io.github.ggozad/haiku-rag

MCPcommunity
v0.57.0io.github.ggozadUnknownUpdated 22d agoGitHub

Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling

Agentic RAG built on LanceDB, Pydantic AI, and Docling. New: vision and multimodal search. Picture-aware ingestion captures embedded figure bytes; vision-capable QA models receive them alongside text. Multimodal embedders put picture vectors in the same space as text, enabling text-as-query → figure hits and image-as-query retrieval. Hybrid search — Vector + full-text with Reciprocal Rank Fusion…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
22d agoLast update
Package
Authorio.github.ggozad
LicenseUnknown
Version0.57.0
Sourcemcp-registry
Trust Status
B
60/100Good
Listed in Forge index+10/10
Publisher identity verified+0/25
Publisher: run `forge publish` from the package repo to claim ownership
Ed25519 publish signature+0/10
Included automatically when the publisher runs `forge publish`
Domain verification+0/5
Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
CVE scan · clean+30/30
Static analysis · clean+20/20
npm provenance (Sigstore)+0/5
Publish from GitHub Actions with the --provenance flag
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain
Provenance
DependenciesNot audited
Tool surface
Security scan✓ Cleanv0.57.0 · 19d ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

Agentic RAG built on LanceDB, Pydantic AI, and Docling. New: vision and multimodal search. Picture-aware ingestion captures embedded figure bytes; vision-capable QA models receive them alongside text. Multimodal embedders put picture vectors in the same space as text, enabling text-as-query → figure hits and image-as-query retrieval. Hybrid search — Vector + full-text with Reciprocal Rank Fusion Multimodal & cross-modal search — Multimodal embedders (vLLM) put picture vectors in the same space…

Keywords
mcp