Instructions for Manipulating Semi-Structured Data via RAG Method

A new solution to improve semantic search and answer generation has been introduced, known as the LangChain RAG pipeline. This innovative system, detailed in the LLaMA2 research paper, addresses a common real-world problem: handling documents with mixed text and tables.

The LangChain RAG pipeline begins with intelligent data parsing, utilising the Unstructured library to analyse a document's layout and separate text and tables cleanly. The PDF is processed, and Unstructured's function is used to identify tables and chunk the document's text.

Following data parsing, the multi-vector retriever comes into play. This component creates concise summaries for large tables and long text blocks, and it allows for storing multiple representations of data to enhance semantic search and answer generation. The multi-vector retriever also links a summary in the vector store with its corresponding raw document in the docstore using unique IDs.

The summaries are processed concurrently using a batch method for speed, ensuring that the complex structure of documents becomes a strength, not a weakness, by providing the language model with complete context in an easy-to-understand manner, leading to better, more reliable answers.

The system successfully found the summary of Table 1, which discusses model parameters and training data, and provided the full, raw table to the language model to answer a question correctly.

To generate these summaries, a LangChain chain is employed. The overall workflow includes intelligent data parsing, multi-vector retriever, and passing full, raw data to the language model for answer generation.

The full code for this RAG pipeline can be accessed on the Colab notebook or the GitHub repository. Necessary Python packages, including LangChain, Unstructured, and Chroma, are installed for its implementation.

It's worth noting that traditional RAG pipelines struggle with mixed-content documents, such as tables being chopped in half. However, this new approach overcomes such challenges by preserving the original meaning and structure of data during preparation and retrieval.

In summary, the LangChain RAG pipeline offers a robust and accurate solution to semantic search and answer generation, particularly in documents with a mix of text and tables. This innovative system provides a significant step forward in the field of AI and information retrieval.