From 8895d468cb375dea6329858b2aa2b211ada3fc0f Mon Sep 17 00:00:00 2001 From: Jorge Piedrahita Ortiz Date: Sun, 27 Oct 2024 10:08:15 -0500 Subject: [PATCH 1/7] community: sambastudio llm refactor (#27215) **Description:** - Sambastudio LLM refactor - Sambastudio openai compatible API support added - docs updated --- docs/docs/integrations/llms/sambanova.ipynb | 190 ---- docs/docs/integrations/llms/sambastudio.ipynb | 239 +++++ .../langchain_community/llms/sambanova.py | 869 +++++++++--------- libs/community/scripts/check_pydantic.sh | 4 +- 4 files changed, 667 insertions(+), 635 deletions(-) delete mode 100644 docs/docs/integrations/llms/sambanova.ipynb create mode 100644 docs/docs/integrations/llms/sambastudio.ipynb diff --git a/docs/docs/integrations/llms/sambanova.ipynb b/docs/docs/integrations/llms/sambanova.ipynb deleted file mode 100644 index 618a02363f51f..0000000000000 --- a/docs/docs/integrations/llms/sambanova.ipynb +++ /dev/null @@ -1,190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SambaNova\n", - "\n", - "**[SambaNova](https://sambanova.ai/)'s** [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n", - "\n", - "This example goes over how to use LangChain to interact with SambaNova models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SambaStudio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**SambaStudio** allows you to train, run batch inference jobs, and deploy online inference endpoints to run open source models that you fine tuned yourself." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A SambaStudio environment is required to deploy a model. Get more information at [sambanova.ai/products/enterprise-ai-platform-sambanova-suite](https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite)\n", - "\n", - "The [sseclient-py](https://pypi.org/project/sseclient-py/) package is required to run streaming predictions " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install --quiet sseclient-py==1.8.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register your environment variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "sambastudio_base_url = \"\"\n", - "sambastudio_base_uri = \"\" # optional, \"api/predict/generic\" set as default\n", - "sambastudio_project_id = \"\"\n", - "sambastudio_endpoint_id = \"\"\n", - "sambastudio_api_key = \"\"\n", - "\n", - "# Set the environment variables\n", - "os.environ[\"SAMBASTUDIO_BASE_URL\"] = sambastudio_base_url\n", - "os.environ[\"SAMBASTUDIO_BASE_URI\"] = sambastudio_base_uri\n", - "os.environ[\"SAMBASTUDIO_PROJECT_ID\"] = sambastudio_project_id\n", - "os.environ[\"SAMBASTUDIO_ENDPOINT_ID\"] = sambastudio_endpoint_id\n", - "os.environ[\"SAMBASTUDIO_API_KEY\"] = sambastudio_api_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call SambaStudio models directly from LangChain!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_community.llms.sambanova import SambaStudio\n", - "\n", - "llm = SambaStudio(\n", - " streaming=False,\n", - " model_kwargs={\n", - " \"do_sample\": True,\n", - " \"max_tokens_to_generate\": 1000,\n", - " \"temperature\": 0.01,\n", - " # \"repetition_penalty\": 1.0,\n", - " # \"top_k\": 50,\n", - " # \"top_logprobs\": 0,\n", - " # \"top_p\": 1.0\n", - " },\n", - ")\n", - "\n", - "print(llm.invoke(\"Why should I use open source models?\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Streaming response\n", - "\n", - "from langchain_community.llms.sambanova import SambaStudio\n", - "\n", - "llm = SambaStudio(\n", - " streaming=True,\n", - " model_kwargs={\n", - " \"do_sample\": True,\n", - " \"max_tokens_to_generate\": 1000,\n", - " \"temperature\": 0.01,\n", - " # \"repetition_penalty\": 1.0,\n", - " # \"top_k\": 50,\n", - " # \"top_logprobs\": 0,\n", - " # \"top_p\": 1.0\n", - " },\n", - ")\n", - "\n", - "for chunk in llm.stream(\"Why should I use open source models?\"):\n", - " print(chunk, end=\"\", flush=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also call a CoE endpoint expert model " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Using a CoE endpoint\n", - "\n", - "from langchain_community.llms.sambanova import SambaStudio\n", - "\n", - "llm = SambaStudio(\n", - " streaming=False,\n", - " model_kwargs={\n", - " \"do_sample\": True,\n", - " \"max_tokens_to_generate\": 1000,\n", - " \"temperature\": 0.01,\n", - " \"process_prompt\": False,\n", - " \"select_expert\": \"Meta-Llama-3-8B-Instruct\",\n", - " # \"repetition_penalty\": 1.0,\n", - " # \"top_k\": 50,\n", - " # \"top_logprobs\": 0,\n", - " # \"top_p\": 1.0\n", - " },\n", - ")\n", - "\n", - "print(llm.invoke(\"Why should I use open source models?\"))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/docs/integrations/llms/sambastudio.ipynb b/docs/docs/integrations/llms/sambastudio.ipynb new file mode 100644 index 0000000000000..dd977f37c9532 --- /dev/null +++ b/docs/docs/integrations/llms/sambastudio.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SambaStudio\n", + "\n", + "**[SambaNova](https://sambanova.ai/)'s** [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform that allows you to train, run batch inference jobs, and deploy online inference endpoints to run open source models that you fine tuned yourself.\n", + "\n", + ":::caution\n", + "You are currently on a page documenting the use of SambaStudio models as [text completion models](/docs/concepts/#llms). We recommend you to use the [chat completion models](/docs/concepts/#chat-models).\n", + "\n", + "You may be looking for [SambaStudio Chat Models](/docs/integrations/chat/sambastudio/) .\n", + ":::\n", + "\n", + "## Overview\n", + "### Integration details\n", + "\n", + "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n", + "| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n", + "| [SambaStudio](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.sambanova.SambaStudio.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.