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classify_extract_sec

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- - __init__.py
  - config.py
  - export_types.py
  - process_file.py
  - schemas.py
- test_placeholder.py
- - App.tsx
  - index.css
  - main.tsx
  - vite-env.d.ts
- .gitignore
- components.json
- index.html
- package.json
- pnpm-lock.yaml
- postcss.config.mjs
- README.md
- tsconfig.json
- vite.config.ts
.copier-answers.yml
.env.template
.gitignore
AGENTS.md
CLAUDE.md
GEMINI.md
IMPLEMENTATION_NOTES.md
pyproject.toml
QUICK_START.md
README.md
uv.lock
WORKFLOW_DIAGRAM.md
WORKFLOW_SUMMARY.md

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// repository documentation

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This is a starter for a Llama Agents. See the [documentation](https://developers.llamaindex.ai/python/cloud/llamaagents/getting-started) for more info.

The backend contains a single workflow that runs LlamaCloud Extraction, given your schema. The frontend exposes an
extraction review UI, where you can review and correct extractions.

## Customizing the schema.

The starter contains a placeholder `MySchema` that is used for extraction. See [`schema.py`](./src/extraction_review_tmp5_classify_sec/schemas.py).

You should customize this `schema.py` for your use case to modify the extracted data. You can also rename the schema from `MySchema` to 
something more appropriate for your use case. Do a find and replace on "MySchema" to also fix the frontend references.

The frontend has a copy of the schema as a json schema, that it uses to introspect and generate an editing UI. Run `uv run export-types` to regenerate the frontend json schema.

## Customizing the application

This is meant to just be a starting place. You can add more workflows, and trigger them from the UI. For example, you could
add functionality sync to a downstream data sink to export the corrected data after review. Or you could add a workflow
that monitors a data source, and automatically triggers the extraction against the file.