Data source - Pinecone (preview)

Raksts
08/28/2024

The configurable options of Pinecone when using Azure OpenAI On Your Data. This data source is supported in API version 2024-02-15-preview.

Name	Type	Required	Description
`parameters`	Parameters	True	The parameters to use when configuring Pinecone.
`type`	string	True	Must be `pinecone`.

Parameters

Name	Type	Required	Description
`environment`	string	True	The environment name of Pinecone.
`index_name`	string	True	The name of the Pinecone database index.
`fields_mapping`	FieldsMappingOptions	True	Customized field mapping behavior to use when interacting with the search index.
`authentication`	ApiKeyAuthenticationOptions	True	The authentication method to use when accessing the defined data source.
`embedding_dependency`	DeploymentNameVectorizationSource	True	The embedding dependency for vector search.
`in_scope`	boolean	False	Whether queries should be restricted to use of indexed data. Default is `True`.
`role_information`	string	False	Give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality and tell it how to format responses.
`strictness`	integer	False	The configured strictness of the search relevance filtering. The higher of strictness, the higher of the precision but lower recall of the answer. Default is `3`.
`top_n_documents`	integer	False	The configured top number of documents to feature for the configured query. Default is `5`.

API key authentication options

The authentication options for Azure OpenAI On Your Data when using an API key.

Name	Type	Required	Description
`key`	string	True	The API key to use for authentication.
`type`	string	True	Must be `api_key`.

Deployment name vectorization source

The details of the vectorization source, used by Azure OpenAI On Your Data when applying vector search. This vectorization source is based on an internal embeddings model deployment name in the same Azure OpenAI resource. This vectorization source enables you to use vector search without Azure OpenAI api-key and without Azure OpenAI public network access.

Name	Type	Required	Description
`deployment_name`	string	True	The embedding model deployment name within the same Azure OpenAI resource.
`type`	string	True	Must be `deployment_name`.

Fields mapping options

The settings to control how fields are processed.

Name	Type	Required	Description
`content_fields`	string[]	True	The names of index fields that should be treated as content.
`content_fields_separator`	string	False	The separator pattern that content fields should use. Default is `\n`.
`filepath_field`	string	False	The name of the index field to use as a filepath.
`title_field`	string	False	The name of the index field to use as a title.
`url_field`	string	False	The name of the index field to use as a URL.

Examples

Prerequisites:

Configure the role assignments from the user to the Azure OpenAI resource. Required role: Cognitive Services OpenAI User.
Install Az CLI and run az login.
Define the following environment variables: AzureOpenAIEndpoint, ChatCompletionsDeploymentName,Environment, IndexName, Key, EmbeddingDeploymentName.

export AzureOpenAIEndpoint=https://example.openai.azure.com/
export ChatCompletionsDeploymentName=turbo
export Environment=testenvironment
export Key=***
export IndexName=pinecone-test-index
export EmbeddingDeploymentName=ada

Python 1.x
REST

Install the latest pip packages openai, azure-identity.

import os
from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

endpoint = os.environ.get("AzureOpenAIEndpoint")
deployment = os.environ.get("ChatCompletionsDeploymentName")
environment = os.environ.get("Environment")
key = os.environ.get("Key")
index_name = os.environ.get("IndexName")
embedding_deployment_name = os.environ.get("EmbeddingDeploymentName")

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")

client = AzureOpenAI(
    azure_endpoint=endpoint,
    azure_ad_token_provider=token_provider,
    api_version="2024-02-15-preview",
)

completion = client.chat.completions.create(
    model=deployment,
    messages=[
        {
            "role": "user",
            "content": "Who is DRI?",
        },
    ],
    extra_body={
        "data_sources": [
            {
                "type": "pinecone",
                "parameters": {
                    "environment": environment,
                    "authentication": {
                        "type": "api_key",
                        "key": key
                    },
                    "index_name": index_name,
                    "fields_mapping": {
                        "content_fields": [
                            "content"
                        ]
                    },
                    "embedding_dependency": {
                        "type": "deployment_name",
                        "deployment_name": embedding_deployment_name
                    }
                }}
        ],
    }
)

print(completion.model_dump_json(indent=2))


az rest --method POST \
 --uri $AzureOpenAIEndpoint/openai/deployments/$ChatCompletionsDeploymentName/chat/completions?api-version=2024-02-15-preview \
 --resource https://cognitiveservices.azure.com/ \
 --body \
'
{
    "data_sources": [
      {
        "type": "pinecone",
        "parameters": {
          "environment": "'$Environment'",
          "authentication": {
            "type": "api_key",
            "key": "'$Key'"
          },
          "index_name": "'$IndexName'",
          "fields_mapping": {
            "content_fields": [
              "content"
            ]
          },
          "embedding_dependency": {
            "type": "deployment_name",
            "deployment_name": "'$EmbeddingDeploymentName'"
          }
        }
      }
    ],
    "messages": [
      {
        "role": "user",
        "content": "Who is DRI?"
      }
    ]
}
'

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