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", + "This example goes over how to use LangChain to interact with SambaStudio models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "### Credentials\n", + "A SambaStudio environment is required to deploy a model. Get more information at [sambanova.ai/products/enterprise-ai-platform-sambanova-suite](https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite)\n", + "\n", + "you'll need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/endpoints.html) and set the `SAMBASTUDIO_URL` and `SAMBASTUDIO_API_KEY` environment variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if \"SAMBASTUDIO_URL\" not in os.environ:\n", + " os.environ[\"SAMBASTUDIO_URL\"] = getpass.getpass()\n", + "if \"SAMBASTUDIO_API_KEY\" not in os.environ:\n", + " os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation\n", + "\n", + "The integration lives in the `langchain-community` package. We also need to install the [sseclient-py](https://pypi.org/project/sseclient-py/) package this is required to run streaming predictions " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --quiet -U langchain-community sseclient-py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instantiation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.llms.sambanova import SambaStudio\n", + "\n", + "llm = SambaStudio(\n", + " model_kwargs={\n", + " \"do_sample\": True,\n", + " \"max_tokens\": 1024,\n", + " \"temperature\": 0.01,\n", + " \"process_prompt\": True, # set if using CoE endpoints\n", + " \"model\": \"Meta-Llama-3-70B-Instruct-4096\", # set if using CoE endpoints\n", + " # \"repetition_penalty\": 1.0,\n", + " # \"top_k\": 50,\n", + " # \"top_logprobs\": 0,\n", + " # \"top_p\": 1.0\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Invocation\n", + "\n", + "Now we can instantiate our model object and generate chat completions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Using open source models can have numerous benefits. Here are some reasons why you should consider using open source models:\\n\\n1. **Cost-effective**: Open source models are often free to use, modify, and distribute, which can significantly reduce costs compared to proprietary models.\\n2. **Customizability**: Open source models can be modified to fit your specific needs, allowing you to tailor the model to your project's requirements.\\n3. **Transparency**: Open source models provide complete transparency into the model's architecture, training data, and algorithms, which can be essential for understanding how the model works and identifying potential biases.\\n4. **Community involvement**: Open source models are often maintained by a community of developers, researchers, and users, which can lead to faster bug fixes, new feature additions, and improved performance.\\n5. **Flexibility**: Open source models can be used in a variety of applications, from research to production, and can be easily integrated into different workflows and systems.\\n6. **Auditability**: With open source models, you can audit the model's performance, data, and algorithms, which is critical in regulated industries or when working with sensitive data.\\n7. **No vendor lock-in**: By using open source models, you're not tied to a specific vendor or proprietary technology, giving you more freedom to switch or modify your approach as needed.\\n8. **Improved security**: Open source models can be reviewed and audited by the community, which can help identify and fix security vulnerabilities more quickly.\\n9. **Access to cutting-edge research**: Open source models can provide access to the latest research and advancements in AI and machine learning, allowing you to leverage the work of experts in the field.\\n10. **Ethical considerations**: By using open source models, you can ensure that your AI systems are transparent, explainable, and fair, which is essential for building trust in AI applications.\\n11. **Reduced risk of bias**: Open source models can help reduce the risk of bias by providing transparency into the model's development, training data, and algorithms.\\n12. **Faster development**: Open source models can accelerate your development process by providing pre-trained models, datasets, and tools that can be easily integrated into your project.\\n13. **Improved collaboration**: Open source models can facilitate collaboration among researchers, developers, and organizations, leading to faster progress and innovation in AI and machine learning.\\n14. **Access to large datasets**: Open source models can provide access to large datasets, which can be essential for training and testing AI models.\\n15. **Compliance with regulations**: In some cases, using open source models can help ensure compliance with regulations, such as GDPR, HIPAA, or CCPA, which require transparency and explainability in AI systems.\\n\\nOverall, using open source models can provide numerous benefits, from cost savings to improved transparency and customizability. By leveraging open source models, you can accelerate your AI and machine learning projects while ensuring that your systems are transparent, explainable, and fair.\"" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_text = \"Why should I use open source models?\"\n", + "\n", + "completion = llm.invoke(input_text)\n", + "completion" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using open source models can have numerous benefits. Here are some reasons why you should consider using open source models:\n", + "\n", + "1. **Cost-effective**: Open source models are often free to use, modify, and distribute, which can significantly reduce costs compared to proprietary models.\n", + "2. **Customizability**: Open source models can be modified to fit your specific needs, allowing you to tailor the model to your project's requirements.\n", + "3. **Transparency**: Open source models provide complete transparency into the model's architecture, training data, and algorithms, which can be essential for understanding how the model works and identifying potential biases.\n", + "4. **Community involvement**: Open source models are often maintained by a community of developers, researchers, and users, which can lead to faster bug fixes, new feature additions, and improved performance.\n", + "5. **Flexibility**: Open source models can be used in a variety of applications, from research to production, and can be easily integrated into different workflows and systems.\n", + "6. **Auditability**: With open source models, you can audit the model's performance, data, and algorithms, which is critical in regulated industries or when working with sensitive data.\n", + "7. **No vendor lock-in**: By using open source models, you're not tied to a specific vendor or proprietary technology, giving you more freedom to switch or modify your approach as needed.\n", + "8. **Improved security**: Open source models can be reviewed and audited by the community, which can help identify and fix security vulnerabilities more quickly.\n", + "9. **Access to cutting-edge research**: Open source models can provide access to the latest research and advancements in AI and machine learning, allowing you to leverage the work of experts in the field.\n", + "10. **Ethical considerations**: By using open source models, you can ensure that your AI systems are transparent, explainable, and fair, which is essential for building trust in AI applications.\n", + "11. **Reduced risk of bias**: Open source models can help reduce the risk of bias by providing transparency into the model's development, training data, and algorithms.