Detailed Introduction
CocoIndex is a data transformation and indexing framework designed for AI workloads, providing ultra-performance processing with support for incremental and real-time indexing. It focuses on pipeline-ing and normalizing raw data into formats suitable for vectorization and retrieval, accelerating semantic search and Retrieval-Augmented Generation (RAG) workflows. The project offers end-to-end processing components that balance throughput and latency for large-scale, continuously-updated indexes.
Main Features
- High-performance data transformation and indexing with parallel and incremental processing.
- Native support for semantic indexing and vectorization pipelines to integrate with vector databases.
- Composable processor components and adapters for connecting diverse data sources and downstream systems.
Use Cases
CocoIndex is suitable for converting massive heterogeneous data into searchable semantic indexes, such as knowledge base construction, real-time log/event indexing, document and code search, and upstream data processing for RAG pipelines. It is also well-suited for engineering applications requiring low-latency incremental indexing and continuous data synchronization.
Technical Features
- Pipeline-oriented modular design with support for custom transformers and connectors.
- Engineered for performance and scalability using efficient concurrency and incremental computation strategies.
- Integrates with common vector databases and retrieval components, enabling CI/CD verification of data consistency and index quality.