How does OpenAI's knowledge retrieval handle document storage, alternatives, and search performance?

Below is some detailed information addressing your questions:

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1. Document Storage Duration

Documents uploaded via the knowledge retrieval tool (using the OpenAI hosted vector database) are stored persistently until you explicitly delete them. There isn’t an automatic expiration period defined in the documentation, so unless you remove the document, it remains available for search.

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2. Alternative to Other Vector Databases

Yes, you can use the OpenAI hosted vector database as an alternative to services like Pinecone or Elasticsearch for many use cases. However, note that:

• It’s designed to integrate easily with the file search tool and semantic search capabilities.
• It may not offer all the advanced configuration options or customizability found in dedicated third-party vector database solutions.

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3. Performance Considerations and Limitations

Regarding search performance when using a shared vector database:

• Multi-Tenancy: Since the system is shared across OpenAI’s apps, it is designed to scale and isolate usage. Generally, there should be no significant performance loss for typical use cases.
• Usage Limits: While the experience is optimized for most applications, there could be rate limits or resource constraints if your usage is extremely heavy.
• Feature Set: Keep in mind that as a managed and currently beta service, some advanced features or custom tuning options available in self-hosted solutions (like Pinecone or Elasticsearch) might not be present.

In summary, the hosted vector database offers a convenient, out-of-the-box solution for knowledge retrieval. However, if you need very high throughput, specialized features, or deep customization, you might still consider a dedicated vector database solution.

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Feel free to ask if you need further clarifications on integrating with or using these tools.