Azure AI Search can function as a vector database search when configured with vectorization and semantic search capabilities. Here’s how it works and where the vectors are stored:
✅ How Azure AI Search Works as a Vector DB
When you set up Azure AI Search to import and vectorize data (e.g., documents from a selected folder in a Blob Storage container), it performs the following:
- Data Ingestion:
- You define a data source (Blob Storage).
- Azure Search pulls documents from the selected folder.
- Vectorization:
- You can use built-in vectorization (via Azure OpenAI embedding models) or bring your own embeddings.
- Each document (or chunk of text) is converted into a vector representation.
- Indexing:
- These vectors are stored in a vector field within the Azure Search index.
- You define this field in your index schema (e.g., contentVector).
- Search:
- You can perform vector similarity search using cosine similarity or other metrics.
- Combine it with a keyword search for hybrid search scenarios.
📍 Where Are Vectors Stored?
The vectors are stored inside the Azure AI Search index itself. Specifically:
- Each document in the index has a field (e.g., vector) that holds the vector embedding.
- Azure Search indexes these vectors and uses them for similarity search.
- You can configure the vector field with parameters like dimensions, vectorSearchAlgorithmConfiguration, etc.
🧠 Example Use Case
If you’re indexing PDFs or text files from Blob Storage:
- Azure Search will chunk the documents.
- Each chunk gets vectorized.
- Vectors are stored in the index.
- You can then query using a vector (e.g., from a user query) to retrieve semantically similar chunks.
Leave a Reply