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  3. Retrieval Augmented Generation (RAG) with LangChain
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Exercise

Building the retrieval chain

Now for the finale of the chapter! You'll create a retrieval chain using LangChain's Expression Language (LCEL). This will combine the vector store containing your embedded document chunks from the RAG paper you loaded earlier, a prompt template, and an LLM so you can begin talking to your documents.

Here's a reminder of the prompt_template you created in the previous exercise, and which is available for you to use:

Use the only the context provided to answer the following question. If you don't know the answer, reply that you are unsure.
Context: {context}
Question: {question}

The vector_store of embedded document chunks that you created previously has also been loaded for you, along with all of the libraries and classes required.

Instructions

100 XP
  • Convert the Chroma vector_store into a retriever object for use in the LCEL retrieval chain.
  • Create the LCEL retrieval chain to combine the retriever, the prompt_template, the llm, and a string output parser so it can answer input questions.
  • Invoke the chain on the question provided.