
cocoindex
Data transformation framework for AI. Ultra performant, with incremental processing.
Stars: 2831

CocoIndex is the world's first open-source engine that supports both custom transformation logic and incremental updates specialized for data indexing. Users declare the transformation, CocoIndex creates & maintains an index, and keeps the derived index up to date based on source update, with minimal computation and changes. It provides a Python library for data indexing with features like text embedding, code embedding, PDF parsing, and more. The tool is designed to simplify the process of indexing data for semantic search and structured information extraction.
README:
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.
⭐ Drop a star to help us grow!
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 - as easy as assembling building blocks.
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
- 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 |
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 |
Multi Format Indexing | Build visual document index from PDFs and images with ColPali for semantic search |
Custom Output Files | Convert markdown files to HTML files and save them to a local directory, using CocoIndex Custom Targets |
Patient intake form extraction | Use LLM to extract structured data from patient intake forms with different formats |
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:
- 🌟 Star us on GitHub
- 👋 Join our Discord community
▶️ Subscribe to our YouTube channel- 📜 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 ⭐ at GitHub repo to stay tuned and help us grow.
CocoIndex is Apache 2.0 licensed.
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