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=["\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}