Vector Search Platform for RAG

Vector Search Platform for RAG

Vector search finds documents by semantic similarity rather than keyword match, by comparing high-dimensional embeddings that capture meaning. It is the retrieval backbone of retrieval-augmented generation (RAG), where a language model answers using relevant context fetched from a knowledge base. This platform runs on Azure and combines vector and keyword (hybrid) search for accuracy. Use it to ground LLM answers in private data and reduce hallucination.

Components

• Blob Storage: stores source documents to be indexed.
• Azure Functions: serverless pipeline that chunks documents, calls the embedding model, and upserts vectors.
• Embedding model (Azure OpenAI): turns text chunks and queries into vectors.
• Azure AI Search: the vector and full-text index supporting hybrid search and filtering.
• Cosmos DB: stores document metadata, chunk references, and access scopes.
• Azure OpenAI: the chat model that generates grounded answers from retrieved context.
• API gateway: fronts the RAG service with authentication and rate limiting.

Data Flow

An ingestion pipeline in Azure Functions splits documents into chunks, generates embeddings, and writes vectors plus metadata into Azure AI Search and Cosmos DB. At query time, the user's question is embedded and sent to AI Search, which runs hybrid vector-plus-keyword retrieval with metadata filters for access control. The top chunks are assembled into a prompt and passed to Azure OpenAI, which returns an answer with citations back to source documents. Interactions are logged for evaluation.

Scaling and Resilience

Azure AI Search scales with replicas for query throughput and partitions for index size. Functions autoscale ingestion with the document backlog. Cosmos DB scales throughput elastically and replicates across regions. Re-embedding is idempotent so reindexing after a model change is safe. Caching frequent queries and answers reduces model cost and latency, and retries with backoff handle transient model throttling.

Security

Access control filters restrict retrieval to documents a user may see, enforced at query time. Prompt-injection defenses sanitize retrieved content and constrain tool use. Data is encrypted at rest and in transit, with private endpoints keeping traffic off the public internet. Managed identities avoid stored secrets, and request and response logging supports auditing and evaluation of answer quality.

Trade-offs and Alternatives

Hybrid search improves recall over pure vector search but adds tuning of chunk size, ranking, and filters. Vector indexes grow with corpus size and embedding dimension, raising cost. Alternatives include open-source vector databases like pgvector, Milvus, Qdrant, or Weaviate, and managed options such as Pinecone. Choose this Azure design when you want managed hybrid retrieval tightly integrated with Azure OpenAI for enterprise RAG.