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https://github.com/hwchase17/langchain
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LLaMA2 SQL Chat cookbook (#11685)
This commit is contained in:
Lance Martin
GitHub
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8bf16d5275
363
cookbook/LLaMA2_sql_chat.ipynb
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363
cookbook/LLaMA2_sql_chat.ipynb
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{
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"cells": [
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{
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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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"This Cookbook shows how to use LangChain's `SQLDatabaseChain` with LLaMA2 to chat about structured data stored in a SQL DB. \n",
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"\n",
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"* As the 2023-24 NBA season is around the corner, we use the NBA roster info saved in a SQLite DB to show you how to ask Llama2 questions about your favorite teams or players. \n",
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"\n",
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"* Because the SQLDatabaseChain API implementation is still in the langchain_experimental package, you'll see more issues that come with using the cutting edge experimental features, and how we succeed resolving some of the issues but fail on some others."
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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 langchain_experimental"
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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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"Use Replicate API for llama-2-13b-chat."
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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": 27,
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"id": "416ecce7-8aec-4145-b3f1-587a9b8a4fe9",
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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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"from getpass import getpass\n",
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"from langchain.llms import Replicate\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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"\n",
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"# Replicate API\n",
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"llama2_13b_chat = \"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\"\n",
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"\n",
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"# Set the system_prompt so that LLaMA will generate only the SQL statement, instead of being wordy and adding something like\n",
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"# \"Sure! Here's the SQL query for the given input question: \" before the SQL query; otherwise custom parsing will be needed.\n",
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"llm = Replicate(\n",
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" model=llama2_13b_chat,\n",
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" input={\"temperature\": 0.01, \n",
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" \"max_length\": 500, \n",
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" \"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": "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 SQL DB, which in this case is in this same directory."
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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": 28,
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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.utilities import SQLDatabase\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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"def get_schema(_):\n",
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" return db.get_table_info()\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 DB \n",
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"\n",
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"Follow the 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": 72,
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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 * FROM nba_roster WHERE NAME = 'Klay Thompson';\""
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]
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},
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"execution_count": 72,
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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.prompts import ChatPromptTemplate\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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" (\"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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"# Chain to query\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.schema.output_parser import StrOutputParser\n",
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"from langchain.schema.runnable 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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"The [LangSmith trace](https://smith.langchain.com/public/afa56a06-b4e2-469a-a60f-c1746e75e42b/r) gives us visibility into the chain! "
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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": 68,
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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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"\" Sure! Here's the natural language response based on the given SQL query and response:\\n\\nThere are 30 unique teams in the NBA roster.\""
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]
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},
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"execution_count": 68,
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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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" (\"system\", \"Given an input question and SQL response, convert it to a natural langugae answer. 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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"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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"Again, the [LangSmith trace](https://smith.langchain.com/public/10420721-746a-4806-8ecf-d6dc6399d739/r) gives us visibility into the chain! "
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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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"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": 74,
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"id": "1985aa1c-eb8f-4fb1-a54f-c8aa10744687",
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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 \\nFROM nba_roster \\nWHERE NAME = 'Klay Thompson'\""
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]
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},
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"execution_count": 74,
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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.prompts import ChatPromptTemplate\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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" (\"system\", \"Given an input question, convert it to a SQL query. No pre-amble.\"),\n",
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" MessagesPlaceholder(variable_name=\"history\"),\n",
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" (\"human\", template)\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.memory import ConversationBufferMemory\n",
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"from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain.schema.runnable import RunnableLambda, GetLocalVar, PutLocalVar\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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" )| prompt\n",
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" | model.bind(stop=[\"\\nSQLResult:\"])\n",
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" | StrOutputParser()\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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"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": 75,
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"id": "0b45818a-1498-441d-b82d-23c29428c2bb",
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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 SALARY \\nFROM nba_roster \\nWHERE NAME = 'Klay Thompson'\""
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]
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},
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"execution_count": 75,
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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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"sql_response_memory.invoke({\"question\": \"What is his salary?\"})"
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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": 76,
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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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"\" Sure thing! Here's the natural language response based on the given SQL query and response:\\n\\nKlay Thompson plays for the Golden State Warriors.\""
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]
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},
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"execution_count": 76,
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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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" (\"system\", \"Given an input question and SQL response, convert it to a natural langugae answer. 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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"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 team is Klay Thompson on?\"})"
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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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}
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