LLM, RAG & Conversational AI
Agentic & Strictly-Grounded RAG
HawkEyes · AI-Agent_MongoDB · HE-Sherlock · 2026
A self-correcting Planner→Executor→Critic agent over MongoDB, plus strict-grounding RAG over Bangla PDFs.
LangGraphMongoDB Atlas Vector SearchFAISSsentence-transformersSSE
- P→E→C
- self-correcting agent loop
- SSE
- streaming + full observability
- bn+en
- low-resource grounding
Problem
Answer enterprise data questions grounded in a real database — reliably, without hallucination — and do the same over Bangla PDF documents where the model must refuse when the answer isn't in context.
Approach
- 01Built a Planner→Executor→Critic→Memory agent loop that generates MongoDB queries / vector searches, evaluates groundedness, and retries within bounds.
- 02Added SSE streaming, pluggable embeddings, and full observability (structured logs + feedback capture).
- 03Companion HE-Sherlock pipeline: PyMuPDF + Tesseract (ben+eng) OCR → FAISS index → Gemini answering under a strict 'answer only from context' prompt that refuses (in Bangla) when the answer is absent.
- 04Simpler sibling (mongoRAG) provides a clean, readable E5-embedding + vector-retrieval baseline.
Results & Impact
- A complete agentic-RAG reference: query generation, groundedness verification, bounded retries, and observability.
- HE-Sherlock delivers production RAG on a low-resource language end to end, with explicit refusal behavior (22 commits).
Related repositories
AI-Agent_MongoDB
Multi-agent, self-correcting RAG over MongoDB (Planner→Executor→Critic).
HE-Sherlock
Bangla document RAG Q&A with strict grounding (FAISS + Gemini).
mongoRAG
Clean MongoDB retrieval-augmented-generation pipeline (E5 embeddings).
Langgraph
LangGraph learning notebooks & agent-workflow experiments.