Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system(gist.github.com)
gist.github.com
Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system
https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125601945ceeb
2 comments
did you test this in a production environment?
preparing to and found a pilot project with endboss pdf which im able to handle already.... but i focus on 100% quality in the db, so there is always a hitl below 100% confi...cant wait to proof im able to have a "hallucination free" rag..... main goal so far....next headache will be the update of data in the rag
While most tutorials stop at "LangChain + VectorDB", I found that making this legally defensible and operationally stable required about 40+ additional components.
We moved from a simple ingestion script to a "Multi-Lane Consensus Engine" (inspired by Six Sigma) because standard OCR/extraction was too hallucination-prone for our use case. We had to build extensive auditing, RBAC down to the document level, and a hybrid Graph+Vector retrieval to get acceptable accuracy
The current architecture includes:
Ingestion: 4 parallel extraction lanes (Vision, Layout, Text, Legal) with a Consensus Engine ("Solomon") that only indexes data confirmed by multiple sources
Retrieval: Hybrid Neo4j (Graph) + ChromaDB (Vector) with Reciprocal Rank Fusion
Performance: Semantic Caching (Redis) specifically for similar-meaning queries (40x speedup)
Security: Full RBAC, Audit Logging of every prompt/retrieval, and PII masking.
I documented the complete feature list and gap analysis
https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125...
My question to the community: Looking at this list – where is the line between "robust production engineering" and "over-engineering"?
For those working in Fintech/Medtech RAG: what critical failure modes am I still missing in this list?