telegram-search
🔍 导出并模糊搜索 Telegram 聊天记录 | Export and fuzzy search your Telegram chat history
claude mcp add --transport stdio groupultra-telegram-search docker run -i ghcr.io/groupultra/telegram-search:latest \ --env HOST="0.0.0.0 (optional; default per image)" \ --env PORT="3333 (optional; default per image)" \ --env MINIO_URL="http://minio:9000 (optional)" \ --env PROXY_URL="http://proxy:port (optional)" \ --env BACKEND_URL="http://127.0.0.1:3333 (optional)" \ --env DATABASE_URL="postgresql://postgres:password@host:5432/database" \ --env MINIO_BUCKET="telegram-media (optional)" \ --env DATABASE_TYPE="postgres|pglite (optional; defaults will be used)" \ --env TELEGRAM_API_ID="Your Telegram API ID" \ --env MINIO_ACCESS_KEY="minioadmin (optional)" \ --env MINIO_SECRET_KEY="minioadmin (optional)" \ --env TELEGRAM_API_HASH="Your Telegram API Hash" \ --env TELEGRAM_BOT_TOKEN="Your Telegram Bot Token"
How to use
Telegram Search provides cutting-edge local search capabilities for your Telegram messages, including vector embeddings, multi-language tokenization, and RAG-enabled Q&A. Run the server with the provided Docker image to expose an HTTP/WebSocket API for indexing, searching, and exporting chat data. Use the integrated bot features to search and export messages via a Telegram Bot, or access the HTTP/WebSocket endpoints for programmatic search and retrieval. The product supports media embedding, image search, and smart summaries of unread messages, making it easy to back up and locate information across languages. Authenticate and configure through the app’s API settings, including embedding and LLM options per account.
How to install
Prerequisites:\n- Docker (and optionally Docker Compose) installed on your host\n- Basic familiarity with environment variable configuration for your deployment.\n\nStep-by-step:\n1) Install Docker if not present on your system.\n2) Create a dedicated directory for the Telegram Search deployment.\n3) Run the Docker-based deployment using the command below (adjust env vars as needed):\n\nbash\ndocker run -d --name telegram-search \ -p 3333:3333 \ -e TELEGRAM_API_ID=YOUR_API_ID \ -e TELEGRAM_API_HASH=YOUR_API_HASH \ -e DATABASE_TYPE=postgres \ -e DATABASE_URL=postgresql://postgres:password@localhost:5432/postgres \ -e TELEGRAM_BOT_TOKEN=YOUR_BOT_TOKEN \ ghcr.io/groupultra/telegram-search:latest\n\n\n4) If using Docker Compose, prepare a docker-compose.yml as provided by the project and start services with:\n\n```bash\ndocker compose -f docker/docker-compose.yml up -d\n````\n4) Open the UI at http://localhost:3333 (or the port you configured) to begin configuring API access, embedding, and search options.\n\nOptional: If you need MinIO or other services, follow the repository’s Docker Compose setup to run all components together.\n
Additional notes
Tips and common issues:\n- Ensure your Telegram API credentials and Bot Token are valid; invalid credentials will prevent indexing.\n- If you change environment variables, restart the Docker container to apply changes.\n- The image may require network access to Telegram services; ensure outbound access is allowed.\n- For production, consider mounting persistent volumes for data and media storage instead of relying on ephemeral containers.\n- If you are using a reverse proxy (nginx/traefik), set BACKEND_URL and ports accordingly to expose /api and /ws endpoints.\n- Check the container logs (docker logs telegram-search) for troubleshooting startup issues.\n
Related MCP Servers
toolsdk -registry
MCPSDK.dev(ToolSDK.ai)'s Awesome MCP Servers and Packages Registry and Database with Structured JSON configurations. Supports OAuth2.1, DCR...
mcp-typescript-sdk
A TypeScript SDK for implementing Model Context Protocol (MCP) over MQTT, supporting both browser and Node.js environments.
mcp -js
MCP server that exposes YepCode processes as callable tools for AI platforms. Securely connect AI assistants to your YepCode workflows, APIs, and automations.
docmole
Dig through any documentation with AI - MCP server for Claude, Cursor, and other AI assistants
alris
Alris is an AI automation tool that transforms natural language commands into task execution.
akyn-sdk
Turn any data source into an MCP server in 5 minutes. Build AI-agents-ready knowledge bases.