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Next Steps (Advanced RAG)

What we have built so far is often referred to as Naive RAG. It is incredibly powerful, but in complex, real-world Big Data environments, data is messy. Documents are massive, vocabularies are highly technical, and a simple semantic search might not always retrieve the perfect context.

If you want to take your project to the next level, here are the core concepts of Advanced RAG you should explore next:

1. Advanced Chunking Strategies

In our example, we ingested tiny, perfectly sized documents. In reality, you will be parsing 100-page PDFs. You cannot embed an entire book into a single vector, nor should you split it randomly in the middle of a sentence.

  • Goal: Look into intelligent text splitting techniques (like Recursive Character Chunking or Semantic Chunking) to ensure that each piece of text retains its full context before being vectorized.

Semantic search is brilliant for understanding concepts, but it sometimes struggles with exact identifiers (e.g., searching for a specific product code like “TX-9902” or a specific person’s name).

  • Goal: Weaviate natively supports Hybrid Search, which runs a traditional keyword search (BM25) and a vector search simultaneously, merging the results. You can easily implement this by changing collection.query.near_text() to collection.query.hybrid().

3. Re-Ranking (Two-Stage Retrieval)

When you query a massive database, retrieving the top 100 results is fast, but the absolute best document might end up at position #15 instead of #1.

  • Goal: A Re-ranker is a specialized AI model that takes the initial broad results from Weaviate and carefully scores and re-orders them based on ultimate relevance to the user’s prompt, ensuring the LLM gets only the absolute best context.

4. Orchestration Frameworks

While writing pure Python code with the Weaviate client is the best way to learn the fundamentals, building complex AI agents often requires higher-level tools.

  • Goal: Explore frameworks like LangChain or LlamaIndex. They have built-in integrations for Weaviate and offer pre-built modules for parsing complex PDFs, managing conversational memory (chat history), and routing queries to different databases.

Final Thoughts

The field of AI and Big Data is evolving at a breakneck pace, but the underlying mechanics of Vector Databases and Retrieval-Augmented Generation are here to stay.

You now have the fundamental knowledge to not just use AI, but to actively build with it. Happy coding, and good luck with your Big Data projects!