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community[minor]: add Kinetica LLM wrapper (#17879)
**Description:** Initial pull request for Kinetica LLM wrapper **Issue:** N/A **Dependencies:** No new dependencies for unit tests. Integration tests require gpudb, typeguard, and faker **Twitter handle:** @chad_juliano Note: There is another pull request for Kinetica vectorstore. Ultimately we would like to make a partner package but we are starting with a community contribution.
This commit is contained in:
Chad Juliano
GitHub
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docs/docs/integrations/chat/kinetica.ipynb
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654
docs/docs/integrations/chat/kinetica.ipynb
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Kinetica\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Kinetica SqlAssist LLM Demo\n",
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"\n",
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"This notebook demonstrates how to use Kinetica to transform natural language into SQL\n",
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"and simplify the process of data retrieval. This demo is intended to show the mechanics\n",
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"of creating and using a chain as opposed to the capabilities of the LLM.\n",
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"\n",
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"## Overview\n",
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"\n",
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"With the Kinetica LLM workflow you create an LLM context in the database that provides\n",
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"information needed for infefencing that includes tables, annotations, rules, and\n",
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"samples. Invoking ``ChatKinetica.load_messages_from_context()`` will retrieve the\n",
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"context information from the database so that it can be used to create a chat prompt.\n",
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"\n",
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"The chat prompt consists of a ``SystemMessage`` and pairs of\n",
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"``HumanMessage``/``AIMessage`` that contain the samples which are question/SQL\n",
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"pairs. You can append pairs samples to this list but it is not intended to\n",
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"facilitate a typical natural language conversation.\n",
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"\n",
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"When you create a chain from the chat prompt and execute it, the Kinetica LLM will\n",
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"generate SQL from the input. Optionally you can use ``KineticaSqlOutputParser`` to\n",
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"execute the SQL and return the result as a dataframe.\n",
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"\n",
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"Currently, 2 LLM's are supported for SQL generation: \n",
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"\n",
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"1. **Kinetica SQL-GPT**: This LLM is based on OpenAI ChatGPT API.\n",
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"2. **Kinetica SqlAssist**: This LLM is purpose built to integrate with the Kinetica\n",
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" database and it can run in a secure customer premise.\n",
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"\n",
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"For this demo we will be using **SqlAssist**. See the [Kinetica Documentation\n",
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"site](https://docs.kinetica.com/7.1/sql-gpt/concepts/) for more information.\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"To get started you will need a Kinetica DB instance. If you don't have one you can\n",
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"obtain a [free development instance](https://cloud.kinetica.com/trynow).\n",
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"\n",
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"You will need to install the following packages..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"# Install Langchain community and core packages\n",
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"%pip install --upgrade --quiet langchain-core langchain-community\n",
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"\n",
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"# Install Kineitca DB connection package\n",
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"%pip install --upgrade --quiet gpudb typeguard\n",
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"\n",
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"# Install packages needed for this tutorial\n",
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"%pip install --upgrade --quiet faker"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Database Connection\n",
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"\n",
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"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",
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"* `KINETICA_URL`: Database connection URL\n",
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"* `KINETICA_USER`: Database user\n",
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"* `KINETICA_PASSWD`: Secure password.\n",
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"\n",
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"If you can create an instance of `KineticaChatLLM` then you are successfully connected."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models.kinetica import ChatKinetica\n",
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"\n",
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"kinetica_llm = ChatKinetica()\n",
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"\n",
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"# Test table we will create\n",
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"table_name = \"demo.user_profiles\"\n",
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"\n",
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"# LLM Context we will create\n",
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"kinetica_ctx = \"demo.test_llm_ctx\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create test data\n",
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"\n",
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"Before we can generate SQL we will need to create a Kinetica table and an LLM context that can inference the table.\n",
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"\n",
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"### Create some fake user profiles\n",
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"\n",
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"We will use the `faker` package to create a dataframe with 100 fake profiles."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>username</th>\n",
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" <th>name</th>\n",
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" <th>sex</th>\n",
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" <th>address</th>\n",
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" <th>mail</th>\n",
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" <th>birthdate</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>id</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>eduardo69</td>\n",
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" <td>Haley Beck</td>\n",
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" <td>F</td>\n",
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" <td>59836 Carla Causeway Suite 939\\nPort Eugene, I...</td>\n",
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" <td>meltondenise@yahoo.com</td>\n",
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" <td>1997-09-09</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>lbarrera</td>\n",
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" <td>Joshua Stephens</td>\n",
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" <td>M</td>\n",
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" <td>3108 Christina Forges\\nPort Timothychester, KY...</td>\n",
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" <td>erica80@hotmail.com</td>\n",
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" <td>1924-05-05</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>bburton</td>\n",
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" <td>Paula Kaiser</td>\n",
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" <td>F</td>\n",
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" <td>Unit 7405 Box 3052\\nDPO AE 09858</td>\n",
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" <td>timothypotts@gmail.com</td>\n",
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" <td>1933-09-06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>melissa49</td>\n",
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" <td>Wendy Reese</td>\n",
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" <td>F</td>\n",
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" <td>6408 Christopher Hill Apt. 459\\nNew Benjamin, ...</td>\n",
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" <td>dadams@gmail.com</td>\n",
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" <td>1988-07-28</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>melissacarter</td>\n",
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" <td>Manuel Rios</td>\n",
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" <td>M</td>\n",
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" <td>2241 Bell Gardens Suite 723\\nScottside, CA 38463</td>\n",
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" <td>williamayala@gmail.com</td>\n",
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" <td>1930-12-19</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" username name sex \\\n",
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"id \n",
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"0 eduardo69 Haley Beck F \n",
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"1 lbarrera Joshua Stephens M \n",
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"2 bburton Paula Kaiser F \n",
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"3 melissa49 Wendy Reese F \n",
