keyphrases-mcp

# Configuration [← Back to README](../README.md) ## Environment Variables > **Note:** All variables below are optional. If not set, the server will use the default values shown. | Variable | Description | Default Value | |--------------------------------------|------------------------------------------------|-----------------------------------------| | MCP_KEYPHRASES_EMBEDDINGS_MODEL | BERT embeddings model | `paraphrase-multilingual-MiniLM-L12-v2` | | MCP_KEYPHRASES_SPACY_TOKENIZER_MODEL | spaCy tokenizer model | `en_core_web_trf` | | MCP_KEYPHRASES_LOG_LEVEL | Log lever | `INFO` | | MCP_KEYPHRASES_MAX_TEXT_LEN | Maximal length of the input text in characters | `6000` | | MCP_KEYPHRASES_MAX_KEYPHRASES_COUNT | Maximal number of keyphrases to extract | `elastic` | | PORT | HTTP port for MCP server | `apple_health_data` | There are various [pretrained embedding models](https://www.sbert.net/docs/sentence_transformer/pretrained_models.html) for BERT. The `"paraphrase-multilingual-MiniLM-L12-v2"` for multi-lingual documents or any other language that is used by default. You can specify `"all-MiniLM-L6-v2"` model for English documents. The are various spaCy [pretrained models](https://spacy.io/models/en/#en_core_web_trf). If you want to build docker image with custom models, then provide MCP_KEYPHRASES_EMBEDDINGS_MODEL and MCP_KEYPHRASES_SPACY_TOKENIZER_MODEL variables both as arguments to build the docker image: ```bash docker build \ --build-arg MCP_KEYPHRASES_EMBEDDINGS_MODEL="<selecled_embeddings_model>" \ --build-arg MCP_KEYPHRASES_SPACY_TOKENIZER_MODEL="<selected_spacy_model>" \ -t mcp-keyphrases:latest . ``` and as environment variables to run the container: ```bash docker run --rm \ --name keyphrases-mcp-server \ -i \ -v <path_to_documents>:/app/documents \ -p 8000:8000 \ --gpus all \ -e MCP_KEYPHRASES_EMBEDDINGS_MODEL="<selecled_embeddings_model>" \ -e MCP_KEYPHRASES_SPACY_TOKENIZER_MODEL="<selected_spacy_model>" \ mcp-keyphrases:latest ```