cognee-community-vector-adapter-azure 0.0.2

Azure AI search vector database adapter for cognee

Azure AI Search Adapter for Cognee

This adapter provides integration between Cognee and Azure AI Search (formerly Azure Cognitive Search) for vector storage and retrieval operations.

Features

• Full vector search capabilities using Azure AI Search
• Hybrid search (combining text and vector search)
• HNSW algorithm for efficient similarity search
• Async/await support for all operations
• Batch operations for improved performance

Installation

If published, the package can be simply installed via pip:

pip install cognee-community-vector-adapter-azure

In case it is not published yet, you can use poetry to locally build the adapter package:

pip install poetry
poetry install # run this command in the directory containing the pyproject.toml file

Configuration

The adapter requires the following credentials:

• endpoint: Your Azure AI Search service endpoint (e.g., https://your-service.search.windows.net)
• api_key: Your Azure AI Search API key
• embedding_engine: An instance of EmbeddingEngine for text vectorization

Usage

from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
from packages.vector.azureaisearch import AzureAISearchAdapter

# Initialize the adapter
embedding_engine = EmbeddingEngine(...)  # Your embedding engine
adapter = AzureAISearchAdapter(
    endpoint="https://your-service.search.windows.net",
    api_key="your-api-key",
    embedding_engine=embedding_engine
)

# Create a collection (index)
await adapter.create_collection("my_collection")

# Add data points
await adapter.create_data_points("my_collection", data_points)

# Search
results = await adapter.search(
    collection_name="my_collection",
    query_text="search query",
    limit=10
)

# Batch search
results = await adapter.batch_search(
    collection_name="my_collection",
    query_texts=["query1", "query2"],
    limit=10
)

Key Differences from Other Vector Databases

1. Collections as Indexes: In Azure AI Search, what other vector databases call "collections" are called "indexes"
2. Document Structure: Documents in Azure AI Search have a specific schema with defined fields
3. Batch Operations: Azure AI Search doesn't have native batch search, so batch operations are parallelized
4. Scoring: Azure AI Search returns @search.score which is normalized differently than other vector databases

Vector Search Configuration

The adapter uses HNSW (Hierarchical Navigable Small World) algorithm with the following default parameters:

• m: 4 (number of bi-directional links)
• efConstruction: 400 (size of the dynamic list)
• efSearch: 500 (size of the dynamic list for search)
• metric: cosine (similarity metric)

These parameters can be adjusted in the create_collection method if needed.