diff --git a/docs/docs/integrations/chat/kinetica.ipynb b/docs/docs/integrations/chat/kinetica.ipynb new file mode 100644 index 0000000000000..c99eb711456c3 --- /dev/null +++ b/docs/docs/integrations/chat/kinetica.ipynb @@ -0,0 +1,648 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: Kinetica\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kinetica SqlAssist LLM Demo\n", + "\n", + "This notebook demonstrates how to use Kinetica to transform natural language into SQL\n", + "and simplify the process of data retrieval. This demo is intended to show the mechanics\n", + "of creating and using a chain as opposed to the capabilities of the LLM.\n", + "\n", + "## Overview\n", + "\n", + "With the Kinetica LLM workflow you create an LLM context in the database that provides\n", + "information needed for infefencing that includes tables, annotations, rules, and\n", + "samples. Invoking ``load_messages_from_context()`` will retrieve the contxt\n", + "information from the database so that it can be used to create a chat prompt.\n", + "\n", + "The chat prompt consists of a ``SystemMessage`` and pairs of\n", + "``HumanMessage``/``AIMessage`` that contain the samples which are question/SQL\n", + "pairs. You can append pairs samples to this list but it is not intended to\n", + "facilitate a typical natural language conversation.\n", + "\n", + "When you create a chain from the chat prompt and execute it, the Kinetica LLM will\n", + "generate SQL from the input. Optionally you can use ``KineticaSqlOutputParser`` to\n", + "execute the SQL and return the result as a dataframe.\n", + "\n", + "Currently, 2 LLM's are supported for SQL generation: \n", + "\n", + "1. **Kinetica SQL-GPT**: This LLM is based on OpenAI ChatGPT API.\n", + "2. **Kinetica SqlAssist**: This LLM is purpose built to integrate with the Kinetica\n", + " database and it can run in a secure customer premise.\n", + "\n", + "For this demo we will be using **SqlAssist**. See the [Kinetica Documentation\n", + "site](https://docs.kinetica.com/7.1/sql-gpt/concepts/) for more information.\n", + "\n", + "## Prerequisites\n", + "\n", + "To get started you will need a Kinetica DB instance. If you don't have one you can\n", + "obtain a [free development instance](https://cloud.kinetica.com/trynow).\n", + "\n", + "You will need to install the following packages..." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "# Install Langchain community and core packages\n", + "%pip install --upgrade --quiet langchain-core langchain-community\n", + "\n", + "# Install Kineitca DB connection package\n", + "%pip install --upgrade --quiet gpudb typeguard\n", + "\n", + "# Install packages needed for this tutorial\n", + "%pip install --upgrade --quiet faker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Database Connection\n", + "\n", + "You must set the database connection in the following environment variables. If you are using a virtual environment you can set them in the `.env` file of the project:\n", + "* `KINETICA_URL`: Database connection URL\n", + "* `KINETICA_USER`: Database user\n", + "* `KINETICA_PASSWD`: Secure password.\n", + "\n", + "If you can create an instance of `KineticaChatLLM` then you are successfully connected." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_models.kinetica import KineticaChatLLM\n", + "\n", + "kinetica_llm = KineticaChatLLM()\n", + "\n", + "# Test table we will create\n", + "table_name = \"demo.user_profiles\"\n", + "\n", + "# LLM Context we will create\n", + "kinetica_ctx = \"demo.test_llm_ctx\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create test data\n", + "\n", + "Before we can generate SQL we will need to create a Kinetica table and an LLM context that can inference the table.\n", + "\n", + "### Create some fake user profiles\n", + "\n", + "We will use the `faker` package to create a dataframe with 100 fake profiles." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0eduardo69Haley BeckF59836 Carla Causeway Suite 939\\nPort Eugene, I...meltondenise@yahoo.com1997-09-08
1lbarreraJoshua StephensM3108 Christina Forges\\nPort Timothychester, KY...erica80@hotmail.com1924-05-04
2bburtonPaula KaiserFUnit 7405 Box 3052\\nDPO AE 09858timothypotts@gmail.com1933-09-05
3melissa49Wendy ReeseF6408 Christopher Hill Apt. 459\\nNew Benjamin, ...dadams@gmail.com1988-07-27
4melissacarterManuel RiosM2241 Bell Gardens Suite 723\\nScottside, CA 38463williamayala@gmail.com1930-12-18
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" + ], + "text/plain": [ + " username name sex \\\n", + "id \n", + "0 eduardo69 Haley Beck F \n", + "1 lbarrera Joshua Stephens M \n", + "2 bburton Paula Kaiser F \n", + "3 melissa49 Wendy Reese F \n", + "4 melissacarter Manuel Rios M \n", + "\n", + " address mail \\\n", + "id \n", + "0 59836 Carla Causeway Suite 939\\nPort Eugene, I... meltondenise@yahoo.com \n", + "1 3108 Christina Forges\\nPort Timothychester, KY... erica80@hotmail.com \n", + "2 Unit 7405 Box 3052\\nDPO AE 09858 timothypotts@gmail.com \n", + "3 6408 Christopher Hill Apt. 459\\nNew Benjamin, ... dadams@gmail.com \n", + "4 2241 Bell Gardens Suite 723\\nScottside, CA 38463 williamayala@gmail.com \n", + "\n", + " birthdate \n", + "id \n", + "0 1997-09-08 \n", + "1 1924-05-04 \n", + "2 1933-09-05 \n", + "3 1988-07-27 \n", + "4 1930-12-18 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from typing import Generator\n", + "\n", + "import pandas as pd\n", + "from faker import Faker\n", + "\n", + "Faker.seed(5467)\n", + "faker = Faker(locale=\"en-US\")\n", + "\n", + "\n", + "def profile_gen(count: int) -> Generator:\n", + " for id in range(0, count):\n", + " rec = dict(id=id, **faker.simple_profile())\n", + " rec[\"birthdate\"] = pd.Timestamp(rec[\"birthdate\"])\n", + " yield rec\n", + "\n", + "\n", + "load_df = pd.DataFrame.from_records(data=profile_gen(100), index=\"id\")\n", + "load_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a Kinetica table from the Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " name type properties\n", + "0 username string [char32]\n", + "1 name string [char32]\n", + "2 sex string [char1]\n", + "3 address string [char64]\n", + "4 mail string [char32]\n", + "5 birthdate long [timestamp]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gpudb import GPUdbTable\n", + "\n", + "gpudb_table = GPUdbTable.from_df(\n", + " load_df,\n", + " db=kinetica_llm.kdbc,\n", + " table_name=table_name,\n", + " clear_table=True,\n", + " load_data=True,\n", + ")\n", + "\n", + "# See the Kinetica column types\n", + "gpudb_table.type_as_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the LLM context\n", + "\n", + "You can create an LLM Context using the Kinetica Workbench UI or you can manually create it with the `CREATE OR REPLACE CONTEXT` syntax. \n", + "\n", + "Here we create a context from the SQL syntax referencing the table we created." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'OK',\n", + " 'message': '',\n", + " 'data_type': 'execute_sql_response',\n", + " 'response_time': 0.02299}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create an LLM context for the table.\n", + "\n", + "from gpudb import GPUdbException\n", + "\n", + "sql = f\"\"\"\n", + "CREATE OR REPLACE CONTEXT {kinetica_ctx}\n", + "(\n", + " TABLE = demo.test_profiles\n", + " COMMENT = 'Contains user profiles.'\n", + "),\n", + "(\n", + " SAMPLES = (\n", + " 'How many male users are there?' = \n", + " 'select count(1) as num_users\n", + " from demo.test_profiles\n", + " where sex = ''M'';')\n", + ")\n", + "\"\"\"\n", + "\n", + "\n", + "def _check_error(response: dict) -> None:\n", + " status = response[\"status_info\"][\"status\"]\n", + " if status != \"OK\":\n", + " message = response[\"status_info\"][\"message\"]\n", + " raise GPUdbException(\"[%s]: %s\" % (status, message))\n", + "\n", + "\n", + "response = kinetica_llm.kdbc.execute_sql(sql)\n", + "_check_error(response)\n", + "response[\"status_info\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Langchain for inferencing\n", + "\n", + "In the example below we will create a chain from the previously created table and LLM context. This chain will generate SQL and return the resulting data as a dataframe.\n", + "\n", + "### Load the chat prompt from the Kinetica DB\n", + "\n", + "The `load_messages_from_context()` function will retrieve a context from the DB and convert it into a list of chat messages that we use to create a ``ChatPromptTemplate``." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m System Message \u001b[0m================================\n", + "\n", + "CREATE TABLE demo.test_profiles AS\n", + "(\n", + " username VARCHAR (32) NOT NULL,\n", + " name VARCHAR (32) NOT NULL,\n", + " sex VARCHAR (1) NOT NULL,\n", + " address VARCHAR (64) NOT NULL,\n", + " mail VARCHAR (32) NOT NULL,\n", + " birthdate TIMESTAMP NOT NULL\n", + ");\n", + "COMMENT ON TABLE demo.test_profiles IS 'Contains user profiles.';\n", + "\n", + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "How many male users are there?\n", + "\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "select count(1) as num_users\n", + " from demo.test_profiles\n", + " where sex = 'M';\n", + "\n", + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n" + ] + } + ], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "# load the context from the database\n", + "ctx_messages = kinetica_llm.load_messages_from_context(kinetica_ctx)\n", + "\n", + "# Add the input prompt. This is where input question will be substituted.\n", + "ctx_messages.append((\"human\", \"{input}\"))\n", + "\n", + "# Create the prompt template.\n", + "prompt_template = ChatPromptTemplate.from_messages(ctx_messages)\n", + "prompt_template.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the chain\n", + "\n", + "The last element of this chain is `KineticaSqlOutputParser` that will execute the SQL and return a dataframe. This is optional and if we left it out then only SQL would be returned." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_models.kinetica import (\n", + " KineticaSqlOutputParser,\n", + " KineticaSqlResponse,\n", + ")\n", + "\n", + "chain = prompt_template | kinetica_llm | KineticaSqlOutputParser(kdbc=kinetica_llm.kdbc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate the SQL\n", + "\n", + "The chain we created will take a question as input and return a KineticaSqlResponse containing the generated SQL and data. The question must be relevant to the to LLM context we used to create the prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SQL: SELECT username\n", + " FROM demo.test_profiles\n", + " WHERE sex = 'F'\n", + " ORDER BY username;\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " username\n", + "0 alexander40\n", + "1 bburton\n", + "2 brian12\n", + "3 brownanna\n", + "4 carl19" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here you must ask a question relevant to the LLM context provided in the prompt template.\n", + "response: KineticaSqlResponse = chain.invoke(\n", + " {\"input\": \"What are the female users ordered by username?\"}\n", + ")\n", + "\n", + "print(f\"SQL: {response.sql}\")\n", + "response.dataframe.head()" + ] + } + ], + "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": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/integrations/providers/kinetica.mdx b/docs/docs/integrations/providers/kinetica.mdx new file mode 100644 index 0000000000000..e9b4b532c3c99 --- /dev/null +++ b/docs/docs/integrations/providers/kinetica.mdx @@ -0,0 +1,28 @@ +# Kinetica + +[Kinetica](https://www.kinetica.com/) is a real-time database purpose built for enabling +analytics and generative AI on time-series & spatial data. + +## Chat Model + +The Kinetica LLM wrapper uses the [Kinetica SqlAssist +LLM](https://docs.kinetica.com/7.2/sql-gpt/concepts/) to transform natural language into +SQL to simplify the process of data retrieval. + +See [Kinetica SqlAssist LLM Demo](/docs/integrations/chat/kinetica) for usage. + +```python +from langchain_community.chat_models.kinetica import KineticaChatLLM +``` + +## Vector Store + +The Kinetca vectorstore wrapper leverages Kinetica's native support for [vector +similarity search](https://docs.kinetica.com/7.2/vector_search/). + +See [Kinetica Vectorsore API](/docs/integrations/vectorstores/kinetica) for usage. + +```python +from langchain_community.vectorstores import Kinetica +``` + diff --git a/docs/docs/integrations/vectorstores/kinetica.ipynb b/docs/docs/integrations/vectorstores/kinetica.ipynb new file mode 100644 index 0000000000000..a13546609801c --- /dev/null +++ b/docs/docs/integrations/vectorstores/kinetica.