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"4 melissacarter Manuel Rios M \n",
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"\n",
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" address mail \\\n",
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"id \n",
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"0 59836 Carla Causeway Suite 939\\nPort Eugene, I... meltondenise@yahoo.com \n",
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"1 3108 Christina Forges\\nPort Timothychester, KY... erica80@hotmail.com \n",
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"2 Unit 7405 Box 3052\\nDPO AE 09858 timothypotts@gmail.com \n",
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"3 6408 Christopher Hill Apt. 459\\nNew Benjamin, ... dadams@gmail.com \n",
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"4 2241 Bell Gardens Suite 723\\nScottside, CA 38463 williamayala@gmail.com \n",
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"\n",
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" birthdate \n",
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"id \n",
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"0 1997-09-09 \n",
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"1 1924-05-05 \n",
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"2 1933-09-06 \n",
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"3 1988-07-28 \n",
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"4 1930-12-19 "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from typing import Generator\n",
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"\n",
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"import pandas as pd\n",
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"from faker import Faker\n",
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"\n",
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"Faker.seed(5467)\n",
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"faker = Faker(locale=\"en-US\")\n",
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"\n",
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"\n",
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"def profile_gen(count: int) -> Generator:\n",
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" for id in range(0, count):\n",
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" rec = dict(id=id, **faker.simple_profile())\n",
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" rec[\"birthdate\"] = pd.Timestamp(rec[\"birthdate\"])\n",
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" yield rec\n",
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"\n",
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"\n",
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"load_df = pd.DataFrame.from_records(data=profile_gen(100), index=\"id\")\n",
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"load_df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create a Kinetica table from the Dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name</th>\n",
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" <th>type</th>\n",
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" <th>properties</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>username</td>\n",
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" <td>string</td>\n",
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" <td>[char32]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>name</td>\n",
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" <td>string</td>\n",
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" <td>[char32]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>sex</td>\n",
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" <td>string</td>\n",
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" <td>[char1]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>address</td>\n",
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" <td>string</td>\n",
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" <td>[char64]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>mail</td>\n",
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" <td>string</td>\n",
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" <td>[char32]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>birthdate</td>\n",
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" <td>long</td>\n",
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" <td>[timestamp]</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" name type properties\n",
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"0 username string [char32]\n",
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"1 name string [char32]\n",
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"2 sex string [char1]\n",
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"3 address string [char64]\n",
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"4 mail string [char32]\n",
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"5 birthdate long [timestamp]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from gpudb import GPUdbTable\n",
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"\n",
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"gpudb_table = GPUdbTable.from_df(\n",
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" load_df,\n",
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" db=kinetica_llm.kdbc,\n",
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" table_name=table_name,\n",
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" clear_table=True,\n",
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" load_data=True,\n",
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")\n",
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"\n",
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"# See the Kinetica column types\n",
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"gpudb_table.type_as_df()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create the LLM context\n",
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"\n",
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"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",
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"\n",
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"Here we create a context from the SQL syntax referencing the table we created."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'status': 'OK',\n",
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" 'message': '',\n",
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" 'data_type': 'execute_sql_response',\n",
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" 'response_time': 0.0148}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# create an LLM context for the table.\n",
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"\n",
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"from gpudb import GPUdbException\n",
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"\n",
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"sql = f\"\"\"\n",
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"CREATE OR REPLACE CONTEXT {kinetica_ctx}\n",
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"(\n",
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" TABLE = demo.test_profiles\n",
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" COMMENT = 'Contains user profiles.'\n",
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"),\n",
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"(\n",
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" SAMPLES = (\n",
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" 'How many male users are there?' = \n",
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" 'select count(1) as num_users\n",
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" from demo.test_profiles\n",
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" where sex = ''M'';')\n",
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")\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"def _check_error(response: dict) -> None:\n",
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" status = response[\"status_info\"][\"status\"]\n",
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" if status != \"OK\":\n",
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" message = response[\"status_info\"][\"message\"]\n",
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" raise GPUdbException(\"[%s]: %s\" % (status, message))\n",
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"\n",
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"\n",
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"response = kinetica_llm.kdbc.execute_sql(sql)\n",
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"_check_error(response)\n",
|
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"response[\"status_info\"]"
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]
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},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"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",
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||||
"\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": 5,
|
||||
"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": 6,
|
||||
"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": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SQL: SELECT username, name\n",
|
||||
" FROM demo.test_profiles\n",
|
||||
" WHERE sex = 'F'\n",
|
||||
" ORDER BY username;\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>username</th>\n",
|
||||
" <th>name</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>alexander40</td>\n",
|
||||
" <td>Tina Ramirez</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>bburton</td>\n",
|
||||
" <td>Paula Kaiser</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>brian12</td>\n",
|
||||
" <td>Stefanie Williams</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>brownanna</td>\n",
|
||||
" <td>Jennifer Rowe</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>carl19</td>\n",
|
||||
" <td>Amanda Potts</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" username name\n",
|
||||
"0 alexander40 Tina Ramirez\n",
|
||||
"1 bburton Paula Kaiser\n",
|
||||
"2 brian12 Stefanie Williams\n",
|
||||
"3 brownanna Jennifer Rowe\n",
|
||||
"4 carl19 Amanda Potts"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"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
|
||||
}
|
||||
28
docs/docs/integrations/providers/kinetica.mdx
Normal file
28
docs/docs/integrations/providers/kinetica.mdx
Normal file
@@ -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 ChatKinetica
|
||||
```
|
||||
|
||||
## 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
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user