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Plugin Parameters​

See plugin common configurations for configuration options available to all plugins.

  • embeddings_provider

    object

    required

    Embedding model provider configurations.

    • azure_openai

      object

      required

      Azure OpenAI embedding model configurations.

      • endpoint

        string

        required

        Azure OpenAI embedding model endpoint.

      • api_key

        string

        required

        Azure OpenAI API key. The value is encrypted with AES before being stored in etcd.

  • vector_search_provider

    object

    required

    Vector search provider configurations.

    • azure_ai_search

      object

      required

      Configurations of Azure AI Search.

      • endpoint

        string

        required

        Azure AI Search endpoint.

      • api_key

        string

        required

        Azure AI Search API key. The value is encrypted with AES before being stored in etcd.

  • ssl_verify

    boolean

    default: true

    If true, verify TLS certificates when calling the embedding and vector search endpoints. Available in API7 Enterprise from version 3.9.10 and APISIX from version 3.17.0.

Request Body Parameters​

The request body should follow the below configurations.

  • ai_rag

    object

    required

    Request body RAG specifications.

    • embeddings

      object

      required

      Embedding specifications.

      The following parameters are available if you are working with Azure OpenAI.

      • input

        string

        required

        Input prompt to the LLM, which will be used to compute embeddings and generate a RAG-enhanced response.

      • user

        string

        A unique identifier representing your end user, which helps in monitoring and detecting abuse.

      • encoding_format

        string

        default: float

        vaild vaule:

        float or base64

        Data type of the returned embeddings.

      • dimensions

        integer

        Dimensions limit of the embedding model used to output the vectors. It should match the dimension of your embedding model. For instance, the dimensions for text-embedding-ada-002 are fixed at 1536. For text-embedding-3-small or text-embedding-3-large, dimensions range from 1 to 1536 and 3072, respectively.

    • vector_search

      object

      required

      Vector search specifications.

      The following parameter is available if you are working with Azure AI Search.

      • fields

        string

        required

        Fields for the vector search.