How to use retriever in Langchain? – Langchain

The Problem:

I am trying to build a chatbot with user custom data. the data is stored into pinecone, and I want the bot to chat with the stored data. I am using Pinecone for storing and retrieving the data and langchain for the chatbot part. However, the issue is that the chatbot is not referring to the stored data in pinecone and is using regular chatGPT’s knowledge. I am receiving an error message Found document with no text key. Skipping. when trying to use the RetrievalQA class from langchain. What is the cause of the issue, and how can I resolve it to make the chatbot utilize the custom data stored in pinecone?

The Solutions:

Solution 1: Use Retriever with Langchain

To incorporate a retriever into your Langchain setup for chatbot use, follow these steps:

1. Preprocess the User Data:

   • Before storing the user data in Pinecone, preprocess it using the doc_preprocessing function to extract text and prepare it for embedding.
   • The embedding_db function can then be used to store the vectors, embedding data with user-specific metadata for individual users.

2. Create a Retrieval-Based QA Chain:

   • Construct a Langchain chain that includes a retriever and an LLM for question answering:

   rag_chain = (
       {"context": retriever | format_docs, "question": RunnablePassthrough()}
       | prompt
       | llm
       | StrOutputParser()
   )
   

   • The retriever is responsible for retrieving relevant documents from Pinecone based on the user query.
   • The formatted documents are then passed to the LLM for context-aware question answering.

3. Execute the Chain for User Queries:

   • To generate responses to user queries, invoke the retrieval_answer function, passing the user ID and the query as arguments.
   • The function performs a similarity search in Pinecone, filtering results by user ID, and returns the generated response.

4. Provide a Custom Prompt Template:

   • In the prompt step within the Langchain chain, use a custom prompt template that includes references to the retrieved documents as context.
   • The prompt should guide the LLM to answer the user’s query using the knowledge extracted from the relevant documents.

5. Explore Langchain Documentation:

   • Visit the Langchain documentation for additional information on constructing and executing Langchain chains:
     https://python.langchain.com/docs/use_cases/question_answering/quickstart

6. Customize According to Your Data and Use Case:

   • Adapt the preprocessing step, retriever implementation, and prompt template to suit your specific data and use case requirements.

Video Explanation:

The following video, titled "LangChain Multi-Query Retriever for RAG - YouTube", provides additional insights and in-depth exploration related to the topics discussed in this post.

... use OpenAI's text-embedding-ada-002, gpt-3.5-turbo, Pinecone vector database, and of course the LangChain library. Code: https://github ...