data: str, cache_name: str) -> requests.Response: An http Response containing the result of the operation """ api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key - response = requests.post( + response = self._h2c.post( api_url, - data, + content=data, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT, ) return response - def put(self, key: str, data: str, cache_name: str) -> requests.Response: + def put(self, key: str, data: str, cache_name: str) -> Response: """Put an entry Args: key(str): key of the entry @@ -472,15 +592,15 @@ def put(self, key: str, data: str, cache_name: str) -> requests.Response: An http Response containing the result of the operation """ api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key - response = requests.put( + response = self._h2c.put( api_url, - data, + content=data, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT, ) return response - def get(self, key: str, cache_name: str) -> requests.Response: + def get(self, key: str, cache_name: str) -> Response: """Get an entry Args: key(str): key of the entry @@ -489,12 +609,12 @@ def get(self, key: str, cache_name: str) -> requests.Response: An http Response containing the entry or errors """ api_url = self._default_node + self._cache_url + "/" + cache_name + "/" + key - response = requests.get( + response = self._h2c.get( api_url, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT ) return response - def schema_post(self, name: str, proto: str) -> requests.Response: + def schema_post(self, name: str, proto: str) -> Response: """Deploy a schema Args: name(str): name of the schema. Will be used as a key @@ -503,10 +623,10 @@ def schema_post(self, name: str, proto: str) -> requests.Response: An http Response containing the result of the operation """ api_url = self._default_node + self._schema_url + "/" + name - response = requests.post(api_url, proto, timeout=REST_TIMEOUT) + response = self._h2c.post(api_url, content=proto, timeout=REST_TIMEOUT) return response - def cache_post(self, name: str, config: str) -> requests.Response: + def cache_post(self, name: str, config: str) -> Response: """Create a cache Args: name(str): name of the cache. @@ -515,15 +635,15 @@ def cache_post(self, name: str, config: str) -> requests.Response: An http Response containing the result of the operation """ api_url = self._default_node + self._cache_url + "/" + name - response = requests.post( + response = self._h2c.post( api_url, - config, + content=config, headers={"Content-Type": "application/json"}, timeout=REST_TIMEOUT, ) return response - def schema_delete(self, name: str) -> requests.Response: + def schema_delete(self, name: str) -> Response: """Delete a schema Args: name(str): name of the schema. @@ -531,10 +651,10 @@ def schema_delete(self, name: str) -> requests.Response: An http Response containing the result of the operation """ api_url = self._default_node + self._schema_url + "/" + name - response = requests.delete(api_url, timeout=REST_TIMEOUT) + response = self._h2c.delete(api_url, timeout=REST_TIMEOUT) return response - def cache_delete(self, name: str) -> requests.Response: + def cache_delete(self, name: str) -> Response: """Delete a cache Args: name(str): name of the cache. @@ -542,10 +662,10 @@ def cache_delete(self, name: str) -> requests.Response: An http Response containing the result of the operation """ api_url = self._default_node + self._cache_url + "/" + name - response = requests.delete(api_url, timeout=REST_TIMEOUT) + response = self._h2c.delete(api_url, timeout=REST_TIMEOUT) return response - def cache_clear(self, cache_name: str) -> requests.Response: + def cache_clear(self, cache_name: str) -> Response: """Clear a cache Args: cache_name(str): name of the cache. @@ -555,7 +675,7 @@ def cache_clear(self, cache_name: str) -> requests.Response: api_url = ( self._default_node + self._cache_url + "/" + cache_name + "?action=clear" ) - response = requests.post(api_url, timeout=REST_TIMEOUT) + response = self._h2c.post(api_url, timeout=REST_TIMEOUT) return response def cache_exists(self, cache_name: str) -> bool: @@ -570,18 +690,17 @@ def cache_exists(self, cache_name: str) -> bool: ) return self.resource_exists(api_url) - @staticmethod - def resource_exists(api_url: str) -> bool: + def resource_exists(self, api_url: str) -> bool: """Check if a resource exists Args: api_url(str): url of the resource. Returns: true if resource exists """ - response = requests.head(api_url, timeout=REST_TIMEOUT) - return response.ok + response = self._h2c.head(api_url, timeout=REST_TIMEOUT) + return response.status_code == self.Codes.OK - def index_clear(self, cache_name: str) -> requests.Response: + def index_clear(self, cache_name: str) -> Response: """Clear an index on a cache Args: cache_name(str): name of the cache. @@ -595,9 +714,9 @@ def index_clear(self, cache_name: str) -> requests.Response: + cache_name + "/search/indexes?action=clear" ) - return requests.post(api_url, timeout=REST_TIMEOUT) + return self._h2c.post(api_url, timeout=REST_TIMEOUT) - def index_reindex(self, cache_name: str) -> requests.Response: + def index_reindex(self, cache_name: str) -> Response: """Rebuild index on a cache Args: cache_name(str): name of the cache. @@ -611,4 +730,4 @@ def index_reindex(self, cache_name: str) -> requests.Response: + cache_name + "/search/indexes?action=reindex" ) - return requests.post(api_url, timeout=REST_TIMEOUT) + return self._h2c.post(api_url, timeout=REST_TIMEOUT) diff --git a/libs/community/langchain_community/vectorstores/myscale.py b/libs/community/langchain_community/vectorstores/myscale.py index ecd2108d419ba..d3dec65c8b71e 100644 --- a/libs/community/langchain_community/vectorstores/myscale.py +++ b/libs/community/langchain_community/vectorstores/myscale.py @@ -193,6 +193,13 @@ def __init__( password=self.config.password, **kwargs, ) + try: + self.client.command("SET allow_experimental_json_type=1") + except Exception as _: + logger.debug( + f"Clickhouse version={self.client.server_version} - " + "There is no allow_experimental_json_type parameter." + ) self.client.command("SET allow_experimental_object_type=1") self.client.command(schema_) diff --git a/libs/community/langchain_community/vectorstores/neo4j_vector.py b/libs/community/langchain_community/vectorstores/neo4j_vector.py index 6f21eedd4534e..bb0be02afdd74 100644 --- a/libs/community/langchain_community/vectorstores/neo4j_vector.py +++ b/libs/community/langchain_community/vectorstores/neo4j_vector.py @@ -623,7 +623,7 @@ def query( params = params or {} try: data, _, _ = self._driver.execute_query( - query, database=self._database, parameters_=params + query, database_=self._database, parameters_=params ) return [r.data() for r in data] except Neo4jError as e: @@ -646,7 +646,7 @@ def query( ): raise # Fallback to allow implicit transactions - with self._driver.session() as session: + with self._driver.session(database=self._database) as session: data = session.run(Query(text=query), params) return [r.data() for r in data] diff --git a/libs/community/langchain_community/vectorstores/pgvector.py b/libs/community/langchain_community/vectorstores/pgvector.py index afeca95b568ef..85814bef99488 100644 --- a/libs/community/langchain_community/vectorstores/pgvector.py +++ b/libs/community/langchain_community/vectorstores/pgvector.py @@ -219,12 +219,12 @@ def _results_to_docs(docs_and_scores: Any) -> List[Document]: since="0.0.31", message=( "This class is pending deprecation and may be removed in a future version. " - "You can swap to using the `PGVector`" - " implementation in `langchain_postgres`. " + "You can swap to using the `PGVector` " + "implementation in `langchain_postgres`. " "Please read the guidelines in the doc-string of this class " "to follow prior to migrating as there are some differences " "between the implementations. " - "See for details about" + "See for details about " "the new implementation." ), alternative="from langchain_postgres import PGVector;", @@ -331,11 +331,11 @@ def __init__( message=( "Please use JSONB instead of JSON for metadata. " "This change will allow for more efficient querying that " - "involves filtering based on metadata." + "involves filtering based on metadata. " "Please note that filtering operators have been changed " - "when using JSOB metadata to be prefixed with a $ sign " + "when using JSONB metadata to be prefixed with a $ sign " "to avoid name collisions with columns. " - "If you're using an existing database, you will need to create a" + "If you're using an existing database, you will need to create a " "db migration for your metadata column to be JSONB and update your " "queries to use the new operators. " ), diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index e463dd0ec7dce..706e8cfc4d89b 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,114 +1,114 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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integrations." authors = [] license = "MIT" @@ -33,13 +33,13 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = "^0.3.6" -langchain = "^0.3.1" +langchain-core = "^0.3.10" +langchain = "^0.3.3" SQLAlchemy = ">=1.4,<3" requests = "^2" PyYAML = ">=5.3" aiohttp = "^3.8.3" -tenacity = "^8.1.0,!=8.4.0" +tenacity = ">=8.1.0,!