Data Extraction and Ingestion

This is a starter for LlamaAgents. See the LlamaAgents (llamactl) getting started guide for context on local development and deployment.

To run the application, install uv and run uvx llamactl serve.

Simple customizations

For some basic customizations, you can modify src/extraction_review/config.py

• USE_REMOTE_EXTRACTION_SCHEMA: Set to False to define your own Pydantic ExtractionSchema in this file. Set to True to reuse the schema from an existing LlamaCloud Extraction Agent.
• EXTRACTION_AGENT_NAME: Logical name for your Extraction Agent. When USE_REMOTE_EXTRACTION_SCHEMA is False, this name is used to upsert the agent with your local schema; when True, it is used to fetch an existing agent.
• EXTRACTED_DATA_COLLECTION: The Agent Data collection name used to store extractions (namespaced by agent name and environment).
• ExtractionSchema: When using a local schema, edit this Pydantic model to match the fields you want extracted. Prefer optional types where possible to allow for partial extractions.

The UI fetches the JSON Schema and collection name from the backend metadata workflow at runtime, and dynamically generates an editing UI based on the schema.

Complex customizations

For more complex customizations, you can edit the rest of the application. For example, you could

• Modify the existing file processing workflow to provide additional context for the extraction process
• Take further action based on the extracted data.
• Add additional workflows to submit data upon approval.

Linting and type checking

Python and javascript packages contain helpful scripts to lint, format, and type check the code.

To check and fix python code:

uv run hatch run lint
uv run hatch run typecheck
uv run hatch run test
# run all at once
uv run hatch run all-fix

To check and fix javascript code, within the ui directory:

pnpm run lint
pnpm run typecheck
pnpm run test
# run all at once
pnpm run all-fix