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https://github.com/iterative/datachain

AI-data warehouse to enrich, transform and analyze data from cloud storages
https://github.com/iterative/datachain

ai cv data-analytics data-wrangling embeddings llm llm-eval mlops multimodal

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AI-data warehouse to enrich, transform and analyze data from cloud storages

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README 

        
================

|logo| DataChain

================



|PyPI| |Python Version| |Codecov| |Tests|



.. |logo| image:: docs/assets/datachain.svg

   :height: 24

.. |PyPI| image:: https://img.shields.io/pypi/v/datachain.svg

   :target: https://pypi.org/project/datachain/

   :alt: PyPI

.. |Python Version| image:: https://img.shields.io/pypi/pyversions/datachain

   :target: https://pypi.org/project/datachain

   :alt: Python Version

.. |Codecov| image:: https://codecov.io/gh/iterative/datachain/graph/badge.svg?token=byliXGGyGB

   :target: https://codecov.io/gh/iterative/datachain

   :alt: Codecov

.. |Tests| image:: https://github.com/iterative/datachain/actions/workflows/tests.yml/badge.svg

   :target: https://github.com/iterative/datachain/actions/workflows/tests.yml

   :alt: Tests



DataChain is a modern Pythonic data-frame library designed for artificial intelligence.

It is made to organize your unstructured data into datasets and wrangle it at scale on

your local machine. Datachain does not abstract or hide the AI models and API calls, but helps to integrate them into the postmodern data stack.



Key Features

============



📂 **Storage as a Source of Truth.**

   - Process unstructured data without redundant copies from S3, GCP, Azure, and local

     file systems.

   - Multimodal data support: images, video, text, PDFs, JSONs, CSVs, parquet.

   - Unite files and metadata together into persistent, versioned, columnar datasets.



🐍 **Python-friendly data pipelines.**

   - Operate on Python objects and object fields.

   - Built-in parallelization and out-of-memory compute without SQL or Spark.



🧠 **Data Enrichment and Processing.**

   - Generate metadata using local AI models and LLM APIs.

   - Filter, join, and group by metadata. Search by vector embeddings.

   - Pass datasets to Pytorch and Tensorflow, or export them back into storage.



🚀 **Efficiency.**

   - Parallelization, out-of-memory workloads and data caching.

   - Vectorized operations on Python object fields: sum, count, avg, etc.

   - Optimized vector search.



Quick Start

-----------



.. code:: console



   $ pip install datachain



Selecting files using JSON metadata

======================================



A storage consists of images of cats and dogs (`dog.1048.jpg`, `cat.1009.jpg`),

annotated with ground truth and model inferences in the 'json-pairs' format,

where each image has a matching JSON file like `cat.1009.json`:



.. code:: json



    {

        "class": "cat", "id": "1009", "num_annotators": 8,

        "inference": {"class": "dog", "confidence": 0.68}

    }



Example of downloading only "high-confidence cat" inferred images using JSON metadata:



.. code:: py



    from datachain import Column, DataChain



    meta = DataChain.from_json("gs://datachain-demo/dogs-and-cats/*json", object_name="meta")

    images = DataChain.from_storage("gs://datachain-demo/dogs-and-cats/*jpg")



    images_id = images.map(id=lambda file: file.path.split('.')[-2])

    annotated = images_id.merge(meta, on="id", right_on="meta.id")



    likely_cats = annotated.filter((Column("meta.inference.confidence") > 0.93) \

                                   & (Column("meta.inference.class_") == "cat"))

    likely_cats.export_files("high-confidence-cats/", signal="file")



Data curation with a local AI model

===================================

Batch inference with a simple sentiment model using the `transformers` library:



.. code:: shell



    pip install transformers



The code below downloads files the cloud, and applies a user-defined function

to each one of them. All files with a positive sentiment

detected are then copied to the local directory.



