Use Multiple Vector Stores With The OpenAI Assistants API

Use multiple vector stores in an OpenAI Assistants API service to enable parallel querying across each store that each handle 5M vectors—500x OpenAI's current limit.

The OpenAI Assistants API does not natively support using more than one vector store at the moment. But the Ragwalla implementation of the OpenAI Assistants API does.

Multi-Store RAG: Breaking Through Vector Database Limitations

Retrieval Augmented Generation (RAG) has become a cornerstone of modern LLM applications, enabling models to access and reason about vast amounts of domain-specific knowledge. However, developers building RAG solutions often encounter limitations with existing services, particularly around vector store capacity and flexibility.

The Vector Store Challenge

OpenAI's Assistants API, while powerful, constrains developers to a single vector store with a 10,000 vector limit. This creates significant challenges for applications requiring broader knowledge bases or more nuanced retrieval strategies. Enterprise applications, technical documentation systems, and large-scale knowledge management solutions frequently exceed these boundaries.

Ragwalla's Multi-Store Architecture

Ragwalla introduces a multi-store RAG architecture that addresses these limitations head-on. The implementation supports parallel querying across multiple vector stores, with each store capable of handling 5,000,000 vectors—500 times the capacity of OpenAI's current limit.

Key Technical Advantages

The multi-store approach offers several architectural benefits:

  1. Parallel Retrieval: Queries execute simultaneously across all configured vector stores, minimizing latency impact.

  2. Segregated Knowledge Domains: Different vector stores can maintain separate semantic spaces, improving retrieval precision for domain-specific queries.

  3. Scalability: The 5,000,000 vector per store limit, combined with multi-store support, enables effectively unlimited knowledge base size.

Implementation Considerations

When implementing a multi-store RAG system:

  • Query Orchestration: Design your retrieval layer to handle concurrent queries efficiently. Consider implementing timeout mechanisms and failure handling for individual store queries.

  • Result Aggregation: Develop a strategy for combining and ranking results from multiple stores. Simple approaches like round-robin or score-based merging can work, but more sophisticated methods might consider store-specific weights or context-aware ranking.

  • Vector Store Selection: Different stores may be optimal for different content types or query patterns. Consider allowing per-store configuration of embedding models and similarity metrics.

Deployment Architecture

[Client] → [API Layer]
             ↓
    [Query Orchestrator]
        ↙     ↓     ↘
[Store 1] [Store 2] [Store N]
        ↘     ↓     ↙
    [Result Aggregator]
             ↓
      [LLM Interface]