=8.4.0,<10" dataclasses-json = ">= 0.5.7, < 0.7" pydantic-settings = "^2.4.0" langsmith = "^0.1.125" diff --git a/libs/community/scripts/check_pydantic.sh b/libs/community/scripts/check_pydantic.sh index 1b5d616257885..6d6f6dbcef919 100755 --- a/libs/community/scripts/check_pydantic.sh +++ b/libs/community/scripts/check_pydantic.sh @@ -20,7 +20,7 @@ count=$(git grep -E '(@root_validator)|(@validator)|(@field_validator)|(@pre_ini # PRs that increase the current count will not be accepted. # PRs that decrease update the code in the repository # and allow decreasing the count of are welcome! -current_count=128 +current_count=127 if [ "$count" -gt "$current_count" ]; then echo "The PR seems to be introducing new usage of @root_validator and/or @field_validator." diff --git a/libs/community/tests/integration_tests/chat_models/test_litellm_router.py b/libs/community/tests/integration_tests/chat_models/test_litellm_router.py index 7bbaddb162a96..23f60398d6f74 100644 --- a/libs/community/tests/integration_tests/chat_models/test_litellm_router.py +++ b/libs/community/tests/integration_tests/chat_models/test_litellm_router.py @@ -266,7 +266,6 @@ def test_litellm_router_streaming_callback( fake_completion.check_inputs(expected_num_calls=1) -@pytest.mark.asyncio @pytest.mark.scheduled async def test_async_litellm_router( fake_completion: FakeCompletion, litellm_router: Any @@ -295,7 +294,6 @@ async def test_async_litellm_router( fake_completion.check_inputs(expected_num_calls=2) -@pytest.mark.asyncio @pytest.mark.scheduled async def test_async_litellm_router_streaming( fake_completion: FakeCompletion, litellm_router: Any diff --git a/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py b/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py index d286aca33b8b2..6749090eb2c6b 100644 --- a/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py +++ b/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py @@ -2,7 +2,6 @@ from typing import Any, cast -import pytest from langchain_core.callbacks import CallbackManager from langchain_core.messages import ( AIMessage, @@ -201,7 +200,6 @@ def test_stream() -> None: assert len(list(res)) >= 1 -@pytest.mark.asyncio async def test_async_invoke() -> None: chat = QianfanChatEndpoint() # type: ignore[call-arg] res = await chat.ainvoke([HumanMessage(content="Hello")]) @@ -209,7 +207,6 @@ async def test_async_invoke() -> None: assert res.content != "" -@pytest.mark.asyncio async def test_async_generate() -> None: """Tests chat agenerate works.""" chat = QianfanChatEndpoint() # type: ignore[call-arg] @@ -229,7 +226,6 @@ async def test_async_generate() -> None: assert isinstance(generation.text, str) -@pytest.mark.asyncio async def test_async_stream() -> None: chat = QianfanChatEndpoint(streaming=True) # type: ignore[call-arg] async for token in chat.astream( diff --git a/libs/community/tests/integration_tests/chat_models/test_yuan2.py b/libs/community/tests/integration_tests/chat_models/test_yuan2.py index 5499e0919b706..bbe18d75cfebd 100644 --- a/libs/community/tests/integration_tests/chat_models/test_yuan2.py +++ b/libs/community/tests/integration_tests/chat_models/test_yuan2.py @@ -100,7 +100,6 @@ def test_chat_yuan2_streaming() -> None: assert isinstance(response, BaseMessage) -@pytest.mark.asyncio async def test_async_chat_yuan2() -> None: """Test async generation.""" chat = ChatYuan2( # type: ignore[call-arg] @@ -124,7 +123,6 @@ async def test_async_chat_yuan2() -> None: assert generation.text == generation.message.content -@pytest.mark.asyncio async def test_async_chat_yuan2_streaming() -> None: """Test that streaming correctly invokes on_llm_new_token callback.""" callback_handler = FakeCallbackHandler() diff --git a/libs/community/tests/integration_tests/indexes/test_document_manager.py b/libs/community/tests/integration_tests/indexes/test_document_manager.py index f9d88d8398ae7..b2b99b449c685 100644 --- a/libs/community/tests/integration_tests/indexes/test_document_manager.py +++ b/libs/community/tests/integration_tests/indexes/test_document_manager.py @@ -43,7 +43,6 @@ def test_update(manager: MongoDocumentManager) -> None: assert sorted(all_keys) == sorted(read_keys + updated_keys) -@pytest.mark.asyncio @pytest.mark.requires("motor") async def test_aupdate(amanager: MongoDocumentManager) -> None: """Test updating records in the MongoDB.""" diff --git a/libs/community/tests/integration_tests/llms/test_vertexai.py b/libs/community/tests/integration_tests/llms/test_vertexai.py index 613e63d3d7bf3..598af7abc1936 100644 --- a/libs/community/tests/integration_tests/llms/test_vertexai.py +++ b/libs/community/tests/integration_tests/llms/test_vertexai.py @@ -150,7 +150,6 @@ def test_model_garden_generate( assert len(output.generations) == 2 -@pytest.mark.asyncio @pytest.mark.parametrize( "endpoint_os_variable_name,result_arg", [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)], diff --git a/libs/community/tests/integration_tests/retrievers/test_contextual_compression.py b/libs/community/tests/integration_tests/retrievers/test_contextual_compression.py index fb643a81c2e3b..ae8acca430bd3 100644 --- a/libs/community/tests/integration_tests/retrievers/test_contextual_compression.py +++ b/libs/community/tests/integration_tests/retrievers/test_contextual_compression.py @@ -1,4 +1,3 @@ -import pytest from langchain.retrievers.contextual_compression import ContextualCompressionRetriever from langchain.retrievers.document_compressors import EmbeddingsFilter @@ -27,7 +26,6 @@ def test_contextual_compression_retriever_get_relevant_docs() -> None: assert texts[-1] not in [d.page_content for d in actual] -@pytest.mark.asyncio async def test_acontextual_compression_retriever_get_relevant_docs() -> None: """Test get_relevant_docs.""" texts = [ diff --git a/libs/community/tests/integration_tests/storage/test_sql.py b/libs/community/tests/integration_tests/storage/test_sql.py index a454029b86cdf..a9eac4a500d74 100644 --- a/libs/community/tests/integration_tests/storage/test_sql.py +++ b/libs/community/tests/integration_tests/storage/test_sql.py @@ -45,7 +45,6 @@ def test_mget(sql_engine: Engine) -> None: assert result == [b"value1", b"value2"] -@pytest.mark.asyncio async def test_amget(sql_aengine: AsyncEngine) -> None: """Test mget method.""" store = SQLStore(engine=sql_aengine, namespace="test") @@ -85,7 +84,6 @@ def test_mset(sql_engine: Engine) -> None: session.commit() -@pytest.mark.asyncio async def test_amset(sql_aengine: AsyncEngine) -> None: """Test that multiple keys can be set.""" store = SQLStore(engine=sql_aengine, namespace="test") @@ -131,7 +129,6 @@ def test_mdelete(sql_engine: Engine) -> None: session.commit() -@pytest.mark.asyncio async def test_amdelete(sql_aengine: AsyncEngine) -> None: """Test that deletion works as expected.""" store = SQLStore(engine=sql_aengine, namespace="test") @@ -172,7 +169,6 @@ def test_yield_keys(sql_engine: Engine) -> None: assert sorted(store.yield_keys(prefix="lang")) == [] -@pytest.mark.asyncio async def test_ayield_keys(sql_aengine: AsyncEngine) -> None: store = SQLStore(engine=sql_aengine, namespace="test") await store.acreate_schema() diff --git a/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan.sh b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan.sh new file mode 100755 index 0000000000000..8202f6604a9ce --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan.sh @@ -0,0 +1,4 @@ +#/bin/sh + +cd infinispan +docker compose up diff --git a/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/groups.properties b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/groups.properties new file mode 100644 index 0000000000000..e0775f747b42e --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/groups.properties @@ -0,0 +1,2 @@ +#Fri May 03 10:19:58 CEST 2024 +user=ADMIN,admin diff --git a/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/infinispan.xml b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/infinispan.xml new file mode 100644 index 0000000000000..fc0bbb2ca7600 --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/infinispan.xml @@ -0,0 +1,62 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/users.properties b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/users.properties new file mode 100644 index 0000000000000..b629aace59f28 --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/users.properties @@ -0,0 +1,4 @@ +#$REALM_NAME=default$ +#$ALGORITHM=encrypted$ +#Fri May 03 10:19:58 CEST 2024 +user=scram-sha-1\:BYGcIAws2gznU/kpezoSb1VQNVd+YMX9r+9SAINFoZtPHaHTAQ\=\=;scram-sha-256\:BYGcIAwRiWiD+8f7dyQEs1Wsum/64MOcjGJ2UcmZFQB6DZJqwRDJ4NrvII4NttmxlA\=\=;scram-sha-384\:BYGcIAz+Eud65N8GWK4TMwhSCZpeE5EFSdynywdryQj3ZwBEgv+KF8hRUuGxiq3EyRxsby6w7DHK3CICGZLsPrM\=;scram-sha-512\:BYGcIAwWxVY9DHn42kHydivyU3s9LSPmyfPPJkIFYyt/XsMASFHGoy5rzk4ahX4HjpJgb+NjdCwhGfi33CY0azUIrn439s62Yg5mq9i+ISto;digest-md5\:AgR1c2VyB2RlZmF1bHSYYyzPjRDR7MhrsdFSK03P;digest-sha\:AgR1c2VyB2RlZmF1bHTga5gDNnNYh7/2HqhBVOdUHjBzhw\=\=;digest-sha-256\:AgR1c2VyB2RlZmF1bHTig5qZQIxqtJBTUp3EMh5UIFoS4qOhz9Uk5aOW9ZKCfw\=\=;digest-sha-384\:AgR1c2VyB2RlZmF1bHT01pAN/pRMLS5afm4Q9S0kuLlA0NokuP8F0AISTwXCb1E8RMsFHlBVPOa5rC6Nyso\=;digest-sha-512\:AgR1c2VyB2RlZmF1bHTi+cHn1Ez2Ze41CvPXb9eP/7JmRys7m1f5qPMQWhAmDOuuUXNWEG4yKSI9k2EZgQvMKTd5hDbR24ul1BsYP8X5; diff --git a/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/docker-compose.yaml b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/docker-compose.yaml new file mode 100644 index 0000000000000..20204795a48a6 --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/docker-compose.yaml @@ -0,0 +1,16 @@ +version: "3.7" + +services: + infinispan: + image: quay.io/infinispan/server:15.0 + ports: + - '11222:11222' + - '11232:11232' + - '11242:11242' + deploy: + resources: + limits: + memory: 25Gb + volumes: + - ./conf:/user-config + command: -c /user-config/infinispan.xml diff --git a/libs/community/tests/integration_tests/vectorstores/test_cassandra.py b/libs/community/tests/integration_tests/vectorstores/test_cassandra.py index 014629220f4b8..fd55bab2d3163 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_cassandra.py +++ b/libs/community/tests/integration_tests/vectorstores/test_cassandra.py @@ -17,6 +17,17 @@ ) +def _strip_docs(documents: List[Document]) -> List[Document]: + return [_strip_doc(doc) for doc in documents] + + +def _strip_doc(document: Document) -> Document: + return Document( + page_content=document.page_content, + metadata=document.metadata, + ) + + def _vectorstore_from_texts( texts: List[str], metadatas: Optional[List[dict]] = None, @@ -110,9 +121,9 @@ async def test_cassandra() -> None: texts = ["foo", "bar", "baz"] docsearch = _vectorstore_from_texts(texts) output = docsearch.similarity_search("foo", k=1) - assert output == [Document(page_content="foo")] + assert _strip_docs(output) == _strip_docs([Document(page_content="foo")]) output = await docsearch.asimilarity_search("foo", k=1) - assert output == [Document(page_content="foo")] + assert _strip_docs(output) == _strip_docs([Document(page_content="foo")]) async def test_cassandra_with_score() -> None: @@ -130,13 +141,13 @@ async def test_cassandra_with_score() -> None: output = docsearch.similarity_search_with_score("foo", k=3) docs = [o[0] for o in output] scores = [o[1] for o in output] - assert docs == expected_docs + assert _strip_docs(docs) == _strip_docs(expected_docs) assert scores[0] > scores[1] > scores[2] output = await docsearch.asimilarity_search_with_score("foo", k=3) docs = [o[0] for o in output] scores = [o[1] for o in output] - assert docs == expected_docs + assert _strip_docs(docs) == _strip_docs(expected_docs) assert scores[0] > scores[1] > scores[2] @@ -239,7 +250,7 @@ async def test_cassandra_no_drop_async() -> None: def test_cassandra_delete() -> None: """Test delete methods from vector store.""" texts = ["foo", "bar", "baz", "gni"] - metadatas = [{"page": i} for i in range(len(texts))] + metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))] docsearch = _vectorstore_from_texts([], metadatas=metadatas) ids = docsearch.add_texts(texts, metadatas) @@ -263,11 +274,21 @@ def test_cassandra_delete() -> None: output = docsearch.similarity_search("foo", k=10) assert len(output) == 0 + docsearch.add_texts(texts, metadatas) + num_deleted = docsearch.delete_by_metadata_filter({"mod2": 0}, batch_size=1) + assert num_deleted == 2 + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 2 + docsearch.clear() + + with pytest.raises(ValueError): + docsearch.delete_by_metadata_filter({}) + async def test_cassandra_adelete() -> None: """Test delete methods from vector store.""" texts = ["foo", "bar", "baz", "gni"] - metadatas = [{"page": i} for i in range(len(texts))] + metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))] docsearch = await _vectorstore_from_texts_async([], metadatas=metadatas) ids = await docsearch.aadd_texts(texts, metadatas) @@ -291,6 +312,16 @@ async def test_cassandra_adelete() -> None: output = docsearch.similarity_search("foo", k=10) assert len(output) == 0 + await docsearch.aadd_texts(texts, metadatas) + num_deleted = await docsearch.adelete_by_metadata_filter({"mod2": 0}, batch_size=1) + assert num_deleted == 2 + output = await docsearch.asimilarity_search("foo", k=10) + assert len(output) == 2 + await docsearch.aclear() + + with pytest.raises(ValueError): + await docsearch.adelete_by_metadata_filter({}) + def test_cassandra_metadata_indexing() -> None: """Test comparing metadata indexing policies.""" @@ -316,3 +347,107 @@ def test_cassandra_metadata_indexing() -> None: with pytest.raises(ValueError): # "Non-indexed metadata fields cannot be used in queries." vstore_f1.similarity_search("bar", filter={"field2": "b"}, k=2) + + +def test_cassandra_replace_metadata() -> None: + """Test of replacing metadata.""" + N_DOCS = 100 + REPLACE_RATIO = 2 # one in ... will have replaced metadata + BATCH_SIZE = 3 + + vstore_f1 = _vectorstore_from_texts( + texts=[], + metadata_indexing=("allowlist", ["field1", "field2"]), + table_name="vector_test_table_indexing", + ) + orig_documents = [ + Document( + page_content=f"doc_{doc_i}", + id=f"doc_id_{doc_i}", + metadata={"field1": f"f1_{doc_i}", "otherf": "pre"}, + ) + for doc_i in range(N_DOCS) + ] + vstore_f1.add_documents(orig_documents) + + ids_to_replace = [ + f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0 + ] + + # various kinds of replacement at play here: + def _make_new_md(mode: int, doc_id: str) -> dict[str, str]: + if mode == 0: + return {} + elif mode == 1: + return {"field2": f"NEW_{doc_id}"} + elif mode == 2: + return {"field2": f"NEW_{doc_id}", "ofherf2": "post"} + else: + return {"ofherf2": "post"} + + ids_to_new_md = { + doc_id: _make_new_md(rep_i % 4, doc_id) + for rep_i, doc_id in enumerate(ids_to_replace) + } + + vstore_f1.replace_metadata(ids_to_new_md, batch_size=BATCH_SIZE) + # thorough check + expected_id_to_metadata: dict[str, dict] = { + **{(document.id or ""): document.metadata for document in orig_documents}, + **ids_to_new_md, + } + for hit in vstore_f1.similarity_search("doc", k=N_DOCS + 1): + assert hit.id is not None + assert hit.metadata == expected_id_to_metadata[hit.id] + + +async def test_cassandra_areplace_metadata() -> None: + """Test of replacing metadata.""" + N_DOCS = 100 + REPLACE_RATIO = 2 # one in ... will have replaced metadata + BATCH_SIZE = 3 + + vstore_f1 = _vectorstore_from_texts( + texts=[], + metadata_indexing=("allowlist", ["field1", "field2"]), + table_name="vector_test_table_indexing", + ) + orig_documents = [ + Document( + page_content=f"doc_{doc_i}", + id=f"doc_id_{doc_i}", + metadata={"field1": f"f1_{doc_i}", "otherf": "pre"}, + ) + for doc_i in range(N_DOCS) + ] + await vstore_f1.aadd_documents(orig_documents) + + ids_to_replace = [ + f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0 + ] + + # various kinds of replacement at play here: + def _make_new_md(mode: int, doc_id: str) -> dict[str, str]: + if mode == 0: + return {} + elif mode == 1: + return {"field2": f"NEW_{doc_id}"} + elif mode == 2: + return {"field2": f"NEW_{doc_id}", "ofherf2": "post"} + else: + return {"ofherf2": "post"} + + ids_to_new_md = { + doc_id: _make_new_md(rep_i % 4, doc_id) + for rep_i, doc_id in enumerate(ids_to_replace) + } + + await vstore_f1.areplace_metadata(ids_to_new_md, concurrency=BATCH_SIZE) + # thorough check + expected_id_to_metadata: dict[str, dict] = { + **{(document.id or ""): document.metadata for document in orig_documents}, + **ids_to_new_md, + } + for hit in await vstore_f1.asimilarity_search("doc", k=N_DOCS + 1): + assert hit.id is not None + assert hit.metadata == expected_id_to_metadata[hit.id] diff --git a/libs/community/tests/integration_tests/vectorstores/test_infinispanvs.py b/libs/community/tests/integration_tests/vectorstores/test_infinispanvs.py index 3f8cd70407323..ff7534cce15ce 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_infinispanvs.py +++ b/libs/community/tests/integration_tests/vectorstores/test_infinispanvs.py @@ -1,7 +1,9 @@ """Test Infinispan functionality.""" +import warnings from typing import Any, List, Optional +import httpx import pytest from langchain_core.documents import Document @@ -11,9 +13,18 @@ fake_texts, ) +""" +cd tests/integration_tests/vectorstores/docker-compose +./infinispan.sh -def _infinispan_setup_noautoconf() -> None: - ispnvs = InfinispanVS(auto_config=False) +Current Infinispan implementation relies on httpx: `pip install "httpx[http2]"` +if not installed. HTTP/2 is enable by default, if it's not +wanted use `pip install "httpx"`. +""" + + +def _infinispan_setup_noautoconf(**kwargs: Any) -> None: + ispnvs = InfinispanVS(http2=_hasHttp2(), auto_config=False, **kwargs) ispnvs.cache_delete() ispnvs.schema_delete() proto = """ @@ -54,64 +65,104 @@ def _infinispanvs_from_texts( ids=ids, clear_old=clear_old, auto_config=auto_config, + http2=_hasHttp2(), **kwargs, ) +def _hasHttp2() -> bool: + try: + httpx.Client(http2=True) + return True + except Exception: + return False + + @pytest.mark.parametrize("autoconfig", [False, True]) +@pytest.mark.parametrize( + "conn_opts", + [ + {}, + { + "user": "user", + "password": "password", + "hosts": ["localhost:11232"], + "schema": "http", + }, + { + "user": "user", + "password": "password", + "hosts": ["localhost:11242"], + "schema": "https", + "verify": False, + }, + ], +) class TestBasic: - def test_infinispan(self, autoconfig: bool) -> None: + def test_infinispan(self, autoconfig: bool, conn_opts: dict) -> None: """Test end to end construction and search.""" if not autoconfig: - _infinispan_setup_noautoconf() - docsearch = _infinispanvs_from_texts(auto_config=autoconfig) + _infinispan_setup_noautoconf(**conn_opts) + docsearch = _infinispanvs_from_texts(auto_config=autoconfig, **conn_opts) output = docsearch.similarity_search("foo", k=1) assert output == [Document(page_content="foo")] - def test_infinispan_with_metadata(self, autoconfig: bool) -> None: + def test_infinispan_with_auth(self, autoconfig: bool, conn_opts: dict) -> None: + """Test end to end construction and search.""" + if not autoconfig: + _infinispan_setup_noautoconf(**conn_opts) + docsearch = _infinispanvs_from_texts(auto_config=autoconfig, **conn_opts) + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo")] + + def test_infinispan_with_metadata(self, autoconfig: bool, conn_opts: dict) -> None: """Test with metadata""" if not autoconfig: - _infinispan_setup_noautoconf() + _infinispan_setup_noautoconf(**conn_opts) meta = [] for _ in range(len(fake_texts)): meta.append({"label": "test"}) - docsearch = _infinispanvs_from_texts(metadatas=meta, auto_config=autoconfig) + docsearch = _infinispanvs_from_texts( + metadatas=meta, auto_config=autoconfig, **conn_opts + ) output = docsearch.similarity_search("foo", k=1) assert output == [Document(page_content="foo", metadata={"label": "test"})] def test_infinispan_with_metadata_with_output_fields( - self, autoconfig: bool + self, autoconfig: bool, conn_opts: dict ) -> None: """Test with metadata""" if not autoconfig: - _infinispan_setup_noautoconf() + _infinispan_setup_noautoconf(**conn_opts) metadatas = [ {"page": i, "label": "label" + str(i)} for i in range(len(fake_texts)) ] c = {"output_fields": ["label", "page", "text"]} docsearch = _infinispanvs_from_texts( - metadatas=metadatas, configuration=c, auto_config=autoconfig + metadatas=metadatas, configuration=c, auto_config=autoconfig, **conn_opts ) output = docsearch.similarity_search("foo", k=1) assert output == [ Document(page_content="foo", metadata={"label": "label0", "page": 0}) ] - def test_infinispanvs_with_id(self, autoconfig: bool) -> None: + def test_infinispanvs_with_id(self, autoconfig: bool, conn_opts: dict) -> None: """Test with ids""" ids = ["id_" + str(i) for i in range(len(fake_texts))] - docsearch = _infinispanvs_from_texts(ids=ids, auto_config=autoconfig) + docsearch = _infinispanvs_from_texts( + ids=ids, auto_config=autoconfig, **conn_opts + ) output = docsearch.similarity_search("foo", k=1) assert output == [Document(page_content="foo")] - def test_infinispan_with_score(self, autoconfig: