The code snippet defines a function that creates a retrieval chain for text retrieval. It initializes a RetrievalChain object with a language model and a retriever, and then returns the RetrievalChain object. The retriever is created using a vectorstore and

Text Retrieval Chain with Vectorstore and RetrievalQA.md

Text Retrieval Chain with Vectorstore and RetrievalQA

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def text_retrieval_chain():
    # main retrieval chain class
    class RetrievalChain:
        def __init__(self, llm, retriever):
            self.chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)

        def run(self, prompt):
            response = self.chain(prompt)
            return response['result']

    # vectorstore
    index_name = "testing"
    vectorstore = Pinecone.from_existing_index(
        index_name=index_name, namespace="test", embedding=embeddings
    )
    retriever = whitepaper_vectorstore.as_retriever(search_kwargs={"k": 4})
    return RetrievalChain(llm=tool_llm, retriever=retriever)

Associated Context
Type	Code Snippet ( .py )
Associated Tags	text_retrieval_chain main retrieval chain class RetrievalQA object run method prompt parameter response variable Pinecone from_existing_index function whitepaper_vectorstore as_retriever decorator tool_llm keyword Framework: Pinecone framework. text retrieval chain retrieval chain class main retrieval chain vectorstore Pinecone existing index namespace embeddings retriever search kwargs
📝 Custom Description	Snippet saved via the Pieces Web Extension
💡 Smart Description	The code snippet defines a class called "text_retrieval_chain" that implements the RetrievalQA interface. It also includes a run method to retrieve data from an existing index and returns its result as it's retriever using whitepaper The code snippet defines a function that creates a retrieval chain for text retrieval. It initializes a RetrievalChain object with a language model and a retriever, and then returns the RetrievalChain object. The retriever is created using a vectorstore and
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