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: Kinetica\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kinetica Vectorstore API\n", + "\n", + ">[Kinetica](https://www.kinetica.com/) is a database with integrated support for vector similarity search\n", + "\n", + "It supports:\n", + "- exact and approximate nearest neighbor search\n", + "- L2 distance, inner product, and cosine distance\n", + "\n", + "This notebook shows how to use the Kinetica vector store (`Kinetica`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This needs an instance of Kinetica which can easily be setup using the instructions given here - [installation instruction](https://www.kinetica.com/developer-edition/)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: gpudb==7.2.0.0b in /home/anindyam/kinetica/kinetica-github/langchain/libs/langchain/.venv/lib/python3.8/site-packages (7.2.0.0b0)\n", + "Requirement already satisfied: future in /home/anindyam/kinetica/kinetica-github/langchain/libs/langchain/.venv/lib/python3.8/site-packages (from gpudb==7.2.0.0b) (0.18.3)\n", + "Requirement already satisfied: pyzmq in /home/anindyam/kinetica/kinetica-github/langchain/libs/langchain/.venv/lib/python3.8/site-packages (from gpudb==7.2.0.0b) (25.1.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "# Pip install necessary package\n", + "%pip install --upgrade --quiet langchain-openai\n", + "%pip install gpudb==7.2.0.0b\n", + "%pip install --upgrade --quiet tiktoken" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Loading Environment Variables\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.docstore.document import Document\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_community.document_loaders import TextLoader\n", + "from langchain_community.vectorstores import (\n", + " DistanceStrategy,\n", + " KineticaSettings,\n", + " KineticaVectorStore,\n", + ")\n", + "from langchain_openai import OpenAIEmbeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", + "documents = loader.load()\n", + "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Kinetica needs the connection to the database.\n", + "# This is how to set it up.\n", + "HOST = os.getenv(\"KINETICA_HOST\", \"http://127.0.0.1:9191\")\n", + "USERNAME = os.getenv(\"KINETICA_USERNAME\", \"\")\n", + "PASSWORD = os.getenv(\"KINETICA_PASSWORD\", \"\")\n", + "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\", \"\")\n", + "\n", + "\n", + "def create_config() -> KineticaSettings:\n", + " return KineticaSettings(host=HOST, username=USERNAME, password=PASSWORD)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Similarity Search with Euclidean Distance (Default)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# The Kinetica Module will try to create a table with the name of the collection.\n", + "# So, make sure that the collection name is unique and the user has the permission to create a table.\n", + "\n", + "COLLECTION_NAME = \"state_of_the_union_test\"\n", + "connection = create_config()\n", + "\n", + "db = KineticaVectorStore.from_documents(\n", + " embedding=embeddings,\n", + " documents=docs,\n", + " collection_name=COLLECTION_NAME,\n", + " config=connection,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs_with_score = db.similarity_search_with_score(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Score: 0.6077010035514832\n", + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + "Score: 0.6077010035514832\n", + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + "Score: 0.6596046090126038\n", + "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n", + "\n", + "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n", + "\n", + "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n", + "\n", + "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n", + "\n", + "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n", + "\n", + "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + "Score: 0.6597143411636353\n", + "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n", + "\n", + "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n", + "\n", + "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n", + "\n", + "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n", + "\n", + "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n", + "\n", + "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "for doc, score in docs_with_score:\n", + " print(\"-\" * 80)\n", + " print(\"Score: \", score)\n", + " print(doc.page_content)\n", + " print(\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Maximal Marginal Relevance Search (MMR)\n", + "Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "docs_with_score = db.max_marginal_relevance_search_with_score(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Score: 0.6077010035514832\n", + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + "Score: 0.6852865219116211\n", + "It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China. \n", + "\n", + "As I’ve told Xi Jinping, it is never a good bet to bet against the American people. \n", + "\n", + "We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. \n", + "\n", + "And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice. \n", + "\n", + "We’ll build a national network of 500,000 electric vehicle charging stations, begin to replace poisonous lead pipes—so every child—and every American—has clean water to drink at home and at school, provide affordable high-speed internet for every American—urban, suburban, rural, and tribal communities. \n", + "\n", + "4,000 projects have already been announced. \n", + "\n", + "And tonight, I’m announcing that this year we will start fixing over 65,000 miles of highway and 1,500 bridges in disrepair.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + "Score: 0.6866700053215027\n", + "We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n", + "\n", + "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n", + "\n", + "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n", + "\n", + "Officer Mora was 27 years old. \n", + "\n", + "Officer Rivera was 22. \n", + "\n", + "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n", + "\n", + "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n", + "\n", + "I’ve worked on these issues a long time. \n", + "\n", + "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + "Score: 0.6936529278755188\n", + "But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. \n", + "\n", + "Danielle says Heath was a fighter to the very end. \n", + "\n", + "He didn’t know how to stop fighting, and neither did she. \n", + "\n", + "Through her pain she found purpose to demand we do better. \n", + "\n", + "Tonight, Danielle—we are. \n", + "\n", + "The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits. \n", + "\n", + "And tonight, I’m announcing we’re expanding eligibility to veterans suffering from nine respiratory cancers. \n", + "\n", + "I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve. \n", + "\n", + "And fourth, let’s end cancer as we know it. \n", + "\n", + "This is personal to me and Jill, to Kamala, and to so many of you. \n", + "\n", + "Cancer is the #2 cause of death in America–second only to heart disease.\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "for doc, score in docs_with_score:\n", + " print(\"-\" * 80)\n", + " print(\"Score: \", score)\n", + " print(doc.page_content)\n", + " print(\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working with vectorstore\n", + "\n", + "Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.\n", + "In order to do that, we can initialize it directly." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "store = KineticaVectorStore(\n", + " collection_name=COLLECTION_NAME,\n", + " config=connection,\n", + " embedding_function=embeddings,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add documents\n", + "We can add documents to the existing vectorstore." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['b94dc67c-ce7e-11ee-b8cb-b940b0e45762']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "store.add_documents([Document(page_content=\"foo\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "docs_with_score = db.similarity_search_with_score(\"foo\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Document(page_content='foo'), 0.0)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs_with_score[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n", + " 0.6946534514427185)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs_with_score[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overriding a vectorstore\n", + "\n", + "If you have an existing collection, you override it by doing `from_documents` and setting `pre_delete_collection` = True" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "db = KineticaVectorStore.from_documents(\n", + " documents=docs,\n", + " embedding=embeddings,\n", + " collection_name=COLLECTION_NAME,\n", + " config=connection,\n", + " pre_delete_collection=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "docs_with_score = db.similarity_search_with_score(\"foo\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt'}),\n", + " 0.6946534514427185)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs_with_score[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a VectorStore as a Retriever" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "retriever = store.as_retriever()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tags=['Kinetica', 'OpenAIEmbeddings'] vectorstore=\n" + ] + } + ], + "source": [ + "print(retriever)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/libs/community/Makefile b/libs/community/Makefile index acec04864a614..e7b1b6465a15e 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -36,7 +36,7 @@ extended_tests: PYTHON_FILES=. MYPY_CACHE=.mypy_cache lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/community -name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/community --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint_package: PYTHON_FILES=langchain_community lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test diff --git a/libs/community/langchain_community/chat_models/__init__.py b/libs/community/langchain_community/chat_models/__init__.py index 8a8fd1fd78df2..d5d4a65311598 100644 --- a/libs/community/langchain_community/chat_models/__init__.py +++ b/libs/community/langchain_community/chat_models/__init__.py @@ -38,6 +38,7 @@ from langchain_community.chat_models.hunyuan import ChatHunyuan from langchain_community.chat_models.javelin_ai_gateway import ChatJavelinAIGateway from langchain_community.chat_models.jinachat import JinaChat +from langchain_community.chat_models.kinetica import KineticaChatLLM from langchain_community.chat_models.konko import ChatKonko from langchain_community.chat_models.litellm import ChatLiteLLM from langchain_community.chat_models.litellm_router import ChatLiteLLMRouter @@ -97,4 +98,5 @@ "GPTRouter", "ChatYuan2", "ChatZhipuAI", + "KineticaChatLLM", ] diff --git a/libs/community/langchain_community/chat_models/kinetica.py b/libs/community/langchain_community/chat_models/kinetica.py new file mode 100644 index 0000000000000..236bd638431c9 --- /dev/null +++ b/libs/community/langchain_community/chat_models/kinetica.py @@ -0,0 +1,598 @@ +## +# Copyright (c) 2024, Chad Juliano, Kinetica DB Inc. +## +"""Kinetica SQL generation LLM API.""" + +import json +import logging +import os +import re +from importlib.metadata import version +from pathlib import Path +from typing import TYPE_CHECKING, Any, Dict, List, Optional, cast + +if TYPE_CHECKING: + import gpudb + +from langchain_core.callbacks import CallbackManagerForLLMRun +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import ( + AIMessage, + BaseMessage, + HumanMessage, + SystemMessage, +) +from langchain_core.output_parsers.transform import BaseOutputParser +from langchain_core.outputs import ChatGeneration, ChatResult, Generation +from langchain_core.pydantic_v1 import BaseModel, Field, root_validator + +LOG = logging.getLogger(__name__) + +# Kinetica pydantic API datatypes + + +class _KdtSuggestContext(BaseModel): + """pydantic API request type""" + + table: Optional[str] = Field(default=None, title="Name of table") + description: Optional[str] = Field(default=None, title="Table description") + columns: List[str] = Field(default=None, title="Table columns list") + rules: Optional[List[str]] = Field( + default=None, title="Rules that apply to the table." + ) + samples: Optional[Dict] = Field( + default=None, title="Samples that apply to the entire context." + ) + + def to_system_str(self) -> str: + lines = [] + lines.append(f"CREATE TABLE {self.table} AS") + lines.append("(") + + if not self.columns or len(self.columns) == 0: + ValueError(detail="columns list can't be null.") # type: ignore + + columns = [] + for column in self.columns: + column = column.replace('"', "").strip() + columns.append(f" {column}") + lines.append(",\n".join(columns)) + lines.append(");") + + if self.description: + lines.append(f"COMMENT ON TABLE {self.table} IS '{self.description}';") + + if self.rules and len(self.rules) > 0: + lines.append( + f"-- When querying table {self.table} the following rules apply:" + ) + for rule in self.rules: + lines.append(f"-- * {rule}") + + result = "\n".join(lines) + return result + + +class _KdtSuggestPayload(BaseModel): + """pydantic API request type""" + + question: Optional[str] + context: List[_KdtSuggestContext] + + def get_system_str(self) -> str: + lines = [] + for table_context in self.context: + if table_context.table is None: + continue + context_str = table_context.to_system_str() + lines.append(context_str) + return "\n\n".join(lines) + + def get_messages(self) -> List[Dict]: + messages = [] + for context in self.context: + if context.samples is None: + continue + for question, answer in context.samples.items(): + # unescape double quotes + answer = answer.replace("''", "'") + + messages.append(dict(role="user", content=question or "")) + messages.append(dict(role="assistant", content=answer)) + return messages + + def to_completion(self) -> Dict: + messages = [] + messages.append(dict(role="system", content=self.get_system_str())) + messages.extend(self.get_messages()) + messages.append(dict(role="user", content=self.question or "")) + response = dict(messages=messages) + return response + + +class _KdtoSuggestRequest(BaseModel): + """pydantic API request type""" + + payload: _KdtSuggestPayload + + +class _KdtMessage(BaseModel): + """pydantic API response type""" + + role: str = Field(default=None, title="One of [user|assistant|system]") + content: str + + +class _KdtChoice(BaseModel): + """pydantic API response type""" + + index: int + message: _KdtMessage = Field(default=None, title="The generated SQL") + finish_reason: str + + +class _KdtUsage(BaseModel): + """pydantic API response type""" + + prompt_tokens: int + completion_tokens: int + total_tokens: int + + +class _KdtSqlResponse(BaseModel): + """pydantic API response type""" + + id: str + object: str + created: int + model: str + choices: List[_KdtChoice] + usage: _KdtUsage + prompt: str = Field(default=None, title="The input question") + + +class _KdtCompletionResponse(BaseModel): + """pydantic API response type""" + + status: str + data: _KdtSqlResponse + + +class _KineticaLlmFileContextParser: + """Parser for Kinetica LLM context datafiles.""" + + # parse line into a dict containing role and content + PARSER = re.compile(r"^<\|(?P\w+)\|>\W*(?P.*)$", re.DOTALL) + + @classmethod + def _removesuffix(cls, text: str, suffix: str) -> str: + if suffix and text.endswith(suffix): + return text[: -len(suffix)] + return text + + @classmethod + def parse_dialogue_file(cls, input_file: os.PathLike) -> Dict: + path = Path(input_file) + # schema = path.name.removesuffix(".txt") python 3.9 + schema = cls._removesuffix(path.name, ".txt") + + lines = open(input_file).read() + return cls.parse_dialogue(lines, schema) + + @classmethod + def parse_dialogue(cls, text: str, schema: str) -> Dict: + messages = [] + system = None + + lines = text.split("<|end|>") + user_message = None + + for idx, line in enumerate(lines): + line = line.strip() + + if len(line) == 0: + continue + + match = cls.PARSER.match(line) + if match is None: + raise ValueError(f"Could not find starting token in: {line}") + + groupdict = match.groupdict() + role = groupdict["role"] + + if role == "system": + if system is not None: + raise ValueError(f"Only one system token allowed in: {line}") + system = groupdict["content"] + elif role == "user": + if user_message is not None: + raise ValueError( + f"Found user token without assistant token: {line}" + ) + user_message = groupdict + elif role == "assistant": + if user_message is None: + raise Exception(f"Found assistant token without user token: {line}") + messages.append(user_message) + messages.append(groupdict) + user_message = None + else: + raise ValueError(f"Unknown token: {role}") + + return {"schema": schema, "system": system, "messages": messages} + + +class KineticaUtil: + """Kinetica utility functions.""" + + @classmethod + def create_kdbc( + cls, + url: Optional[str] = None, + user: Optional[str] = None, + passwd: Optional[str] = None, + ) -> "gpudb.GPUdb": + """Create a connectica connection object and verify connectivity. + + If None is passed for one or more of the parameters then an attempt will be made + to retrieve the value from the related environment variable. + + Args: + url: The Kinetica URL or ``KINETICA_URL`` if None. + user: The Kinetica user or ``KINETICA_USER`` if None. + passwd: The Kinetica password or ``KINETICA_PASSWD`` if None. + + Returns: + The Kinetica connection object. + """ + + try: + import gpudb + except ModuleNotFoundError: + raise ImportError( + "Could not import Kinetica python package. " + "Please install it with `pip install gpudb`." + ) + + url = cls._get_env("KINETICA_URL", url) + user = cls._get_env("KINETICA_USER", user) + passwd = cls._get_env("KINETICA_PASSWD", passwd) + + options = gpudb.GPUdb.Options() + options.username = user + options.password = passwd + options.skip_ssl_cert_verification = True + options.disable_failover = True + options.logging_level = "INFO" + kdbc = gpudb.GPUdb(host=url, options=options) + + LOG.info( + "Connected to Kinetica: {}. (api={}, server={})".format( + kdbc.get_url(), version("gpudb"), kdbc.server_version + ) + ) + + return kdbc + + @classmethod + def _get_env(cls, name: str, default: Optional[str]) -> str: + """Get an environment variable or use a default.""" + if default is not None: + return default + + result = os.getenv(name) + if result is not None: + return result + + raise ValueError( + f"Parameter was not passed and not found in the environment: {name}" + ) + + +class KineticaChatLLM(BaseChatModel): + """Kinetica LLM Chat Model API. + + Prerequisites for using this API: + + * The ``gpudb`` and ``typeguard`` packages installed. + * A Kinetica DB instance. + * Kinetica host specified in ``KINETICA_URL`` + * Kinetica login specified ``KINETICA_USER``, and ``KINETICA_PASSWD``. + * An LLM context that specifies the tables and samples to use for inferencing. + + This API is intended to interact with the Kinetica SqlAssist LLM that supports + generation of SQL from natural language. + + In the Kinetica LLM workflow you create an LLM context in the database that provides + information needed for infefencing that includes tables, annotations, rules, and + samples. Invoking ``load_messages_from_context()`` will retrieve the contxt + information from the database so that it can be used to create a chat prompt. + + The chat prompt consists of a ``SystemMessage`` and pairs of + ``HumanMessage``/``AIMessage`` that contain the samples which are question/SQL + pairs. You can append pairs samples to this list but it is not intended to + facilitate a typical natural language conversation. + + When you create a chain from the chat prompt and execute it, the Kinetica LLM will + generate SQL from the input. Optionally you can use ``KineticaSqlOutputParser`` to + execute the SQL and return the result as a dataframe. + + The following example creates an LLM using the environment variables for the + Kinetica connection. This will fail if the API is unable to connect to the database. + + Example: + .. code-block:: python + from langchain_community.chat_models.kinetica import KineticaChatLLM + kinetica_llm = KineticaChatLLM() + + If you prefer to pass connection information directly then you can create a + connection using ``KineticaUtil.create_kdbc()``. + + Example: + .. code-block:: python + from langchain_community.chat_models.kinetica import ( + KineticaChatLLM, KineticaUtil) + kdbc = KineticaUtil._create_kdbc(url=url, user=user, passwd=passwd) + kinetica_llm = KineticaChatLLM(kdbc=kdbc) + """ + + kdbc: Any = Field(exclude=True) + """ Kinetica DB connection. """ + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Pydantic object validator.""" + + kdbc = values.get("kdbc", None) + if kdbc is None: + kdbc = KineticaUtil.create_kdbc() + values["kdbc"] = kdbc + return values + + @property + def _llm_type(self) -> str: + return "kinetica-sqlassist" + + @property + def _identifying_params(self) -> Dict[str, Any]: + return dict( + kinetica_version=str(self.kdbc.server_version), api_version=version("gpudb") + ) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if stop is not None: + raise ValueError("stop kwargs are not permitted.") + + dict_messages = [self._convert_message_to_dict(m) for m in messages] + sql_response = self._submit_completion(dict_messages) + + response_message = sql_response.choices[0].message + # generated_dict = response_message.model_dump() # pydantic v2 + generated_dict = response_message.dict() + + generated_message = self._convert_message_from_dict(generated_dict) + + llm_output = dict( + input_tokens=sql_response.usage.prompt_tokens, + output_tokens=sql_response.usage.completion_tokens, + model_name=sql_response.model, + ) + return ChatResult( + generations=[ChatGeneration(message=generated_message)], + llm_output=llm_output, + ) + + def load_messages_from_context(self, context_name: str) -> List: + """Load a lanchain prompt from a Kinetica context. + + A Kinetica Context is an object created with the Kinetica Workbench UI or with + SQL syntax. This function will convert the data in the context to a list of + messages that can be used as a prompt. The messages will contain a + ``SystemMessage`` followed by pairs of ``HumanMessage``/``AIMessage`` that + contain the samples. + + Args: + context_name: The name of an LLM context in the database. + + Returns: + A list of messages containing the information from the context. + """ + + # query kinetica for the prompt + sql = f"GENERATE PROMPT WITH OPTIONS (CONTEXT_NAMES = '{context_name}')" + result = self._execute_sql(sql) + prompt = result["Prompt"] + prompt_json = json.loads(prompt) + + # convert the prompt to messages + # request = SuggestRequest.model_validate(prompt_json) # pydantic v2 + request = _KdtoSuggestRequest.parse_obj(prompt_json) + payload = request.payload + + dict_messages = [] + dict_messages.append(dict(role="system", content=payload.get_system_str())) + dict_messages.extend(payload.get_messages()) + messages = [self._convert_message_from_dict(m) for m in dict_messages] + return messages + + def _submit_completion(self, messages: List[Dict]) -> _KdtSqlResponse: + """Submit a /chat/completions request to Kinetica.""" + + request = dict(messages=messages) + request_json = json.dumps(request) + response_raw = self.kdbc._GPUdb__submit_request_json( + "/chat/completions", request_json + ) + response_json = json.loads(response_raw) + + status = response_json["status"] + if status != "OK": + message = response_json["message"] + match_resp = re.compile(r"response:({.