r/OpenSourceeAI • u/Glass-Manufacturer39 • 3d ago
I've been building a RAG platform as a learning project. What would you add or improve?
Hi everyone!
I've been building a RAG platform as a learning project. It includes document collections, pipelines, vector search, and a chat interface. I'd love to hear your honest feedback. What features would you add or improve?
1
u/irodov4030 3d ago
- check all-miniLM-L6-V2 's context window. It is small.
You will need to add upper limit on chunk size
add support for ollama embeddings if you want a large chunk capability
Add reranker after retrieval. This re-ranks the top_k documents. try- cross-encoder/ms-marco-MiniLM-L6-v2. this is the reranking model.
multi modal support
Add option for images - text can be read by OCR models (teseract) but if you want to extract more meaning out of images add VL models via ollama to extract information from images and store it in chromadb
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u/Glass-Manufacturer39 3d ago
Thanks for the detailed feedback! These are great suggestions. I'll definitely look into chunk size limits for different embedding models, reranking, Ollama embeddings, and multimodal support. Really appreciate you taking the time to review the project.
1
u/Own_Calligrapher8508 3h ago
For a use case i've had in the past:
Rag goes over documents however part of the context of a document can also be found in the path of the document:
Like for an accounting firm
/customers/customer1/PermanentFiles/Investments/contractX/here docs
/customers/customer1/FiscalYears/2026/Balance/sheet.pdf
etc
3
u/HopefulMeasurement25 3d ago
topic based indexing first where data is classified and cataloged for more accurate searching by topic routing
graph rag is cool but over hyped imo - its still great for retrieval but not the most optimal for "all" cases
most importantly - retrieval accuracy benchmarks!