Rust Local RAG

Rust Docs

Discover an advanced approach to managing Rust crate documentation through seamless integration with LlamaIndex's HTML reader. Benefit from smart file selection algorithms, eliminating duplicates, and enjoy flexible parsing options for in-depth analysis.

Minima (Local RAG)

Efficiently access and fetch contextual information from nearby documents for RAG applications.

RAG Docs

Enhances information retrieval through semantic search functionality and a vector database (Qdrant), facilitating streamlined access to extensive document repositories.

RAG Documentation

Experience advanced knowledge access with seamless integration of Qdrant vector search and documentation retrieval in Model Context Protocol (MCP). Unlock context-aware responses and enable semantic querying for a richer user experience.

Rust Docs

Explore Rust documentation seamlessly on docs.rs with seamless integration, allowing for convenient search, access to crates, retrieval of documentation, type details, feature flags, versions, and source code for your Rust initiatives.

Journal RAG

Easily search and retrieve personal notes and reflections from your markdown journal using advanced vector database technology, enhancing the way you recall past memories, ideas, and events.

Qdrant Docs Rag

Efficiently capture and retrieve real-time contextual information using vector-based search with Qdrant technology.

RAG Documentation Search

Enhances document search with semantic vectors for contextually relevant results from specified document repositories.

RagRabbit

Unlocks the power of RagRabbit integration for website crawling, vector embedding generation, and facilitating domain-specific information retrieval through advanced search and question answering capabilities.

RAG Memory

Enhance your information retrieval with a cutting-edge system integrating vector search and graph-based relationships, enriched by a knowledge graph. Access contextual information seamlessly from persistent memory with our advanced Model Context Protocol (MCP).

Rust Documentation

Discover a comprehensive resource hub for AI applications, offering easily accessible Rust documentation, coding templates, and troubleshooting answers. Accessible via a TypeScript server, this platform aggregates and organizes content from docs.rs, GitHub, and various community outlets.

RAGDocs (Vector Documentation Search)

Unlock the ability to search and retrieve semantic documentation effortlessly through vector databases. Get URL extraction, source oversight, and index queuing, paired with diverse embedding providers such as Ollama and OpenAI.

PDF Search

Enhance Zed's search capabilities on PDF files with a powerful combination of Qdrant vector database and OpenAI embeddings for advanced semantic indexing.

Rust Docs

Discover comprehensive Rust crate documentation through the Model Context Protocol (MCP), offering crucial insights for enhancing Rust programming projects.

Docs RAG

Utilizing a RAG framework developed with TypeScript, LlamaIndex, and Gemini embeddings, this protocol empowers artificial intelligence to search and assess native files and Git repositories effectively.