.. code:: py



    from transformers import pipeline

    from datachain import DataChain, Column



    classifier = pipeline("sentiment-analysis", device="cpu",

                    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")



    def is_positive_dialogue_ending(file) -> bool:

        dialogue_ending = file.read()[-512:]

        return classifier(dialogue_ending)[0]["label"] == "POSITIVE"



    chain = (

       DataChain.from_storage("gs://datachain-demo/chatbot-KiT/",

                              object_name="file", type="text")

       .settings(parallel=8, cache=True)

       .map(is_positive=is_positive_dialogue_ending)

       .save("file_response")

    )



    positive_chain = chain.filter(Column("is_positive") == True)

    positive_chain.export_files("./output")



    print(f"{positive_chain.count()} files were exported")



13 files were exported



.. code:: shell



    $ ls output/datachain-demo/chatbot-KiT/

    15.txt 20.txt 24.txt 27.txt 28.txt 29.txt 33.txt 37.txt 38.txt 43.txt ...

    $ ls output/datachain-demo/chatbot-KiT/ | wc -l

    13



LLM judging chatbots

=============================



LLMs can work as universal classifiers. In the example below,

we employ a free API from Mistral to judge the `publicly available`_ chatbot dialogs. Please get a free

Mistral API key at https://console.mistral.ai



.. code:: shell



    $ pip install mistralai (Requires version >=1.0.0)

    $ export MISTRAL_API_KEY=_your_key_



DataChain can parallelize API calls; the free Mistral tier supports up to 4 requests at the same time.



.. code:: py



    from mistralai import Mistral

    from datachain import File, DataChain, Column



    PROMPT = "Was this dialog successful? Answer in a single word: Success or Failure."



    def eval_dialogue(file: File) -> bool:

         client = Mistral()

         response = client.chat.complete(

             model="open-mixtral-8x22b",

             messages=[{"role": "system", "content": PROMPT},

                       {"role": "user", "content": file.read()}])

         result = response.choices[0].message.content

         return result.lower().startswith("success")



    chain = (

       DataChain.from_storage("gs://datachain-demo/chatbot-KiT/", object_name="file")

       .settings(parallel=4, cache=True)

       .map(is_success=eval_dialogue)

       .save("mistral_files")

    )



    successful_chain = chain.filter(Column("is_success") == True)

    successful_chain.export_files("./output_mistral")



    print(f"{successful_chain.count()} files were exported")



With the instruction above, the Mistral model considers 31/50 files to hold the successful dialogues:



.. code:: shell



    $ ls output_mistral/datachain-demo/chatbot-KiT/

    1.txt  15.txt 18.txt 2.txt  22.txt 25.txt 28.txt 33.txt 37.txt 4.txt  41.txt ...

    $ ls output_mistral/datachain-demo/chatbot-KiT/ | wc -l

    31



Serializing Python-objects

==========================



LLM responses may contain valuable information for analytics – such as the number of tokens used, or the

model performance parameters.



Instead of extracting this information from the Mistral response data structure (class

`ChatCompletionResponse`), DataChain can serialize the entire LLM response to the internal DB:



.. code:: py



    from mistralai import Mistral

    from mistralai.models import ChatCompletionResponse

    from datachain import File, DataChain, Column



    PROMPT = "Was this dialog successful? Answer in a single word: Success or Failure."



    def eval_dialog(file: File) -> ChatCompletionResponse:

         client = MistralClient()

         return client.chat(

             model="open-mixtral-8x22b",

             messages=[{"role": "system", "content": PROMPT},

                       {"role": "user", "content": file.read()}])



    chain = (

       DataChain.from_storage("gs://datachain-demo/chatbot-KiT/", object_name="file")

       .settings(parallel=4, cache=True)

       .map(response=eval_dialog)

       .map(status=lambda response: response.choices[0].message.content.lower()[:7])

       .save("response")

    )



    chain.select("file.name", "status", "response.usage").show(5)



    success_rate = chain.filter(Column("status") == "success").count() / chain.count()

    print(f"{100*success_rate:.1f}% dialogs were successful")



Output:



.. code:: shell



         file   status      response     response          response

         name                  usage        usage             usage

                       prompt_tokens total_tokens completion_tokens

    0   1.txt  success           547          548                 1

    1  10.txt  failure          3576         3578                 2

    2  11.txt  failure           626          628                 2

    3  12.txt  failure          1144         1182                38

    4  13.txt  success          1100         1101                 1



    [Limited by 5 rows]

    64.0% dialogs were successful



Iterating over Python data structures

=============================================



In the previous examples, datasets were saved in the embedded database

(`SQLite`_ in folder `.datachain` of the working directory).

