ai-rag - Apisix — ContextQMD

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Description

The ai-rag Plugin provides Retrieval-Augmented Generation (RAG) capabilities with LLMs. It facilitates the efficient retrieval of relevant documents or information from external data sources, which are used to enhance the LLM responses, thereby improving the accuracy and contextual relevance of the generated outputs.

The Plugin supports using Azure OpenAI and Azure AI Search services for generating embeddings and performing vector search. PRs for introducing support for other service providers are welcomed.

Plugin Attributes

Name	Type	Required	Description
`embeddings_provider`	object	True	Embedding model provider configurations.
`embeddings_provider.azure_openai`	object	True	Azure OpenAI embedding model configurations.
`embeddings_provider.azure_openai.endpoint`	string	True	Azure OpenAI embedding model endpoint.
`embeddings_provider.azure_openai.api_key`	string	True	Azure OpenAI API key.
`vector_search_provider`	object	True	Vector search provider configurations.
`vector_search_provider.azure_ai_search`	object	True	Configurations of Azure AI Search.
`vector_search_provider.azure_ai_search.endpoint`	string	True	Azure AI Search endpoint.
`vector_search_provider.azure_ai_search.api_key`	string	True	Azure AI Search API key.

Request Body Format

The following fields must be present in the request body.

Field	Type	Description
`ai_rag`	object	Request body RAG specifications.
`ai_rag.embeddings`	object	Request parameters required to generate embeddings. Contents will depend on the API specification of the configured provider.
`ai_rag.vector_search`	object	Request parameters required to perform vector search. Contents will depend on the API specification of the configured provider.

Parameters of ai_rag.embeddings

Azure OpenAI

Name	Required	Type	Description
`input`	True	string	Input text used to compute embeddings, encoded as a string.
`user`	False	string	A unique identifier representing your end user, which can help in monitoring and detecting abuse.
`encoding_format`	False	string	The format to return the embeddings in. Can be either `float` or `base64`. Defaults to `float`.
`dimensions`	False	integer	The number of dimensions the resulting output embeddings should have. 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.

For other parameters please refer to the Azure OpenAI embeddings documentation.

Parameters of ai_rag.vector_search
- Azure AI Search
Field Required Type Description
fields True string Fields for the vector search.

For other parameters please refer to the Azure AI Search documentation. In addition, these vector query parameters are also supported.

Field	Required	Type	Description
`fields`	True	string	Fields for the vector search.

Example request body:

json

{
  "ai_rag": {
    "vector_search": { "fields": "contentVector" },
    "embeddings": {
      "input": "which service is good for devops",
      "dimensions": 1024
    }
  }
}

Examples

To follow along the example, create an Azure account and complete the following steps:

In Azure AI Foundry, deploy a generative chat model, such as gpt-4o, and an embedding model, such as text-embedding-3-large. Obtain the API key and model endpoints.
Follow Azure's example to prepare for a vector search in Azure AI Search using Python. The example will create a search index called vectest with the desired schema and upload the sample data which contains 108 descriptions of various Azure services, for embeddings titleVector and contentVector to be generated based on title and content. Complete all the setups before performing vector searches in Python.
In Azure AI Search, obtain the Azure vector search API key and the search service endpoint.

Save the API keys and endpoints to environment variables:

shell

# replace with your values

AZ_OPENAI_DOMAIN=https://ai-plugin-developer.openai.azure.com
AZ_OPENAI_API_KEY=9m7VYroxITMDEqKKEnpOknn1rV7QNQT7DrIBApcwMLYJQQJ99ALACYeBjFXJ3w3AAABACOGXGcd
AZ_CHAT_ENDPOINT=${AZ_OPENAI_DOMAIN}/openai/deployments/gpt-4o/chat/completions?api-version=2024-02-15-preview
AZ_EMBEDDING_MODEL=text-embedding-3-large
AZ_EMBEDDINGS_ENDPOINT=${AZ_OPENAI_DOMAIN}/openai/deployments/${AZ_EMBEDDING_MODEL}/embeddings?api-version=2023-05-15

AZ_AI_SEARCH_SVC_DOMAIN=https://ai-plugin-developer.search.windows.net
AZ_AI_SEARCH_KEY=IFZBp3fKVdq7loEVe9LdwMvVdZrad9A4lPH90AzSeC06SlR
AZ_AI_SEARCH_INDEX=vectest
AZ_AI_SEARCH_ENDPOINT=${AZ_AI_SEARCH_SVC_DOMAIN}/indexes/${AZ_AI_SEARCH_INDEX}/docs/search?api-version=2024-07-01

:::note

You can fetch the admin_key from config.yaml and save to an environment variable with the following command:

shell

admin_key=$(yq '.deployment.admin.admin_key[0].key' conf/config.yaml | sed 's/"//g')

:::

Integrate with Azure for RAG-Enhanced Responses

The following example demonstrates how you can use the ai-proxy Plugin to proxy requests to Azure OpenAI LLM and use the ai-rag Plugin to generate embeddings and perform vector search to enhance LLM responses.

