Agentic RAG for local and self-hosted document search: hybrid retrieval, reranking and multimodal RAG on embedded LanceDB, with Docling parsing and an MCP server
-
Updated
Aug 24, 2026 - Python
8000
Agentic RAG for local and self-hosted document search: hybrid retrieval, reranking and multimodal RAG on embedded LanceDB, with Docling parsing and an MCP server
Provenance-first extractive RAG: return verbatim source spans with citations using local ModernBERT or optional LLM-assisted extraction.
Reliable research infrastructure for AI agents. Evidence-backed web search with citations, confidence scores, and Clarity anti-hallucination. MCP server, REST API, Python SDK.
Deterministic policy language for AI agents. Z3 + TLA+ dual-engine formal verification. Runtime enforcement <1ms.
re!think it. System prompt teaching LLMs to execute two core tasks: complex answers without hallucinations, and creative ideas without clichés. Written in math-like logic, which LLMs parse better than plain language. Built for mid-to-high complexity tasks, featuring a Bypass branch to execute simple prompts directly without added cognitive overhead
Zero-hallucination reference verification for Claude Code — live CrossRef/S2/PubMed verification, verbatim abstract traceability, retraction check
Comprehensive guide to building production AI agent systems - Scale by Subtraction methodology
Stop hallucinations by verifying citations.
W.A.Y? (Who Are You? : I'm not a developer) — a personal AI harness that learns you, not a framework you learn
AI agent skill that creates formal, verifiable proofs of claims — every fact computed or cited, never asserted
AI advisory board as a Claude Code skill + MCP server: verifies every quote word-for-word against public-domain sources, or abstains. Fail-closed fidelity.
Deterministic factual substrate for multi-agent AI. Shared evidence-backed facts and reproducible grounding through dataset_hash.
TrustScoreEval: Trust Scores for AI/LLM Responses — Detect hallucinations, flags misinformation & Validate outputs. Build trustworthy AI.
Build a Production RAG System with Python, LangChain, and ChromaDB Build ShopBot from scratch — a production RAG assistant that answers from retrieved evidence, not training memory. Ten chapters, one complete system.
Framework structures causes for AI hallucinations and provides countermeasures
A robust RAG backend featuring semantic chunking, embedding caching, and a similarity-gated retrieval pipeline. Uses GPT-4 and FAISS to provide verifiable, source-backed answers from PDFs, DOCX, and Markdown.
Verification skills for academic research — grounds every claim in fetched evidence, not memory. Citation/figure/contradiction checking, arXiv monitoring, NRF grant writing.
Developer toolkit for EthersFlow — a multi-model trust layer that verifies AI outputs through adversarial consensus. MCP server, SDKs, and API docs.
Track the source of every factual claim in AI agent output. Inline citations and source diversity scoring. Works with OpenClaw, Claude Code, Cursor, and any agent platform.
CLI tool for AI agents that moves blocks text addressed by reference to save context and ensure faithful byte-level edits within a file or between multiple files.
Add a description, image, and links to the hallucination-prevention topic page so that developers can more easily learn about it.
To associate your repository with the hallucination-prevention topic, visit your repo's landing page and select "manage topics."