Load Fireworks AI data in Python using dltHub

Build a Fireworks AI-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Fireworks data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def fireworks_ai_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fireworks.ai/v1/",
            {'auth': {'type': 'bearer', 'token': 'access_token'}},
        },
        "resources": [
            models,,weather,,transcribe
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='fireworks_ai_pipeline',
        destination='duckdb',
        dataset_name='fireworks_ai_data', 
    )
    # Load the data
    load_info = pipeline.run(fireworks_ai_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from fireworks_ai’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Models: Endpoints for managing and interacting with various machine learning models.
  • Weather: Endpoints related to weather data.
  • Transcribe: Endpoints for transcribing audio inputs.
  • Embeddings: Endpoints for generating embeddings from text inputs.
  • Fine-Tuning: Endpoints for fine-tuning models for specific tasks.
  • Completions: Endpoints for generating text completions based on prompts.
  • Deployments: Endpoints for managing model deployments.

You will then debug the Fireworks pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with Fireworks support.

    dlt init dlthub:fireworks_ai duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Fireworks API, as specified in @fireworks_ai-docs.yaml 
Start with endpoints models and  and skip incremental loading for now. 
Place the code in fireworks_ai_pipeline.py and name the pipeline fireworks_ai_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python fireworks_ai_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is done using an API key located in the header, specifically in the 'Authorization' header.

    To get the appropriate API keys, please visit the original source at https://www.fireworks.ai/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python fireworks_ai_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline fireworks_ai load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fireworks_ai_data
The duckdb destination used duckdb:/fireworks_ai.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline fireworks_ai_pipeline show --dashboard

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("fireworks_ai_pipeline").dataset()
# get odel table as Pandas frame
data.odel.df().head()

Running into errors?

Ensure that the API key is provided in the correct format as a Bearer token. The API may have rate limits that need to be managed to avoid throttling. Some endpoints may require specific request formats, and certain features may necessitate additional setup.

Extra resources:

Next steps

def get_data() -> None: # Connect to destination pipeline = dlt.pipeline( pipeline_name='fireworks_ai_pipeline', destination='duckdb', dataset_name='fireworks_ai_data', ) # Load the data load_info = pipeline.run(fireworks_ai_source()) print(load_info)


### Why use dltHub Workspace with LLM Context to generate Python pipelines?

- Accelerate pipeline development with AI-native context
- Debug pipelines, validate schemas and data with the integrated **Pipeline Dashboard**
- Build Python notebooks for end users of your data
- **Low maintenance** thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
- dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

## What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from fireworks_ai’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

- Models: Endpoints for managing and interacting with various machine learning models.
- Weather: Endpoints related to weather data.
- Transcribe: Endpoints for transcribing audio inputs.
- Embeddings: Endpoints for generating embeddings from text inputs.
- Fine-Tuning: Endpoints for fine-tuning models for specific tasks.
- Completions: Endpoints for generating text completions based on prompts.
- Deployments: Endpoints for managing model deployments.

You will then debug the Fireworks pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

## Setup & steps to follow

```default
Before getting started, let's make sure Cursor is set up correctly:
   - We suggest using a model like Claude 3.7 Sonnet or better
   - Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as **@dlt rest api**
   - [Read our full steps on setting up Cursor](https://dlthub.com/docs/dlt-ecosystem/llm-tooling/cursor-restapi#23-configuring-cursor-with-documentation)

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with Fireworks support.

    dlt init dlthub:fireworks_ai duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Fireworks API, as specified in @fireworks_ai-docs.yaml 
Start with endpoints models and  and skip incremental loading for now. 
Place the code in fireworks_ai_pipeline.py and name the pipeline fireworks_ai_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python fireworks_ai_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is done using an API key located in the header, specifically in the 'Authorization' header.

    To get the appropriate API keys, please visit the original source at https://www.fireworks.ai/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python fireworks_ai_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline fireworks_ai load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fireworks_ai_data
The duckdb destination used duckdb:/fireworks_ai.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline fireworks_ai_pipeline show --dashboard

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("fireworks_ai_pipeline").dataset()
# get odel table as Pandas frame
data.odel.df().head()

Running into errors?

Ensure that the API key is provided in the correct format as a Bearer token. The API may have rate limits that need to be managed to avoid throttling. Some endpoints may require specific request formats, and certain features may necessitate additional setup.

Extra resources:

Next steps