*})") + result = match_resp.search(message) + if result is not None: + response = result.group(1) + response_json = json.loads(response) + message = response_json["message"] + raise ValueError(message) + + data = response_json["data"] + # response = CompletionResponse.model_validate(data) # pydantic v2 + response = _KdtCompletionResponse.parse_obj(data) + if response.status != "OK": + raise ValueError("SQL Generation failed") + return response.data + + def _execute_sql(self, sql: str) -> Dict: + """Execute an SQL query and return the result.""" + + response = self.kdbc.execute_sql_and_decode( + sql, limit=1, get_column_major=False + ) + + status_info = response["status_info"] + if status_info["status"] != "OK": + message = status_info["message"] + raise ValueError(message) + + records = response["records"] + if len(records) != 1: + raise ValueError("No records returned.") + + record = records[0] + response_dict = {} + for col, val in record.items(): + response_dict[col] = val + return response_dict + + @classmethod + def load_messages_from_datafile(cls, sa_datafile: Path) -> List[BaseMessage]: + """Load a lanchain prompt from a Kinetica context datafile.""" + datafile_dict = _KineticaLlmFileContextParser.parse_dialogue_file(sa_datafile) + messages = cls._convert_dict_to_messages(datafile_dict) + return messages + + @classmethod + def _convert_message_to_dict(cls, message: BaseMessage) -> Dict: + """Convert a single message to a BaseMessage.""" + + content = cast(str, message.content) + if isinstance(message, HumanMessage): + role = "user" + elif isinstance(message, AIMessage): + role = "assistant" + elif isinstance(message, SystemMessage): + role = "system" + else: + raise ValueError(f"Got unsupported message type: {message}") + + result_message = dict(role=role, content=content) + return result_message + + @classmethod + def _convert_message_from_dict(cls, message: Dict) -> BaseMessage: + """Convert a single message from a BaseMessage.""" + + role = message["role"] + content = message["content"] + if role == "user": + return HumanMessage(content=content) + elif role == "assistant": + return AIMessage(content=content) + elif role == "system": + return SystemMessage(content=content) + else: + raise ValueError(f"Got unsupported role: {role}") + + @classmethod + def _convert_dict_to_messages(cls, sa_data: Dict) -> List[BaseMessage]: + """Convert a dict to a list of BaseMessages.""" + + schema = sa_data["schema"] + system = sa_data["system"] + messages = sa_data["messages"] + LOG.info(f"Importing prompt for schema: {schema}") + + result_list: List[BaseMessage] = [] + result_list.append(SystemMessage(content=system)) + result_list.extend([cls._convert_message_from_dict(m) for m in messages]) + return result_list + + +class KineticaSqlResponse(BaseModel): + """Response containing SQL and the fetched data. + + This object is returned by a chain with ``KineticaSqlOutputParser`` and it contains + the generated SQL and related Pandas Dataframe fetched from the database. + """ + + sql: str = Field(default=None) + """The generated SQL.""" + + # dataframe: "pd.DataFrame" = Field(default=None) + dataframe: Any = Field(default=None) + """The Pandas dataframe containing the fetched data.""" + + class Config: + """Configuration for this pydantic object.""" + + arbitrary_types_allowed = True + + +class KineticaSqlOutputParser(BaseOutputParser[KineticaSqlResponse]): + """Fetch and return data from the Kinetica LLM. + + This object is used as the last element of a chain to execute generated SQL and it + will output a ``KineticaSqlResponse`` containing the SQL and a pandas dataframe with + the fetched data. + + Example: + .. code-block:: python + from langchain_community.chat_models.kinetica import ( + KineticaChatLLM, KineticaSqlOutputParser) + kinetica_llm = KineticaChatLLM() + + # create chain + ctx_messages = kinetica_llm.load_messages_from_context(self.context_name) + ctx_messages.append(("human", "{input}")) + prompt_template = ChatPromptTemplate.from_messages(ctx_messages) + chain = ( + prompt_template + | kinetica_llm + | KineticaSqlOutputParser(kdbc=kinetica_llm.kdbc) + ) + sql_response: KineticaSqlResponse = chain.invoke( + {"input": "What are the female users ordered by username?"} + ) + + assert isinstance(sql_response, KineticaSqlResponse) + LOG.info(f"SQL Response: {sql_response.sql}") + assert isinstance(sql_response.dataframe, pd.DataFrame) + """ + + kdbc: Any = Field(exclude=True) + """ Kinetica DB connection. """ + + class Config: + """Configuration for this pydantic object.""" + + arbitrary_types_allowed = True + + def parse(self, text: str) -> KineticaSqlResponse: + df = self.kdbc.to_df(text) + return KineticaSqlResponse(sql=text, dataframe=df) + + def parse_result( + self, result: List[Generation], *, partial: bool = False + ) -> KineticaSqlResponse: + return self.parse(result[0].text) + + @property + def _type(self) -> str: + return "kinetica_sql_output_parser" diff --git a/libs/community/langchain_community/vectorstores/__init__.py b/libs/community/langchain_community/vectorstores/__init__.py index da412557f3753..3f6ad32f20067 100644 --- a/libs/community/langchain_community/vectorstores/__init__.py +++ b/libs/community/langchain_community/vectorstores/__init__.py @@ -216,6 +216,24 @@ def _import_hanavector() -> Any: return HanaDB +def _import_kinetica() -> Any: + from langchain_community.vectorstores.kinetica import KineticaVectorStore + + return KineticaVectorStore + + +def _import_kinetica_settings() -> Any: + from langchain_community.vectorstores.kinetica import KineticaSettings + + return KineticaSettings + + +def _import_distance_strategy() -> Any: + from langchain_community.vectorstores.kinetica import DistanceStrategy + + return DistanceStrategy + + def _import_hologres() -> Any: from langchain_community.vectorstores.hologres import Hologres @@ -553,6 +571,12 @@ def __getattr__(name: str) -> Any: return _import_hologres() elif name == "KDBAI": return _import_kdbai() + elif name == "DistanceStrategy": + return _import_distance_strategy() + elif name == "KineticaSettings": + return _import_kinetica_settings() + elif name == "KineticaVectorStore": + return _import_kinetica() elif name == "LanceDB": return _import_lancedb() elif name == "LLMRails": @@ -673,6 +697,9 @@ def __getattr__(name: str) -> Any: "HanaDB", "Hologres", "KDBAI", + "DistanceStrategy", + "Kinetica", + "KineticaSettings", "LanceDB", "LLMRails", "Marqo", diff --git a/libs/community/langchain_community/vectorstores/kinetica.py b/libs/community/langchain_community/vectorstores/kinetica.py new file mode 100644 index 0000000000000..baf239814992f --- /dev/null +++ b/libs/community/langchain_community/vectorstores/kinetica.py @@ -0,0 +1,919 @@ +from __future__ import annotations + +import asyncio +import enum +import json +import logging +import struct +import uuid +from collections import OrderedDict +from enum import Enum +from functools import partial +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type + +import numpy as np +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.pydantic_v1 import BaseSettings +from langchain_core.vectorstores import VectorStore + +from langchain_community.vectorstores.utils import maximal_marginal_relevance + + +class DistanceStrategy(str, enum.Enum): + """Enumerator of the Distance strategies.""" + + EUCLIDEAN = "l2" + COSINE = "cosine" + MAX_INNER_PRODUCT = "inner" + + +def _results_to_docs(docs_and_scores: Any) -> List[Document]: + """Return docs from docs and scores.""" + return [doc for doc, _ in docs_and_scores] + + +class Dimension(int, Enum): + """Some default dimensions for known embeddings.""" + + OPENAI = 1536 + + +DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN + +_LANGCHAIN_DEFAULT_SCHEMA_NAME = "langchain" ## Default Kinetica schema name +_LANGCHAIN_DEFAULT_COLLECTION_NAME = ( + "langchain_kinetica_embeddings" ## Default Kinetica table name +) + + +class KineticaSettings(BaseSettings): + """`KineticaVectorStore` client configuration. + + Attribute: + host (str) : An URL to connect to MyScale backend. + Defaults to 'localhost'. + port (int) : URL port to connect with HTTP. Defaults to 8443. + username (str) : Username to login. Defaults to None. + password (str) : Password to login. Defaults to None. + database (str) : Database name to find the table. Defaults to 'default'. + table (str) : Table name to operate on. + Defaults to 'vector_table'. + metric (str) : Metric to compute distance, + supported are ('angular', 'euclidean', 'manhattan', 'hamming', + 'dot'). Defaults to 'angular'. + https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169 + + """ + + host: str = "http://127.0.0.1" + port: int = 9191 + + username: Optional[str] = None + password: Optional[str] = None + + database: str = _LANGCHAIN_DEFAULT_SCHEMA_NAME + table: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME + metric: str = DEFAULT_DISTANCE_STRATEGY.value + + def __getitem__(self, item: str) -> Any: + return getattr(self, item) + + class Config: + env_file = ".env" + env_prefix = "kinetica_" + env_file_encoding = "utf-8" + + +class KineticaVectorStore(VectorStore): + """`Kinetica` vector store. + + To use, you should have the ``gpudb`` python package installed. + + Args: + kinetica_settings: Kinetica connection settings class. + embedding_function: Any embedding function implementing + `langchain.embeddings.base.Embeddings` interface. + collection_name: The name of the collection to use. (default: langchain) + NOTE: This is not the name of the table, but the name of the collection. + The tables will be created when initializing the store (if not exists) + So, make sure the user has the right permissions to create tables. + distance_strategy: The distance strategy to use. (default: COSINE) + pre_delete_collection: If True, will delete the collection if it exists. + (default: False). Useful for testing. + engine_args: SQLAlchemy's create engine arguments. + + Example: + .. code-block:: python + + from langchain_community.vectorstores + import KineticaVectorStore, KineticaSettings + from langchain_community.embeddings.openai import OpenAIEmbeddings + + kinetica_settings = KineticaSettings( + host="http://127.0.0.1", username="", password="" + ) + COLLECTION_NAME = "kinetica_store" + embeddings = OpenAIEmbeddings() + vectorstore = KineticaVectorStore.from_documents( + documents=docs, + embedding=embeddings, + collection_name=COLLECTION_NAME, + config=kinetica_settings, + ) + """ + + def __init__( + self, + config: KineticaSettings, + embedding_function: Embeddings, + collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, + schema_name: str = _LANGCHAIN_DEFAULT_SCHEMA_NAME, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + pre_delete_collection: bool = False, + logger: Optional[logging.Logger] = None, + relevance_score_fn: Optional[Callable[[float], float]] = None, + ) -> None: + """Constructor for the KineticaVectorStore class + + Args: + config (KineticaSettings): a `KineticaSettings` instance + embedding_function (Embeddings): embedding function to use + collection_name (str, optional): the Kinetica table name. + Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. + schema_name (str, optional): the Kinetica table name. + Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME. + distance_strategy (DistanceStrategy, optional): _description_. + Defaults to DEFAULT_DISTANCE_STRATEGY. + pre_delete_collection (bool, optional): _description_. Defaults to False. + logger (Optional[logging.Logger], optional): _description_. + Defaults to None. + """ + + self._config = config + self.embedding_function = embedding_function + self.collection_name = collection_name + self.schema_name = schema_name + self._distance_strategy = distance_strategy + self.pre_delete_collection = pre_delete_collection + self.logger = logger or logging.getLogger(__name__) + self.override_relevance_score_fn = relevance_score_fn + self._db = self.