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 ChatOpenAItemplate = """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=["\nSQLResult:"])| 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}