\n", + "12. **Faster development**: Open source models can accelerate your development process by providing pre-trained models, datasets, and tools that can be easily integrated into your project.\n", + "13. **Improved collaboration**: Open source models can facilitate collaboration among researchers, developers, and organizations, leading to faster progress and innovation in AI and machine learning.\n", + "14. **Access to large datasets**: Open source models can provide access to large datasets, which can be essential for training and testing AI models.\n", + "15. **Compliance with regulations**: In some cases, using open source models can help ensure compliance with regulations, such as GDPR, HIPAA, or CCPA, which require transparency and explainability in AI systems.\n", + "\n", + "Overall, using open source models can provide numerous benefits, from cost savings to improved transparency and customizability. By leveraging open source models, you can accelerate your AI and machine learning projects while ensuring that your systems are transparent, explainable, and fair." + ] + } + ], + "source": [ + "# Streaming response\n", + "for chunk in llm.stream(\"Why should I use open source models?\"):\n", + " print(chunk, end=\"\", flush=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chaining" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'In German, you can say:\\n\\n\"Ich liebe das Programmieren.\"\\n\\nHere\\'s a breakdown of the sentence:\\n\\n* \"Ich\" means \"I\"\\n* \"liebe\" is the verb \"to love\" in the present tense, first person singular (I love)\\n* \"das\" is the definite article \"the\"\\n* \"Programmieren\" is the noun \"programming\"\\n\\nSo, \"Ich liebe das Programmieren\" literally means \"I love the programming\".\\n\\nIf you want to make it sound more casual, you can say:\\n\\n\"Ich liebe\\'s Programmieren.\"\\n\\nThe apostrophe in \"liebe\\'s\" is a contraction of \"liebe es\", which is a more informal way of saying \"I love it\".\\n\\nAlternatively, you can also say:\\n\\n\"Programmieren ist meine Leidenschaft.\"\\n\\nThis sentence means \"Programming is my passion\". Here, \"Programmieren\" is the subject, \"ist\" is the verb \"to be\" in the present tense, and \"meine Leidenschaft\" means \"my passion\".'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_core.prompts import PromptTemplate\n", + "\n", + "prompt = PromptTemplate.from_template(\"How to say {input} in {output_language}:\\n\")\n", + "\n", + "chain = prompt | llm\n", + "chain.invoke(\n", + " {\n", + " \"output_language\": \"German\",\n", + " \"input\": \"I love programming.\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "For detailed documentation of all `SambaStudio` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/community/llms/langchain_community.llms.sambanova.SambaStudio.html" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "multimodalenv", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/libs/community/langchain_community/llms/sambanova.py b/libs/community/langchain_community/llms/sambanova.py index 42c22b951c8dc..4ae6a1e6a2809 100644 --- a/libs/community/langchain_community/llms/sambanova.py +++ b/libs/community/langchain_community/llms/sambanova.py @@ -1,318 +1,195 @@ import json -from typing import Any, Dict, Generator, Iterator, List, Optional, Union +from typing import Any, Dict, Iterator, List, Optional, Tuple, Union import requests from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.utils import get_from_dict_or_env, pre_init -from pydantic import ConfigDict +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import Field, SecretStr +from requests import Response -class SSEndpointHandler: - """ - SambaNova Systems Interface for SambaStudio model endpoints. - - :param str host_url: Base URL of the DaaS API service +class SambaStudio(LLM): """ + SambaStudio large language models. - def __init__(self, host_url: str, api_base_uri: str): - """ - Initialize the SSEndpointHandler. - - :param str host_url: Base URL of the DaaS API service - :param str api_base_uri: Base URI of the DaaS API service - """ - self.host_url = host_url - self.api_base_uri = api_base_uri - self.http_session = requests.Session() - - def _process_response(self, response: requests.Response) -> Dict: - """ - Processes the API response and returns the resulting dict. - - All resulting dicts, regardless of success or failure, will contain the - `status_code` key with the API response status code. - - If the API returned an error, the resulting dict will contain the key - `detail` with the error message. - - If the API call was successful, the resulting dict will contain the key - `data` with the response data. - - :param requests.Response response: the response object to process - :return: the response dict - :type: dict - """ - result: Dict[str, Any] = {} - try: - result = response.json() - except Exception as e: - result["detail"] = str(e) - if "status_code" not in result: - result["status_code"] = response.status_code - return result - - def _process_streaming_response( - self, - response: requests.Response, - ) -> Generator[Dict, None, None]: - """Process the streaming response""" - if "api/predict/nlp" in self.api_base_uri: - try: - import sseclient - except ImportError: - raise ImportError( - "could not import sseclient library" - "Please install it with `pip install sseclient-py`." - ) - client = sseclient.SSEClient(response) - close_conn = False - for event in client.events(): - if event.event == "error_event": - close_conn = True - chunk = { - "event": event.event, - "data": event.data, - "status_code": response.status_code, + Setup: + To use, you should have the environment variables + ``SAMBASTUDIO_URL`` set with your SambaStudio environment URL. + ``SAMBASTUDIO_API_KEY`` set with your SambaStudio endpoint API key. + https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite + read extra documentation in https://docs.sambanova.ai/sambastudio/latest/index.html + Example: + .. code-block:: python + from langchain_community.llms.sambanova import SambaStudio + SambaStudio( + sambastudio_url="your-SambaStudio-environment-URL", + sambastudio_api_key="your-SambaStudio-API-key, + model_kwargs={ + "model" : model or expert name (set for CoE endpoints), + "max_tokens" : max number of tokens to generate, + "temperature" : model temperature, + "top_p" : model top p, + "top_k" : model top k, + "do_sample" : wether to do sample + "process_prompt": wether to process prompt + (set for CoE generic v1 and v2 endpoints) + }, + ) + Key init args — completion params: + model: str + The name of the model to use, e.g., Meta-Llama-3-70B-Instruct-4096 + (set for CoE endpoints). + streaming: bool + Whether to use streaming handler when using non streaming methods + model_kwargs: dict + Extra Key word arguments to pass to the model: + max_tokens: int + max tokens to generate + temperature: float + model temperature + top_p: float + model top p + top_k: int + model top k + do_sample: bool + wether to do sample + process_prompt: + wether to process prompt (set for CoE generic v1 and v2 endpoints) + Key init args — client params: + sambastudio_url: str + SambaStudio endpoint Url + sambastudio_api_key: str + SambaStudio endpoint api key + + Instantiate: + .. code-block:: python + + from langchain_community.llms import SambaStudio + + llm = SambaStudio=( + sambastudio_url = set with your SambaStudio deployed endpoint URL, + sambastudio_api_key = set with your SambaStudio deployed endpoint Key, + model_kwargs = { + "model" : model or expert name (set for CoE endpoints), + "max_tokens" : max number of tokens to generate, + "temperature" : model temperature, + "top_p" : model top p, + "top_k" : model top k, + "do_sample" : wether to do sample + "process_prompt" : wether to process prompt + (set for CoE generic v1 and v2 endpoints) } - yield chunk - if close_conn: - client.close() - elif ( - "api/v2/predict/generic" in self.api_base_uri - or "api/predict/generic" in self.api_base_uri - ): - try: - for line in response.iter_lines(): - chunk = json.loads(line) - if "status_code" not in chunk: - chunk["status_code"] = response.status_code - yield chunk - except Exception as e: - raise RuntimeError(f"Error processing streaming response: {e}") - else: - raise ValueError( - f"handling of endpoint uri: {self.api_base_uri} not implemented" ) - def _get_full_url(self, path: str) -> str: - """ - Return the full API URL for a given path. + Invoke: + .. code-block:: python + prompt = "tell me a joke" + response = llm.invoke(prompt) - :param str path: the sub-path - :returns: the full API URL for the sub-path - :type: str - """ - return f"{self.host_url}/{self.api_base_uri}/{path}" + Stream: + .. code-block:: python - def nlp_predict( - self, - project: str, - endpoint: str, - key: str, - input: Union[List[str], str], - params: Optional[str] = "", - stream: bool = False, - ) -> Dict: - """ - NLP predict using inline input string. - - :param str project: Project ID in which the endpoint exists - :param str endpoint: Endpoint ID - :param str key: API Key - :param str input_str: Input string - :param str params: Input params string - :returns: Prediction results - :type: dict - """ - if isinstance(input, str): - input = [input] - if "api/predict/nlp" in self.api_base_uri: - if params: - data = {"inputs": input, "params": json.loads(params)} - else: - data = {"inputs": input} - elif "api/v2/predict/generic" in self.api_base_uri: - items = [{"id": f"item{i}", "value": item} for i, item in enumerate(input)] - if params: - data = {"items": items, "params": json.loads(params)} - else: - data = {"items": items} - elif "api/predict/generic" in self.api_base_uri: - if params: - data = {"instances": input, "params": json.loads(params)} - else: - data = {"instances": input} - else: - raise ValueError( - f"handling of endpoint uri: {self.api_base_uri} not implemented" - ) - response = self.http_session.post( - self._get_full_url(f"{project}/{endpoint}"), - headers={"key": key}, - json=data, - ) - return self._process_response(response) + for chunk in llm.stream(prompt): + print(chunk, end="", flush=True) - def nlp_predict_stream( - self, - project: str, - endpoint: str, - key: str, - input: Union[List[str], str], - params: Optional[str] = "", - ) -> Iterator[Dict]: - """ - NLP predict using inline input string. - - :param str project: Project ID in which the endpoint exists - :param str endpoint: Endpoint ID - :param str key: API Key - :param str input_str: Input string - :param str params: Input params string - :returns: Prediction results - :type: dict - """ - if "api/predict/nlp" in self.api_base_uri: - if isinstance(input, str): - input = [input] - if params: - data = {"inputs": input, "params": json.loads(params)} - else: - data = {"inputs": input} - elif "api/v2/predict/generic" in self.api_base_uri: - if isinstance(input, str): - input = [input] - items = [{"id": f"item{i}", "value": item} for i, item in enumerate(input)] - if params: - data = {"items": items, "params": json.loads(params)} - else: - data = {"items": items} - elif "api/predict/generic" in self.api_base_uri: - if isinstance(input, list): - input = input[0] - if params: - data = {"instance": input, "params": json.loads(params)} - else: - data = {"instance": input} - else: - raise ValueError( - f"handling of endpoint uri: {self.api_base_uri} not implemented" - ) - # Streaming output - response = self.http_session.post( - self._get_full_url(f"stream/{project}/{endpoint}"), - headers={"key": key}, - json=data, - stream=True, - ) - for chunk in self._process_streaming_response(response): - yield chunk + Async: + .. code-block:: python + response = llm.ainvoke(prompt) + await response -class SambaStudio(LLM): """ - SambaStudio large language models. - To use, you should have the environment variables - ``SAMBASTUDIO_BASE_URL`` set with your SambaStudio environment URL. - ``SAMBASTUDIO_BASE_URI`` set with your SambaStudio api base URI. - ``SAMBASTUDIO_PROJECT_ID`` set with your SambaStudio project ID. - ``SAMBASTUDIO_ENDPOINT_ID`` set with your SambaStudio endpoint ID. - ``SAMBASTUDIO_API_KEY`` set with your SambaStudio endpoint API key. - - https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite - - read extra documentation in https://docs.sambanova.ai/sambastudio/latest/index.html - - Example: - .. code-block:: python - - from langchain_community.llms.sambanova import SambaStudio - SambaStudio( - sambastudio_base_url="your-SambaStudio-environment-URL", - sambastudio_base_uri="your-SambaStudio-base-URI", - sambastudio_project_id="your-SambaStudio-project-ID", - sambastudio_endpoint_id="your-SambaStudio-endpoint-ID", - sambastudio_api_key="your-SambaStudio-endpoint-API-key, - streaming=False - model_kwargs={ - "do_sample": False, - "max_tokens_to_generate": 1000, - "temperature": 0.7, - "top_p": 1.0, - "repetition_penalty": 1, - "top_k": 50, - #"process_prompt": False, - #"select_expert": "Meta-Llama-3-8B-Instruct" - }, - ) - """ + sambastudio_url: str = Field(default="") + """SambaStudio Url""" - sambastudio_base_url: str = "" - """Base url to use""" + sambastudio_api_key: SecretStr = Field(default="") + """SambaStudio api key""" - sambastudio_base_uri: str = "" - """endpoint base uri""" + base_url: str = Field(default="", exclude=True) + """SambaStudio non streaming URL""" - sambastudio_project_id: str = "" - """Project