__get_db(self._config) + + def __post_init__(self, dimensions: int) -> None: + """ + Initialize the store. + """ + try: + from gpudb import GPUdbTable + except ImportError: + raise ImportError( + "Could not import Kinetica python API. " + "Please install it with `pip install gpudb==7.2.0.0b`." + ) + + self.dimensions = dimensions + dimension_field = f"vector({dimensions})" + + if self.pre_delete_collection: + self.delete_schema() + + self.table_name = self.collection_name + if self.schema_name is not None and len(self.schema_name) > 0: + self.table_name = f"{self.schema_name}.{self.collection_name}" + + self.table_schema = [ + ["text", "string"], + ["embedding", "bytes", dimension_field], + ["metadata", "string", "json"], + ["id", "string", "uuid"], + ] + + self.create_schema() + self.EmbeddingStore: GPUdbTable = self.create_tables_if_not_exists() + + def __get_db(self, config: KineticaSettings) -> Any: + try: + from gpudb import GPUdb + except ImportError: + raise ImportError( + "Could not import Kinetica python API. " + "Please install it with `pip install gpudb==7.2.0.0b`." + ) + + options = GPUdb.Options() + options.username = config.username + options.password = config.password + options.skip_ssl_cert_verification = True + return GPUdb(host=config.host, options=options) + + @property + def embeddings(self) -> Embeddings: + return self.embedding_function + + @classmethod + def __from( + cls, + config: KineticaSettings, + texts: List[str], + embeddings: List[List[float]], + embedding: Embeddings, + dimensions: int, + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + pre_delete_collection: bool = False, + logger: Optional[logging.Logger] = None, + **kwargs: Any, + ) -> KineticaVectorStore: + """Class method to assist in constructing the `KineticaVectorStore` store instance + using different combinations of parameters + + Args: + config (KineticaSettings): a `KineticaSettings` instance + texts (List[str]): The list of texts to generate embeddings for and store + embeddings (List[List[float]]): List of embeddings + embedding (Embeddings): the Embedding function + dimensions (int): The number of dimensions the embeddings have + metadatas (Optional[List[dict]], optional): List of JSON data associated + with each text. Defaults to None. + ids (Optional[List[str]], optional): List of unique IDs (UUID by default) + associated with each text. Defaults to None. + collection_name (str, optional): Kinetica schema name. + Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. + distance_strategy (DistanceStrategy, optional): Not used for now. + Defaults to DEFAULT_DISTANCE_STRATEGY. + pre_delete_collection (bool, optional): Whether to delete the Kinetica + schema or not. Defaults to False. + logger (Optional[logging.Logger], optional): Logger to use for logging at + different levels. Defaults to None. + + Returns: + KineticaVectorStore: An instance of `KineticaVectorStore` class + """ + if ids is None: + ids = [str(uuid.uuid1()) for _ in texts] + + if not metadatas: + metadatas = [{} for _ in texts] + + store = cls( + config=config, + collection_name=collection_name, + embedding_function=embedding, + distance_strategy=distance_strategy, + pre_delete_collection=pre_delete_collection, + logger=logger, + **kwargs, + ) + + store.__post_init__(dimensions) + + store.add_embeddings( + texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs + ) + + return store + + def create_tables_if_not_exists(self) -> Any: + """Create the table to store the texts and embeddings""" + + try: + from gpudb import GPUdbTable + except ImportError: + raise ImportError( + "Could not import Kinetica python API. " + "Please install it with `pip install gpudb==7.2.0.0b`." + ) + return GPUdbTable( + _type=self.table_schema, + name=self.table_name, + db=self._db, + options={"is_replicated": "true"}, + ) + + def drop_tables(self) -> None: + """Delete the table""" + self._db.clear_table( + f"{self.table_name}", options={"no_error_if_not_exists": "true"} + ) + + def create_schema(self) -> None: + """Create a new Kinetica schema""" + self._db.create_schema(self.schema_name) + + def delete_schema(self) -> None: + """Delete a Kinetica schema with cascade set to `true` + This method will delete a schema with all tables in it. + """ + self.logger.debug("Trying to delete collection") + self._db.drop_schema( + self.schema_name, {"no_error_if_not_exists": "true", "cascade": "true"} + ) + + def add_embeddings( + self, + texts: Iterable[str], + embeddings: List[List[float]], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Add embeddings to the vectorstore. + + Args: + texts: Iterable of strings to add to the vectorstore. + embeddings: List of list of embedding vectors. + metadatas: List of metadatas associated with the texts. + ids: List of ids for the text embedding pairs + kwargs: vectorstore specific parameters + """ + if ids is None: + ids = [str(uuid.uuid1()) for _ in texts] + + if not metadatas: + metadatas = [{} for _ in texts] + + records = [] + for text, embedding, metadata, id in zip(texts, embeddings, metadatas, ids): + buf = struct.pack("%sf" % self.dimensions, *embedding) + records.append([text, buf, json.dumps(metadata), id]) + + self.EmbeddingStore.insert_records(records) + + return ids + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas (JSON data) associated with the texts. + ids: List of IDs (UUID) for the texts supplied; will be generated if None + kwargs: vectorstore specific parameters + + Returns: + List of ids from adding the texts into the vectorstore. + """ + embeddings = self.embedding_function.embed_documents(list(texts)) + self.dimensions = len(embeddings[0]) + if not hasattr(self, "EmbeddingStore"): + self.__post_init__(self.dimensions) + return self.add_embeddings( + texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs + ) + + def similarity_search( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + **kwargs: Any, + ) -> List[Document]: + """Run similarity search with Kinetica with distance. + + Args: + query (str): Query text to search for. + k (int): Number of results to return. Defaults to 4. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List of Documents most similar to the query. + """ + embedding = self.embedding_function.embed_query(text=query) + return self.similarity_search_by_vector( + embedding=embedding, + k=k, + filter=filter, + ) + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + ) -> List[Tuple[Document, float]]: + """Return docs most similar to query. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List of Documents most similar to the query and score for each + """ + embedding = self.embedding_function.embed_query(query) + docs = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k, filter=filter + ) + return docs + + def similarity_search_with_score_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[dict] = None, + ) -> List[Tuple[Document, float]]: + resp: Dict = self.__query_collection(embedding, k, filter) + + records: OrderedDict = resp["records"] + results = list(zip(*list(records.values()))) + + return self._results_to_docs_and_scores(results) + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[dict] = None, + **kwargs: Any, + ) -> List[Document]: + """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 (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List of Documents most similar to the query vector. + """ + docs_and_scores = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k, filter=filter + ) + return [doc for doc, _ in docs_and_scores] + + def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]: + """Return docs and scores from results.""" + docs = [ + ( + Document( + page_content=result[0], + metadata=json.loads(result[1]), + ), + result[2] if self.embedding_function is not None else None, + ) + for result in results + ] + return docs + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + The 'correct' relevance function + may differ depending on a few things, including: + - the distance / similarity metric used by the VectorStore + - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) + - embedding dimensionality + - etc. + """ + if self.override_relevance_score_fn is not None: + return self.override_relevance_score_fn + + # Default strategy is to rely on distance strategy provided + # in vectorstore constructor + if self._distance_strategy == DistanceStrategy.COSINE: + return self._cosine_relevance_score_fn + elif self._distance_strategy == DistanceStrategy.EUCLIDEAN: + return self._euclidean_relevance_score_fn + elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: + return self._max_inner_product_relevance_score_fn + else: + raise ValueError( + "No supported normalization function" + f" for distance_strategy of {self._distance_strategy}." + "Consider providing relevance_score_fn to KineticaVectorStore constructor." + ) + + @property + def distance_strategy(self) -> str: + if self._distance_strategy == DistanceStrategy.EUCLIDEAN: + return "l2_distance" + elif self._distance_strategy == DistanceStrategy.COSINE: + return "cosine_distance" + elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: + return "dot_product" + else: + raise ValueError( + f"Got unexpected value for distance: {self._distance_strategy}. " + f"Should be one of {', '.join([ds.value for ds in DistanceStrategy])}." + ) + + def __query_collection( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, str]] = None, + ) -> Dict: + """Query the collection.""" + # if filter is not None: + # filter_clauses = [] + # for key, value in filter.items(): + # IN = "in" + # if isinstance(value, dict) and IN in map(str.lower, value): + # value_case_insensitive = { + # k.lower(): v for k, v in value.items() + # } + # filter_by_metadata = self.EmbeddingStore.cmetadata[ + # key + # ].astext.in_(value_case_insensitive[IN]) + # filter_clauses.append(filter_by_metadata) + # else: + # filter_by_metadata = self.EmbeddingStore.cmetadata[ + # key + # ].astext == str(value) + # filter_clauses.append(filter_by_metadata) + + json_filter = json.dumps(filter) if filter is not None else None + where_clause = ( + f" where '{json_filter}' = JSON(metadata) " + if json_filter is not None + else "" + ) + + embedding_str = "[" + ",".join([str(x) for x in embedding]) + "]" + + dist_strategy = self.distance_strategy + + query_string = f""" + SELECT text, metadata, {dist_strategy}(embedding, '{embedding_str}') + as distance, embedding + FROM {self.table_name} + {where_clause} + ORDER BY distance asc NULLS LAST + LIMIT {k} + """ + + self.logger.debug(query_string) + resp = self._db.execute_sql_and_decode(query_string) + self.logger.debug(resp) + return resp + + def max_marginal_relevance_search_with_score_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Return docs selected using the maximal marginal relevance with score + to embedding vector. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding: Embedding to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. + Defaults to 20. + lambda_mult (float): Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List[Tuple[Document, float]]: List of Documents selected by maximal marginal + relevance to the query and score for each. + """ + resp = self.__query_collection(embedding=embedding, k=fetch_k, filter=filter) + records: OrderedDict = resp["records"] + results = list(zip(*list(records.values()))) + + embedding_list = [ + struct.unpack("%sf" % self.dimensions, embedding) + for embedding in records["embedding"] + ] + + mmr_selected = maximal_marginal_relevance( + np.array(embedding, dtype=np.float32), + embedding_list, + k=k, + lambda_mult=lambda_mult, + ) + + candidates = self._results_to_docs_and_scores(results) + + return [r for i, r in enumerate(candidates) if i in mmr_selected] + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query (str): Text to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. + Defaults to 20. + lambda_mult (float): Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List[Document]: List of Documents selected by maximal marginal relevance. + """ + embedding = self.embedding_function.embed_query(query) + return self.max_marginal_relevance_search_by_vector( + embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + filter=filter, + **kwargs, + ) + + def max_marginal_relevance_search_with_score( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[dict] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Return docs selected using the maximal marginal relevance with score. