Load Blip data to DuckDB
Build a Blip to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Blip API base URL, auth, endpoints, and incremental loading.
Blip is a conversational platform that provides a REST API to manage and interact with chatbot services and channels. Everything needed to build a working Blip → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.
Build your Blip to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Blip to DuckDB and run it on dltHub
That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Blip API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →
Prefer to write it yourself? Every fact the agent uses is below.
Blip API at a glance
| Base URL | https://{{contract_id}}.http.msging.net |
| Example endpoint | GET v1/inboxes |
| Authentication | all requests require a Key token in the Authorization header — sent in the Authorization header |
| Pagination | Offset-based |
| API reference | https://docs.blip.ai/ |
These values come from the Blip API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Blip API?
All requests require an 'Authorization' header in the format 'Key {YOUR_TOKEN}', where the token is your bot's access key retrieved from the Blip Portal.
1. Get your credentials
To obtain your Blip REST API credentials, log in to the Blip Portal and select your chatbot. Navigate to the left-hand menu, click on Configurations (gear icon), and select Connection information. Within the HTTP Endpoints section, you can locate or generate your Authorization token (API key). If a new key is needed, navigate to the Access keys section under settings, click New key, assign it a name, and copy the generated token immediately as it cannot be retrieved later for security reasons.
2. Add them to .dlt/secrets.toml
[sources.blip_source] api_key = "REPLACE_ME"
dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.
What Blip data can I load into DuckDB?
These are the Blip endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| inboxes | /v1/inboxes | GET | List all inboxes | |
| /v1/emails/{id} | GET | Get full email content | ||
| api_keys | /v1/api-keys | GET | List API keys | |
| webhooks | /v1/webhooks | GET | List webhooks | |
| webhook_deliveries | /v1/webhooks/{id}/deliveries | GET | List webhook deliveries |
How do I load only new Blip records?
The Blip API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.
{"name": "inboxes", "endpoint": { "path": "v1/inboxes", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"}, }}
On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.
What does the generated Blip pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading messages and commands from the Blip API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def blip_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://{{contract_id}}.http.msging.net", "auth": {"type": "api_key", "api_key": api_key, "name": "Authorization", "location": "header"}, }, "resources": [ {"name": "inboxes", "endpoint": {"path": "v1/inboxes"}}, {"name": "api_keys", "endpoint": {"path": "v1/api-keys"}} ], } yield from rest_api_resources(config) def load_blip_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="blip_pipeline", destination="duckdb", dataset_name="blip_data", ) load_info = pipeline.run(blip_source()) print(load_info) if __name__ == "__main__": load_blip_to_duckdb()
Run it with python blip_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.
How do I query Blip data in DuckDB?
dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.
Python (pandas DataFrame):
import dlt data = dlt.pipeline("blip_pipeline").dataset() df = data.inboxes.df() print(df.head())
SQL:
SELECT * FROM blip_data.inboxes LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Blip to DuckDB pipeline in production?
The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.
- Deploy & schedule — run the pipeline as a managed job with automatic retries.
- Monitor — observable job queues, alerting, and load metrics for every run.
- Transform — promote raw Blip loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load Blip data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example value |
|---|---|
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.
Next steps
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