bool) -> None: + def test_infinispan_with_score(self, autoconfig: bool, conn_opts: dict) -> None: """Test end to end construction and search with scores and IDs.""" if not autoconfig: - _infinispan_setup_noautoconf() + _infinispan_setup_noautoconf(**conn_opts) texts = ["foo", "bar", "baz"] metadatas = [{"page": i} for i in range(len(texts))] docsearch = _infinispanvs_from_texts( - metadatas=metadatas, auto_config=autoconfig + metadatas=metadatas, auto_config=autoconfig, **conn_opts ) output = docsearch.similarity_search_with_score("foo", k=3) docs = [o[0] for o in output] @@ -123,14 +174,14 @@ def test_infinispan_with_score(self, autoconfig: bool) -> None: ] assert scores[0] >= scores[1] >= scores[2] - def test_infinispan_add_texts(self, autoconfig: bool) -> None: + def test_infinispan_add_texts(self, autoconfig: bool, conn_opts: dict) -> None: """Test end to end construction and MRR search.""" if not autoconfig: - _infinispan_setup_noautoconf() + _infinispan_setup_noautoconf(**conn_opts) texts = ["foo", "bar", "baz"] metadatas = [{"page": i} for i in range(len(texts))] docsearch = _infinispanvs_from_texts( - metadatas=metadatas, auto_config=autoconfig + metadatas=metadatas, auto_config=autoconfig, **conn_opts ) docsearch.add_texts(texts, metadatas) @@ -138,19 +189,22 @@ def test_infinispan_add_texts(self, autoconfig: bool) -> None: output = docsearch.similarity_search("foo", k=10) assert len(output) == 6 - def test_infinispan_no_clear_old(self, autoconfig: bool) -> None: + def test_infinispan_no_clear_old(self, autoconfig: bool, conn_opts: dict) -> None: """Test end to end construction and MRR search.""" if not autoconfig: - _infinispan_setup_noautoconf() + _infinispan_setup_noautoconf(**conn_opts) texts = ["foo", "bar", "baz"] metadatas = [{"page": i} for i in range(len(texts))] docsearch = _infinispanvs_from_texts( - metadatas=metadatas, auto_config=autoconfig + metadatas=metadatas, auto_config=autoconfig, **conn_opts ) del docsearch try: docsearch = _infinispanvs_from_texts( - metadatas=metadatas, clear_old=False, auto_config=autoconfig + metadatas=metadatas, + clear_old=False, + auto_config=autoconfig, + **conn_opts, ) except AssertionError: if autoconfig: @@ -159,3 +213,12 @@ def test_infinispan_no_clear_old(self, autoconfig: bool) -> None: raise output = docsearch.similarity_search("foo", k=10) assert len(output) == 6 + + +class TestHttp2: + def test_http2(self) -> None: + try: + httpx.Client(http2=True) + except Exception: + warnings.warn('pip install "httpx[http2]" if you need HTTP/2') + pass diff --git a/libs/community/tests/integration_tests/vectorstores/test_upstash.py b/libs/community/tests/integration_tests/vectorstores/test_upstash.py index 7da52dd8b7de1..148db72fb3d74 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_upstash.py +++ b/libs/community/tests/integration_tests/vectorstores/test_upstash.py @@ -68,7 +68,6 @@ def test_upstash_simple_insert() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_simple_insert_async() -> None: """Test end to end construction and search.""" texts = ["foo", "bar", "baz"] @@ -94,7 +93,6 @@ def test_upstash_with_metadatas() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_with_metadatas_async() -> None: """Test end to end construction and search.""" texts = ["foo", "bar", "baz"] @@ -125,7 +123,6 @@ def test_upstash_with_metadatas_with_scores() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_with_metadatas_with_scores_async() -> None: """Test end to end construction and scored search.""" texts = ["foo", "bar", "baz"] @@ -159,7 +156,6 @@ def test_upstash_with_metadatas_with_scores_using_vector() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_with_metadatas_with_scores_using_vector_async() -> None: """Test end to end construction and scored search, using embedding vector.""" texts = ["foo", "bar", "baz"] @@ -190,7 +186,6 @@ def test_upstash_mmr() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_mmr_async() -> None: """Test end to end construction and search.""" texts = ["foo", "bar", "baz"] @@ -213,7 +208,6 @@ def test_upstash_mmr_by_vector() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_mmr_by_vector_async() -> None: """Test end to end construction and search.""" texts = ["foo", "bar", "baz"] @@ -237,7 +231,6 @@ def test_init_from_index() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_init_from_async_index() -> None: from upstash_vector import AsyncIndex @@ -259,7 +252,6 @@ def test_init_from_credentials() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_init_from_credentials_async() -> None: store = UpstashVectorStore( index_url=os.environ["UPSTASH_VECTOR_REST_URL"], @@ -326,7 +318,6 @@ def test_upstash_similarity_search_with_metadata() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_similarity_search_with_metadata_async() -> None: store = UpstashVectorStore(embedding=FakeEmbeddings()) docs = [ @@ -384,7 +375,6 @@ def test_upstash_similarity_search_by_vector_with_metadata() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_similarity_search_by_vector_with_metadata_async() -> None: store = UpstashVectorStore(embedding=FakeEmbeddings()) docs = [ @@ -434,7 +424,6 @@ def test_upstash_max_marginal_relevance_search_with_metadata() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_upstash_max_marginal_relevance_search_with_metadata_async() -> None: store = UpstashVectorStore(embedding=FakeEmbeddings()) docs = [ @@ -539,7 +528,6 @@ def test_embedding_index() -> None: @pytest.mark.vcr() -@pytest.mark.asyncio async def test_embedding_index_async() -> None: store = UpstashVectorStore( index_url=os.environ["UPSTASH_VECTOR_URL_EMBEDDING"], diff --git a/libs/community/tests/unit_tests/chat_models/test_imports.py b/libs/community/tests/unit_tests/chat_models/test_imports.py index 6be5a41d9ec1c..035ecbca8b1c6 100644 --- a/libs/community/tests/unit_tests/chat_models/test_imports.py +++ b/libs/community/tests/unit_tests/chat_models/test_imports.py @@ -30,6 +30,9 @@ "ChatMLX", "ChatNebula", "ChatOCIGenAI", + "ChatOCIModelDeployment", + "ChatOCIModelDeploymentVLLM", + "ChatOCIModelDeploymentTGI", "ChatOllama", "ChatOpenAI", "ChatPerplexity", diff --git a/libs/community/tests/unit_tests/chat_models/test_oci_data_science.py b/libs/community/tests/unit_tests/chat_models/test_oci_data_science.py new file mode 100644 index 0000000000000..f385f2ddb09a3 --- /dev/null +++ b/libs/community/tests/unit_tests/chat_models/test_oci_data_science.py @@ -0,0 +1,193 @@ +# Copyright (c) 2024, Oracle and/or its affiliates. + +"""Test Chat model for OCI Data Science Model Deployment Endpoint.""" + +import sys +from typing import Any, AsyncGenerator, Dict, Generator +from unittest import mock + +import pytest +from langchain_core.messages import AIMessage, AIMessageChunk +from requests.exceptions import HTTPError + +from langchain_community.chat_models import ( + ChatOCIModelDeploymentTGI, + ChatOCIModelDeploymentVLLM, +) + +CONST_MODEL_NAME = "odsc-vllm" +CONST_ENDPOINT = "https://oci.endpoint/ocid/predict" +CONST_PROMPT = "This is a prompt." +CONST_COMPLETION = "This is a completion." +CONST_COMPLETION_RESPONSE = { + "id": "chat-123456789", + "object": "chat.completion", + "created": 123456789, + "model": "mistral", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": CONST_COMPLETION, + "tool_calls": [], + }, + "logprobs": None, + "finish_reason": "length", + "stop_reason": None, + } + ], + "usage": {"prompt_tokens": 115, "total_tokens": 371, "completion_tokens": 256}, + "prompt_logprobs": None, +} +CONST_COMPLETION_RESPONSE_TGI = {"generated_text": CONST_COMPLETION} +CONST_STREAM_TEMPLATE = ( + 'data: {"id":"chat-123456","object":"chat.completion.chunk","created":123456789,' + '"model":"odsc-llm","choices":[{"index":0,"delta":,"finish_reason":null}]}' +) +CONST_STREAM_DELTAS = ['{"role":"assistant","content":""}'] + [ + '{"content":" ' + word + '"}' for word in CONST_COMPLETION.split(" ") +] +CONST_STREAM_RESPONSE = ( + content + for content in [ + CONST_STREAM_TEMPLATE.replace("", delta).encode() + for delta in CONST_STREAM_DELTAS + ] + + [b"data: [DONE]"] +) + +CONST_ASYNC_STREAM_TEMPLATE = ( + '{"id":"chat-123456","object":"chat.completion.chunk","created":123456789,' + '"model":"odsc-llm","choices":[{"index":0,"delta":,"finish_reason":null}]}' +) +CONST_ASYNC_STREAM_RESPONSE = ( + CONST_ASYNC_STREAM_TEMPLATE.replace("", delta).encode() + for delta in CONST_STREAM_DELTAS +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 9), reason="Requires Python 3.9 or higher" +) + + +class MockResponse: + """Represents a mocked response.""" + + def __init__(self, json_data: Dict, status_code: int = 200): + self.json_data = json_data + self.status_code = status_code + + def raise_for_status(self) -> None: + """Mocked raise for status.""" + if 400 <= self.status_code < 600: + raise HTTPError() + + def json(self) -> Dict: + """Returns mocked json data.""" + return self.json_data + + def iter_lines(self, chunk_size: int = 4096) -> Generator[bytes, None, None]: + """Returns a generator of mocked streaming response.""" + return CONST_STREAM_RESPONSE + + @property + def text(self) -> str: + """Returns the mocked text representation.""" + return "" + + +def mocked_requests_post(url: str, **kwargs: Any) -> MockResponse: + """Method to mock post requests""" + + payload: dict = kwargs.get("json", {}) + messages: list = payload.get("messages", []) + prompt = messages[0].get("content") + + if prompt == CONST_PROMPT: + return MockResponse(json_data=CONST_COMPLETION_RESPONSE) + + return MockResponse( + json_data={}, + status_code=404, + ) + + +@pytest.mark.requires("ads") +@pytest.mark.requires("langchain_openai") +@mock.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) +@mock.patch("requests.post", side_effect=mocked_requests_post) +def test_invoke_vllm(*args: Any) -> None: + """Tests invoking vLLM endpoint.""" + llm = ChatOCIModelDeploymentVLLM(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME) + output = llm.invoke(CONST_PROMPT) + assert isinstance(output, AIMessage) + assert output.content == CONST_COMPLETION + + +@pytest.mark.requires("ads") +@pytest.mark.requires("langchain_openai") +@mock.