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query (str): Text to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. + Defaults to 20. + lambda_mult (float): Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List[Tuple[Document, float]]: List of Documents selected by maximal marginal + relevance to the query and score for each. + """ + embedding = self.embedding_function.embed_query(query) + docs = self.max_marginal_relevance_search_with_score_by_vector( + embedding=embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + filter=filter, + **kwargs, + ) + return docs + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance + to embedding vector. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding (str): Text to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. + Defaults to 20. + lambda_mult (float): Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. + + Returns: + List[Document]: List of Documents selected by maximal marginal relevance. + """ + docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector( + embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + filter=filter, + **kwargs, + ) + + return _results_to_docs(docs_and_scores) + + async def amax_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance.""" + + # This is a temporary workaround to make the similarity search + # asynchronous. The proper solution is to make the similarity search + # asynchronous in the vector store implementations. + func = partial( + self.max_marginal_relevance_search_by_vector, + embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + filter=filter, + **kwargs, + ) + return await asyncio.get_event_loop().run_in_executor(None, func) + + @classmethod + def from_texts( + cls: Type[KineticaVectorStore], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + config: KineticaSettings = KineticaSettings(), + collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + ids: Optional[List[str]] = None, + pre_delete_collection: bool = False, + **kwargs: Any, + ) -> KineticaVectorStore: + """Adds the texts passed in to the vector store and returns it + + Args: + cls (Type[KineticaVectorStore]): KineticaVectorStore class + texts (List[str]): A list of texts for which the embeddings are generated + embedding (Embeddings): List of embeddings + metadatas (Optional[List[dict]], optional): List of dicts, JSON + describing the texts/documents. Defaults to None. + config (KineticaSettings): a `KineticaSettings` instance + collection_name (str, optional): Kinetica schema name. + Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. + distance_strategy (DistanceStrategy, optional): Distance strategy + e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY. + ids (Optional[List[str]], optional): A list of UUIDs for each + text/document. Defaults to None. + pre_delete_collection (bool, optional): Indicates whether the Kinetica + schema is to be deleted or not. Defaults to False. + + Returns: + KineticaVectorStore: a `KineticaVectorStore` instance + """ + + if len(texts) == 0: + raise ValueError("texts is empty") + + try: + first_embedding = embedding.embed_documents(texts[0:1]) + except NotImplementedError: + first_embedding = [embedding.embed_query(texts[0])] + + dimensions = len(first_embedding[0]) + embeddings = embedding.embed_documents(list(texts)) + + kinetica_store = cls.__from( + texts=texts, + embeddings=embeddings, + embedding=embedding, + dimensions=dimensions, + config=config, + metadatas=metadatas, + ids=ids, + collection_name=collection_name, + distance_strategy=distance_strategy, + pre_delete_collection=pre_delete_collection, + **kwargs, + ) + + return kinetica_store + + @classmethod + def from_embeddings( + cls: Type[KineticaVectorStore], + text_embeddings: List[Tuple[str, List[float]]], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + config: KineticaSettings = KineticaSettings(), + dimensions: int = Dimension.OPENAI, + collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + ids: Optional[List[str]] = None, + pre_delete_collection: bool = False, + **kwargs: Any, + ) -> KineticaVectorStore: + """Adds the embeddings passed in to the vector store and returns it + + Args: + cls (Type[KineticaVectorStore]): KineticaVectorStore class + text_embeddings (List[Tuple[str, List[float]]]): A list of texts + and the embeddings + embedding (Embeddings): List of embeddings + metadatas (Optional[List[dict]], optional): List of dicts, JSON describing + the texts/documents. Defaults to None. + config (KineticaSettings): a `KineticaSettings` instance + dimensions (int, optional): Dimension for the vector data, if not passed a + default will be used. Defaults to Dimension.OPENAI. + collection_name (str, optional): Kinetica schema name. + Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. + distance_strategy (DistanceStrategy, optional): Distance strategy + e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY. + ids (Optional[List[str]], optional): A list of UUIDs for each text/document. + Defaults to None. + pre_delete_collection (bool, optional): Indicates whether the + Kinetica schema is to be deleted or not. Defaults to False. + + Returns: + KineticaVectorStore: a `KineticaVectorStore` instance + """ + + texts = [t[0] for t in text_embeddings] + embeddings = [t[1] for t in text_embeddings] + dimensions = len(embeddings[0]) + + return cls.__from( + texts=texts, + embeddings=embeddings, + embedding=embedding, + dimensions=dimensions, + config=config, + metadatas=metadatas, + ids=ids, + collection_name=collection_name, + distance_strategy=distance_strategy, + pre_delete_collection=pre_delete_collection, + **kwargs, + ) + + @classmethod + def from_documents( + cls: Type[KineticaVectorStore], + documents: List[Document], + embedding: Embeddings, + config: KineticaSettings = KineticaSettings(), + metadatas: Optional[List[dict]] = None, + collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + ids: Optional[List[str]] = None, + pre_delete_collection: bool = False, + **kwargs: Any, + ) -> KineticaVectorStore: + """Adds the list of `Document` passed in to the vector store and returns it + + Args: + cls (Type[KineticaVectorStore]): KineticaVectorStore class + texts (List[str]): A list of texts for which the embeddings are generated + embedding (Embeddings): List of embeddings + config (KineticaSettings): a `KineticaSettings` instance + metadatas (Optional[List[dict]], optional): List of dicts, JSON describing + the texts/documents. Defaults to None. + collection_name (str, optional): Kinetica schema name. + Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. + distance_strategy (DistanceStrategy, optional): Distance strategy + e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY. + ids (Optional[List[str]], optional): A list of UUIDs for each text/document. + Defaults to None. + pre_delete_collection (bool, optional): Indicates whether the Kinetica + schema is to be deleted or not. Defaults to False. + + Returns: + KineticaVectorStore: a `KineticaVectorStore` instance + """ + + texts = [d.page_content for d in documents] + metadatas = [d.metadata for d in documents] + + return cls.from_texts( + texts=texts, + embedding=embedding, + metadatas=metadatas, + config=config, + collection_name=collection_name, + distance_strategy=distance_strategy, + ids=ids, + pre_delete_collection=pre_delete_collection, + **kwargs, + ) diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index be5c69eebb2ad..1957816f99ecd 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "aenum" @@ -2230,6 +2230,16 @@ smb = ["smbprotocol"] ssh = ["paramiko"] tqdm = ["tqdm"] +[[package]] +name = "future" +version = "0.18.3" +description = "Clean single-source support for Python 3 and 2" +optional = true +python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +files = [ + {file = "future-0.18.3.tar.gz", hash = "sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307"}, +] + [[package]] name = "geomet" version = "0.2.1.post1" @@ -2381,6 +2391,55 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4 [package.extras] grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"] +[[package]] +name = "gpudb" +version = "7.2.0.0" +description = "Python client for Kinetica DB" +optional = true +python-versions = "*" +files = [ + {file = "gpudb-7.2.0.0-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:a992345e2531bb88fce37b1f4bb0294ebe62e8dea06d56deef3b673ffcbcfa05"}, + {file = "gpudb-7.2.0.0-cp27-cp27m-manylinux1_x86_64.whl", hash = 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"chardet", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gpudb", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "typeguard", "upstash-redis", "xata", "xmltodict", "zhipuai"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "5fdd9b2eb766411463fa27e19433daf5d5325f2af01ddd93b6a594e3e02a31de" +content-hash = "4932b632e610a0d80b29a4b4585d1bfa6c4bb9b65cc7279556ffcf942b61c855" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index abf8dc89b6384..123b6e1383ac3 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -94,6 +94,8 @@ hdbcli = {version = "^2.19.21", optional = true} oci = {version = "^2.119.1", optional = true} rdflib = {version = "7.0.0", optional = true} nvidia-riva-client = {version = "^2.14.0", optional = true} +gpudb = {version = "^7.2.0.0", optional = true} +typeguard = {version = "^4.1.5", optional = true} [tool.poetry.group.test] optional = true @@ -259,7 +261,9 @@ extended_testing = [ "elasticsearch", "hdbcli", "oci", - "rdflib" + "rdflib", + "gpudb", + "typeguard" ] [tool.ruff] diff --git a/libs/community/tests/integration_tests/.env.example b/libs/community/tests/integration_tests/.env.example index 99be838353376..2a6c04c53eb13 100644 --- a/libs/community/tests/integration_tests/.env.example +++ b/libs/community/tests/integration_tests/.env.example @@ -50,3 +50,9 @@ POWERBI_NUMROWS=_num_rows_in_your_test_table # MongoDB Atlas Vector Search MONGODB_ATLAS_URI=your_mongodb_atlas_connection_string + + +# Kinetica +KINETICA_URL = _db_connection_url_here +KINETICA_USER = _login_user_here +KINETICA_PASSWD = _password_here diff --git a/libs/community/tests/integration_tests/chat_models/cassettes/TestKineticaChatLLM.test_create_llm.yaml b/libs/community/tests/integration_tests/chat_models/cassettes/TestKineticaChatLLM.test_create_llm.yaml new file mode 100644 index 0000000000000..149100d550614 --- /dev/null +++ b/libs/community/tests/integration_tests/chat_models/cassettes/TestKineticaChatLLM.test_create_llm.yaml @@ -0,0 +1,710 @@ +interactions: +- request: + body: "\0" + headers: + Accept: + - application/octet-stream + Content-type: + - application/octet-stream + authorization: + - DUMMY + method: POST + uri: 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1; mode=block + x-request-time-secs: + - '0.00070' + status: + code: 200 + message: OK +- request: + body: "\0" + headers: + Accept: + - application/octet-stream + Content-type: + - application/octet-stream + authorization: + - DUMMY + method: POST + uri: http://172.31.33.30:9191/show/system/properties + response: + body: + string: !!binary | + BE9LAD5zaG93X3N5c3RlbV9wcm9wZXJ0aWVzX3Jlc3BvbnNlzvUCpAk8Y29uZi5haS5hcGkuY29u + bmVjdGlvbl90aW1lb3V0BDkwHmNvbmYuYWkuYXBpLmtleQAoY29uZi5haS5hcGkucHJvdmlkZXIW + a2luZXRpY2FsbG0eY29uZi5haS5hcGkudXJsSGh0dHA6Ly8xNzIuMzEuMzEuMTM6ODA1MC9zcWwv + c3VnZ2VzdDBjb25mLmFsZXJ0X2Rpc2tfYWJzb2x1dGUANGNvbmYuYWxlcnRfZGlza19wZXJjZW50 + YWdlGDEsIDUsIDEwLCAyMBxjb25mLmFsZXJ0X2V4ZQAsY29uZi5hbGVydF9ob3N0X3N0YXR1cwhU + UlVFOmNvbmYuYWxlcnRfaG9zdF9zdGF0dXNfZmlsdGVyIGZhdGFsX2luaXRfZXJyb3I4Y29uZi5h + bGVydF9tYXhfc3RvcmVkX2FsZXJ0cwYxMDA0Y29uZi5hbGVydF9tZW1vcnlfYWJzb2x1dGUAOGNv + bmYuYWxlcnRfbWVtb3J5X3BlcmNlbnRhZ2UYMSwgNSwgMTAsIDIwNGNvbmYuYWxlcnRfcmFua19j + 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headers: + Accept: + - application/octet-stream + Content-type: + - application/octet-stream + authorization: + - DUMMY + method: POST + uri: http://172.31.33.30:9191/show/system/properties + response: + body: + string: !!binary | + BE9LAD5zaG93X3N5c3RlbV9wcm9wZXJ0aWVzX3Jlc3BvbnNlku8Ckgk8Y29uZi5haS5hcGkuY29u + bmVjdGlvbl90aW1lb3V0BDkwHmNvbmYuYWkuYXBpLmtleQAoY29uZi5haS5hcGkucHJvdmlkZXIW + a2luZXRpY2FsbG0eY29uZi5haS5hcGkudXJsSGh0dHA6Ly8xNzIuMzEuMzEuMTM6ODA1MC9zcWwv + c3VnZ2VzdDBjb25mLmFsZXJ0X2Rpc2tfYWJzb2x1dGUANGNvbmYuYWxlcnRfZGlza19wZXJjZW50 + YWdlGDEsIDUsIDEwLCAyMBxjb25mLmFsZXJ0X2V4ZQAsY29uZi5hbGVydF9ob3N0X3N0YXR1cwhU + UlVFOmNvbmYuYWxlcnRfaG9zdF9zdGF0dXNfZmlsdGVyIGZhdGFsX2luaXRfZXJyb3I4Y29uZi5h + bGVydF9tYXhfc3RvcmVkX2FsZXJ0cwYxMDA0Y29uZi5hbGVydF9tZW1vcnlfYWJzb2x1dGUAOGNv + bmYuYWxlcnRfbWVtb3J5X3BlcmNlbnRhZ2UYMSwgNSwgMTAsIDIwNGNvbmYuYWxlcnRfcmFua19j + dWRhX2Vycm9yCFRSVUVEY29uZi5hbGVydF9yYW5rX2ZhbGxiYWNrX2FsbG9jYXRvcghUUlVFLGNv + 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X-XSS-Protection: + - 1; mode=block + x-request-time-secs: + - '0.00061' + status: + code: 200 + message: OK +- request: + body: !!binary | + hAFHRU5FUkFURSBQUk9NUFQgV0lUSCBPUFRJT05TIChDT05URVhUX05BTUVTID0gJ2RlbW8udGVz + dF9sbG1fY3R4JykAAgxiaW5hcnkAAAA= + headers: + Accept: + - application/octet-stream + Content-type: + - application/octet-stream + authorization: + - DUMMY + method: POST + uri: http://172.31.33.30:9191/execute/sql + response: + body: + string: !!binary | + BE9LAChleGVjdXRlX3NxbF9yZXNwb25zZe4MAPYDeyJuYW1lIjoiZ2VuZXJpY19yZXNwb25zZSIs + InR5cGUiOiJyZWNvcmQiLCJmaWVsZHMiOlt7Im5hbWUiOiJjb2x1bW5fMSIsInR5cGUiOnsidHlw + ZSI6ImFycmF5IiwiaXRlbXMiOiJzdHJpbmcifX0seyJuYW1lIjoiY29sdW1uX2hlYWRlcnMiLCJ0 + eXBlIjp7InR5cGUiOiJhcnJheSIsIml0ZW1zIjoic3RyaW5nIn19LHsibmFtZSI6ImNvbHVtbl9k + YXRhdHlwZXMiLCJ0eXBlIjp7InR5cGUiOiJhcnJheSIsIml0ZW1zIjoic3RyaW5nIn19XX2qBwL+ + BnsicGF5bG9hZCI6eyJjb250ZXh0IjpbeyJ0YWJsZSI6ImRlbW8udGVzdF9wcm9maWxlcyIsImNv + 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X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-Kinetica-Group: + - DDL + X-XSS-Protection: + - 1; mode=block + x-request-time-secs: + - '0.00784' + status: + code: 200 + message: OK +- request: + body: '{"messages": [{"role": "system", "content": "CREATE TABLE demo.test_profiles + AS\n(\n username VARCHAR (32) NOT NULL,\n name VARCHAR (32) NOT NULL,\n sex + VARCHAR (1) NOT NULL,\n address VARCHAR (64) NOT NULL,\n mail VARCHAR (32) + NOT NULL,\n birthdate TIMESTAMP NOT NULL\n);\nCOMMENT ON TABLE demo.test_profiles + IS ''Contains user profiles.'';"}, {"role": "user", "content": "How many male + users are there?"}, {"role": "assistant", "content": "select count(1) as num_users\n from + demo.test_profiles\n where sex = ''M'';"}, {"role": "user", "content": + "What are the female users ordered by username?"}]}' + headers: + Accept: + - text/plain + Content-type: + - application/json + authorization: + - DUMMY + method: POST + uri: http://172.31.33.30:9191/chat/completions + response: + body: + string: '{"status":"OK","data":{"status":"OK","data":{"id":"chatCompl-1708381178","object":"chat.completion","created":1708381178,"model":"sqlassist-1.1","choices":[{"index":0,"message":{"role":"assistant","content":"SELECT + username, name\n FROM demo.test_profiles\n WHERE sex + = ''F''\n ORDER BY username;"},"finish_reason":"stop"}],"usage":{"prompt_tokens":140,"completion_tokens":22,"total_tokens":162},"prompt":"What + are the female users ordered by username?"}}}' + headers: + Access-Control-Allow-Origin: + - '*' + Access-Control-Expose-Headers: + - x-request-time-secs + Connection: + - Close + Content-Type: + - application/json + Date: + - Mon, 19 Feb 2024 22:19:37 GMT + Strict-Transport-Security: + - max-age=31536000; includeSubDomains + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + x-request-time-secs: + - '0.86368' + status: + code: 200 + message: OK 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''Contains user profiles.'';"}, {"role": "user", "content": "How many male + users are there?"}, {"role": "assistant", "content": "select count(1) as num_users\n from + demo.test_profiles\n where sex = ''M'';"}, {"role": "user", "content": + "What are the female users ordered by username?"}]}' + headers: + Accept: + - text/plain + Content-type: + - application/json + authorization: + - DUMMY + method: POST + uri: http://172.31.33.30:9191/chat/completions + response: + body: + string: '{"status":"OK","data":{"status":"OK","data":{"id":"chatCompl-1708381176","object":"chat.completion","created":1708381177,"model":"sqlassist-1.1","choices":[{"index":0,"message":{"role":"assistant","content":"SELECT + username, name\n FROM demo.test_profiles\n WHERE sex + = ''F''\n ORDER BY username;"},"finish_reason":"stop"}],"usage":{"prompt_tokens":140,"completion_tokens":22,"total_tokens":162},"prompt":"What + are the female users ordered by username?"}}}' + headers: + Access-Control-Allow-Origin: + - '*' + Access-Control-Expose-Headers: + - x-request-time-secs + Connection: + - Close + Content-Type: + - application/json + Date: + - Mon, 19 Feb 2024 22:19:36 GMT + Strict-Transport-Security: + - max-age=31536000; includeSubDomains + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + x-request-time-secs: + - '0.86274' + status: + code: 200 + message: OK +version: 1 diff --git a/libs/community/tests/integration_tests/chat_models/cassettes/TestKineticaChatLLM.test_load_context.yaml b/libs/community/tests/integration_tests/chat_models/cassettes/TestKineticaChatLLM.test_load_context.yaml new file mode 100644 index 0000000000000..1ddb7c08d0ec3 --- /dev/null +++ b/libs/community/tests/integration_tests/chat_models/cassettes/TestKineticaChatLLM.test_load_context.yaml @@ -0,0 +1,740 @@ +interactions: +- request: + body: "\0" + headers: + Accept: + - application/octet-stream + Content-type: + - application/octet-stream + authorization: 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Access-Control-Allow-Origin: + - '*' + Access-Control-Expose-Headers: + - x-request-time-secs + Connection: + - Close + Content-Type: + - application/octet-stream + Date: + - Mon, 19 Feb 2024 22:19:35 GMT + Strict-Transport-Security: + - max-age=31536000; includeSubDomains + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + x-request-time-secs: + - '0.00057' + status: + code: 200 + message: OK +- request: + body: "\0" + headers: + Accept: + - application/octet-stream + Content-type: + - application/octet-stream + authorization: + - DUMMY + method: POST + uri: http://172.31.33.30:9191/show/system/properties + response: + body: + string: !!binary | + BE9LAD5zaG93X3N5c3RlbV9wcm9wZXJ0aWVzX3Jlc3BvbnNlku8Ckgk8Y29uZi5haS5hcGkuY29u + bmVjdGlvbl90aW1lb3V0BDkwHmNvbmYuYWkuYXBpLmtleQAoY29uZi5haS5hcGkucHJvdmlkZXIW + a2luZXRpY2FsbG0eY29uZi5haS5hcGkudXJsSGh0dHA6Ly8xNzIuMzEuMzEuMTM6ODA1MC9zcWwv + 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Generator + +import pandas as pd +import pytest +from langchain_core.messages import ( + AIMessage, + HumanMessage, + SystemMessage, +) +from langchain_core.prompts import ChatPromptTemplate + +from langchain_community.chat_models.kinetica import ( + KineticaChatLLM, + KineticaSqlOutputParser, + KineticaSqlResponse, + KineticaUtil, +) + +if TYPE_CHECKING: + import gpudb + + +LOG = logging.getLogger(__name__) + + +@pytest.fixture(scope="module") +def vcr_config() -> dict: + return { + # Replace the Authorization request header with "DUMMY" in cassettes + "filter_headers": [("authorization", "DUMMY")], + } + + +class TestKineticaChatLLM: + """Integration tests for `Kinetica` chat models. + + You must have `gpudb`, `typeguard`, and `faker` packages installed to run these + tests. pytest-vcr cassettes are provided for offline testing. + + For more information see https://docs.kinetica.com/7.1/sql-gpt/concepts/. + + These integration tests follow a workflow: + + 1. The `test_setup()` will create a table with fake user profiles and and a related + LLM context for the table. + + 2. The LLM context is retrieved from the DB and used to create a chat prompt + template. + + 3. A chain is constructed from the chat prompt template. + + 4. The chain is executed to generate the SQL and execute the query. + """ + + table_name = "demo.test_profiles" + context_name = "demo.test_llm_ctx" + num_records = 100 + + @classmethod + @pytest.mark.vcr() + def test_setup(cls) -> "gpudb.GPUdb": + """Create the connection, test table, and LLM context.""" + + kdbc = KineticaUtil.create_kdbc() + cls._create_test_table(kdbc, cls.table_name, cls.num_records) + cls._create_llm_context(kdbc, cls.context_name) + return kdbc + + @pytest.mark.vcr() + def test_create_llm(self) -> None: + """Create an LLM instance.""" + import gpudb + + kinetica_llm = KineticaChatLLM() + LOG.info(kinetica_llm._identifying_params) + + assert isinstance(kinetica_llm.kdbc, gpudb.GPUdb) + assert kinetica_llm._llm_type == "kinetica-sqlassist" + + @pytest.mark.vcr() + def test_load_context(self) -> None: + """Load the LLM context from the DB.""" + kinetica_llm = KineticaChatLLM() + ctx_messages = kinetica_llm.load_messages_from_context(self.context_name) + + system_message = ctx_messages[0] + assert isinstance(system_message, SystemMessage) + + last_question = ctx_messages[-2] + assert isinstance(last_question, HumanMessage) + assert last_question.content == "How many male users are there?" + + @pytest.mark.vcr() + def test_generate(self) -> None: + """Generate SQL from a chain.""" + kinetica_llm = KineticaChatLLM() + + # create chain + ctx_messages = kinetica_llm.load_messages_from_context(self.context_name) + ctx_messages.append(("human", "{input}")) + prompt_template = ChatPromptTemplate.from_messages(ctx_messages) + chain = prompt_template | kinetica_llm + + resp_message = chain.invoke( + {"input": "What are the female users ordered by username?"} + ) + LOG.info(f"SQL Response: {resp_message.content}") + assert isinstance(resp_message, AIMessage) + + @pytest.mark.vcr() + def test_full_chain(self) -> None: + """Generate SQL from a chain and execute the query.""" + kinetica_llm = KineticaChatLLM() + + # create chain + ctx_messages = kinetica_llm.load_messages_from_context(self.context_name) + ctx_messages.append(("human", "{input}")) + prompt_template = ChatPromptTemplate.from_messages(ctx_messages) + chain = ( + prompt_template + | kinetica_llm + | KineticaSqlOutputParser(kdbc=kinetica_llm.kdbc) + ) + sql_response: KineticaSqlResponse = chain.invoke( + {"input": "What are the female users ordered by username?"