from qdrant_client import QdrantClient
from qdrant_client.http import models
from qdrant_client.http.models import Distance, VectorParams
def setup_collection():
# Connect to Qdrant
client = QdrantClient(
url='https://e67ee53a-6e03-4526-9e41-3fde622323a9.us-east4-0.gcp.cloud.qdrant.io:6333',
api_key='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIiwiZXhwIjoxNzQ1MTAyNzQ3fQ.3gvK8M7dJxZkSpyzpJtTGVUhjyjgbYEhEvl2aG7JodM'
)
collection_name = "mcp-codebase-insight"
try:
# Check if collection exists
collections = client.get_collections().collections
exists = any(c.name == collection_name for c in collections)
# If collection exists, recreate it
if exists:
print(f"\nRemoving existing collection '{collection_name}'")
client.delete_collection(collection_name=collection_name)
# Create a new collection with named vector configurations
print(f"\nCreating collection '{collection_name}' with named vectors")
# Create named vectors configuration
vectors_config = {
# For the default MCP server embedding model (all-MiniLM-L6-v2)
"fast-all-minilm-l6-v2": VectorParams(
size=384, # all-MiniLM-L6-v2 produces 384-dimensional vectors
distance=Distance.COSINE
)
}
client.create_collection(
collection_name=collection_name,
vectors_config=vectors_config
)
# Verify the collection was created properly
collection_info = client.get_collection(collection_name=collection_name)
print(f"\nCollection '{collection_name}' created successfully")
print(f"Vector configuration: {collection_info.config.params.vectors}")
print("\nCollection is ready for the MCP server")
except Exception as e:
print(f"\nError setting up collection: {e}")
if __name__ == '__main__':
setup_collection()