🔒 100% Private RAG Stack with EmbeddingGemma, SQLite-vec & Ollama - Zero Cost, Offline Capable
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Updated
Sep 10, 2025 - Jupyter Notebook
🔒 100% Private RAG Stack with EmbeddingGemma, SQLite-vec & Ollama - Zero Cost, Offline Capable
This VL-JEPA implimentation takes direct insperation from the original VL-JEPA paper
A high-performance, production-ready Rust microservice for Google's EmbeddingGemma-300M, serving embeddings via HTTP and gRPC with ONNX Runtime.
Semantic caching service using Redis vector search and EmbeddingGemma (via Ollama) for multilingual LLM query caching. Supports Matryoshka dimensions (768/512/256/128) for flexible quality vs storage trade-offs.
An open-source Agentic RAG solution for seamless local Vector store retrieval and real-time web search. Automatically decides whether to query your internal Vector store or scout the Live Web for the most relevant information.
A Retrieval-Augmented Generation service that crawls websites, indexes content into a vector database, and answers questions with explicit source citations. Designed for correctness, safety, and observability within practical engineering constraints.
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