id on sambastudio for model""" + streaming_url: str = Field(default="", exclude=True) + """SambaStudio streaming URL""" - sambastudio_endpoint_id: str = "" - """endpoint id on sambastudio for model""" + streaming: bool = Field(default=False) + """Whether to use streaming handler when using non streaming methods""" - sambastudio_api_key: str = "" - """sambastudio api key""" - - model_kwargs: Optional[dict] = None + model_kwargs: Optional[Dict[str, Any]] = None """Key word arguments to pass to the model.""" - streaming: Optional[bool] = False - """Streaming flag to get streamed response.""" - - model_config = ConfigDict( - extra="forbid", - ) + class Config: + populate_by_name = True @classmethod def is_lc_serializable(cls) -> bool: + """Return whether this model can be serialized by Langchain.""" return True + @property + def lc_secrets(self) -> Dict[str, str]: + return { + "sambastudio_url": "sambastudio_url", + "sambastudio_api_key": "sambastudio_api_key", + } + @property def _identifying_params(self) -> Dict[str, Any]: - """Get the identifying parameters.""" - return {**{"model_kwargs": self.model_kwargs}} + """Return a dictionary of identifying parameters. + + This information is used by the LangChain callback system, which + is used for tracing purposes make it possible to monitor LLMs. + """ + return {"streaming": self.streaming, **{"model_kwargs": self.model_kwargs}} @property def _llm_type(self) -> str: """Return type of llm.""" - return "Sambastudio LLM" + return "sambastudio-llm" - @pre_init - def validate_environment(cls, values: Dict) -> Dict: - """Validate that api key and python package exists in environment.""" - values["sambastudio_base_url"] = get_from_dict_or_env( - values, "sambastudio_base_url", "SAMBASTUDIO_BASE_URL" - ) - values["sambastudio_base_uri"] = get_from_dict_or_env( - values, - "sambastudio_base_uri", - "SAMBASTUDIO_BASE_URI", - default="api/predict/generic", + def __init__(self, **kwargs: Any) -> None: + """init and validate environment variables""" + kwargs["sambastudio_url"] = get_from_dict_or_env( + kwargs, "sambastudio_url", "SAMBASTUDIO_URL" ) - values["sambastudio_project_id"] = get_from_dict_or_env( - values, "sambastudio_project_id", "SAMBASTUDIO_PROJECT_ID" - ) - values["sambastudio_endpoint_id"] = get_from_dict_or_env( - values, "sambastudio_endpoint_id", "SAMBASTUDIO_ENDPOINT_ID" + + kwargs["sambastudio_api_key"] = convert_to_secret_str( + get_from_dict_or_env(kwargs, "sambastudio_api_key", "SAMBASTUDIO_API_KEY") ) - values["sambastudio_api_key"] = get_from_dict_or_env( - values, "sambastudio_api_key", "SAMBASTUDIO_API_KEY" + kwargs["base_url"], kwargs["streaming_url"] = self._get_sambastudio_urls( + kwargs["sambastudio_url"] ) - return values + super().__init__(**kwargs) + + def _get_sambastudio_urls(self, url: str) -> Tuple[str, str]: + """ + Get streaming and non streaming URLs from the given URL + + Args: + url: string with sambastudio base or streaming endpoint url - def _get_tuning_params(self, stop: Optional[List[str]]) -> str: + Returns: + base_url: string with url to do non streaming calls + streaming_url: string with url to do streaming calls + """ + if "openai" in url: + base_url = url + stream_url = url + else: + if "stream" in url: + base_url = url.replace("stream/", "") + stream_url = url + else: + base_url = url + if "generic" in url: + stream_url = "generic/stream".join(url.split("generic")) + else: + raise ValueError("Unsupported URL") + return base_url, stream_url + + def _get_tuning_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]: """ Get the tuning parameters to use when calling the LLM. @@ -321,151 +198,294 @@ def _get_tuning_params(self, stop: Optional[List[str]]) -> str: first occurrence of any of the stop substrings. Returns: - The tuning parameters as a JSON string. + The tuning parameters in the format required by api to use """ + if stop is None: + stop = [] + + # get the parameters to use when calling the LLM. _model_kwargs = self.model_kwargs or {} - _kwarg_stop_sequences = _model_kwargs.get("stop_sequences", []) - _stop_sequences = stop or _kwarg_stop_sequences - # if not _kwarg_stop_sequences: - # _model_kwargs["stop_sequences"] = ",".join( - # f'"{x}"' for x in _stop_sequences - # ) - if "api/v2/predict/generic" in self.sambastudio_base_uri: - tuning_params_dict = _model_kwargs - else: - tuning_params_dict = { + + # handle the case where stop sequences are send in the invocation + # and stop sequences has been also set in the model parameters + _stop_sequences = _model_kwargs.get("stop_sequences", []) + stop + if len(_stop_sequences) > 0: + _model_kwargs["stop_sequences"] = _stop_sequences + + # set the parameters structure depending of the API + if "openai" in self.sambastudio_url: + if "select_expert" in _model_kwargs.keys(): + _model_kwargs["model"] = _model_kwargs.pop("select_expert") + if "max_tokens_to_generate" in _model_kwargs.keys(): + _model_kwargs["max_tokens"] = _model_kwargs.pop( + "max_tokens_to_generate" + ) + if "process_prompt" in _model_kwargs.keys(): + _model_kwargs.pop("process_prompt") + tuning_params = _model_kwargs + + elif "api/v2/predict/generic" in self.sambastudio_url: + if "model" in _model_kwargs.keys(): + _model_kwargs["select_expert"] = _model_kwargs.pop("model") + if "max_tokens" in _model_kwargs.keys(): + _model_kwargs["max_tokens_to_generate"] = _model_kwargs.pop( + "max_tokens" + ) + tuning_params = _model_kwargs + + elif "api/predict/generic" in self.sambastudio_url: + if "model" in _model_kwargs.keys(): + _model_kwargs["select_expert"] = _model_kwargs.pop("model") + if "max_tokens" in _model_kwargs.keys(): + _model_kwargs["max_tokens_to_generate"] = _model_kwargs.pop( + "max_tokens" + ) + + tuning_params = { k: {"type": type(v).__name__, "value": str(v)} for k, v in (_model_kwargs.items()) } - # _model_kwargs["stop_sequences"] = _kwarg_stop_sequences - tuning_params = json.dumps(tuning_params_dict) + + else: + raise ValueError( + f"Unsupported URL{self.sambastudio_url}" + "only openai, generic v1 and generic v2 APIs are supported" + ) + return tuning_params - def _handle_nlp_predict( - self, sdk: SSEndpointHandler, prompt: Union[List[str], str], tuning_params: str - ) -> str: + def _handle_request( + self, + prompt: Union[List[str], str], + stop: Optional[List[str]] = None, + streaming: Optional[bool] = False, + ) -> Response: """ - Perform an NLP prediction using the SambaStudio endpoint handler. + Performs a post request to the LLM API. Args: - sdk: The SSEndpointHandler to use for the prediction. - prompt: The prompt to use for the prediction. - tuning_params: The tuning parameters to use for the prediction. + prompt: The prompt to pass into the model + stop: list of stop tokens + streaming: wether to do a streaming call Returns: - The prediction result. - - Raises: - ValueError: If the prediction fails. + A request Response object """ - response = sdk.nlp_predict( - self.sambastudio_project_id, - self.sambastudio_endpoint_id, - self.sambastudio_api_key, - prompt, - tuning_params, - ) - if response["status_code"] != 200: - optional_detail = response.get("detail") - if optional_detail: - raise RuntimeError( - f"Sambanova /complete call failed with status code " - f"{response['status_code']}.