Qdrant MCP Server

# Qdrant MCP Server Configuration Example # Copy this file to .env and update with your values # Required: OpenAI API Key for generating embeddings OPENAI_API_KEY=sk-your-openai-api-key-here # Qdrant Configuration QDRANT_URL=http://localhost:6333 # QDRANT_API_KEY= # Optional, only needed for Qdrant Cloud QDRANT_COLLECTION_NAME=codebase # Embedding Configuration EMBEDDING_MODEL=text-embedding-3-small EMBEDDING_DIMENSIONS=1536 # Indexing Configuration MAX_FILE_SIZE=1048576 # 1MB in bytes BATCH_SIZE=10 # Number of files to process in parallel MAX_TOKENS_PER_CHUNK=500 CHUNK_OVERLAP=50 # File Patterns (glob patterns) INCLUDE_PATTERNS=**/*.{js,ts,jsx,tsx,py,java,go,rs,cpp,c,h,rb,php,swift,kt,scala,r,m,mm} EXCLUDE_PATTERNS=**/node_modules/**,**/.git/**,**/dist/**,**/build/**,**/__pycache__/**,**/venv/**,**/.env/** # Background Indexer Configuration WATCH_INTERVAL=5000 # milliseconds INDEX_ON_START=true INCREMENTAL_INDEXING=true # Logging Configuration LOG_LEVEL=info # debug, info, warn, error LOG_FILE=~/.qdrant-mcp/logs/indexer.log LOG_MAX_SIZE=10485760 # 10MB LOG_MAX_FILES=5 # Performance Tuning MAX_CONCURRENT_REQUESTS=5 REQUEST_TIMEOUT=30000 # milliseconds RETRY_ATTEMPTS=3 RETRY_DELAY=1000 # milliseconds # Cost Control DAILY_TOKEN_LIMIT=1000000 # Stop indexing after this many tokens COST_PER_1K_TOKENS=0.00002 # For text-embedding-3-small # MCP Server Configuration MCP_SERVER_PORT=3333 # For standalone mode MCP_SERVER_HOST=localhost # Cache Configuration ENABLE_CACHE=true CACHE_DIR=~/.qdrant-mcp/cache CACHE_TTL=86400 # 24 hours in seconds # Development/Debug Options DRY_RUN=false # Set to true to simulate indexing without making API calls VERBOSE=false # Set to true for detailed logging TEST_MODE=false # Set to true to use mock data instead of real API calls