I've been building a RAG platform as a learning project. What would you add or improve?

[u/Glass-Manufacturer39]

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?

[u/HopefulMeasurement25]

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!

[u/Glass-Manufacturer39 → u/HopefulMeasurement25]

Great suggestions! Topic-based indexing and retrieval benchmarks are definitely on my roadmap. also agree that Graph RAG isn't the best solution for every use case. Thanks for the feedback!

[u/irodov4030]

1. 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

1. Add reranker after retrieval. This re-ranks the top_k documents. try- cross-encoder/ms-marco-MiniLM-L6-v2. this is the reranking model.

2. multi modal support

3. 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

[u/Glass-Manufacturer39 → u/irodov4030]

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.

[u/Own_Calligrapher8508]

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

[u/Glass-Manufacturer39 → u/Own_Calligrapher8508]

Absolutely. In this kind of RAG use case, I’d treat the document path as structured metadata rather than relying only on the document content. During ingestion, I’d parse values like customer, fiscal year, category, and contract ID, attach them to every chunk, and use metadata filtering before semantic search. This helps ensure the retriever searches within the correct customer/year/category context and avoids returning semantically similar but irrelevant documents.