cocoindex

Data transformation for AI

Ultra performant data transformation framework for AI, with core engine written in Rust. Support incremental processing and data lineage out-of-box. Exceptional developer velocity. Production-ready at day 0.

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CocoIndex makes it super easy to transform data with AI workloads, and keep source data and target in sync effortlessly.

Either creating embedding, building knowledge graphs, or any data transformations - beyond traditional SQL.

Exceptional velocity

Just declare transformation in dataflow with ~100 lines of python

# import
data['content'] = flow_builder.add_source(...)

# transform
data['out'] = data['content']
    .transform(...)
    .transform(...)

# collect data
collector.collect(...)

# export to db, vector db, graph db ...
collector.export(...)

CocoIndex follows the idea of Dataflow programming model. Each transformation creates a new field solely based on input fields, without hidden states and value mutation. All data before/after each transformation is observable, with lineage out of the box.

Particularly, developers don't explicitly mutate data by creating, updating and deleting. They just need to define transformation/formula for a set of source data.

Build like LEGO

Native builtins for different source, targets and transformations. Standardize interface, make it 1-line code switch between different components.

Data Freshness

CocoIndex keep source data and target in sync effortlessly.

It has out-of-box support for incremental indexing:

• minimal recomputation on source or logic change.
• (re-)processing necessary portions; reuse cache when possible

Quick Start:

If you're new to CocoIndex, we recommend checking out

• 📖 Documentation
• ⚡ Quick Start Guide
• 🎬 Quick Start Video Tutorial

Setup

• Install CocoIndex Python library

pip install -U cocoindex

• Install Postgres if you don't have one. CocoIndex uses it for incremental processing.

Define data flow

Follow Quick Start Guide to define your first indexing flow. An example flow looks like:

@cocoindex.flow_def(name="TextEmbedding")
def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    # Add a data source to read files from a directory
    data_scope["documents"] = flow_builder.add_source(cocoindex.sources.LocalFile(path="markdown_files"))

    # Add a collector for data to be exported to the vector index
    doc_embeddings = data_scope.add_collector()

    # Transform data of each document
    with data_scope["documents"].row() as doc:
        # Split the document into chunks, put into `chunks` field
        doc["chunks"] = doc["content"].transform(
            cocoindex.functions.SplitRecursively(),
            language="markdown", chunk_size=2000, chunk_overlap=500)

        # Transform data of each chunk
        with doc["chunks"].row() as chunk:
            # Embed the chunk, put into `embedding` field
            chunk["embedding"] = chunk["text"].transform(
                cocoindex.functions.SentenceTransformerEmbed(
                    model="sentence-transformers/all-MiniLM-L6-v2"))

            # Collect the chunk into the collector.
            doc_embeddings.collect(filename=doc["filename"], location=chunk["location"],
                                   text=chunk["text"], embedding=chunk["embedding"])

    # Export collected data to a vector index.
    doc_embeddings.export(
        "doc_embeddings",
        cocoindex.targets.Postgres(),
        primary_key_fields=["filename", "location"],
        vector_indexes=[
            cocoindex.VectorIndexDef(
                field_name="embedding",
                metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])

It defines an index flow like this:

🚀 Examples and demo

Example	Description
Text Embedding	Index text documents with embeddings for semantic search
Code Embedding	Index code embeddings for semantic search
PDF Embedding	Parse PDF and index text embeddings for semantic search
Manuals LLM Extraction	Extract structured information from a manual using LLM
Amazon S3 Embedding	Index text documents from Amazon S3
Azure Blob Storage Embedding	Index text documents from Azure Blob Storage
Google Drive Text Embedding	Index text documents from Google Drive
Docs to Knowledge Graph	Extract relationships from Markdown documents and build a knowledge graph
Embeddings to Qdrant	Index documents in a Qdrant collection for semantic search
FastAPI Server with Docker	Run the semantic search server in a Dockerized FastAPI setup
Product Recommendation	Build real-time product recommendations with LLM and graph database
Image Search with Vision API	Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend
Face Recognition	Recognize faces in images and build embedding index
Paper Metadata	Index papers in PDF files, and build metadata tables for each paper

More coming and stay tuned 👀!

📖 Documentation

For detailed documentation, visit CocoIndex Documentation, including a Quickstart guide.

🤝 Contributing

We love contributions from our community ❤️. For details on contributing or running the project for development, check out our contributing guide.

👥 Community

Welcome with a huge coconut hug 🥥⋆｡˚🤗. We are super excited for community contributions of all kinds - whether it's code improvements, documentation updates, issue reports, feature requests, and discussions in our Discord.

Join our community here:

• 🌟 Star us on GitHub
• 👋 Join our Discord community
• ▶️ Subscribe to our YouTube channel
• 📜 Read our blog posts

Support us:

We are constantly improving, and more features and examples are coming soon. If you love this project, please drop us a star ⭐ at GitHub repo to stay tuned and help us grow.

License

CocoIndex is Apache 2.0 licensed.