mirror of
https://github.com/hwchase17/langchain
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103 lines
2.6 KiB
Python
103 lines
2.6 KiB
Python
from pathlib import Path
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from langchain.memory import ConversationBufferMemory
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from langchain_community.chat_models import ChatOllama
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from langchain_community.utilities import SQLDatabase
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableLambda, RunnablePassthrough
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# Add the LLM downloaded from Ollama
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ollama_llm = "zephyr"
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llm = ChatOllama(model=ollama_llm)
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db_path = Path(__file__).parent / "nba_roster.db"
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rel = db_path.relative_to(Path.cwd())
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db_string = f"sqlite:///{rel}"
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db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0)
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def get_schema(_):
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return db.get_table_info()
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def run_query(query):
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return db.run(query)
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# Prompt
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template = """Based on the table schema below, write a SQL query that would answer the user's question:
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{schema}
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Question: {question}
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SQL Query:""" # noqa: E501
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "Given an input question, convert it to a SQL query. No pre-amble."),
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MessagesPlaceholder(variable_name="history"),
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("human", template),
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]
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)
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memory = ConversationBufferMemory(return_messages=True)
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# Chain to query with memory
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sql_chain = (
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RunnablePassthrough.assign(
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schema=get_schema,
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history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]),
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)
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| prompt
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| llm.bind(stop=["\nSQLResult:"])
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| StrOutputParser()
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)
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def save(input_output):
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output = {"output": input_output.pop("output")}
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memory.save_context(input_output, output)
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return output["output"]
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sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save
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# Chain to answer
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template = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
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{schema}
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Question: {question}
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SQL Query: {query}
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SQL Response: {response}""" # noqa: E501
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prompt_response = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"Given an input question and SQL response, convert it to a natural "
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"language answer. No pre-amble.",
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),
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("human", template),
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]
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)
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# Supply the input types to the prompt
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class InputType(BaseModel):
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question: str
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chain = (
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RunnablePassthrough.assign(query=sql_response_memory).with_types(
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input_type=InputType
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)
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| RunnablePassthrough.assign(
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schema=get_schema,
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response=lambda x: db.run(x["query"]),
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)
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| prompt_response
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| llm
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)
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