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) +@mock.patch("requests.post", side_effect=mocked_requests_post) +def test_invoke_tgi(*args: Any) -> None: + """Tests invoking TGI endpoint using OpenAI Spec.""" + llm = ChatOCIModelDeploymentTGI(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME) + output = llm.invoke(CONST_PROMPT) + assert isinstance(output, AIMessage) + assert output.content == CONST_COMPLETION + + +@pytest.mark.requires("ads") +@pytest.mark.requires("langchain_openai") +@mock.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) +@mock.patch("requests.post", side_effect=mocked_requests_post) +def test_stream_vllm(*args: Any) -> None: + """Tests streaming with vLLM endpoint using OpenAI spec.""" + llm = ChatOCIModelDeploymentVLLM( + endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True + ) + output = None + count = 0 + for chunk in llm.stream(CONST_PROMPT): + assert isinstance(chunk, AIMessageChunk) + if output is None: + output = chunk + else: + output += chunk + count += 1 + assert count == 5 + assert output is not None + if output is not None: + assert str(output.content).strip() == CONST_COMPLETION + + +async def mocked_async_streaming_response( + *args: Any, **kwargs: Any +) -> AsyncGenerator[bytes, None]: + """Returns mocked response for async streaming.""" + for item in CONST_ASYNC_STREAM_RESPONSE: + yield item + + +@pytest.mark.asyncio +@pytest.mark.requires("ads") +@pytest.mark.requires("langchain_openai") +@mock.patch( + "ads.common.auth.default_signer", return_value=dict(signer=mock.MagicMock()) +) +@mock.patch( + "langchain_community.utilities.requests.Requests.apost", + mock.MagicMock(), +) +async def test_stream_async(*args: Any) -> None: + """Tests async streaming.""" + llm = ChatOCIModelDeploymentVLLM( + endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True + ) + with mock.patch.object( + llm, + "_aiter_sse", + mock.MagicMock(return_value=mocked_async_streaming_response()), + ): + chunks = [str(chunk.content) async for chunk in llm.astream(CONST_PROMPT)] + assert "".join(chunks).strip() == CONST_COMPLETION diff --git a/libs/community/tests/unit_tests/chat_models/test_oci_model_deployment_endpoint.py b/libs/community/tests/unit_tests/chat_models/test_oci_model_deployment_endpoint.py new file mode 100644 index 0000000000000..62afd4828b8c0 --- /dev/null +++ b/libs/community/tests/unit_tests/chat_models/test_oci_model_deployment_endpoint.py @@ -0,0 +1,98 @@ +"""Test OCI Data Science Model Deployment Endpoint.""" + +import pytest +import responses +from langchain_core.messages import AIMessage, HumanMessage +from pytest_mock import MockerFixture + +from langchain_community.chat_models import ChatOCIModelDeployment + + +@pytest.mark.requires("ads") +def test_initialization(mocker: MockerFixture) -> None: + """Test chat model initialization.""" + mocker.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) + chat = ChatOCIModelDeployment( + model="odsc", + endpoint="test_endpoint", + model_kwargs={"temperature": 0.2}, + ) + assert chat.model == "odsc" + assert chat.endpoint == "test_endpoint" + assert chat.model_kwargs == {"temperature": 0.2} + assert chat._identifying_params == { + "endpoint": chat.endpoint, + "model_kwargs": {"temperature": 0.2}, + "model": chat.model, + "stop": chat.stop, + "stream": chat.streaming, + } + + +@pytest.mark.requires("ads") +@responses.activate +def test_call(mocker: MockerFixture) -> None: + """Test valid call to oci model deployment endpoint.""" + endpoint = "https://MD_OCID/predict" + responses.add( + responses.POST, + endpoint, + json={ + "id": "cmpl-88159e77c92f46088faad75fce2e26a1", + "object": "chat.completion", + "created": 274246, + "model": "odsc-llm", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello World", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 10, + "total_tokens": 20, + "completion_tokens": 10, + }, + }, + status=200, + ) + mocker.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) + + chat = ChatOCIModelDeployment(endpoint=endpoint) + output = chat.invoke("this is a test.") + assert isinstance(output, AIMessage) + assert output.response_metadata == { + "token_usage": { + "prompt_tokens": 10, + "total_tokens": 20, + "completion_tokens": 10, + }, + "model_name": "odsc-llm", + "system_fingerprint": "", + "finish_reason": "stop", + } + + +@pytest.mark.requires("ads") +@responses.activate +def test_construct_json_body(mocker: MockerFixture) -> None: + """Tests constructing json body that will be sent to endpoint.""" + mocker.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) + messages = [ + HumanMessage(content="User message"), + ] + chat = ChatOCIModelDeployment( + endpoint="test_endpoint", model_kwargs={"temperature": 0.2} + ) + payload = chat._construct_json_body(messages, chat._invocation_params(stop=None)) + assert payload == { + "messages": [{"content": "User message", "role": "user"}], + "model": chat.model, + "stop": None, + "stream": chat.streaming, + "temperature": 0.2, + } diff --git a/libs/community/tests/unit_tests/embeddings/test_infinity_local.py b/libs/community/tests/unit_tests/embeddings/test_infinity_local.py index 9d58307b9598b..b6eadfbc87a9e 100644 --- a/libs/community/tests/unit_tests/embeddings/test_infinity_local.py +++ b/libs/community/tests/unit_tests/embeddings/test_infinity_local.py @@ -13,7 +13,6 @@ @pytest.mark.skipif(not IMPORTED_TORCH, reason="torch not installed") -@pytest.mark.asyncio async def test_local_infinity_embeddings() -> None: embedder = InfinityEmbeddingsLocal( model="TaylorAI/bge-micro-v2", diff --git a/libs/community/tests/unit_tests/llms/test_bedrock.py b/libs/community/tests/unit_tests/llms/test_bedrock.py index 42af3a011210e..bb64f4d29d6d3 100644 --- a/libs/community/tests/unit_tests/llms/test_bedrock.py +++ b/libs/community/tests/unit_tests/llms/test_bedrock.py @@ -273,7 +273,6 @@ async def async_gen_mock_streaming_response() -> AsyncGenerator[Dict, None]: yield item -@pytest.mark.asyncio async def test_bedrock_async_streaming_call() -> None: # Mock boto3 import mock_boto3 = MagicMock() diff --git a/libs/community/tests/unit_tests/llms/test_imports.py b/libs/community/tests/unit_tests/llms/test_imports.py index dd1a089fe4d11..df0fe68b59fd4 100644 --- a/libs/community/tests/unit_tests/llms/test_imports.py +++ b/libs/community/tests/unit_tests/llms/test_imports.py @@ -56,6 +56,7 @@ "Modal", "MosaicML", "Nebula", + "OCIModelDeploymentLLM", "OCIModelDeploymentTGI", "OCIModelDeploymentVLLM", "OCIGenAI", diff --git a/libs/community/tests/unit_tests/llms/test_oci_model_deployment_endpoint.py b/libs/community/tests/unit_tests/llms/test_oci_model_deployment_endpoint.py index 655231617625d..c87b05f12a12d 100644 --- a/libs/community/tests/unit_tests/llms/test_oci_model_deployment_endpoint.py +++ b/libs/community/tests/unit_tests/llms/test_oci_model_deployment_endpoint.py @@ -1,47 +1,170 @@ -"""Test OCI Data Science Model Deployment Endpoint.""" +# Copyright (c) 2023, 2024, Oracle and/or its affiliates. + +"""Test LLM for OCI Data Science Model Deployment Endpoint.""" + +import sys +from typing import Any, AsyncGenerator, Dict, Generator +from unittest import mock import pytest -import responses -from pytest_mock import MockerFixture +from requests.exceptions import HTTPError -from langchain_community.llms import OCIModelDeploymentTGI, OCIModelDeploymentVLLM +from langchain_community.llms.oci_data_science_model_deployment_endpoint import ( + OCIModelDeploymentTGI, + OCIModelDeploymentVLLM, +) +CONST_MODEL_NAME = "odsc-vllm" +CONST_ENDPOINT = "https://oci.endpoint/ocid/predict" +CONST_PROMPT = "This is a prompt." +CONST_COMPLETION = "This is a completion." +CONST_COMPLETION_RESPONSE = { + "choices": [ + { + "index": 0, + "text": CONST_COMPLETION, + "logprobs": 0.1, + "finish_reason": "length", + } + ], +} +CONST_COMPLETION_RESPONSE_TGI = {"generated_text": CONST_COMPLETION} +CONST_STREAM_TEMPLATE = ( + 'data: {"id":"","object":"text_completion","created":123456,' + + '"choices":[{"index":0,"text":"","finish_reason":""}]}' +) +CONST_STREAM_RESPONSE = ( + CONST_STREAM_TEMPLATE.replace("", " " + word).encode() + for word in CONST_COMPLETION.split(" ") +) -@pytest.mark.requires("ads") -@responses.activate -def test_call_vllm(mocker: MockerFixture) -> None: - """Test valid call to oci model deployment endpoint.""" - endpoint = "https://MD_OCID/predict" - responses.add( - responses.POST, - endpoint, - json={ - "choices": [{"index": 0, "text": "This is a completion."}], - }, - status=200, +CONST_ASYNC_STREAM_TEMPLATE = ( + '{"id":"","object":"text_completion","created":123456,' + + '"choices":[{"index":0,"text":"","finish_reason":""}]}' +) +CONST_ASYNC_STREAM_RESPONSE = ( + CONST_ASYNC_STREAM_TEMPLATE.replace("", " " + word).encode() + for word in CONST_COMPLETION.split(" ") +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 9), reason="Requires Python 3.9 or higher" +) + + +class MockResponse: + """Represents a mocked response.""" + + def __init__(self, json_data: Dict, status_code: int = 200) -> None: + self.json_data = json_data + self.status_code = status_code + + def raise_for_status(self) -> None: + """Mocked raise for status.""" + if 400 <= self.status_code < 600: + raise HTTPError() + + def json(self) -> Dict: + """Returns mocked json data.""" + return self.json_data + + def iter_lines(self, chunk_size: int = 4096) -> Generator[bytes, None, None]: + """Returns a generator of mocked streaming response.""" + return CONST_STREAM_RESPONSE + + @property + def text(self) -> str: + """Returns the mocked text representation.""" + return "" + + +def mocked_requests_post(url: str, **kwargs: Any) -> MockResponse: + """Method to mock post requests""" + + payload: dict = kwargs.get("json", {}) + if "inputs" in payload: + prompt = payload.get("inputs") + is_tgi = True + else: + prompt = payload.get("prompt") + is_tgi = False + + if prompt == CONST_PROMPT: + if is_tgi: + return MockResponse(json_data=CONST_COMPLETION_RESPONSE_TGI) + return MockResponse(json_data=CONST_COMPLETION_RESPONSE) + + return MockResponse( + json_data={}, + status_code=404, ) - mocker.