Create a Route as such:

shell

curl "http://127.0.0.1:9180/apisix/admin/routes/1" -X PUT \
  -H "X-API-KEY: ${admin_key}" \
  -d '{
  "uri": "/rag",
  "plugins": {
    "ai-rag": {
      "embeddings_provider": {
        "azure_openai": {
          "endpoint": "'"$AZ_EMBEDDINGS_ENDPOINT"'",
          "api_key": "'"$AZ_OPENAI_API_KEY"'"
        }
      },
      "vector_search_provider": {
        "azure_ai_search": {
          "endpoint": "'"$AZ_AI_SEARCH_ENDPOINT"'",
          "api_key": "'"$AZ_AI_SEARCH_KEY"'"
        }
      }
    },
    "ai-proxy": {
      "provider": "openai",
      "auth": {
        "header": {
          "api-key": "'"$AZ_OPENAI_API_KEY"'"
        }
      },
      "model": "gpt-4o",
      "override": {
        "endpoint": "'"$AZ_CHAT_ENDPOINT"'"
      }
    }
  }
}'

</TabItem> <TabItem value="adc" label="ADC">

Create a Route with the ai-rag and ai-proxy Plugins configured as such:

yaml

services:
  - name: ai-rag-service
    routes:
      - name: ai-rag-route
        uris:
          - /rag
        methods:
          - POST
        plugins:
          ai-rag:
            embeddings_provider:
              azure_openai:
                endpoint: "${AZ_EMBEDDINGS_ENDPOINT}"
                api_key: "${AZ_OPENAI_API_KEY}"
            vector_search_provider:
              azure_ai_search:
                endpoint: "${AZ_AI_SEARCH_ENDPOINT}"
                api_key: "${AZ_AI_SEARCH_KEY}"
          ai-proxy:
            provider: openai
            auth:
              header:
                api-key: "${AZ_OPENAI_API_KEY}"
            model: gpt-4o
            override:
              endpoint: "${AZ_CHAT_ENDPOINT}"

Synchronize the configuration to the gateway:

shell

adc sync -f adc.yaml

</TabItem> <TabItem value="ingress" label="Ingress Controller"> <Tabs groupId="k8s-api"> <TabItem value="gateway-api" label="Gateway API">

Create a Route with the ai-rag and ai-proxy Plugins configured as such:

yaml

apiVersion: apisix.apache.org/v1alpha1
kind: PluginConfig
metadata:
  namespace: aic
  name: ai-rag-plugin-config
spec:
  plugins:
    - name: ai-rag
      config:
        embeddings_provider:
          azure_openai:
            endpoint: "https://your-openai-resource.openai.azure.com/openai/deployments/text-embedding-3-large/embeddings?api-version=2023-05-15"
            api_key: "Bearer your-api-key"
        vector_search_provider:
          azure_ai_search:
            endpoint: "https://your-search-service.search.windows.net/indexes/vectest/docs/search?api-version=2024-07-01"
            api_key: "Bearer your-api-key"
    - name: ai-proxy
      config:
        provider: openai
        auth:
          header:
            api-key: "Bearer your-api-key"
        model: gpt-4o
        override:
          endpoint: "https://your-openai-resource.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-02-15-preview"
---
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  namespace: aic
  name: ai-rag-route
spec:
  parentRefs:
    - name: apisix
  rules:
    - matches:
        - path:
            type: Exact
            value: /rag
          method: POST
      filters:
        - type: ExtensionRef
          extensionRef:
            group: apisix.apache.org
            kind: PluginConfig
            name: ai-rag-plugin-config

</TabItem> <TabItem value="ingress" label="APISIX Ingress Controller">

Create a Route with the ai-rag and ai-proxy Plugins configured as such:

yaml

apiVersion: apisix.apache.org/v2
kind: ApisixRoute
metadata:
  namespace: aic
  name: ai-rag-route
spec:
  ingressClassName: apisix
  http:
    - name: ai-rag-route
      match:
        paths:
          - /rag
        methods:
          - POST
      plugins:
        - name: ai-rag
          enable: true
          config:
            embeddings_provider:
              azure_openai:
                endpoint: "https://your-openai-resource.openai.azure.com/openai/deployments/text-embedding-3-large/embeddings?api-version=2023-05-15"
                api_key: "Bearer your-api-key"
            vector_search_provider:
              azure_ai_search:
                endpoint: "https://your-search-service.search.windows.net/indexes/vectest/docs/search?api-version=2024-07-01"
                api_key: "Bearer your-api-key"
        - name: ai-proxy
          enable: true
          config:
            provider: openai
            auth:
              header:
                api-key: "Bearer your-api-key"
            model: gpt-4o
            override:
              endpoint: "https://your-openai-resource.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-02-15-preview"

</TabItem> </Tabs>

Apply the configuration to your cluster:

shell

kubectl apply -f ai-rag-ic.yaml

</TabItem> </Tabs>

Send a POST request to the Route with the vector fields name, embedding model dimensions, and an input prompt in the request body:

shell

curl "http://127.0.0.1:9080/rag" -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "ai_rag":{
      "vector_search":{
        "fields":"contentVector"
      },
      "embeddings":{
        "input":"Which Azure services are good for DevOps?",
        "dimensions":1024
      }
    }
  }'

You should receive an HTTP/1.1 200 OK response similar to the following:

json

{
  "choices": [
    {
      "content_filter_results": {
        ...
      },
      "finish_reason": "length",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Here is a list of Azure services ...",
        "role": "assistant"
      }
    }
  ],
  "created": 1740625850,
  "id": "chatcmpl-B54gQdumpfioMPIybFnirr6rq9ZZS",
  "model": "gpt-4o-2024-05-13",
  "object": "chat.completion",
  "prompt_filter_results": [
    {
      "prompt_index": 0,
      "content_filter_results": {
        ...
      }
    }
  ],
  "system_fingerprint": "fp_65792305e4",
  "usage": {
    ...
  }
}