mirror of
https://github.com/hwchase17/langchain
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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import os
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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from langchain.vectorstores import Vectara
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if os.environ.get("VECTARA_CUSTOMER_ID", None) is None:
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raise Exception("Missing `VECTARA_CUSTOMER_ID` environment variable.")
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if os.environ.get("VECTARA_CORPUS_ID", None) is None:
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raise Exception("Missing `VECTARA_CORPUS_ID` environment variable.")
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if os.environ.get("VECTARA_API_KEY", None) is None:
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raise Exception("Missing `VECTARA_API_KEY` environment variable.")
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# If you want to ingest data then use this code.
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# Note that no document chunking is needed, as this is
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# done efficiently in the Vectara backend.
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# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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# docs = loader.load()
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# vec_store = Vectara.from_document(docs)
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# retriever = vec_store.as_retriever()
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# Otherwise, if data is already loaded into Vectara then use this code:
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retriever = Vectara().as_retriever()
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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model = ChatOpenAI()
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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