Welcome to our third lesson of this course about improving Retrieval-Augmented Generation (RAG) pipelines! In our previous sessions, we explored constrained generation to reduce hallucinations and iterative retrieval to refine how we search for relevant context. Now, we will focus on managing multiple, potentially repetitive chunks of text by detecting overlaps and summarizing them. This ensures that your final answer is both concise and comprehensive. Let's jump in!

Sometimes your system will retrieve numerous chunks that carry the same core insight, especially when your corpus has repeated sections. Directly showing all of that content might confuse the end user and clutter the final answer.

By integrating overlap detection and summarization, you can:

1. Reduce Redundancy: Merge repetitive chunks so readers don't have to sift through duplicated text.
2. Enhance Readability: Provide a cleaner, streamlined overview rather than repeating the same facts.
3. Improve LLM Performance: Concentrate the LLM's attention on crucial details, helping it generate more accurate output.

This strategy elevates your RAG pipeline: first, detect if multiple chunks are too similar; then decide whether to compile them into a single summary or simply present them as-is.

To illustrate how you might detect repeated content, here's a simple function that checks lexical (word-level) overlap among chunks. In a more robust system, you would rely on embeddings-based similarity, but this example captures the core concept:

What's happening here?

• We set a similarity_threshold to decide when two chunks have an especially large overlap in vocabulary.
• If that threshold is exceeded, the function returns True, signaling significant redundancy.

While this placeholder approach is simplistic, it's enough for demonstration. Embeddings-based techniques are more advanced, capturing semantic overlap rather than just word overlap.

When you detect overlapping chunks — or simply have many chunks — it often makes sense to condense them into a single summary. Doing so keeps the final context more focused:

How it works:

1. We combine chunks into a single string.
2. A prompt is formed, explicitly asking the LLM for a brief but thorough summary.
3. If the LLM produces something unusually short or “not possible”, the function simply returns the original text, ensuring nothing is lost.

After deciding whether to use a direct set of chunks or a merged summary, you need to craft the actual response for the user's query. Take a look:

Key points:

• If no context is available, we immediately let the user know.
• When context is present, we embed both the user query and the retrieved text into a prompt, so the LLM can produce a final, context-aware answer.

Below is an example flow that ties these functions together — from retrieving chunks to deciding if a summary is needed, and then generating the final answer. Each line includes minimal but essential commentary to guide you:

Step-by-step overview:

1. Load & Build: We load the corpus into chunked_docs and build a vector-based collection.
2. Query the Collection: We fetch the top five relevant documents for a given user query.
3. Overlap Logic: If these chunks are numerous (more than three) or appear heavily duplicated, we consolidate them into a summary. Otherwise, we present them as a list.
4. Final Generation: We create a user-facing answer by combining the query with our selected context (summarized or raw).

You've now learned how to detect overlapping chunks in retrieved text and generate a summarized version where it makes sense. This intermediate step can significantly improve readability and relevance for your end users, especially when working with large and repetitive corpora.

Keep experimenting, and have fun optimizing your RAG system!