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Data source - Pinecone (preview)

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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

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))