AI 🔗 DataChain

DataChain is an open-source Python library for processing and curating unstructured data at scale.

🤖 AI-Driven Data Curation: Use local ML models or LLM APIs calls to enrich your data.

🚀 GenAI Dataset scale: Handle tens of millions of multimodal files.

🐍 Python-friendly: Use strictly-typed Pydantic objects instead of JSON.

Datachain supports parallel processing, parallel data downloads, and out-of-memory computing. It excels at optimizing offline batch operations.

The typical use cases include Computer Vision data curation, LLM analytics, and validation of multimodal AI applications.

$ pip install datachain

Quick Start

Data curation with a local model

We will evaluate chatbot dialogs stored as text files in Google Cloud Storage - 50 files total in this example. These dialogs involve users chatting with a bot while looking for better wireless plans. Our goal is to identify the successful dialogs.

The data used in the examples is publicly available. The sample code is designed to run on a local machine.

First, we'll show batch inference with a simple sentiment model using the transformers library:

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.

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

$ 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 efficient universal classifiers. In the example below, we employ a free API from Mistral to judge the chatbot performance. Please get a free Mistral API key at https://console.mistral.ai

$ pip install mistralai
$ export MISTRAL_API_KEY=_your_key_

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

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
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 = MistralClient()
     response = client.chat(
         model="open-mixtral-8x22b",
         messages=[ChatMessage(role="system", content=PROMPT),
                   ChatMessage(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:

$ 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:

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage, 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=[ChatMessage(role="system", content=PROMPT),
                   ChatMessage(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:

     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:

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:

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:

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:

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:

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)

Contributions

Contributions are very welcome. To learn more, see the Contributor Guide.

Community and Support

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