Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
URL of your Qdrant instance (for local: 'http://127.0.0.1:6333', for cloud: 'https://your-cluster-url.qdrant.tech')
http://127.0.0.1:6333Model to use for embeddings (Ollama defaults to 'nomic-embed-text', OpenAI defaults to 'text-embedding-3-small')
Choice of embedding provider ('ollama' or 'openai')
ollamaOpenAI API key (required if using OpenAI)
API key for Qdrant Cloud (required when using cloud instance)
Security Notice
Your environment variables and credentials are securely stored and encrypted. LangDB never shares these configuration values with third parties.
Discover shared experiences
Shared threads will appear here, showcasing real-world applications and insights from the community. Check back soon for updates!
An MCP server implementation that provides tools for retrieving and processing documentation through...
Model Context Protocol (MCP) server implementation for semantic search and memory management using T...
Enables Claude and other LLMs to search through any published MkDocs documentation site using the Lu...
A Model Context Protocol server that provides image generation capabilities using Google's Gemini 2 ...
Creates travel agent-level flight plans using the fast-flights API, allowing users to search for fli...