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) - llm = OCIModelDeploymentVLLM(endpoint=endpoint, model="my_model") - output = llm.invoke("This is a prompt.") - assert isinstance(output, str) + +async def mocked_async_streaming_response( + *args: Any, **kwargs: Any +) -> AsyncGenerator[bytes, None]: + """Returns mocked response for async streaming.""" + for item in CONST_ASYNC_STREAM_RESPONSE: + yield item @pytest.mark.requires("ads") -@responses.activate -def test_call_tgi(mocker: MockerFixture) -> None: - """Test valid call to oci model deployment endpoint.""" - endpoint = "https://MD_OCID/predict" - responses.add( - responses.POST, - endpoint, - json={ - "generated_text": "This is a completion.", - }, - status=200, +@mock.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) +@mock.patch("requests.post", side_effect=mocked_requests_post) +def test_invoke_vllm(*args: Any) -> None: + """Tests invoking vLLM endpoint.""" + llm = OCIModelDeploymentVLLM(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME) + output = llm.invoke(CONST_PROMPT) + assert output == CONST_COMPLETION + + +@pytest.mark.requires("ads") +@mock.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) +@mock.patch("requests.post", side_effect=mocked_requests_post) +def test_stream_tgi(*args: Any) -> None: + """Tests streaming with TGI endpoint using OpenAI spec.""" + llm = OCIModelDeploymentTGI( + endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True ) - mocker.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) + output = "" + count = 0 + for chunk in llm.stream(CONST_PROMPT): + output += chunk + count += 1 + assert count == 4 + assert output.strip() == CONST_COMPLETION + - llm = OCIModelDeploymentTGI(endpoint=endpoint) - output = llm.invoke("This is a prompt.") - assert isinstance(output, str) +@pytest.mark.requires("ads") +@mock.patch("ads.common.auth.default_signer", return_value=dict(signer=None)) +@mock.patch("requests.post", side_effect=mocked_requests_post) +def test_generate_tgi(*args: Any) -> None: + """Tests invoking TGI endpoint using TGI generate spec.""" + llm = OCIModelDeploymentTGI( + endpoint=CONST_ENDPOINT, api="/generate", model=CONST_MODEL_NAME + ) + output = llm.invoke(CONST_PROMPT) + assert output == CONST_COMPLETION + + +@pytest.mark.asyncio +@pytest.mark.requires("ads") +@mock.patch( + "ads.common.auth.default_signer", return_value=dict(signer=mock.MagicMock()) +) +@mock.patch( + "langchain_community.utilities.requests.Requests.apost", + mock.MagicMock(), +) +async def test_stream_async(*args: Any) -> None: + """Tests async streaming.""" + llm = OCIModelDeploymentTGI( + endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True + ) + with mock.patch.object( + llm, + "_aiter_sse", + mock.MagicMock(return_value=mocked_async_streaming_response()), + ): + chunks = [chunk async for chunk in llm.astream(CONST_PROMPT)] + assert "".join(chunks).strip() == CONST_COMPLETION diff --git a/libs/community/tests/unit_tests/retrievers/test_you.py b/libs/community/tests/unit_tests/retrievers/test_you.py index 2118fb0c901a8..a5a189d769d91 100644 --- a/libs/community/tests/unit_tests/retrievers/test_you.py +++ b/libs/community/tests/unit_tests/retrievers/test_you.py @@ -1,6 +1,5 @@ from unittest.mock import AsyncMock, patch -import pytest import responses from langchain_community.retrievers.you import YouRetriever @@ -63,7 +62,6 @@ def test_invoke_news(self) -> None: expected_result = NEWS_RESPONSE_PARSED assert results == expected_result - @pytest.mark.asyncio async def test_ainvoke(self) -> None: instance = YouRetriever(ydc_api_key="test_api_key") diff --git a/libs/community/tests/unit_tests/tools/test_you.py b/libs/community/tests/unit_tests/tools/test_you.py index 08caf91077362..4c383a98d524b 100644 --- a/libs/community/tests/unit_tests/tools/test_you.py +++ b/libs/community/tests/unit_tests/tools/test_you.py @@ -1,6 +1,5 @@ from unittest.mock import AsyncMock, patch -import pytest import responses from langchain_community.tools.you import YouSearchTool @@ -68,7 +67,6 @@ def test_invoke_news(self) -> None: expected_result = NEWS_RESPONSE_PARSED assert results == expected_result - @pytest.mark.asyncio async def test_ainvoke(self) -> None: you_tool = YouSearchTool(api_wrapper=YouSearchAPIWrapper(ydc_api_key="test")) # type: ignore[call-arg] diff --git a/libs/community/tests/unit_tests/utilities/test_you.py b/libs/community/tests/unit_tests/utilities/test_you.py index 5ac5009d22da3..ef6d972f62ff8 100644 --- a/libs/community/tests/unit_tests/utilities/test_you.py +++ b/libs/community/tests/unit_tests/utilities/test_you.py @@ -1,7 +1,6 @@ from typing import Any, Dict, List, Optional, Union from unittest.mock import AsyncMock, patch -import pytest import responses from langchain_core.documents import Document @@ -189,7 +188,6 @@ def test_results_news() -> None: assert raw_results == expected_result -@pytest.mark.asyncio async def test_raw_results_async() -> None: instance = YouSearchAPIWrapper(ydc_api_key="test_api_key") @@ -208,7 +206,6 @@ async def test_raw_results_async() -> None: assert results == MOCK_RESPONSE_RAW -@pytest.mark.asyncio async def test_results_async() -> None: instance = YouSearchAPIWrapper(ydc_api_key="test_api_key") @@ -227,7 +224,6 @@ async def test_results_async() -> None: assert results == MOCK_PARSED_OUTPUT -@pytest.mark.asyncio async def test_results_news_async() -> None: instance = YouSearchAPIWrapper(endpoint_type="news", ydc_api_key="test_api_key") diff --git a/libs/core/langchain_core/rate_limiters.py b/libs/core/langchain_core/rate_limiters.py index 043e54cc38f9b..11588020f6ba7 100644 --- a/libs/core/langchain_core/rate_limiters.py +++ b/libs/core/langchain_core/rate_limiters.py @@ -249,7 +249,13 @@ async def aacquire(self, *, blocking: bool = True) -> bool: return self._consume() while not self._consume(): - await asyncio.sleep(self.check_every_n_seconds) + # This code ignores the ASYNC110 warning which is a false positive in this + # case. + # There is no external actor that can mark that the Event is done + # since the tokens are managed by the rate limiter itself. + # It needs to wake up to re-fill the tokens. + # https://docs.astral.sh/ruff/rules/async-busy-wait/ + await asyncio.sleep(self.check_every_n_seconds) # ruff: noqa: ASYNC110 return True diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index 45513dcd0101a..6946b0b969ffd 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "annotated-types" @@ -902,13 +902,13 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339- [[package]] name = "jsonschema-specifications" -version = "2023.12.1" +version = "2024.10.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"}, - {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"}, + {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"}, + {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"}, ] [package.dependencies] @@ -1215,13 +1215,13 @@ url = "../text-splitters" [[package]] name = "langsmith" -version = "0.1.129" +version = "0.1.132" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.129-py3-none-any.whl", hash = "sha256:31393fbbb17d6be5b99b9b22d530450094fab23c6c37281a6a6efb2143d05347"}, - {file = "langsmith-0.1.129.tar.gz", hash = "sha256:6c3ba66471bef41b9f87da247cc0b493268b3f54656f73648a256a205261b6a0"}, + {file = "langsmith-0.1.132-py3-none-any.whl", hash = "sha256:2320894203675c1c292b818cbecf68b69e47a9f7814d4e950237d1faaafd5dee"}, + {file = "langsmith-0.1.132.tar.gz", hash = "sha256:007b8fac469138abdba89db931900a26c5d316640e27ff4660d28c92a766aae1"}, ] [package.dependencies] @@ -1232,74 +1232,76 @@ pydantic = [ {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""}, ] requests = ">=2,<3" +requests-toolbelt = ">=1.0.0,<2.0.0" [[package]] name = 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pytest = ">=7.0.0,<9.0.0" [[package]] name = "tenacity" -version = "8.5.0" +version = "9.0.0" description = "Retry code until it succeeds" optional = false python-versions = ">=3.8" files = [ - {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"}, - {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"}, + {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"}, + {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"}, ] [package.extras] @@ -2849,13 +2875,13 @@ files = [ [[package]] name = "types-python-dateutil" -version = "2.9.0.20240906" +version = "2.9.0.20241003" description = "Typing stubs for python-dateutil" optional = false python-versions = ">=3.8" files = [ - {file = "types-python-dateutil-2.9.0.20240906.tar.gz", hash 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[build-system] -requires = [ "poetry-core>=1.0.0",] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-core" -version = "0.3.9" +version = "0.3.10" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -12,10 +12,16 @@ readme = "README.md" repository = "https://github.com/langchain-ai/langchain" [tool.mypy] -exclude = [ "notebooks", "examples", "example_data", "langchain_core/pydantic", "tests/unit_tests/utils/test_function_calling.py",] +exclude = [ + "notebooks", + "examples", + "example_data", + "langchain_core/pydantic", + "tests/unit_tests/utils/test_function_calling.py", +] disallow_untyped_defs = "True" [[tool.mypy.overrides]] -module = [ "numpy", "pytest",] +module = ["numpy", "pytest"] ignore_missing_imports = true [tool.ruff] @@ -28,7 +34,7 @@ target-version = "py39" [tool.poetry.dependencies] python = ">=3.9,<4.0" langsmith = "^0.1.125" -tenacity = "^8.1.0,!=8.4.0" +tenacity = ">=8.1.0,!