Segmind Python API Docs | dltHub

Segmind is a developer platform providing hosted machine learning model APIs for inference and model‑serving. The REST API base URL is https://api.segmind.com/v1 and All requests require an x-api-key header for authentication..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Segmind data in under 10 minutes.

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What data can I load from Segmind?

Here are some of the endpoints you can load from Segmind:

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How do I authenticate with the Segmind API?

Segmind uses an API key passed in the HTTP header named x-api-key. Include Content-Type: application/json for JSON requests; image‑generation endpoints may return binary (image/*) responses.

1. Get your credentials

1. Sign in to https://www.segmind.com and open your account/dashboard.
2. Navigate to the "API keys" or "Create an API key" section in the developer/docs area.
3. Create a new key, copy the key value and store it securely.
4. Use the key as the value of the x-api-key header in requests.

2. Add them to .dlt/secrets.toml

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[sources.segmind_consistent_character_ai_source]
api_key = "YOUR_SEGMIND_API_KEY"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Segmind API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python segmind_consistent_character_ai_pipeline.py

If everything is configured correctly, you'll see output like this:

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

Inspect your pipeline and data:

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dlt pipeline segmind_consistent_character_ai_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads consistent_character_neolemon_v2 and fast_flux_schnell from the Segmind API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def segmind_consistent_character_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.segmind.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "consistent_character_neolemon_v2", "endpoint": {"path": "consistent-character-AI-neolemon-v2"}},
            {"name": "fast_flux_schnell", "endpoint": {"path": "fast-flux-schnell"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="segmind_consistent_character_ai_pipeline",
        destination="duckdb",
        dataset_name="segmind_consistent_character_ai_data",
    )
    load_info = pipeline.run(segmind_consistent_character_ai_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("segmind_consistent_character_ai_pipeline").dataset()
sessions_df = data.consistent_character_neolemon_v2.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM segmind_consistent_character_ai_data.consistent_character_neolemon_v2 LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("segmind_consistent_character_ai_pipeline").dataset()
data.consistent_character_neolemon_v2.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Segmind data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive HTTP 401 Unauthorized, verify you set the x-api-key header with a valid API key. Ensure there are no extra quotes or whitespace around the key.

Rate limits and credits

Responses with HTTP 406 indicate insufficient credits. The docs also list 405 Method Not Allowed, 404 Not Found and 500 Server Error; handle these by retry/backoff and ensure correct endpoint/method.

Binary/image responses and content-type

Image-generation endpoints commonly return image/* (e.g., image/jpeg) or binary content. When expecting images set response handling to binary (stream) rather than JSON.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack