# Using uvx (recommended - no installation needed)
uvx mcp-semantic-search

# Or install with pip
pip install mcp-semantic-search

export GEMINI_API_KEY="your_gemini_api_key"
export QDRANT_URL="http://localhost:6333"

# Embedding model (default: text-embedding-004)
export GEMINI_EMBEDDING_MODEL="text-embedding-004"

# Chunk configuration (defaults: 50/10/5)
export CHUNK_MAX_LINES=50        # Max lines per chunk
export CHUNK_OVERLAP_LINES=10    # Overlap between chunks
export CHUNK_MIN_LINES=5         # Min lines for valid chunk

GEMINI_API_KEY=your_gemini_api_key
QDRANT_URL=http://localhost:6333

# Using Docker
docker run -p 6333:6333 qdrant/qdrant

# Or using docker-compose
echo '
services:
  qdrant:
    image: qdrant/qdrant
    ports:
      - "6333:6333"
' | docker-compose -f - up

{
  "mcpServers": {
    "semantic-search": {
      "type": "stdio",
      "command": "uvx",
      "args": ["mcp-semantic-search"],
      "env": {
        "GEMINI_API_KEY": "your_gemini_api_key_here",
        "QDRANT_URL": "http://localhost:6333"
      }
    }
  }
}

{
  "mcpServers": {
    "semantic-search": {
      "type": "stdio",
      "command": "mcp-semantic-search",
      "env": {
        "GEMINI_API_KEY": "your_gemini_api_key_here",
        "QDRANT_URL": "http://localhost:6333"
      }
    }
  }
}

{
  "mcpServers": {
    "semantic-search": {
      "type": "stdio",
      "command": "uvx",
      "args": ["mcp-semantic-search"],
      "env": {
        "GEMINI_API_KEY": "your_gemini_api_key_here",
        "QDRANT_URL": "http://localhost:6333",
        "CHUNK_MAX_LINES": "50",
        "CHUNK_OVERLAP_LINES": "10",
        "CHUNK_MIN_LINES": "5"
      }
    }
  }
}

claude mcp add semantic-search \
  -e GEMINI_API_KEY="$GEMINI_API_KEY" \
  -e QDRANT_URL="$QDRANT_URL" \
  -- uvx mcp-semantic-search

# Index your codebase (auto-starts on first use)
index_codebase(root_dir="/path/to/project")
# Returns: {"status": "success", "files_queued": 1234}

# Search for code using natural language
search_code("how does authentication work")
# Returns:
# {
#   "query": "...",
#   "count": 5,
#   "results": [
#     {
#       "file": "src/auth/middleware.py",
#       "lines": "10-25",
#       "score": 0.876,
#       "content": "..."
#     },
#     ...
#   ]
# }

# Check indexing status
get_status()
# Returns:
# {
#   "collection": {"total_chunks": 12345, "files_indexed": 1234},
#   "queue": {"running": true, "queued": 0, "pending": 0}
# }

# Enable live watching (auto-index on file changes)
start_live_watch(root_dir="/path/to/project")

# Smaller chunks = more precise results, more storage
export CHUNK_MAX_LINES=30

# Larger chunks = more context per result
export CHUNK_MAX_LINES=100

# Adjust overlap for context continuity
export CHUNK_OVERLAP_LINES=15

# Adjust search parameters
search_code(
    query="your query",
    limit=20,                 # More results (default: 10)
    score_threshold=0.3       # Lower threshold = more results (default: 0.5)
)

# Clone the repository
git clone https://github.com/yourusername/mcp-semantic-search.git
cd mcp-semantic-search

# Install in development mode
pip install -e .

# Test with a small subset
python -c "
from mcp_semantic_search import GeminiEmbedder, QdrantCodeStore, index_repository

embedder = GeminiEmbedder()
store = QdrantCodeStore()

# Test with just 5 files
stats = index_repository(
    root_dir='.',
    embedder=embedder,
    store=store,
    max_files=5
)
print(stats)
"

# Test semantic search
python -c "
from mcp_semantic_search import GeminiEmbedder, QdrantCodeStore

embedder = GeminiEmbedder()
store = QdrantCodeStore()

query_embedding = embedder.embed_query('authentication')
results = store.search(query_embedding, limit=5)

for r in results:
    print(f'{r[\"file_path\"]}:{r[\"start_line\"]} ({r[\"score\"]:.2f})')
    print(r['content'][:200])
    print('---')
"