安装依赖
pip install --upgrade --quiet langchain-core langchain-community langchain-openai编写代码
from langchain_core.prompts import ChatPromptTemplate from langchain_community.utilities import SQLDatabase from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI template = """Based on the table schema below, write a SQL query that would answer the user's question: {schema} Question: {question} SQL Query:""" prompt = ChatPromptTemplate.from_template(template) db = SQLDatabase.from_uri("sqlite:///./Chinook.db") def get_schema(_): return db.get_table_info() def run_query(query): return db.run(query) model = ChatOpenAI( model="gpt-3.5-turbo", ) sql_response = ( RunnablePassthrough.assign(schema=get_schema) | prompt | model.bind(stop=[" SQLResult:"]) | StrOutputParser() ) template = """Based on the table schema below, question, sql query, and sql response, write a natural language response: {schema} Question: {question} SQL Query: {query} SQL Response: {response}""" prompt_response = ChatPromptTemplate.from_template(template) full_chain = ( RunnablePassthrough.assign(query=sql_response).assign( schema=get_schema, response=lambda x: db.run(x["query"]), ) | prompt_response | model ) message = full_chain.invoke({"question": "How many employees are there?"}) print(f"message: {message}")运行结果
? python3 test09.py message: content='There are a total of 8 employees in the database.' response_metadata={'finish_reason': 'stop', 'logprobs': None}
