Airbyte
Open-source data integration engine for ELT pipelines across data sources
CocoIndex
Ultra-performant data transformation framework for AI pipelines
Rust-powered data transformation framework for AI with incremental processing, data lineage, and declarative Python API. Build vector indexes, knowledge graphs, and custom transformations effortlessly.
Overview
Transform Data for AI with Exceptional Velocity
CocoIndex is a high-performance data transformation framework designed specifically for AI workloads. Built with a Rust core engine and a declarative Python API, it enables developers to build production-ready data pipelines in ~100 lines of code.
Following a dataflow programming model, CocoIndex treats transformations as pure functions that create new fields without hidden state or mutations. This approach provides complete observability and automatic data lineage tracking. Developers simply declare transformations on source data—no manual CRUD operations required.
Built for Production from Day Zero
The framework excels at keeping source and target data in sync through intelligent incremental processing. When source data or transformation logic changes, CocoIndex automatically recomputes only the necessary portions while reusing cached results wherever possible. This minimizes computational overhead and ensures data freshness.
CocoIndex provides plug-and-play building blocks for diverse sources (local files, S3, Azure Blob, Google Drive), transformations (embeddings, LLM extraction, chunking), and targets (Postgres, Qdrant, LanceDB, knowledge graphs). Standardized interfaces make switching components as simple as changing a single line of code. Whether building RAG vector indexes, extracting structured data with LLMs, or constructing knowledge graphs, CocoIndex delivers exceptional developer velocity without sacrificing performance.
Highlights
Rust-powered core engine for ultra-high performance data transformation
Automatic incremental processing with intelligent caching and minimal recomputation
Built-in data lineage and observability across all transformation stages
Declarative dataflow API with plug-and-play sources, transforms, and targets
Pros
- Exceptional performance with Rust core while maintaining Python developer experience
- Automatic incremental updates eliminate manual sync logic and reduce compute costs
- Complete data lineage and observability built into the framework
- Rich ecosystem of pre-built connectors for common AI data workflows
Considerations
- Requires Postgres installation for incremental processing capabilities
- Dataflow programming model may require mindset shift from imperative approaches
- Relatively new project with evolving API and ecosystem
- Limited to declarative transformations; complex stateful logic may be challenging
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Fit guide
Great for
- Building and maintaining vector indexes for RAG applications with live data
- Teams needing production-ready AI data pipelines with minimal code
- Projects requiring automatic data freshness and incremental updates
- Developers wanting observable, reproducible data transformations with lineage
Not ideal when
- Simple one-time data transformations without incremental update requirements
- Teams unable to deploy Postgres for state management
- Projects requiring complex stateful processing or imperative control flow
- Organizations needing mature enterprise support and long-term API stability guarantees
How teams use it
Semantic Search with Live Updates
Build vector indexes from document collections that automatically stay synchronized as source files change, with minimal recomputation overhead
Knowledge Graph Construction
Extract entities and relationships from documents using LLMs and maintain an up-to-date knowledge graph as content evolves
Multi-Modal AI Indexing
Process images with vision models, generate embeddings, and build searchable indexes that incrementally update when new images arrive
Structured Data Extraction
Use LLMs to extract structured information from unstructured documents like PDFs and forms, with automatic reprocessing on schema changes
Tech snapshot
Rust78%
Python22%
Handlebars1%
Frequently asked questions
Why does CocoIndex require Postgres?
Postgres stores metadata and state needed for incremental processing, enabling CocoIndex to track which data has changed and minimize recomputation while maintaining data lineage.
How does incremental processing work?
CocoIndex automatically detects changes in source data or transformation logic, then reprocesses only affected portions while reusing cached results for unchanged data, significantly reducing compute costs.
Can I use CocoIndex with my existing vector database?
Yes, CocoIndex provides built-in targets for Postgres, Qdrant, and LanceDB, plus a custom target API for integrating with other databases or storage systems.
What's the difference between CocoIndex and traditional ETL tools?
CocoIndex uses a declarative dataflow model optimized for AI workloads, with automatic incremental updates and data lineage. Traditional ETL tools typically require manual orchestration and lack AI-specific transformations.
Is CocoIndex production-ready?
Yes, CocoIndex is designed to be production-ready from day zero with its Rust core engine providing high performance and reliability for demanding AI data pipelines.
Project at a glance
Active- Stars
- 5,884
- Watchers
- 5,884
- Forks
- 432
LicenseApache-2.0
Repo age11 months old
Last commit2 days ago
Primary languagePython
Last synced 2 days ago