LlamaIndex’s Post

Do you have any recommendations on RAG workflow for parsing pdfs with plots?

This is fantastic news! The improvements to markdown-based table reconstruction will surely enhance the capabilities of LlamaParse in handling complex documents. How do these changes impact the overall performance and speed of the parsing process?

Hi LlamaIndex, We discovered that some document text extraction solutions are not always accurate. We tested SimpleDirectoryReader and LamaParse. The problem arises when columns have multiple columns (common in financial reports) or if they have multiple rows (stacked into one). I suspect that additional OCR enhancement could help the model recognize the text more accurately, but probably at the cost of time. Time that stakeholders do not want to waste. 🙄

I am curious, I have also built similar to this but without LlamaParse. I want to know what makes it really good at reading complex tables. I have used Table Transformers in combination with Camelot and with that I achieve 87%+ accuracy on really complex documents. What’s the backbone model used in this ?

Retrieval-Augmented Generation (RAG) significantly enhances document analysis by enabling more accurate and comprehensive retrieval of information from large datasets. It combines the retrieval of relevant documents with generative models to synthesize coherent responses, improving the understanding of complex documents and data analysis.

Wonderful. Been using Llama Parse since its release...now my go-to tool for parsing complex PDFs, especially Annual Reports and 10-Ks. Unmatched results. Easy to use via API and amazing markdown outputs, especially for tables.

Please put this into pdf benchmark out there so we can see how it compares to other solutions quantitatively.

Retrieval-Augmented Generation (RAG) benefits AI applications by enhancing accuracy, efficiency, and scalability. It improves response precision and contextual relevance, making it crucial for customer service and content creation.

Retrieval-Augmented Generation (RAG) impacts AI learning efficiency by dynamically incorporating relevant external knowledge during the learning process. This approach enhances the model's understanding and reasoning capabilities, leading to more accurate and contextually relevant outputs.