[Question]: Extracting Implicit Information from Vector Databases Using Llama Index's RAG Setup

[BennisonDevadoss]

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Question

Understanding the Problem Statement

Problem Statement: When querying a vector database (e.g., Chroma) that stores document embeddings, retrieving specific information can be challenging if the query terms do not explicitly match the keywords in the document. For instance, querying a resume for a candidate's name may fail if the resume does not explicitly contain the terms 'candidate' or 'name'. The issue is how to extract relevant information from the vector database in such cases.

Example Scenario:

1. File Upload: A resume is uploaded and its content is stored as embeddings in a vector database like Chroma.
2. Query: A user queries the vector database with the question, "What is the candidate name?".
3. Challenge: The resume might not explicitly mention the keywords 'candidate' or 'name', making it difficult for the vector database to retrieve the desired information.

Possible Solution with Llama Index's RAG Setup: Can this issue be addressed using Llama Index's Retrieval-Augmented Generation (RAG) setups? Specifically, can RAG setups help in understanding and extracting relevant information even when explicit keywords are missing?

Discussion: Exploring the capabilities of Llama Index's RAG setups to handle such cases effectively.

• How does RAG improve the retrieval process?
• Can RAG infer context and relationships between different pieces of information?
• Examples of successful implementation of RAG in similar scenarios.

Feel free to provide your input, insights, and potential solutions for this problem.

[dosubot[bot]]

Hey @BennisonDevadoss! 👋 I'm here to help you with any bugs, questions, or contribution you might have. Let's tackle this together!

Yes, the issue of retrieving specific information from a vector database, such as a candidate's name from a resume, can be addressed using Llama Index's Retrieval-Augmented Generation (RAG) setups.

RAG improves the retrieval process by indexing your data into vector embeddings, which allows for efficient and accurate querying. When a user query is made, the system filters the data to find the most relevant context, which is then used by the LLM to generate a response. This process ensures that the retrieval is both contextually relevant and efficient.

Supporting evidence from the document:

• "In RAG, your data is loaded and prepared for queries or 'indexed'. User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response."
• "A retriever defines how to efficiently retrieve relevant context from an index when given a query. Your retrieval strategy is key to the relevancy of the data retrieved and the efficiency with which it's done" ^[1].

Additionally, LlamaIndex offers various techniques to handle different types of data, including unstructured documents like resumes. For instance, semantic search can be used to match the query terms and/or semantic intent, making it suitable for extracting specific details such as a candidate's name.

By using RAG and potentially SELF-RAG, you can effectively retrieve specific information like a candidate's name from a resume by leveraging the indexing and querying capabilities of Llama Index. This ensures that the retrieval process is both accurate and efficient, even when dealing with complex or unstructured data sources.

New insights from the RAG Bootcamp:

• The process involves three main steps: building external knowledge, retrieving against a query, and generating the final response.
• The LlamaIndex framework supports the ingestion of documents, chunking, encoding, and storing them into a vector store, which is crucial for efficient retrieval.
• The framework also provides a QueryEngine that handles retrieval and context augmentation to generate the final response, ensuring that the information retrieved is highly relevant and contextually accurate ^[2].

These steps and tools provided by LlamaIndex make it well-suited for tasks like extracting specific information from resumes.

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