github langgenius/dify 0.3.31
v0.3.31

latest releases: 0.8.2, 0.8.1, 0.8.0...
10 months ago

New Features

Important Upgrade Notice

Current version adds full-text search and hybrid search functions for datasets, significantly improving search results. If you want to use this feature, you need to make the following configurations:

  • If the vector database you are using is Weaviate
    Please update your version to v1.19.0 or later in /dify/docker/docker-compose.yaml.
    For more details, please refer to the Weaviate release list.
 weaviate:
    image: semitechnologies/weaviate:1.19.0
    restart: always
    volumes:
      # Mount the Weaviate data directory to the container.
      - ./volumes/weaviate:/var/lib/weaviate
    environment:
      # The Weaviate configurations
      # You can refer to the [Weaviate](https://weaviate.io/developers/weaviate/config-refs/env-vars) documentation for more information.
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'false'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      DEFAULT_VECTORIZER_MODULE: 'none'
      CLUSTER_HOSTNAME: 'node1'
      AUTHENTICATION_APIKEY_ENABLED: 'true'
      AUTHENTICATION_APIKEY_ALLOWED_KEYS: 'WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih'
      AUTHENTICATION_APIKEY_USERS: 'hello@dify.ai'
      AUTHORIZATION_ADMINLIST_ENABLED: 'true'
      AUTHORIZATION_ADMINLIST_USERS: 'hello@dify.ai'
  • If the vector database you are using is Qdrant
    1. Since Dify's full-text index relies on the multilingual tokenizer in Qdrant's full-text index, by default, Qdrant does not support Chinese, Japanese, and Korean language tokenization.
    2. Therefore, we provide a full-language tokenizer mirror for Qdrant: langgenius/qdrant:latest, which can be deployed and used directly. You can also build your own Qdrant image using the following command:
    docker buildx build .  --build-arg  FEATURES="multiling-chinese,multiling-japanese,multiling-korean" --tag=langgenius/qdrant
    
    1. Due to the lack of full-text index in previous Qdrant data, you need to execute the following commands in the api container to complete the process:
      1. Start a bash shell inside the running Docker container:
       docker exec -it docker-api-1 bash
      
      1. Execute the command to create the full-text-index index:
        flask add-qdrant-full-text-index
      
  • Milvus/Zilliz temporarily does not support Hybrid search

What's Changed

New Contributors

Full Changelog: 0.3.30...0.3.31

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