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https://github.com/hwchase17/langchain
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ad502e8d50
Thank you for contributing to LangChain! **Description:** update to the Vectara / Langchain integration to integrate new Vectara capabilities: - Full RAG implemented as a Runnable with as_rag() - Vectara chat supported with as_chat() - Both support streaming response - Updated documentation and example notebook to reflect all the changes - Updated Vectara templates **Twitter handle:** ofermend **Add tests and docs**: no new tests or docs, but updated both existing tests and existing docs
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import os
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from langchain.retrievers.multi_query import MultiQueryRetriever
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from langchain_community.vectorstores import Vectara
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from langchain_community.vectorstores.vectara import SummaryConfig, VectaraQueryConfig
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from langchain_openai.chat_models import ChatOpenAI
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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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# Setup the Vectara retriever with your Corpus ID and API Key
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vectara = Vectara()
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# Define the query configuration:
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summary_config = SummaryConfig(is_enabled=True, max_results=5, response_lang="eng")
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config = VectaraQueryConfig(k=10, lambda_val=0.005, summary_config=summary_config)
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# Setup the Multi-query retriever
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llm = ChatOpenAI(temperature=0)
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retriever = MultiQueryRetriever.from_llm(
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retriever=vectara.as_retriever(config=config), llm=llm
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)
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# Setup RAG pipeline with multi-query.
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# We extract the summary from the RAG output, which is the last document in the list.
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# Note that if you want to extract the citation information, you can use res[:-1]]
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| (lambda res: res[-1])
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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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