} + ) + + assert isinstance(sql_response, KineticaSqlResponse) + LOG.info(f"SQL Response: {sql_response.sql}") + assert isinstance(sql_response.dataframe, pd.DataFrame) + users = sql_response.dataframe["username"] + assert users[0] == "alexander40" + + @classmethod + def _create_fake_records(cls, count: int) -> Generator: + """Generator for fake records.""" + import faker + + faker.Faker.seed(5467) + faker_inst = faker.Faker(locale="en-US") + for id in range(0, count): + rec = dict(id=id, **faker_inst.simple_profile()) + rec["birthdate"] = pd.Timestamp(rec["birthdate"]) + yield rec + + @classmethod + def _create_test_table( + cls, kinetica_dbc: "gpudb.GPUdb", table_name: str, num_records: int + ) -> "gpudb.GPUdbTable": + """Create a table from the fake records generator.""" + import gpudb + + table_df = pd.DataFrame.from_records( + data=cls._create_fake_records(num_records), index="id" + ) + + LOG.info(f"Creating test table '{table_name}' with {num_records} records...") + gpudb_table = gpudb.GPUdbTable.from_df( + table_df, + db=kinetica_dbc, + table_name=table_name, + clear_table=True, + load_data=True, + column_types={}, + ) + return gpudb_table + + @classmethod + def _check_error(cls, response: dict) -> None: + """Convert a DB error into an exception.""" + status = response["status_info"]["status"] + if status != "OK": + message = response["status_info"]["message"] + raise Exception("[%s]: %s" % (status, message)) + + @classmethod + def _create_llm_context( + cls, kinetica_dbc: "gpudb.GPUdb", context_name: str + ) -> None: + """Create an LLM context for the table.""" + + sql = f""" + CREATE OR REPLACE CONTEXT {context_name} + ( + TABLE = {cls.table_name} + COMMENT = 'Contains user profiles.' + ), + ( + SAMPLES = ( + 'How many male users are there?' = + 'select count(1) as num_users + from {cls.table_name} + where sex = ''M'';') + ) + """ + LOG.info(f"Creating context: {context_name}") + response = kinetica_dbc.execute_sql(sql) + cls._check_error(response) diff --git a/libs/community/tests/integration_tests/vectorstores/test_kinetica.py b/libs/community/tests/integration_tests/vectorstores/test_kinetica.py new file mode 100644 index 0000000000000..46151806ab93d --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/test_kinetica.py @@ -0,0 +1,300 @@ +import os +from typing import List + +import pytest +from langchain_core.documents import Document + +from langchain_community.embeddings.openai import OpenAIEmbeddings +from langchain_community.vectorstores import ( + DistanceStrategy, + KineticaSettings, + KineticaVectorStore, +) +from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings + +DIMENSIONS = 3 +HOST = os.getenv("KINETICA_HOST", "http://127.0.0.1:9191") +USERNAME = os.getenv("KINETICA_USERNAME", "") +PASSWORD = os.getenv("KINETICA_PASSWORD", "") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "") + + +class FakeEmbeddingsWithAdaDimension(FakeEmbeddings): + """Fake embeddings functionality for testing.""" + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return simple embeddings.""" + return [[float(1.0)] * (DIMENSIONS - 1) + [float(i)] for i in range(len(texts))] + + def embed_query(self, text: str) -> List[float]: + """Return simple embeddings.""" + return [float(1.0)] * (DIMENSIONS - 1) + [float(0.0)] + + +@pytest.fixture +def create_config() -> KineticaSettings: + return KineticaSettings(host=HOST, username=USERNAME, password=PASSWORD) + + +@pytest.mark.requires("gpudb") +def test_kinetica(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"text": text} for text in texts] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica", + pre_delete_collection=True, + ) + output = docsearch.similarity_search("foo", k=1) + assert output[0].page_content == "foo" + + +@pytest.mark.requires("gpudb") +def test_kinetica_embeddings(create_config: KineticaSettings) -> None: + """Test end to end construction with embeddings and search.""" + texts = ["foo", "bar", "baz"] + text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts) + text_embedding_pairs = list(zip(texts, text_embeddings)) + docsearch = KineticaVectorStore.from_embeddings( + config=create_config, + text_embeddings=text_embedding_pairs, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_embeddings", + pre_delete_collection=False, + ) + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo")] + + +@pytest.mark.requires("gpudb") +def test_kinetica_with_metadatas(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_with_metadatas", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo", metadata={"page": "0"})] + + +@pytest.mark.requires("gpudb") +def test_kinetica_with_metadatas_with_scores(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_with_metadatas_with_scores", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search_with_score("foo", k=1) + assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)] + + +@pytest.mark.requires("gpudb") +def test_kinetica_with_filter_match(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_with_filter_match", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"}) + assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)] + + +@pytest.mark.requires("gpudb") +def test_kinetica_with_filter_distant_match(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_with_filter_distant_match", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"}) + assert output == [(Document(page_content="baz", metadata={"page": "2"}), 2.0)] + + +@pytest.mark.skip(reason="Filter condition has IN clause") +@pytest.mark.requires("gpudb") +def test_kinetica_with_filter_in_set(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_with_filter_in_set", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search_with_score( + "foo", k=2, filter={"page": {"IN": ["0", "2"]}} + ) + assert output == [ + (Document(page_content="foo", metadata={"page": "0"}), 0.0), + (Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406), + ] + + +@pytest.mark.requires("gpudb") +def test_kinetica_relevance_score(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + collection_name="test_kinetica_relevance_score", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search_with_relevance_scores("foo", k=3) + assert output == [ + (Document(page_content="foo", metadata={"page": "0"}), 1.0), + (Document(page_content="bar", metadata={"page": "1"}), 0.29289321881345254), + (Document(page_content="baz", metadata={"page": "2"}), -0.4142135623730949), + ] + + +@pytest.mark.requires("openai", "gpudb") +def test_kinetica_max_marginal_relevance_search( + create_config: KineticaSettings, +) -> None: + """Test end to end construction and search.""" + openai = OpenAIEmbeddings(api_key=OPENAI_API_KEY) + texts = ["foo", "bar", "baz"] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + embedding=openai, + distance_strategy=DistanceStrategy.COSINE, + collection_name="test_kinetica_max_marginal_relevance_search", + pre_delete_collection=False, + ) + + output = docsearch.max_marginal_relevance_search("foo", k=1, fetch_k=3) + assert output == [Document(page_content="foo")] + + +@pytest.mark.requires("gpudb") +def test_kinetica_max_marginal_relevance_search_with_score( + create_config: KineticaSettings, +) -> None: + """Test end to end construction and search.""" + texts = ["foo", "bar", "baz"] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + embedding=FakeEmbeddingsWithAdaDimension(), + distance_strategy=DistanceStrategy.EUCLIDEAN, + collection_name="test_kinetica_max_marginal_relevance_search_with_score", + pre_delete_collection=False, + ) + + output = docsearch.max_marginal_relevance_search_with_score("foo", k=1, fetch_k=3) + assert output == [(Document(page_content="foo"), 0.0)] + + +@pytest.mark.requires("openai", "gpudb") +def test_kinetica_with_openai_embeddings(create_config: KineticaSettings) -> None: + """Test end to end construction and search.""" + if OPENAI_API_KEY == "": + assert False + + openai = OpenAIEmbeddings(api_key=OPENAI_API_KEY) + texts = ["foo", "bar", "baz"] + metadatas = [{"text": text} for text in texts] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=openai, + collection_name="kinetica_openai_test", + pre_delete_collection=False, + ) + + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo", metadata={"text": "foo"})] + + +@pytest.mark.requires("gpudb") +def test_kinetica_retriever_search_threshold(create_config: KineticaSettings) -> None: + """Test using retriever for searching with threshold.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + distance_strategy=DistanceStrategy.EUCLIDEAN, + collection_name="test_kinetica_retriever_search_threshold", + pre_delete_collection=False, + ) + + retriever = docsearch.as_retriever( + search_type="similarity_score_threshold", + search_kwargs={"k": 3, "score_threshold": 0.999}, + ) + output = retriever.get_relevant_documents("summer") + assert output == [ + Document(page_content="foo", metadata={"page": "0"}), + ] + + +@pytest.mark.requires("gpudb") +def test_kinetica_retriever_search_threshold_custom_normalization_fn( + create_config: KineticaSettings, +) -> None: + """Test searching with threshold and custom normalization function""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + docsearch = KineticaVectorStore.from_texts( + config=create_config, + texts=texts, + metadatas=metadatas, + embedding=FakeEmbeddingsWithAdaDimension(), + distance_strategy=DistanceStrategy.EUCLIDEAN, + collection_name="test_kinetica_retriever_search_threshold_custom_normalization_fn", + pre_delete_collection=False, + relevance_score_fn=lambda d: d * 0, + ) + + retriever = docsearch.as_retriever( + search_type="similarity_score_threshold", + search_kwargs={"k": 3, "score_threshold": 0.5}, + ) + output = retriever.get_relevant_documents("foo") + assert output == [] 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 58a2901f93160..9d27bab6e38f6 100644 --- a/libs/community/tests/unit_tests/chat_models/test_imports.py +++ b/libs/community/tests/unit_tests/chat_models/test_imports.py @@ -40,6 +40,7 @@ "GPTRouter", "ChatYuan2", "ChatZhipuAI", + "KineticaChatLLM", ] diff --git a/libs/community/tests/unit_tests/chat_models/test_kinetica.py b/libs/community/tests/unit_tests/chat_models/test_kinetica.py new file mode 100644 index 0000000000000..caab95043e445 --- /dev/null +++ b/libs/community/tests/unit_tests/chat_models/test_kinetica.py @@ -0,0 +1,67 @@ +"""Test `Kinetica` chat models""" + + +import logging +from typing import Any + +from langchain_core.messages import AIMessage + +from langchain_community.chat_models.kinetica import KineticaChatLLM, KineticaUtil + +LOG = logging.getLogger(__name__) + + +class TestKineticaChatLLM: + test_ctx_json = """ + { + "payload":{ + "context":[ + { + "table":"demo.test_profiles", + "columns":[ + "username VARCHAR (32) NOT NULL", + "name VARCHAR (32) NOT NULL", + "sex VARCHAR (1) NOT NULL", + "address VARCHAR (64) NOT NULL", + "mail VARCHAR (32) NOT NULL", + "birthdate TIMESTAMP NOT NULL" + ], + "description":"Contains user profiles.", + "rules":[ + + ] + }, + { + "samples":{ + "How many male users are there?": + "select count(1) as num_users from demo.test_profiles where sex = ''M'';" + } + } + ] + } + } +""" + + def test_convert_messages(self, monkeypatch: Any) -> None: + """Test convert messages from context.""" + + def patch_kdbc() -> None: + return None + + monkeypatch.setattr(KineticaUtil, "create_kdbc", patch_kdbc) + + def patch_execute_sql(*args: Any, **kwargs: Any) -> dict: + return dict(Prompt=self.test_ctx_json) + + monkeypatch.setattr(KineticaChatLLM, "_execute_sql", patch_execute_sql) + + kinetica_llm = KineticaChatLLM() + + test_messages = kinetica_llm.load_messages_from_context("test") + LOG.info(f"test_messages: {test_messages}") + ai_message = test_messages[-1] + assert isinstance(ai_message, AIMessage) + assert ( + ai_message.content + == "select count(1) as num_users from demo.test_profiles where sex = 'M';" + ) diff --git a/libs/community/tests/unit_tests/vectorstores/test_imports.py b/libs/community/tests/unit_tests/vectorstores/test_imports.py index fc49a1742d346..2b4657acfb280 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_imports.py +++ b/libs/community/tests/unit_tests/vectorstores/test_imports.py @@ -10,5 +10,7 @@ def test_all_imports() -> None: "AlibabaCloudOpenSearchSettings", "ClickhouseSettings", "MyScaleSettings", + "DistanceStrategy", + "KineticaSettings", ]: assert issubclass(getattr(vectorstores, cls), VectorStore) diff --git a/libs/community/tests/unit_tests/vectorstores/test_public_api.py b/libs/community/tests/unit_tests/vectorstores/test_public_api.py index 48b51accdda87..808da73b55978 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_public_api.py +++ b/libs/community/tests/unit_tests/vectorstores/test_public_api.py @@ -21,6 +21,7 @@ "DatabricksVectorSearch", "DeepLake", "Dingo", + "DistanceStrategy", "DocArrayHnswSearch", "DocArrayInMemorySearch", "ElasticKnnSearch", @@ -31,6 +32,8 @@ "HanaDB", "Hologres", "KDBAI", + "Kinetica", + "KineticaSettings", "LanceDB", "Lantern", "LLMRails", diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 1028d8b813dc4..5818198cf0aa0 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -1,113 +1,99 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. -[[package]] -name = "aiodns" -version = "3.1.1" 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