Avni Python API Docs | dltHub

Avni is a REST+JSON API for extracting health/program data (subjects, enrolments, encounters, programEncounters) from an Avni server. The REST API base URL is https://{your-avni-server-domain}/api and All requests require an auth token obtained via the Avni web app/Cognito (send as header 'auth-token' or Authorization header)..

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 Avni data in under 10 minutes.

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What data can I load from Avni?

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

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How do I authenticate with the Avni API?

Avni uses AWS Cognito for authentication. Create a user in the Avni admin web app (role 'User') and obtain the Cognito-issued token; include it as the 'auth-token' request header (or Authorization: Bearer where supported). The token can be copied from the web app's localStorage 'auth-token' key or obtained programmatically via Cognito initiate_auth flows.

1. Get your credentials

1. Open your Avni admin web application and create a user with role 'User'.
2. Log in once via the web interface (avoid first‑login password change challenge).
3. Open browser dev tools → Application → Local storage → your Avni server origin → copy the 'auth-token' value.
4. Alternatively implement AWS Cognito initiate_auth (USERNAME/PASSWORD) to get the id/access token programmatically.

2. Add them to .dlt/secrets.toml

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[sources.avni_source]
auth_token = "your_auth_token_here"

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 Avni 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 avni_pipeline.py

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

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Pipeline avni_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset avni_data
The duckdb destination used duckdb:/avni.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 avni_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 subjects and enrolments from the Avni 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 avni_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-avni-server-domain}/api",
            "auth": {
                "type": "bearer",
                "token": auth_token,
            },
        },
        "resources": [
            {"name": "subjects", "endpoint": {"path": "api/subjects", "data_selector": "content"}},
            {"name": "enrolments", "endpoint": {"path": "api/enrolments", "data_selector": "content"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="avni_pipeline",
        destination="duckdb",
        dataset_name="avni_data",
    )
    load_info = pipeline.run(avni_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("avni_pipeline").dataset()
sessions_df = data.subjects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM avni_data.subjects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("avni_pipeline").dataset()
data.subjects.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 Avni 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 401 Unauthorized: ensure a valid Cognito token is provided. Copy 'auth-token' from the web app localStorage or call Cognito initiate_auth; include it as header 'auth-token' or 'Authorization: Bearer '. Ensure the user has role 'User' and the account isn't forced to change password.

Authorization / Role errors

403 Forbidden indicates the authenticated user lacks required roles/permissions. Use an admin to grant the 'User' role to API user.

Pagination and incremental sync

Avni list endpoints are paginated (default page size 100). Responses include paging metadata and the array of records under the 'content' key. Use the 'lastModifiedDateTime' audit field on entities to perform incremental reads; store the last seen value and request entries > that timestamp.

Rate limits and server errors

Some deployments may enforce rate limits (429). Implement exponential backoff and retries. 5xx responses indicate server errors—retry with backoff and report to the Avni server admin if persistent.

Data duplication/collisions

Entities can appear multiple times if updated; deduplicate by entity id and use lastModifiedDateTime to determine newest state.

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