CocoIndex is an incremental data indexing engine designed for long-horizon AI agents that need to keep data indexes synchronized with constantly changing sources. It provides high-performance data transformation and semantic indexing that continuously processes updates, ensuring RAG pipelines and search systems always reflect the latest available information.
Incremental Processing
- High-performance data transformation and indexing with parallel and incremental processing
- Efficiently handles continuous source updates without full reprocessing
- Low-latency incremental indexing and continuous data synchronization
- Engineered for performance using efficient concurrency and incremental computation strategies that avoid redundant processing
Semantic Indexing
- Native support for semantic indexing and vectorization pipelines that integrate directly with vector databases
- Composable processor components and adapters for connecting diverse data sources to downstream retrieval systems
- Converts massive heterogeneous data into searchable semantic indexes for knowledge base construction
- Supports real-time log and event indexing, document or code search scenarios
Pipeline Architecture
- Pipeline-oriented modular design with support for custom transformers and connectors tailored to specific data workflows
- Integrates with common vector databases and retrieval components
- CI/CD verification of data consistency and index quality
- Designed for upstream data processing in RAG pipelines that require indexes to stay current with changing source data