Day 18 - Going beyond Basic RAG : Part 3

Feedback Loop for Enhanced Retrieval and Adaptive Retrieval

Hey everyone, welcome back to my 75 Days of Generative AI series. Check here and here for earlier articles about Advanced RAG Techniques. Today, we will explore an advanced RAG technique that takes the traditional Retrieval-Augmented Generation approach to the next level. We'll also explore how to implement a feedback loop that continuously improves the quality and relevance of responses over time and Adaptive Retrieval

The Limitations of Traditional RAG Systems

Traditional RAG systems can sometimes produce inconsistent or irrelevant responses due to limitations in the retrieval process or the underlying knowledge base. This is where a feedback loop comes in - by incorporating user feedback and dynamically adjusting the retrieval process, we can significantly enhance the quality and relevance of responses.

Technique 5: Key Components of the Feedback Loop

So, what are the key components of this advanced RAG system? Let's break it down:

1. PDF Content Extraction: We extract text from PDF documents to create a knowledge base.

2. Vectorstore: We store and index document embeddings for efficient retrieval.

3. Retriever: We fetch relevant documents based on user queries.

4. Language Model: We generate responses using retrieved documents.

5. Feedback Collection: We gather user feedback on response quality and relevance.

6. Feedback Storage: We persist user feedback for future use.

7. Relevance Score Adjustment: We modify document relevance based on feedback.

8. Index Fine-tuning: We periodically update the vectorstore using accumulated feedback.

Method Details

Now, let's dive into the method details:

1. Initial Setup: We read PDF content, create a vectorstore, initialize a retriever, and set up a language model.

2. Query Processing: When a user submits a query, we fetch relevant documents and generate a response.

3. Feedback Collection: We collect user feedback on the response's relevance and quality.

4. Relevance Score Adjustment: We load previous feedback and adjust document relevance scores based on user input.

5. Retriever Update: We update the retriever with adjusted document scores.

6. Periodic Index Fine-tuning: We use high-quality feedback to fine-tune the index at regular intervals.

Benefits of this Approach

1. Continuous Improvement: The system learns from each interaction, gradually enhancing its performance.

2. Personalization: By incorporating user feedback, the system can adapt to individual or group preferences over time.

3. Increased Relevance: The feedback loop helps prioritize more relevant documents in future retrievals.

4. Quality Control: Low-quality or irrelevant responses are less likely to be repeated as the system evolves.

5. Adaptability: The system can adjust to changes in user needs or document contents over time.

Technique 6: Adaptive RAG System

Our advanced RAG system is designed to adapt its retrieval strategy based on the type of query. By leveraging Language Models (LLMs) at various stages, we can provide more accurate and relevant information to users. The system consists of four key components:

1. Query Classifier: Determines the type of query (Factual, Analytical, Opinion, or Contextual).

2. Adaptive Retrieval Strategies: Four distinct strategies tailored to different query types:

• Factual Strategy

• Analytical Strategy

• Opinion Strategy

• Contextual Strategy

3. LLM Integration: LLMs are used throughout the process to enhance retrieval and ranking.

4. LLM Model: Generates the final response using the retrieved documents as context.

How it Works

The system begins by classifying the user's query into one of four categories. Each query type triggers a specific retrieval strategy, which is designed to provide the most relevant and accurate information.

* Factual Strategy: Enhances the original query using an LLM for better precision, retrieves documents based on the enhanced query, and uses an LLM to rank documents by relevance.

* Analytical Strategy: Generates multiple sub-queries using an LLM to cover different aspects of the main query, retrieves documents for each sub-query, and ensures diversity in the final document selection using an LLM.

* Opinion Strategy: Identifies different viewpoints on the topic using an LLM, retrieves documents representing each viewpoint, and uses an LLM to select a diverse range of opinions from the retrieved documents.

* Contextual Strategy: Incorporates user-specific context into the query using an LLM, performs retrieval based on the contextualized query, and ranks documents considering both relevance and user context.

The Benefits of Adaptive RAG

Advanced RAG system offers several benefits, including:

* Improved Accuracy: By tailoring the retrieval strategy to the query type, the system can provide more accurate and relevant information.

* Flexibility: The system adapts to different types of queries, handling a wide range of user needs.

* Context-Awareness: Especially for contextual queries, the system can incorporate user-specific information for more personalized responses.

* Diverse Perspectives: For opinion-based queries, the system actively seeks out and presents multiple viewpoints.

* Comprehensive Analysis: The analytical strategy ensures a thorough exploration of complex topics.

Conclusion

That's a wrap on Advanced RAG Techniques. Thanks for joining me on this journey. If you want to stay updated on the latest Generative AI trends and techniques, follow me on LinkedIn: https://www.linkedin.com/in/varunbhanot/. Our next part of the journey is now using these techniques to create a personal finance manager. Stay tuned as I share my journey!