These datasets were automatically versioned, and can be accessed using

`DataChain.from_dataset("dataset_name")`.



Here is how to retrieve a saved dataset and iterate over the objects:



.. code:: py



    chain = DataChain.from_dataset("response")



    # Iterating one-by-one: support out-of-memory workflow

    for file, response in chain.limit(5).collect("file", "response"):

        # verify the collected Python objects

        assert isinstance(response, ChatCompletionResponse)



        status = response.choices[0].message.content[:7]

        tokens = response.usage.total_tokens

        print(f"{file.get_uri()}: {status}, file size: {file.size}, tokens: {tokens}")



Output:



.. code:: shell



    gs://datachain-demo/chatbot-KiT/1.txt: Success, file size: 1776, tokens: 548

    gs://datachain-demo/chatbot-KiT/10.txt: Failure, file size: 11576, tokens: 3578

    gs://datachain-demo/chatbot-KiT/11.txt: Failure, file size: 2045, tokens: 628

    gs://datachain-demo/chatbot-KiT/12.txt: Failure, file size: 3833, tokens: 1207

    gs://datachain-demo/chatbot-KiT/13.txt: Success, file size: 3657, tokens: 1101



Vectorized analytics over Python objects

========================================



Some operations can run inside the DB without deserialization.

For instance, let's calculate the total cost of using the LLM APIs, assuming the Mixtral call costs $2 per 1M input tokens and $6 per 1M output tokens:



.. code:: py



    chain = DataChain.from_dataset("mistral_dataset")



    cost = chain.sum("response.usage.prompt_tokens")*0.000002 \

               + chain.sum("response.usage.completion_tokens")*0.000006

    print(f"Spent ${cost:.2f} on {chain.count()} calls")



Output:



.. code:: shell



    Spent $0.08 on 50 calls



PyTorch data loader

===================



Chain results can be exported or passed directly to PyTorch dataloader.

For example, if we are interested in passing image and a label based on file

name suffix, the following code will do it:



.. code:: py



    from torch.utils.data import DataLoader

    from transformers import CLIPProcessor



    from datachain import C, DataChain



    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")



    chain = (

        DataChain.from_storage("gs://datachain-demo/dogs-and-cats/", type="image")

        .map(label=lambda name: name.split(".")[0], params=["file.name"])

        .select("file", "label").to_pytorch(

            transform=processor.image_processor,

            tokenizer=processor.tokenizer,

        )

    )

    loader = DataLoader(chain, batch_size=1)



Tutorials

---------



* `Getting Started`_

* `Multimodal `_ (try in `Colab `__)

* `LLM evaluations `_ (try in `Colab `__)

* `Reading JSON metadata `_ (try in `Colab `__)



Contributions

-------------



Contributions are very welcome.

To learn more, see the `Contributor Guide`_.



Community and Support

---------------------



* `Docs `_

* `File an issue`_ if you encounter any problems

* `Discord Chat `_

* `Email `_

* `Twitter `_



.. _PyPI: https://pypi.org/

.. _file an issue: https://github.com/iterative/datachain/issues

.. github-only

.. _Contributor Guide: CONTRIBUTING.rst

.. _Pydantic: https://github.com/pydantic/pydantic

.. _publicly available: https://radar.kit.edu/radar/en/dataset/FdJmclKpjHzLfExE.ExpBot%2B-%2BA%2Bdataset%2Bof%2B79%2Bdialogs%2Bwith%2Ban%2Bexperimental%2Bcustomer%2Bservice%2Bchatbot

.. _SQLite: https://www.sqlite.org/

.. _Getting Started: https://datachain.dvc.ai/

.. |Flowchart| image:: https://github.com/iterative/datachain/blob/main/docs/assets/flowchart.png?raw=true

   :alt: DataChain FlowChart