\n Details: {optional_detail}" + + if isinstance(prompt, str): + prompt = [prompt] + + params = self._get_tuning_params(stop) + + # create request payload for openAI v1 API + if "openai" in self.sambastudio_url: + messages_dict = [{"role": "user", "content": prompt[0]}] + data = {"messages": messages_dict, "stream": streaming, **params} + data = {key: value for key, value in data.items() if value is not None} + headers = { + "Authorization": f"Bearer " + f"{self.sambastudio_api_key.get_secret_value()}", + "Content-Type": "application/json", + } + + # create request payload for generic v1 API + elif "api/v2/predict/generic" in self.sambastudio_url: + if params.get("process_prompt", False): + prompt = json.dumps( + { + "conversation_id": "sambaverse-conversation-id", + "messages": [ + {"message_id": None, "role": "user", "content": prompt[0]} + ], + } ) else: - raise RuntimeError( - f"Sambanova /complete call failed with status code " - f"{response['status_code']}.\n response {response}" - ) - if "api/predict/nlp" in self.sambastudio_base_uri: - return response["data"][0]["completion"] - elif "api/v2/predict/generic" in self.sambastudio_base_uri: - return response["items"][0]["value"]["completion"] - elif "api/predict/generic" in self.sambastudio_base_uri: - return response["predictions"][0]["completion"] + prompt = prompt[0] + items = [{"id": "item0", "value": prompt}] + params = {key: value for key, value in params.items() if value is not None} + data = {"items": items, "params": params} + headers = {"key": self.sambastudio_api_key.get_secret_value()} + + # create request payload for generic v1 API + elif "api/predict/generic" in self.sambastudio_url: + if params.get("process_prompt", False): + if params["process_prompt"].get("value") == "True": + prompt = json.dumps( + { + "conversation_id": "sambaverse-conversation-id", + "messages": [ + { + "message_id": None, + "role": "user", + "content": prompt[0], + } + ], + } + ) + else: + prompt = prompt[0] + else: + prompt = prompt[0] + if streaming: + data = {"instance": prompt, "params": params} + else: + data = {"instances": [prompt], "params": params} + headers = {"key": self.sambastudio_api_key.get_secret_value()} + else: raise ValueError( - f"handling of endpoint uri: {self.sambastudio_base_uri} not implemented" + f"Unsupported URL{self.sambastudio_url}" + "only openai, generic v1 and generic v2 APIs are supported" ) - def _handle_completion_requests( - self, prompt: Union[List[str], str], stop: Optional[List[str]] - ) -> str: + # make the request to SambaStudio API + http_session = requests.Session() + if streaming: + response = http_session.post( + self.streaming_url, headers=headers, json=data, stream=True + ) + else: + response = http_session.post( + self.base_url, headers=headers, json=data, stream=False + ) + if response.status_code != 200: + raise RuntimeError( + f"Sambanova / complete call failed with status code " + f"{response.status_code}." + f"{response.text}." + ) + return response + + def _process_response(self, response: Response) -> str: """ - Perform a prediction using the SambaStudio endpoint handler. + Process a non streaming response from the api Args: - prompt: The prompt to use for the prediction. - stop: stop sequences. + response: A request Response object - Returns: - The prediction result. - - Raises: - ValueError: If the prediction fails. + Returns + completion: a string with model generation """ - ss_endpoint = SSEndpointHandler( - self.sambastudio_base_url, self.sambastudio_base_uri - ) - tuning_params = self._get_tuning_params(stop) - return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params) - def _handle_nlp_predict_stream( - self, sdk: SSEndpointHandler, prompt: Union[List[str], str], tuning_params: str - ) -> Iterator[GenerationChunk]: + # Extract json payload form response + try: + response_dict = response.json() + except Exception as e: + raise RuntimeError( + f"Sambanova /complete call failed couldn't get JSON response {e}" + f"response: {response.text}" + ) + + # process response payload for openai compatible API + if "openai" in self.sambastudio_url: + completion = response_dict["choices"][0]["message"]["content"] + # process response payload for generic v2 API + elif "api/v2/predict/generic" in self.sambastudio_url: + completion = response_dict["items"][0]["value"]["completion"] + # process response payload for generic v1 API + elif "api/predict/generic" in self.sambastudio_url: + completion = response_dict["predictions"][0]["completion"] + else: + raise ValueError( + f"Unsupported URL{self.sambastudio_url}" + "only openai, generic v1 and generic v2 APIs are supported" + ) + return completion + + def _process_stream_response(self, response: Response) -> Iterator[GenerationChunk]: """ - Perform a streaming request to the LLM. + Process a streaming response from the api Args: - sdk: The SVEndpointHandler to use for the prediction. - prompt: The prompt to use for the prediction. - tuning_params: The tuning parameters to use for the prediction. + response: An iterable request Response object - Returns: - An iterator of GenerationChunks. + Yields: + GenerationChunk: a GenerationChunk with model partial generation """ - for chunk in sdk.nlp_predict_stream( - self.sambastudio_project_id, - self.sambastudio_endpoint_id, - self.sambastudio_api_key, - prompt, - tuning_params, - ): - if chunk["status_code"] != 200: - error = chunk.get("error") - if error: - optional_code = error.get("code") - optional_details = error.get("details") - optional_message = error.get("message") - raise ValueError( + + try: + import sseclient + except ImportError: + raise ImportError( + "could not import sseclient library" + "Please install it with `pip install sseclient-py`." + ) + + # process response payload for openai compatible API + if "openai" in self.sambastudio_url: + client = sseclient.SSEClient(response) + for event in client.events(): + if event.event == "error_event": + raise RuntimeError( f"Sambanova /complete call failed with status code " - f"{chunk['status_code']}.