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 effortless to transform data with AI, and keep source data and target in sync. Whether you’re building a vector index for RAG, creating knowledge graphs, or performing any custom data transformations — goes beyond 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:
If you're new to CocoIndex, we recommend checking out
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:
More coming and stay tuned 👀!
For detailed documentation, visit CocoIndex Documentation, including a Quickstart guide.
We love contributions from our community ❤️. For details on contributing or running the project for development, check out our contributing guide.
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:
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.
CocoIndex is Apache 2.0 licensed.
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