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Pull Request Overview
This PR optimizes the Dockerfile by upgrading the Python base image, modernizing package management with uv, and improving server network accessibility in containerized environments.
- Upgrades Python base image from 3.11-slim to 3.13-slim
- Switches from pip-based uv installation to pre-built binary from ghcr.io
- Adds FASTMCP_HOST environment variable to enable network interface binding
Comments suppressed due to low confidence (1)
Dockerfile:6
- The uv version 0.8.3 may not exist. As of my knowledge cutoff in January 2025, the latest stable versions were in the 0.4.x range. Please verify that version 0.8.3 is available at ghcr.io/astral-sh/uv before using it.
COPY --from=ghcr.io/astral-sh/uv:0.8.3 /uv /uvx /bin/
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This commit consolidates changes from PRs qdrant#92, qdrant#75, qdrant#77, qdrant#90, qdrant#20, qdrant#89, qdrant#78, qdrant#76, and qdrant#68: Infrastructure & Configuration (PR qdrant#75, qdrant#77): - Upgrade Dockerfile to Python 3.13-slim - Use UV 0.8.3 from official image - Add FASTMCP_HOST="0.0.0.0" for container networking - Add SettingsConfigDict for proper None value parsing New Embedding Providers (PR qdrant#76, qdrant#92): - Add Model2Vec support for fast, lightweight embeddings - Add OpenAI-compatible API support (oai_compat) - New settings: OAI_COMPAT_ENDPOINT, OAI_COMPAT_API_KEY, OAI_COMPAT_VEC_SIZE Unnamed Vectors & Multiple Collections (PR qdrant#78): - Add support for Qdrant unnamed vectors - Create UnnamedVectorProvider wrapper - Add USE_UNNAMED_VECTORS and COLLECTION_NAMES settings - Update qdrant.py to handle both named and unnamed vectors - Add __main__.py for python -m execution Hybrid Search (PR qdrant#90): - Implement hybrid search combining dense and sparse vectors - Add find_hybrid() method with RRF and DBSF fusion methods - New tool: qdrant-hybrid-find - Add SPARSE_EMBEDDING_MODEL setting - Support configurable limits for dense, sparse, and final results Optional Collection Names (PR qdrant#89): - Make collection_name parameter optional in store() and find() - Use default collection when not specified - Simplify tool usage with fallback to COLLECTION_NAME env var Additional Tools (PR qdrant#68): - Add qdrant-get-point: Retrieve point by ID - Add qdrant-delete-point: Delete point by ID - Add qdrant-update-point-payload: Update point metadata - Add qdrant-get-collections: List all collections - Add qdrant-get-collection-details: Get collection info - Implement corresponding methods in QdrantConnector Documentation: - Update README with all new features - Document new embedding providers - Add examples for unnamed vectors and multiple collections - Update environment variables table - Document hybrid search functionality - Expand tools documentation Dependencies: - Add model2vec==0.6.0 - Add openai>=1.109.1
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This commit consolidates changes from PRs qdrant#92, qdrant#75, qdrant#77, qdrant#90, qdrant#20, qdrant#89, qdrant#78, qdrant#76, and qdrant#68: Infrastructure & Configuration (PR qdrant#75, qdrant#77): - Upgrade Dockerfile to Python 3.13-slim - Use UV 0.8.3 from official image - Add FASTMCP_HOST="0.0.0.0" for container networking - Add SettingsConfigDict for proper None value parsing New Embedding Providers (PR qdrant#76, qdrant#92): - Add Model2Vec support for fast, lightweight embeddings - Add OpenAI-compatible API support (oai_compat) - New settings: OAI_COMPAT_ENDPOINT, OAI_COMPAT_API_KEY, OAI_COMPAT_VEC_SIZE Unnamed Vectors & Multiple Collections (PR qdrant#78): - Add support for Qdrant unnamed vectors - Create UnnamedVectorProvider wrapper - Add USE_UNNAMED_VECTORS and COLLECTION_NAMES settings - Update qdrant.py to handle both named and unnamed vectors - Add __main__.py for python -m execution Hybrid Search (PR qdrant#90): - Implement hybrid search combining dense and sparse vectors - Add find_hybrid() method with RRF and DBSF fusion methods - New tool: qdrant-hybrid-find - Add SPARSE_EMBEDDING_MODEL setting - Support configurable limits for dense, sparse, and final results Optional Collection Names (PR qdrant#89): - Make collection_name parameter optional in store() and find() - Use default collection when not specified - Simplify tool usage with fallback to COLLECTION_NAME env var Additional Tools (PR qdrant#68): - Add qdrant-get-point: Retrieve point by ID - Add qdrant-delete-point: Delete point by ID - Add qdrant-update-point-payload: Update point metadata - Add qdrant-get-collections: List all collections - Add qdrant-get-collection-details: Get collection info - Implement corresponding methods in QdrantConnector Documentation: - Update README with all new features - Document new embedding providers - Add examples for unnamed vectors and multiple collections - Update environment variables table - Document hybrid search functionality - Expand tools documentation Dependencies: - Add model2vec==0.6.0 - Add openai>=1.109.1 Co-authored-by: Claude <noreply@anthropic.com>
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This pull request updates the
Dockerfileto use a newer Python base image, improve package management, and enhance configurability for the server. Key changes include upgrading the Python version, switching to a more efficient package installation method, and adding a new environment variable to allow the server to listen on all network interfaces.Base image and package management updates:
python:3.11-slimtopython:3.13-slimfor compatibility with the latest Python features and improvements.pip-based installation of theuvpackage with a pre-built binary fromghcr.io, improving efficiency and reducing build complexity.mcp-server-qdrantpackage using theuv tool installcommand instead ofuv pip install, aligning with the new package management approach.Server configuration improvements:
FASTMCP_HOSTenvironment variable to make the server listen on all network interfaces (0.0.0.0), improving accessibility in containerized environments.CMDinstruction to use a JSON array format for better compatibility with Docker's execution model.