=8.4.0,<10.0.0" jsonpatch = "^1.33" PyYAML = ">=5.3" packaging = ">=23.2,<25" @@ -73,22 +79,27 @@ select = [ "TID", "UP", "W", - "YTT" + "YTT", ] ignore = [ "COM812", # Messes with the formatter - "UP007", # Incompatible with pydantic + Python 3.9 - "W293", # + "UP007", # Incompatible with pydantic + Python 3.9 + "W293", # ] [tool.coverage.run] -omit = [ "tests/*",] +omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",] +markers = [ + "requires: mark tests as requiring a specific library", + "compile: mark placeholder test used to compile integration tests without running them", +] asyncio_mode = "auto" -filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",] +filterwarnings = [ + "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning", +] [tool.poetry.group.lint] optional = true @@ -106,16 +117,22 @@ optional = true optional = true [tool.ruff.lint.pep8-naming] -classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_init", "pydantic.field_validator", "pydantic.v1.root_validator",] +classmethod-decorators = [ + "classmethod", + "langchain_core.utils.pydantic.pre_init", + "pydantic.field_validator", + "pydantic.v1.root_validator", +] [tool.ruff.lint.per-file-ignores] -"tests/unit_tests/prompts/test_chat.py" = [ "E501",] -"tests/unit_tests/runnables/test_runnable.py" = [ "E501",] -"tests/unit_tests/runnables/test_graph.py" = [ "E501",] +"tests/unit_tests/prompts/test_chat.py" = ["E501"] +"tests/unit_tests/runnables/test_runnable.py" = ["E501"] +"tests/unit_tests/runnables/test_graph.py" = ["E501"] [tool.poetry.group.lint.dependencies] ruff = "^0.5" + [tool.poetry.group.typing.dependencies] mypy = ">=1.10,<1.11" types-pyyaml = "^6.0.12.2" @@ -123,11 +140,13 @@ types-requests = "^2.28.11.5" types-jinja2 = "^2.11.9" simsimd = "^5.0.0" + [tool.poetry.group.dev.dependencies] jupyter = "^1.0.0" setuptools = "^67.6.1" grandalf = "^0.8" + [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" @@ -146,12 +165,15 @@ python = "<3.12" version = "^1.26.0" python = ">=3.12" + [tool.poetry.group.test_integration.dependencies] + [tool.poetry.group.typing.dependencies.langchain-text-splitters] path = "../text-splitters" develop = true + [tool.poetry.group.test.dependencies.langchain-standard-tests] path = "../standard-tests" develop = true diff --git a/libs/core/tests/unit_tests/_api/test_beta_decorator.py b/libs/core/tests/unit_tests/_api/test_beta_decorator.py index 40465813d28f1..55f5cc8025c6c 100644 --- a/libs/core/tests/unit_tests/_api/test_beta_decorator.py +++ b/libs/core/tests/unit_tests/_api/test_beta_decorator.py @@ -118,7 +118,6 @@ def test_beta_function() -> None: assert not inspect.iscoroutinefunction(beta_function) -@pytest.mark.asyncio async def test_beta_async_function() -> None: """Test beta async function.""" with warnings.catch_warnings(record=True) as warning_list: @@ -159,7 +158,6 @@ def test_beta_method() -> None: assert not inspect.iscoroutinefunction(obj.beta_method) -@pytest.mark.asyncio async def test_beta_async_method() -> None: """Test beta method.""" with warnings.catch_warnings(record=True) as warning_list: diff --git a/libs/core/tests/unit_tests/_api/test_deprecation.py b/libs/core/tests/unit_tests/_api/test_deprecation.py index 06ffa55a8d722..a6a35430de54b 100644 --- a/libs/core/tests/unit_tests/_api/test_deprecation.py +++ b/libs/core/tests/unit_tests/_api/test_deprecation.py @@ -137,7 +137,6 @@ def test_deprecated_function() -> None: assert not inspect.iscoroutinefunction(deprecated_function) -@pytest.mark.asyncio async def test_deprecated_async_function() -> None: """Test deprecated async function.""" with warnings.catch_warnings(record=True) as warning_list: @@ -179,7 +178,6 @@ def test_deprecated_method() -> None: assert not inspect.iscoroutinefunction(obj.deprecated_method) -@pytest.mark.asyncio async def test_deprecated_async_method() -> None: """Test deprecated async method.""" with warnings.catch_warnings(record=True) as warning_list: diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr index 85b53552716a2..e74caea6312b5 100644 --- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr +++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr @@ -480,7 +480,7 @@ # --- # name: test_combining_sequences.3 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000004')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000006'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'foobar'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nicer assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='foobar', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000006', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000007'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000006-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000007', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000008'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000009')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000008', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000004')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000006'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'foobar'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nicer assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='foobar', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000006', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000007'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000006-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000007', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000008'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000009')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000008', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_configurable_fields[schema2] @@ -1119,7 +1119,7 @@ # --- # name: test_prompt_with_chat_model.2 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000003')}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000003')}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_prompt_with_chat_model_and_parser @@ -1243,7 +1243,7 @@ # --- # name: test_prompt_with_chat_model_and_parser.1 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000004')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000004')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_prompt_with_chat_model_async @@ -1359,7 +1359,7 @@ # --- # name: test_prompt_with_chat_model_async.2 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000003')}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000003')}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_prompt_with_llm @@ -1469,13 +1469,13 @@ # --- # name: test_prompt_with_llm.1 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_prompt_with_llm.2 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), - RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003.20230101T000000000000Z00000000-0000-4000-8000-000000000004', trace_id=UUID('00000000-0000-4000-8000-000000000003')), RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000003'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000003')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003.20230101T000000000000Z00000000-0000-4000-8000-000000000004', trace_id=UUID('00000000-0000-4000-8000-000000000003')), RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000003'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000003')), ]) # --- # name: test_prompt_with_llm_and_async_lambda @@ -1598,7 +1598,7 @@ # --- # name: test_prompt_with_llm_and_async_lambda.1 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='passthrough', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='passthrough', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_prompt_with_llm_parser @@ -1722,13 +1722,13 @@ # --- # name: test_prompt_with_llm_parser.1 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), ]) # --- # name: test_prompt_with_llm_parser.2 list([ - RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'tomato, lettuce, onion', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'tomato, lettuce, onion'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')), - RunTree(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:1'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000004')), RunTree(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:2'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000006', trace_id=UUID('00000000-0000-4000-8000-000000000004')), RunTree(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:3'], child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000007', trace_id=UUID('00000000-0000-4000-8000-000000000004'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004', trace_id=UUID('00000000-0000-4000-8000-000000000004')), + RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'tomato, lettuce, onion', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, 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start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000004')), RunTree(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000006', trace_id=UUID('00000000-0000-4000-8000-000000000004')), RunTree(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, 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authors = [] license = "MIT" @@ -33,7 +33,7 @@ langchain-server = "langchain.server:main" [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = "^0.3.8" +langchain-core = "^0.3.10" langchain-text-splitters = "^0.3.0" langsmith = "^0.1.17" pydantic = "^2.7.4" @@ -41,7 +41,7 @@ SQLAlchemy = ">=1.4,<3" requests = "^2" PyYAML = ">=5.3" aiohttp = "^3.8.3" -tenacity = "^8.1.0,!=8.4.0" +tenacity = ">=8.1.0,!