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Indexing

Lets queries hit millions of chunks in milliseconds.

What It Does

Indexing creates optimized data structures that allow for fast searching and retrieval of documents and chunks. It maps terms, vectors, or other features to the content that contains them, enabling efficient query processing at scale.

Why It Matters

Effective indexing is crucial for the performance and scalability of your RAG system. It enables quick retrieval of relevant documents from potentially massive collections, making real-time responses possible even with millions of documents.

Common Challenges

  • Choosing the right index type for your specific use case and query patterns
  • Balancing index size with query performance and memory usage
  • Handling index updates as documents are added, modified, or deleted
  • Scaling indexes to large document collections while maintaining speed
  • Optimizing for different query patterns (exact match vs. semantic search)
  • Managing index consistency across distributed systems

Interactive Demo

Vector Space Visualization

2D projection of vectors (green = indexed chunks, blue = query, orange = results)

indexing.lastRefreshed: 1:55:04 PM

indexing.vectorSearch

indexing.searchVectorIndex

indexing.indexStatus 8 indexing.chunks
indexing.distanceMetric: Cosine Similarity
indexing.approximate indexing.search

indexing.searchResults

indexing.runSearchToSeeResults

indexing.noSearchResults

Skip the Complexity

Building a robust Indexing solution is challenging. Respeak's Enterprise RAG Platform handles this complexity for you.

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