DataFlow employs a "code generation" paradigm similar to that of create-react-app or vue-cli. This means that, through command-line invocation, it automatically generates the necessary scripts and entry Python files. After customizing these files (for example, by changing the dataset, using different large model APIs, or re-tuning operators), you can run the Python file to execute the corresponding functions.

Specifically, after successfully installing DataFlow as described in the previous section, please find an empty working directory to get started with DataFlow. Navigate to this directory and execute:

dataflow init

This command will generate three folders named cpu, api, and gpu, as well as an example_data folder for storing default sample data, in your current working directory.

Each of our pre-configured Pipelines is provided in three modes, placed in these three folders respectively. They are categorized based on the resource types required by the operators in the Pipeline, as shown in the table below:

User Category	Operators that only require CPU	Operators that require a large model API	Operators that require a locally deployed GPU
cpu	√
api	√	√
gpu	√	√	√

The same-named Pipelines in different folders have an inclusive relationship. Specifically, the Pipeline in the gpu folder is the most comprehensive, containing all the functions. Removing the operators that require a locally deployed GPU model results in the Pipeline in the api folder. Further removing the operators that require a large model backend results in the Pipeline in the cpu folder.

Notably, the api Pipeline can be modified to use a locally deployed GPU model (such as Qwen-3, llama, etc.) by changing the LLMServing within it. Compared to the gpu Pipeline, the operators removed in the api Pipeline are mainly those that call unconventional LLM models which cannot be deployed using the vllm backend.

Subsequently, by navigating to the corresponding path, you can access the Python files corresponding to our pre-configured Pipelines.

For these files, the default input dataset is stored in the json file within the example_data folder. You can change the first_entry_file_name field in the storage class to point it to your raw dataset.

self.storage = FileStorage(
    first_entry_file_name="../example_data/AgenticRAGPipeline/pipeline_small_chunk.json",
    cache_path="./cache_local", # Cache path
    file_name_prefix="dataflow_cache_step", # Prefix for cache file names
    cache_type="json",  # File type for intermediate cache files
)

Additionally, you may need to modify the LLMServing class according to your device or the api_url you possess, in order to use a locally downloaded model or an online large model API.

If you are using the API method, you need to export the api_key field to the environment variable. On Linux, this can be done using:

export api_key=sh-xxxxx

On Windows, you can set the environment variable using the following command:

set api_key=sh-xxxxx

Or in PowerShell:

$env:api_key = "sh-xxxxx"

After setting this, the program can read the API key from the environment for invocation. Be sure not to expose the key in public code.

Once you have modified the Python script, you can run it to experience DataFlow's comfortable data governance capabilities:

python reasoning_pipeline.py