#embeddings

Local-first markdown context engine for AI coding agents. Indexes a repo's docs into SQLite (FTS5 + local vectors) and exposes CLI, HTTP, and MCP over one service core. Ships a docs registry, skills/agents sync, and code-to-doc governance.

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Embedded PostgreSQL with pgvector extension for Node.js applications

Go toolkit for document ingestion and retrieval.

Convert a document collection to verified Markdown, package it into a single encrypted archive with its search index and provenance, and serve it to agents over the Model Context Protocol. Python 3.11+, Apache-2.0.

Vector memory that gets smarter every time your agent uses it.

Get hundreds of thousands of financial documents into your AI app 🚀

Experimental Rust vector-compression sidecars implementing TurboQuant (ICLR 2026), PolarQuant and QJL research primitives, with exact reranking and workload-scoped benchmark receipts.

Persistent memory layer for Claude Code. Zero API key. Zero config. The only tool that tells compact what to preserve.

Structured local memory storage and retrieval for LLM agents

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A local knowledge vault for your AI agents — saves tokens, remembers across sessions, and turns every model into a disciplined engineer.

Local hybrid (vector + BM25) code search for AI coding agents — AST chunking, MiniLM embeddings on ONNX, Go call graph, MCP server. No external services.

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Embedding search for Go: the whole index scan is one matrix-vector product. 18-38x a hand-written loop. No cgo.

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A Golang Mistral API client