n file. Here's an example `mcp_config.json`: ### Using the server directly If you use a virtual Python environment to install the server, you can use the following configuration - note that MCP host like Claude will not be able to connect to the server if you use a virtual environment, you need to use the absolute path to the Python executable of the virtual environment where you did "pip install" or "uv pip install", for example ```json { "mcpServers": { "kb-server": { "command": "/your/home/project/.venv/bin/kb-mcp-server", "args": [ "--embeddings", "/path/to/knowledge_base.tar.gz" ], "cwd": "/path/to/working/directory" } } } ``` ### Using system default Python If you use your system default Python, you can use the following configuration: ```json { "rag-server": { "command": "python3", "args": [ "-m", "txtai_mcp_server", "--embeddings", "/path/to/knowledge_base.tar.gz", "--enable-causal-boost" ], "cwd": "/path/to/working/directory" } } ``` Alternatively, if you're using uvx, assuming you have uvx installed in your system via "brew install uvx" etc, or you 've installed uvx and made it globally accessible via: ``` # Create a symlink to /usr/local/bin (which is typically in the system PATH) sudo ln -s /Users/cliang/.local/bin/uvx /usr/local/bin/uvx ``` This creates a symbolic link from your user-specific installation to a system-wide location. For macOS applications like Claude Desktop, you can modify the system-wide PATH by creating or editing a launchd configuration file: ``` # Create a plist file to set environment variables for all GUI applications sudo nano /Library/LaunchAgents/environment.plist ``` Add this content: ```xml Label my.startup ProgramArguments sh -c launchctl setenv PATH $PATH:/Users/cliang/.local/bin RunAtLoad ``` Then load it: ``` sudo launchctl load -w /Library/LaunchAgents/environment.plist ``` You'll need to restart your computer for this to take effect, though. ```json { "mcpServers": { "kb-server": { "command": "uvx", "args": [ "kb-mcp-server@0.2.6", "--embeddings", "/path/to/knowledge_base", "--host", "localhost", "--port", "8000" ], "cwd": "/path/to/working/directory" } } } ``` Place this configuration file in a location accessible to your LLM client and configure the client to use it. The exact configuration steps will depend on your specific LLM client. ## Advanced Knowledge Base Configuration Building a knowledge base with txtai requires a YAML configuration file that controls various aspects of the embedding process. This configuration is used by the `kb_builder` tool, not the MCP server itself. One may need to tune segmentation/chunking strategies, embedding models, and scoring methods, as well as configure graph construction, causal boosting, weights of hybrid search, and more. Fortunately, txtai provides a powerful YAML configuration system that requires no coding. Here's an example of a comprehensive configuration for knowledge base building: ```yaml # Path to save/load embeddings index path: ~/.txtai/embeddings writable: true # Content storage in SQLite content: path: sqlite:///~/.txtai/content.db # Embeddings configuration embeddings: # Model settings path: sentence-transformers/nli-mpnet-base-v2 backend: faiss gpu: true batch: 32 normalize: true # Scoring settings scoring: hybrid hybridalpha: 0.75 # Pipeline configuration pipeline: workers: 2 queue: 100 timeout: 300 # Question-answering pipeline extractor: path: distilbert-base-cased-distilled-squad maxlength: 512 minscore: 0.3 # Graph configuration gr

Embedding MCP Server

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