wehub-resource-sync
45 lines
1.8 KiB
Markdown
45 lines
1.8 KiB
Markdown
# Dataroom Q&A
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## Goal
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Answer grounded financial questions over a synthetic 10-K packet.
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The packet uses synthetic company data, but the documents are shaped like annual report excerpts: MD&A text uses 10-K `Part II, Item 7`, while statement PDFs and footnote text use `Part II, Item 8`.
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## Why this is valuable
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This demo shows a retrieval-first agent pattern over a bounded financial corpus where each metric and explanation should stay tied to source files.
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## Setup
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Run the fixture generator and then the Unix-local example from the repository root. Set `OPENAI_API_KEY` in your shell environment before running the example.
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```bash
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uv run python examples/sandbox/tutorials/data/dataroom/setup.py
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uv run python examples/sandbox/tutorials/dataroom_qa/main.py
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```
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After the initial answer, the demo keeps the sandbox session open for Rich-rendered follow-up prompts. Pass `--no-interactive` for a one-shot run.
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To run the same manifest in Docker, build the shared tutorial image once and pass
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`--docker`:
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```bash
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docker build --tag sandbox-tutorials:latest examples/sandbox/tutorials
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uv run python examples/sandbox/tutorials/dataroom_qa/main.py --docker
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```
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## Expected artifacts
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- A direct cited answer in the streamed agent response.
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- Citations use `[n](data/source-file.txt:line:14)` for text excerpts and `[n](data/source-file.pdf:page:1)` for the one-page synthetic PDFs.
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## Demo shape
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- Inputs: 5 synthetic filing text docs and 3 simple filing PDFs from `examples/sandbox/tutorials/data/dataroom/`.
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- Runtime primitives: sandbox-local bash/file search.
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## How instructions are loaded
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At startup, the wrapper loads this folder's `AGENTS.md` into the agent instructions and builds a hard-coded manifest that maps the shared SEC packet from `examples/sandbox/tutorials/data/dataroom/` into the sandbox as `data/...`.
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