Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 11 months ago

MODERATORS

communick@academy.garden

[D] Multiple documents reveal significant limitations of OpenAI's Assistants API for RAG (alien.top)

submitted 10 months ago by ian4321@alien.top to c/machinelearning@academy.garden

6 comments fedilink hide all child comments

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

you are viewing a single comment's thread
view the rest of the comments

[–] Evaworld9@alien.top 1 points 9 months ago

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.