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399 lines
12 KiB
Plaintext
399 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "fc935871-7640-41c6-b798-58514d860fe0",
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"metadata": {},
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"source": [
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"## LLaMA2 chat with SQL\n",
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"\n",
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"Open source, local LLMs are great to consider for any application that demands data privacy.\n",
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"\n",
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"SQL is one good example. \n",
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"\n",
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"This cookbook shows how to perform text-to-SQL using various local versions of LLaMA2 run locally.\n",
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"\n",
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"## 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": null,
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"id": "81adcf8b-395a-4f02-8749-ac976942b446",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install langchain replicate"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8e13ed66-300b-4a23-b8ac-44df68ee4733",
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"metadata": {},
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"source": [
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"## LLM\n",
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"\n",
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"There are a few ways to access LLaMA2.\n",
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"\n",
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"To run locally, we use Ollama.ai. \n",
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"\n",
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"See [here](/docs/integrations/chat/ollama) for details on installation and setup.\n",
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"\n",
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"Also, see [here](/docs/guides/development/local_llms) for our full guide on local LLMs.\n",
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" \n",
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"To use an external API, which is not private, we can use Replicate."
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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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"id": "6a75a5c6-34ee-4ab9-a664-d9b432d812ee",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Init param `input` is deprecated, please use `model_kwargs` instead.\n"
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]
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}
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],
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"source": [
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"# Local\n",
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"from langchain_community.chat_models import ChatOllama\n",
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"\n",
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"llama2_chat = ChatOllama(model=\"llama2:13b-chat\")\n",
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"llama2_code = ChatOllama(model=\"codellama:7b-instruct\")\n",
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"\n",
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"# API\n",
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"from langchain_community.llms import Replicate\n",
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"\n",
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"# REPLICATE_API_TOKEN = getpass()\n",
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"# os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN\n",
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"replicate_id = \"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\"\n",
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"llama2_chat_replicate = Replicate(\n",
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" model=replicate_id, input={\"temperature\": 0.01, \"max_length\": 500, \"top_p\": 1}\n",
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")"
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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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"id": "ce96f7ea-b3d5-44e1-9fa5-a79e04a9e1fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Simply set the LLM we want to use\n",
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"llm = llama2_chat"
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]
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},
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{
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"cell_type": "markdown",
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"id": "80222165-f353-4e35-a123-5f70fd70c6c8",
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"metadata": {},
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"source": [
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"## DB\n",
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"\n",
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"Connect to a SQLite DB.\n",
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"\n",
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"To create this particular DB, you can use the code and follow the steps shown [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb)."
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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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"id": "025bdd82-3bb1-4948-bc7c-c3ccd94fd05c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.utilities import SQLDatabase\n",
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"\n",
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"db = SQLDatabase.from_uri(\"sqlite:///nba_roster.db\", sample_rows_in_table_info=0)\n",
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"\n",
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"\n",
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"def get_schema(_):\n",
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" return db.get_table_info()\n",
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"\n",
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"\n",
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"def run_query(query):\n",
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" return db.run(query)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "654b3577-baa2-4e12-a393-f40e5db49ac7",
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"metadata": {},
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"source": [
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"## Query a SQL Database \n",
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"\n",
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"Follow the runnables workflow [here](https://python.langchain.com/docs/expression_language/cookbook/sql_db)."
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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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"id": "5a4933ea-d9c0-4b0a-8177-ba4490c6532b",
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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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"' SELECT \"Team\" FROM nba_roster WHERE \"NAME\" = \\'Klay Thompson\\';'"
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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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"# Prompt\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"# Update the template based on the type of SQL Database like MySQL, Microsoft SQL Server and so on\n",
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"template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
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"{schema}\n",
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"\n",
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"Question: {question}\n",
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"SQL Query:\"\"\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", \"Given an input question, convert it to a SQL query. No pre-amble.\"),\n",
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" (\"human\", template),\n",
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" ]\n",
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")\n",
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"\n",
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"# Chain to query\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.runnables import RunnablePassthrough\n",
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"\n",
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"sql_response = (\n",
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" RunnablePassthrough.assign(schema=get_schema)\n",
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" | prompt\n",
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" | llm.bind(stop=[\"\\nSQLResult:\"])\n",
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" | StrOutputParser()\n",
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")\n",
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"\n",
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"sql_response.invoke({\"question\": \"What team is Klay Thompson on?\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a0e9e2c8-9b88-4853-ac86-001bc6cc6695",
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"metadata": {},
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"source": [
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"We can review the results:\n",
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"\n",
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"* [LangSmith trace](https://smith.langchain.com/public/afa56a06-b4e2-469a-a60f-c1746e75e42b/r) LLaMA2-13 Replicate API\n",
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"* [LangSmith trace](https://smith.langchain.com/public/2d4ecc72-6b8f-4523-8f0b-ea95c6b54a1d/r) LLaMA2-13 local \n"
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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": 15,
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"id": "2a2825e3-c1b6-4f7d-b9c9-d9835de323bb",
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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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"AIMessage(content=' Based on the table schema and SQL query, there are 30 unique teams in the NBA.')"