\n" - f"Message: {optional_message}\n" - f"Details: {optional_details}\n" - f"Code: {optional_code}\n" + f"{response.status_code}." + f"{event.data}." ) - else: + try: + # check if the response is not a final event ("[DONE]") + if event.data != "[DONE]": + if isinstance(event.data, str): + data = json.loads(event.data) + else: + raise RuntimeError( + f"Sambanova /complete call failed with status code " + f"{response.status_code}." + f"{event.data}." + ) + if data.get("error"): + raise RuntimeError( + f"Sambanova /complete call failed with status code " + f"{response.status_code}." + f"{event.data}." + ) + if len(data["choices"]) > 0: + content = data["choices"][0]["delta"]["content"] + else: + content = "" + generated_chunk = GenerationChunk(text=content) + yield generated_chunk + + except Exception as e: raise RuntimeError( - f"Sambanova /complete call failed with status code " - f"{chunk['status_code']}." - f"{chunk}." + f"Error getting content chunk raw streamed response: {e}" + f"data: {event.data}" ) - if "api/predict/nlp" in self.sambastudio_base_uri: - text = json.loads(chunk["data"])["stream_token"] - elif "api/v2/predict/generic" in self.sambastudio_base_uri: - text = chunk["result"]["items"][0]["value"]["stream_token"] - elif "api/predict/generic" in self.sambastudio_base_uri: - if len(chunk["result"]["responses"]) > 0: - text = chunk["result"]["responses"][0]["stream_token"] - else: - text = "" - else: - raise ValueError( - f"handling of endpoint uri: {self.sambastudio_base_uri}" - f"not implemented" - ) - generated_chunk = GenerationChunk(text=text) - yield generated_chunk + + # process response payload for generic v2 API + elif "api/v2/predict/generic" in self.sambastudio_url: + for line in response.iter_lines(): + try: + data = json.loads(line) + content = data["result"]["items"][0]["value"]["stream_token"] + generated_chunk = GenerationChunk(text=content) + yield generated_chunk + + except Exception as e: + raise RuntimeError( + f"Error getting content chunk raw streamed response: {e}" + f"line: {line}" + ) + + # process response payload for generic v1 API + elif "api/predict/generic" in self.sambastudio_url: + for line in response.iter_lines(): + try: + data = json.loads(line) + content = data["result"]["responses"][0]["stream_token"] + generated_chunk = GenerationChunk(text=content) + yield generated_chunk + + except Exception as e: + raise RuntimeError( + f"Error getting content chunk raw streamed response: {e}" + f"line: {line}" + ) + + else: + raise ValueError( + f"Unsupported URL{self.sambastudio_url}" + "only openai, generic v1 and generic v2 APIs are supported" + ) def _stream( self, @@ -478,56 +498,16 @@ def _stream( Args: prompt: The prompt to pass into the model. - stop: Optional list of stop words to use when generating. - - Returns: - The string generated by the model. - """ - ss_endpoint = SSEndpointHandler( - self.sambastudio_base_url, self.sambastudio_base_uri - ) - tuning_params = self._get_tuning_params(stop) - try: - if self.streaming: - for chunk in self._handle_nlp_predict_stream( - ss_endpoint, prompt, tuning_params - ): - if run_manager: - run_manager.on_llm_new_token(chunk.text) - yield chunk - else: - return - except Exception as e: - # Handle any errors raised by the inference endpoint - raise ValueError(f"Error raised by the inference endpoint: {e}") from e - - def _handle_stream_request( - self, - prompt: Union[List[str], str], - stop: Optional[List[str]], - run_manager: Optional[CallbackManagerForLLMRun], - kwargs: Dict[str, Any], - ) -> str: + stop: a list of strings on which the model should stop generating. + run_manager: A run manager with callbacks for the LLM. + Yields: + chunk: GenerationChunk with model partial generation """ - Perform a streaming request to the LLM. - - Args: - prompt: The prompt to generate from. - stop: Stop words to use when generating. Model output is cut off at the - first occurrence of any of the stop substrings. - run_manager: Callback manager for the run. - kwargs: Additional keyword arguments. directly passed - to the sambastudio model in API call. - - Returns: - The model output as a string. - """ - completion = "" - for chunk in self._stream( - prompt=prompt, stop=stop, run_manager=run_manager, **kwargs - ): - completion += chunk.text - return completion + response = self._handle_request(prompt, stop, streaming=True) + for chunk in self._process_stream_response(response): + if run_manager: + run_manager.on_llm_new_token(chunk.text) + yield chunk def _call( self, @@ -540,17 +520,20 @@ def _call( Args: prompt: The prompt to pass into the model. - stop: Optional list of stop words to use when generating. + stop: a list of strings on which the model should stop generating. Returns: - The string generated by the model. + result: string with model generation """ - if stop is not None: - raise Exception("stop not implemented") - try: - if self.streaming: - return self._handle_stream_request(prompt, stop, run_manager, kwargs) - return self._handle_completion_requests(prompt, stop) - except Exception as e: - # Handle any errors raised by the inference endpoint - raise ValueError(f"Error raised by the inference endpoint: {e}") from e + if self.streaming: + completion = "" + for chunk in self._stream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): + completion += chunk.text + + return completion + + response = self._handle_request(prompt, stop, streaming=False) + completion = self._process_response(response) + return completion diff --git a/libs/community/scripts/check_pydantic.sh b/libs/community/scripts/check_pydantic.sh index 6d6f6dbcef919..99cb222d2b26e 100755 --- a/libs/community/scripts/check_pydantic.sh +++ b/libs/community/scripts/check_pydantic.sh @@ -20,7 +20,7 @@ count=$(git grep -E '(@root_validator)|(@validator)|(@field_validator)|(@pre_ini # PRs that increase the current count will not be accepted. # PRs that decrease update the code in the repository # and allow decreasing the count of are welcome! -current_count=127 +current_count=126 if [ "$count" -gt "$current_count" ]; then echo "The PR seems to be introducing new usage of @root_validator and/or @field_validator." @@ -52,4 +52,4 @@ if [ "$count" -gt "$current_count" ]; then elif [ "$count" -lt "$current_count" ]; then echo "Please update the $current_count variable in ./scripts/check_pydantic.sh to $count" exit 1 -fi \ No newline at end of file +fi From 440c162b8be2c70c22e323fd99a3fc49ece48c06 Mon Sep 17 00:00:00 2001 From: Baptiste Pasquier <50556298+baptiste-pasquier@users.noreply.github.com> Date: Sun, 27 Oct 2024 16:37:21 +0100 Subject: [PATCH 2/7] community: Fix closed session in Infinity (#26933) **Description:** The `aiohttp.ClientSession` is closed at the end of the with statement, which causes an error during a second call. The implemented fix is to define the session directly within the with block, exactly like in the textembed code: https://github.com/langchain-ai/langchain/blob/c6350d636e139bd9018f7225d3257c4be6139d54/libs/community/langchain_community/embeddings/textembed.py#L335-L346 **Issue:** Fix #26932 Co-authored-by: ccurme --- libs/community/langchain_community/embeddings/infinity.