The OpenAI RAG system struggled with multiple documents, showing inconsistent performance with our evaluation framework. However, performance improved markedly when all documents were uploaded as a single document. Despite current limitations, such as a 20-file limit per assistant and challenges in handling multiple documents, there is significant potential for improvement. Enhancing the Assistants API to match GPT quality and reducing restrictions could make it a leading RAG solution.
https://www.tonic.ai/blog/rag-evaluation-series-validating-openai-assistants-rag-performance
The best tool that everyone is eager for would let users upload as many documents as they want, ask questions about them, and the AI would nail the answers, pulling out all the relevant bits from those documents. That’s my goal that I want to build (when I have time) in parallel with my current solution YourDocGPT.com which supports one document at a time but gives outstanding results based on current user feedback.
I have developed an algorithm that when combined with the ChatGPT API, yields very accurate results. The primary challenge with handling multiple documents is the API’s maximum token limit. So, what I’m thinking is, when a user requests information from, say, 100 documents, the system should focus on the most relevant documents (for example, the top 5) and extract the most pertinent information from those five. This approach will require extensive tweaking and testing. However, users who need information from all 100 documents in a single response may find this limitation unsatisfactory.