Self-Reflective Question Answering for Biomedical Reasoning
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Updated
Oct 14, 2025 - Python
Self-Reflective Question Answering for Biomedical Reasoning
Training code for advanced RAG techniques - Adaptive-RAG, Corrective RAG, RQ-RAG, Self-RAG, Agentic RAG, and ReZero. Reproduces paper methodologies to fine-tune LLMs via SFT and GRPO for adaptive retrieval, corrective evaluation, query refinement, self-reflection, and agentic search behaviors.
Production-ready Retrieval-Augmented Generation (RAG) system with hybrid retrieval, Self-RAG agent workflows, cross-encoder reranking, and comprehensive benchmarking.
MBSPro is an AI-assisted billing copilot for Australian GPs. It turns clinical notes (typed; optional STT) into top-N MBS item suggestions with explicit rule/ethics checks, then generates a FHIR-ready claim and draft clinical docs (e.g., referral/care plan), with a lightweight compliance dashboard for errors/rejects and revenue snapshots.
Context-aware tool for automated BDD test generation and execution using RAG, VectorDB, and LLaMA.
Advanced RAG using langgraph which uses websearch functionality to produce relevant documents.
Production adapters and pipelines for PortfolioCore. Vector stores (pgvector, Qdrant), graph stores (Neo4j), embedders (OpenAI), Broadway pipelines, advanced RAG (Self-RAG, CRAG, GraphRAG, Agentic), multi-graph federation, and observability.
This project is built using Python and the Flask web framework, providing a user-friendly web interface for interacting with the RAG system. The core logic, including document processing, embedding generation, retrieval strategies (Self-RAG and Agentic RAG), and integration with the Gemini API, is organized within the utils directory.
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