Building a Production RAG Pipeline That Actually Works: Lessons from DocExtract

This article discusses the architecture and design choices behind DocExtract, a production-ready document retrieval and question-answering system. It highlights the benefits of a multi-service approach and the limitations of pure vector search, leading to the adoption of a hybrid retrieval model using Reciprocal Rank Fusion (RRF).

Details

The article describes the architecture of DocExtract, a document retrieval and question-answering system, which is split into three services: an API, a worker, and a frontend. This decoupled approach allows the slow document processing to be handled asynchronously, improving the responsiveness of the API. The article also discusses the limitations of pure vector search, which struggles to capture exact matches like product codes, invoice numbers, and legal citations. To address this, a hybrid retrieval model is used, combining vector search and BM25 ranking. Reciprocal Rank Fusion (RRF) is employed to combine the rankings from the two retrievers, providing the best of both worlds. Finally, the article introduces a ReAct (Reasoning + Acting) agent that dynamically selects the appropriate retrieval method per query, further improving the system's accuracy.