local-embeddings

Semantic search for Obsidian vaults using LanceDB and cloud or local embedding models

One brain. Every AI agent. Nothing forgotten. — Self-hosted memory layer via MCP + Postgres + pgvector

A Python project that deploys a Local RAG chatbot using Ollama API. Refines answers with Deep Research from external websites, and uses both Embedding and LLM models.

MCP RAG server — local embeddings, your docs never leave your machine. Private knowledge base + web search for Claude, Cursor, and Ollama. Drop your docs, connect your AI client, done.

Turn your voice into intelligent, linked notes inside Obsidian

Semantic Code Search, Code Graphs & Git Diff intelligence for VS Code, powered by NightOwl-CodeEmbedding — a ModernBERT bi-encoder built for code retrieval.

A Python project that deploys a Local RAG chatbot using Ollama API. Refines answers with internal RAG knowledge base, and uses both Embedding and LLM models.

A Python project that deploys a Local RAG chatbot using Ollama API and vLLM API. Refines answers with internal RAG knowledge base, using both Embedding and Rerank models to improve accuracy of context provided to LLM models.

Project-aware collection management based on Qdrant, including a Rust MCP, daemon and CLI: hybrid semantic, pattern and full-text (FTS5) search into single or cross-concerns collection. Dedicated collections for knowledge library, LLM behavioral rules, and an LLM scratchpad

MCP server that runs local LLMs (with full access to MCP tools included). Callable by Python to chain MCP tools with local intelligence.

A Fast API server that provides local text and multi-modal embedding using LlamaIndex Hugging Face Embedding

claude-router is a local prompt router that picks the right Claude model tier and prepends the right scaffold using local embeddings before you call the API. A deterministic routing layer for eval, research, content, and review prompts that helps teams stop overspending on Sonnet and Opus when Haiku plus structure is enough.

Turn Obsidian notes into local knowledge you can ask. Build an ontology-style knowledge graph with optional Jev analysis, use augmented RAG with your AI subscription or API key, and explore insights with visible sources.

Local inference, memory-, chat- subsystem and AI agents

Offline Express.js QA API using Ollama. Parse PDFs, embed locally, search and chat with your private docs — no cloud needed.

An experiment with VectorDB and Semantic Search, in Obsidian

A lightweight Retrieval-Augmented Generation (RAG) agent powered by Groq AI and local embeddings, built to process and understand text data efficiently. It retrieves relevant context from your own files and generates accurate, natural-language responses -all while keeping your data private and running locally.

Memory-as-a-Service for AI Agents & LLMs. Add persistent memory, pgvector-based semantic search, and automatic semantic deduplication with 3 simple REST API endpoints. Comes with an LRU embedding cache and a developer analytics dashboard.

assignment based project

A CPU-friendly, self-hosted ML engine that automatically discovers product catalog schemas, classifies fields, detects category hierarchies, and suggests attribute aliases using profile-based features and local embeddings.