Advanced RAG Techniques

If I missed any details let me know :)

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Long-Context Reorder
The "Long-Context Reorder" documentation on LangChain describes a module that optimizes model performance by reordering document contexts. This tool is crucial for models handling lengthy or multiple documents, ensuring that important information is prioritized. The page details installation, setup, and usage with code examples, emphasizing improved retrieval effectiveness.

For further details, you can access the full documentation https://python.langchain.com/docs/mod...


Chunking
Optimize your data indexing with customizable chunk sizes and overlaps for better retrieval results. Default settings use a 1024 chunk size and 20 overlap, but adjusting these can refine or broaden your embeddings. Smaller chunks increase precision, capturing detailed nuances, while larger sizes provide a broader overview but may overlook specifics. Enhance your vector index by adjusting the `similarity_top_k` parameter to fetch more relevant data per query, ensuring your system remains efficient and effective.

Explore more on optimizing data indexing strategies https://docs.llamaindex.ai/en/stable/...

Self Querying/ Meta data filtering
The "Self-querying" module on LangChain allows for dynamic querying capabilities within a VectorStore. It enables the construction of structured queries using a language model, applying these queries to document metadata for precise retrieval. This self-query mechanism enhances semantic searches by incorporating user-specified filters directly into the query process, ensuring more relevant and targeted search results.

To learn more about the self-querying capabilities, check out the full documentation https://python.langchain.com/docs/mod...