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/libs/community/langchain_community/embeddings/infinity.py b/libs/community/langchain_community/embeddings/infinity.py index dbbc9f83142f6..cc41250b54f70 100644 --- a/libs/community/langchain_community/embeddings/infinity.py +++ b/libs/community/langchain_community/embeddings/infinity.py @@ -306,11 +306,9 @@ async def aembed(self, model: str, texts: List[str]) -> List[List[float]]: perm_texts_batched = self._batch(perm_texts) # Request - if self.aiosession is None: - self.aiosession = aiohttp.ClientSession( - trust_env=True, connector=aiohttp.TCPConnector(limit=32) - ) - async with self.aiosession as session: + async with aiohttp.ClientSession( + trust_env=True, connector=aiohttp.TCPConnector(limit=32) + ) as session: embeddings_batch_perm = await asyncio.gather( *[ self._async_request( From ede953d617f8fe8ecf6638b0afe10df8570d75f8 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Mon, 28 Oct 2024 08:46:47 -0700 Subject: [PATCH 3/7] openai[patch]: fix schema formatting util (#27685) --- .../langchain_openai/chat_models/base.py | 8 +++- .../tests/unit_tests/chat_models/test_base.py | 42 ++++++++++++++++++- 2 files changed, 47 insertions(+), 3 deletions(-) diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index 52cb1e1912d37..0d991cbe4ddda 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -2143,9 +2143,13 @@ def _convert_to_openai_response_format( if isinstance(schema, type) and is_basemodel_subclass(schema): return schema - if "json_schema" in schema and schema.get("type") == "json_schema": + if ( + isinstance(schema, dict) + and "json_schema" in schema + and schema.get("type") == "json_schema" + ): response_format = schema - elif "name" in schema and "schema" in schema: + elif isinstance(schema, dict) and "name" in schema and "schema" in schema: response_format = {"type": "json_schema", "json_schema": schema} else: strict = strict if strict is not None else 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 8b96b89e08329..4ea4da055db01 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 @@ -17,7 +17,8 @@ ToolMessage, ) from langchain_core.messages.ai import UsageMetadata -from pydantic import BaseModel +from pydantic import BaseModel, Field +from typing_extensions import TypedDict from langchain_openai import ChatOpenAI from langchain_openai.chat_models.base import ( @@ -805,3 +806,42 @@ def test__convert_to_openai_response_format() -> None: with pytest.raises(ValueError): _convert_to_openai_response_format(response_format, strict=False) + + +@pytest.mark.parametrize("method", ["function_calling", "json_schema"]) +@pytest.mark.parametrize("strict", [True, None]) +def test_structured_output_strict( + method: Literal["function_calling", "json_schema"], strict: Optional[bool] +) -> None: + """Test to verify structured output with strict=True.""" + + llm = ChatOpenAI(model="gpt-4o-2024-08-06") + + class Joke(BaseModel): + """Joke to tell user.""" + + setup: str = Field(description="question to set up a joke") + punchline: str = Field(description="answer to resolve the joke") + + llm.with_structured_output(Joke, method=method, strict=strict) + # Schema + llm.with_structured_output(Joke.model_json_schema(), method=method, strict=strict) + + +def test_nested_structured_output_strict() -> None: + """Test to verify structured output with strict=True for nested object.""" + + llm = ChatOpenAI(model="gpt-4o-2024-08-06") + + class SelfEvaluation(TypedDict): + score: int + text: str + + class JokeWithEvaluation(TypedDict): + """Joke to tell user.""" + + setup: str + punchline: str + self_evaluation: SelfEvaluation + + llm.with_structured_output(JokeWithEvaluation, method="json_schema") From 6803cb4f3426e3a6642e6880b95514853e89aa62 Mon Sep 17 00:00:00 2001 From: yahya-mouman <103438582+yahya-mouman@users.noreply.github.com> Date: Mon, 28 Oct 2024 20:49:43 +0100 Subject: [PATCH 4/7] openai[patch]: add check for none values when summing token usage (#27585) **Description:** Fixes None addition issues when an empty value is passed on If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --- libs/partners/openai/langchain_openai/chat_models/base.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index 0d991cbe4ddda..7ad8586296005 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -605,6 +605,8 @@ def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: token_usage = output["token_usage"] if token_usage is not None: for k, v in token_usage.items(): + if v is None: + continue if k in overall_token_usage: overall_token_usage[k] = _update_token_usage( overall_token_usage[k], v From 6d524e9566bbb4d2592175ebf87faec9469eb8f1 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Mon, 28 Oct 2024 12:54:20 -0700 Subject: [PATCH 5/7] partners/box: release 0.2.2 (#27690) --- libs/partners/box/poetry.lock | 483 ++++++++++++++++--------------- libs/partners/box/pyproject.toml | 4 +- 2 files changed, 255 insertions(+), 232 deletions(-) diff --git a/libs/partners/box/poetry.lock b/libs/partners/box/poetry.lock index c09158c9e79aa..ff2f7db6dfc86 100644 --- a/libs/partners/box/poetry.lock +++ b/libs/partners/box/poetry.lock @@ -13,13 +13,13 @@ files = [ [[package]] name = "anyio" -version = "4.6.0" +version = "4.6.2.post1" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - 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"poetry.core.masonry.api" [tool.poetry] name = "langchain-huggingface" -version = "0.1.0" +version = "0.1.1" description = "An integration package connecting Hugging Face and LangChain" authors = [] readme = "README.md" @@ -20,7 +20,7 @@ disallow_untyped_defs = "True" [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = ">=0.3.0,<0.4" +langchain-core = "^0.3.13" tokenizers = ">=0.19.1" transformers = ">=4.39.0" sentence-transformers = ">=2.6.0" From 94e5765416037b9b90a27603cfda36cbe86bce71 Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Mon, 28 Oct 2024 13:44:30 -0700 Subject: [PATCH 7/7] docs: packages in homepage (#27693) --- docs/scripts/partner_pkg_table.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/scripts/partner_pkg_table.py b/docs/scripts/partner_pkg_table.py index 36822da3bb968..6706b82aa4552 100644 --- a/docs/scripts/partner_pkg_table.py +++ b/docs/scripts/partner_pkg_table.py @@ -25,6 +25,13 @@ "redis", "weaviate", "upstage", + "mongodb", + "azure-dynamic-sessions", + "ibm", + "unstructured", + "milvus", + "together", + "ai21", } JS_PACKAGES = {