[Question]: Recommended persistence pattern for SummaryIndex at scale in a web service?

[brycecf]

Question Validation

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Question

Does the LlamaIndex team have recommendations on how to use a SummaryIndex, at scale, in a web service?

I currently use OpenSearch as my vector store, and the usage pattern essentially mirrors that of any other web service that needs to query a database: if the web service needs to scale up, it does so and the database should remain effectively accessible (minus implied increased usage).

Unlike with a VectorStoreIndex, with something like the SummaryIndex, that pattern no longer holds true. At least based on the documentation examples, I only see SummaryIndexes locally. At a small scale of documents, you could potentially afford to reconstruct the SummaryIndex each time, but that would quickly become an issue (plus needlessly redundant).

My idea is to use a bucket store or caching service, but I want to confirm there is not already a better way utilizing existing databases/docstores.

[dosubot[bot]]

For scaling a web service using a SummaryIndex in OpenSearch, consider these strategies:

1. Incremental Indexing: Implement an approach where only new or updated documents are added to the index, reducing the resources required for updates.
2. Sharding: Divide the SummaryIndex into smaller shards to distribute the data across multiple nodes, improving scalability and performance.
3. Load Balancing: Distribute requests evenly across nodes to prevent bottlenecks and ensure efficient handling of high volumes of requests.
4. Caching: Use caching to store frequently accessed data in memory, significantly improving response times for common queries.
5. Optimize Indexing Strategies: Analyze and optimize your indexing strategies for efficiency, which can reduce index size and improve query performance.
6. Use of Alias and Index Templates in OpenSearch: Manage indices effectively with aliases for switching without downtime and index templates for consistent settings.

For more efficient approaches leveraging existing databases or document stores:

• Direct Integration with Database/Document Store: Consider integrating directly with OpenSearch using plugins or connectors for automatic data syncing.
• Change Data Capture (CDC): Use CDC to capture database changes in real-time and feed them into OpenSearch, minimizing update latency.
• Use of Queues or Stream Processing: Implement message queues or stream processing systems to efficiently process and route data to OpenSearch, improving scalability and fault tolerance.

In the LlamaIndex repository, the SummaryIndex is scaled through:

• Batch Processing with LLM Calls: The DocumentSummaryIndexLLMRetriever processes summaries in batches, optimizing LLM calls.
• Embedding-Based Retrieval: The DocumentSummaryIndexEmbeddingRetriever uses vector embeddings for efficient similarity search, scaling with large datasets.

These strategies address the challenges of scaling and index reconstruction at large scale, leveraging existing databases or document stores more efficiently.

Sources

• llama-index-legacy/llama_index/legacy/indices/list/base.py
• llama-index-legacy/llama_index/legacy/indices/document_summary/retrievers.py

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