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]
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},
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"execution_count": 15,
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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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"# Chain to answer\n",
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"template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
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"{schema}\n",
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"\n",
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"Question: {question}\n",
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"SQL Query: {query}\n",
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"SQL Response: {response}\"\"\"\n",
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"prompt_response = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.\",\n",
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" ),\n",
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" (\"human\", template),\n",
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" ]\n",
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")\n",
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"\n",
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"full_chain = (\n",
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" RunnablePassthrough.assign(query=sql_response)\n",
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" | RunnablePassthrough.assign(\n",
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" schema=get_schema,\n",
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" response=lambda x: db.run(x[\"query\"]),\n",
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" )\n",
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" | prompt_response\n",
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" | llm\n",
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")\n",
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"\n",
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"full_chain.invoke({\"question\": \"How many unique teams are there?\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec17b3ee-6618-4681-b6df-089bbb5ffcd7",
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"metadata": {},
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"source": [
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"We can review the results:\n",
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"\n",
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"* [LangSmith trace](https://smith.langchain.com/public/10420721-746a-4806-8ecf-d6dc6399d739/r) LLaMA2-13 Replicate API\n",
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"* [LangSmith trace](https://smith.langchain.com/public/5265ebab-0a22-4f37-936b-3300f2dfa1c1/r) LLaMA2-13 local "
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]
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},
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{
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"cell_type": "markdown",
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"id": "1e85381b-1edc-4bb3-a7bd-2ab23f81e54d",
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"metadata": {},
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"source": [
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"## Chat with a SQL DB \n",
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"\n",
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"Next, we can add memory."
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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": 7,
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"id": "022868f2-128e-42f5-8d90-d3bb2f11d994",
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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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"' SELECT \"Team\" FROM nba_roster WHERE \"NAME\" = \\'Klay Thompson\\';'"
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]
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},
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"execution_count": 7,
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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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"# Prompt\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"\n",
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"template = \"\"\"Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:\n",
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"{schema}\n",
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"\"\"\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", template),\n",
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" MessagesPlaceholder(variable_name=\"history\"),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"memory = ConversationBufferMemory(return_messages=True)\n",
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"\n",
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"# Chain to query with memory\n",
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"from langchain_core.runnables import RunnableLambda\n",
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"\n",
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"sql_chain = (\n",
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" RunnablePassthrough.assign(\n",
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" schema=get_schema,\n",
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" history=RunnableLambda(lambda x: memory.load_memory_variables(x)[\"history\"]),\n",
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" )\n",
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" | prompt\n",
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" | llm.bind(stop=[\"\\nSQLResult:\"])\n",
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" | StrOutputParser()\n",
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")\n",
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"\n",
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"\n",
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"def save(input_output):\n",
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" output = {\"output\": input_output.pop(\"output\")}\n",
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" memory.save_context(input_output, output)\n",
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" return output[\"output\"]\n",
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"\n",
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"\n",
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"sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save\n",
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"sql_response_memory.invoke({\"question\": \"What team is Klay Thompson on?\"})"
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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": 21,
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"id": "800a7a3b-f411-478b-af51-2310cd6e0425",
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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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"AIMessage(content=' Sure! Here\\'s the natural language response based on the given input:\\n\\n\"Klay Thompson\\'s salary is $43,219,440.\"')"
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]
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},
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"execution_count": 21,
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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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"# Chain to answer\n",
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"template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
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"{schema}\n",
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"\n",
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"Question: {question}\n",
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"SQL Query: {query}\n",
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"SQL Response: {response}\"\"\"\n",
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"prompt_response = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.\",\n",
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" ),\n",
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" (\"human\", template),\n",
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" ]\n",
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")\n",
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"\n",
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"full_chain = (\n",
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" RunnablePassthrough.assign(query=sql_response_memory)\n",
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" | RunnablePassthrough.assign(\n",
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" schema=get_schema,\n",
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" response=lambda x: db.run(x[\"query\"]),\n",
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" )\n",
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" | prompt_response\n",
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" | llm\n",
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")\n",
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"\n",
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"full_chain.invoke({\"question\": \"What is his salary?\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b77fee61-f4da-4bb1-8285-14101e505518",
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"metadata": {},
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"source": [
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"Here is the [trace](https://smith.langchain.com/public/54794d18-2337-4ce2-8b9f-3d8a2df89e51/r)."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|