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Querying a SQL DB | Документация для разработчиков

Querying a SQL DB

Обновлено 27 февраля 2024

We can replicate our SQLDatabaseChain with Runnables.

from langchain_core.prompts import ChatPromptTemplate

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)
from langchain_community.utilities import SQLDatabase

We'll need the Chinook sample DB for this example. There's many places to download it from, e.g. https://database.guide/2-sample-databases-sqlite/

db = SQLDatabase.from_uri("sqlite:///./Chinook.db")
def get_schema(_):
return db.get_table_info()
def run_query(query):
return db.run(query)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

model = ChatOpenAI()

sql_response = (
RunnablePassthrough.assign(schema=get_schema)
| prompt
| model.bind(stop=["\nSQLResult:"])
| StrOutputParser()
)
sql_response.invoke({"question": "How many employees are there?"})
    'SELECT COUNT(*) FROM Employee'
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
)
full_chain.invoke({"question": "How many employees are there?"})
    AIMessage(content='There are 8 employees.', additional_kwargs={}, example=False)
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