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.
β Drop a star to help us grow!
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.
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.
Native builtins for different source, targets and transformations. Standardize interface, make it 1-line code switch between different components.
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
If you're new to CocoIndex, we recommend checking out
- π Documentation
- β‘ Quick Start Guide
- π¬ Quick Start Video Tutorial
- Install CocoIndex Python library
pip install -U cocoindex- Install Postgres if you don't have one. CocoIndex uses it for incremental processing.
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:
| 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 |
| 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 |
| Paper Metadata | Index papers in PDF files, and build metadata tables for each paper |
More coming and stay tuned π!
For detailed documentation, visit CocoIndex Documentation, including a Quickstart guide.
We love contributions from our community β€οΈ. For details on contributing or running the project for development, check out our contributing guide.
Welcome with a huge coconut hug π₯₯βｑΛπ€. 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:
- π Star us on GitHub
- π Join our Discord community
βΆοΈ Subscribe to our YouTube channel- π Read our blog posts
We are constantly improving, and more features and examples are coming soon. If you love this project, please drop us a star β at GitHub repo
CocoIndex is Apache 2.0 licensed.