=8.4.0,<10" [[tool.poetry.dependencies.numpy]] version = "^1" python = "<3.12" diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py index d7f2643f0f7c0..5f69b62228ab3 100644 --- a/libs/partners/anthropic/langchain_anthropic/chat_models.py +++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py @@ -1,3 +1,4 @@ +import copy import re import warnings from operator import itemgetter @@ -116,7 +117,6 @@ def _merge_messages( """Merge runs of human/tool messages into single human messages with content blocks.""" # noqa: E501 merged: list = [] for curr in messages: - curr = curr.model_copy(deep=True) if isinstance(curr, ToolMessage): if ( isinstance(curr.content, list) @@ -143,12 +143,12 @@ def _merge_messages( if isinstance(last.content, str): new_content: List = [{"type": "text", "text": last.content}] else: - new_content = last.content + new_content = copy.copy(last.content) if isinstance(curr.content, str): new_content.append({"type": "text", "text": curr.content}) else: new_content.extend(curr.content) - last.content = new_content + merged[-1] = curr.model_copy(update={"content": new_content}, deep=False) else: merged.append(curr) return merged @@ -178,9 +178,11 @@ def _format_messages( raise ValueError("System message must be at beginning of message list.") if isinstance(message.content, list): system = [ - block - if isinstance(block, dict) - else {"type": "text", "text": "block"} + ( + block + if isinstance(block, dict) + else {"type": "text", "text": "block"} + ) for block in message.content ] else: diff --git a/libs/partners/anthropic/pyproject.toml b/libs/partners/anthropic/pyproject.toml index 12bec1e3ae233..83e32dbb03007 100644 --- a/libs/partners/anthropic/pyproject.toml +++ b/libs/partners/anthropic/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = [ "poetry-core>=1.0.0",] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] @@ -25,14 +25,17 @@ langchain-core = "^0.3.9" pydantic = "^2.7.4" [tool.ruff.lint] -select = [ "E", "F", "I", "T201",] +select = ["E", "F", "I", "T201"] [tool.coverage.run] -omit = [ "tests/*",] +omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",] +markers = [ + "requires: mark tests as requiring a specific library", + "compile: mark placeholder test used to compile integration tests without running them", +] asyncio_mode = "auto" [tool.poetry.dependencies.defusedxml] diff --git a/libs/partners/azure-dynamic-sessions/pyproject.toml b/libs/partners/azure-dynamic-sessions/pyproject.toml index 9a2407fb9b6a4..4bd203f06b3bd 100644 --- a/libs/partners/azure-dynamic-sessions/pyproject.toml +++ b/libs/partners/azure-dynamic-sessions/pyproject.toml @@ -34,7 +34,6 @@ omit = ["tests/*"] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/chroma/poetry.lock b/libs/partners/chroma/poetry.lock index ab68cd39e91aa..435c4533c7672 100644 --- a/libs/partners/chroma/poetry.lock +++ b/libs/partners/chroma/poetry.lock @@ 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a/libs/partners/exa/pyproject.toml +++ b/libs/partners/exa/pyproject.toml @@ -33,7 +33,6 @@ omit = ["tests/*"] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/fireworks/pyproject.toml b/libs/partners/fireworks/pyproject.toml index dd44828ff59ab..e8889cc2a21fb 100644 --- a/libs/partners/fireworks/pyproject.toml +++ b/libs/partners/fireworks/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = [ "poetry-core>=1.0.0",] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] @@ -27,14 +27,17 @@ requests = "^2" aiohttp = "^3.9.1" [tool.ruff.lint] -select = [ "E", "F", "I", "T201",] +select = ["E", "F", "I", "T201"] [tool.coverage.run] -omit = [ "tests/*",] +omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",] +markers = [ + "requires: mark tests as requiring a specific library", + "compile: mark placeholder test used to compile integration tests without running them", +] asyncio_mode = "auto" [tool.poetry.group.test] diff --git a/libs/partners/huggingface/pyproject.toml b/libs/partners/huggingface/pyproject.toml index 53c0dc204f4f3..429edb95b2e25 100644 --- a/libs/partners/huggingface/pyproject.toml +++ b/libs/partners/huggingface/pyproject.toml @@ -36,7 +36,6 @@ omit = ["tests/*"] addopts = "--strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/mistralai/pyproject.toml b/libs/partners/mistralai/pyproject.toml index 834c61a0a2f9e..e3ed4d85a78a9 100644 --- a/libs/partners/mistralai/pyproject.toml +++ b/libs/partners/mistralai/pyproject.toml @@ -36,7 +36,6 @@ omit = ["tests/*"] addopts = "--strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/mongodb/pyproject.toml b/libs/partners/mongodb/pyproject.toml index e850c36c00cae..a6faef2fd9c2a 100644 --- a/libs/partners/mongodb/pyproject.toml +++ b/libs/partners/mongodb/pyproject.toml @@ -41,7 +41,6 @@ omit = ["tests/*"] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/nomic/pyproject.toml b/libs/partners/nomic/pyproject.toml index d503e8395dfc3..2f323863986b1 100644 --- a/libs/partners/nomic/pyproject.toml +++ b/libs/partners/nomic/pyproject.toml @@ -97,7 +97,6 @@ addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5 # https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index baaa74f637b31..233fa8700a436 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -480,6 +480,15 @@ def build_extra(cls, values: Dict[str, Any]) -> Any: values = _build_model_kwargs(values, all_required_field_names) return values + @model_validator(mode="before") + @classmethod + def validate_temperature(cls, values: Dict[str, Any]) -> Any: + """Currently o1 models only allow temperature=1.""" + model = values.get("model_name") or values.get("model") or "" + if model.startswith("o1") and "temperature" not in values: + values["temperature"] = 1 + return values + @model_validator(mode="after") def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml index d4c3a5875d728..c33100e5ecbd4 100644 --- a/libs/partners/openai/pyproject.toml +++ b/libs/partners/openai/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = [ "poetry-core>=1.0.0",] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] @@ -28,20 +28,26 @@ openai = "^1.40.0" tiktoken = ">=0.7,<1" [tool.ruff.lint] -select = [ "E", "F", "I", "T201",] +select = ["E", "F", "I", "T201"] [tool.ruff.format] docstring-code-format = true skip-magic-trailing-comma = true [tool.coverage.run] -omit = [ "tests/*",] +omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5 --cov=langchain_openai" -markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", "scheduled: mark tests to run in scheduled testing",] +markers = [ + "requires: mark tests as requiring a specific library", + "compile: mark placeholder test used to compile integration tests without running them", + "scheduled: mark tests to run in scheduled testing", +] asyncio_mode = "auto" -filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",] +filterwarnings = [ + "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning", +] [tool.poetry.group.test] optional = true diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py index cc03698e5ef93..c6065a137e5c7 100644 --- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py @@ -35,6 +35,13 @@ def test_openai_model_param() -> None: assert llm.model_name == "foo" +def test_openai_o1_temperature() -> None: + llm = ChatOpenAI(model="o1-preview") + assert llm.temperature == 1 + llm = ChatOpenAI(model_name="o1-mini") # type: ignore[call-arg] + assert llm.temperature == 1 + + def test_function_message_dict_to_function_message() -> None: content = json.dumps({"result": "Example #1"}) name = "test_function" diff --git a/libs/partners/pinecone/pyproject.toml b/libs/partners/pinecone/pyproject.toml index 6dbff9622571f..b37aec50bcd85 100644 --- a/libs/partners/pinecone/pyproject.toml +++ b/libs/partners/pinecone/pyproject.toml @@ -42,7 +42,6 @@ omit = ["tests/*"] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/pinecone/tests/integration_tests/test_embeddings.py b/libs/partners/pinecone/tests/integration_tests/test_embeddings.py index 92e277ee44238..56c9cd3e45e6f 100644 --- a/libs/partners/pinecone/tests/integration_tests/test_embeddings.py +++ b/libs/partners/pinecone/tests/integration_tests/test_embeddings.py @@ -44,7 +44,6 @@ def test_embed_query(embd_client: PineconeEmbeddings) -> None: assert len(out) == DIMENSION -@pytest.mark.asyncio async def test_aembed_query(embd_client: PineconeEmbeddings) -> None: out = await embd_client.aembed_query("Hello, world!") assert isinstance(out, list) @@ -58,7 +57,6 @@ def test_embed_documents(embd_client: PineconeEmbeddings) -> None: assert len(out[0]) == DIMENSION -@pytest.mark.asyncio async def test_aembed_documents(embd_client: PineconeEmbeddings) -> None: out = await embd_client.aembed_documents(["Hello, world!", "This is a test."]) assert isinstance(out, list) diff --git a/libs/partners/prompty/pyproject.toml b/libs/partners/prompty/pyproject.toml index 32e34016c7d7d..d39d06862ca9f 100644 --- a/libs/partners/prompty/pyproject.toml +++ b/libs/partners/prompty/pyproject.toml @@ -91,7 +91,6 @@ addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5 # https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/qdrant/pyproject.toml b/libs/partners/qdrant/pyproject.toml index da4162b7f1639..80c72fc12a54c 100644 --- a/libs/partners/qdrant/pyproject.toml +++ b/libs/partners/qdrant/pyproject.toml @@ -44,7 +44,6 @@ omit = ["tests/*"] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/partners/voyageai/pyproject.toml b/libs/partners/voyageai/pyproject.toml index 62c435a3c23ef..9b416f3ce5970 100644 --- a/libs/partners/voyageai/pyproject.toml +++ b/libs/partners/voyageai/pyproject.toml @@ -96,7 +96,6 @@ addopts = "--strict-markers --strict-config --durations=5" # https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers markers = [ "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/text-splitters/pyproject.toml b/libs/text-splitters/pyproject.toml index 8e74a32429c50..a23a8b09fba5d 100644 --- a/libs/text-splitters/pyproject.toml +++ b/libs/text-splitters/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = [ "poetry-core>=1.0.0",] +requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry] @@ -14,7 +14,14 @@ repository = "https://github.com/langchain-ai/langchain" [tool.mypy] disallow_untyped_defs = "True" [[tool.mypy.overrides]] -module = [ "transformers", "sentence_transformers", "nltk.tokenize", "konlpy.tag", "bs4", "pytest",] +module = [ + "transformers", + "sentence_transformers", + "nltk.tokenize", + "konlpy.tag", + "bs4", + "pytest", +] ignore_missing_imports = "True" [tool.poetry.urls] @@ -26,14 +33,17 @@ python = ">=3.9,<4.0" langchain-core = "^0.3.0" [tool.ruff.lint] -select = [ "E", "F", "I", "T201",] +select = ["E", "F", "I", "T201"] [tool.coverage.run] -omit = [ "tests/*",] +omit = ["tests/*"] [tool.pytest.ini_options] addopts = "--strict-markers --strict-config --durations=5" -markers = [ "requires: mark tests as requiring a specific library", "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them",] +markers = [ + "requires: mark tests as requiring a specific library", + "compile: mark placeholder test used to compile integration tests without running them", +] asyncio_mode = "auto" [tool.poetry.group.lint]