mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
from pathlib import Path
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from langchain_community.llms import Replicate
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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
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnablePassthrough
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# make sure to set REPLICATE_API_TOKEN in your environment
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# use llama-2-13b model in replicate
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replicate_id = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d" # noqa: E501
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llm = Replicate(
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model=replicate_id,
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model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
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)
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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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template_query = """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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("human", template_query),
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]
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)
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sql_response = (
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RunnablePassthrough.assign(schema=get_schema)
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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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template_response = """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_response),
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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).with_types(input_type=InputType)
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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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