wehub-resource-sync
58 lines
2.2 KiB
Markdown
58 lines
2.2 KiB
Markdown
# QA: Multimodal Attachments — PydanticAI
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## Prerequisites
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- Demo deployed and accessible at `/demos/multimodal`
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- Agent backend healthy (check `/api/health`)
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- `OPENAI_API_KEY` set
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- Bundled `public/demo-files/sample.png` and `public/demo-files/sample.pdf`
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present (copied over from the langgraph-python reference assets)
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## Test Steps
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### 1. Basic Functionality
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- [ ] Navigate to `/demos/multimodal`
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- [ ] Verify the header "Multimodal attachments" is visible
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- [ ] Verify the sample-row is visible with two buttons:
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"Try with sample image" and "Try with sample PDF"
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- [ ] Verify `<CopilotChat />` renders a message composer
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### 2. Sample image round-trip
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- [ ] Click "Try with sample image"
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- [ ] Within 10 seconds, an attachment chip labelled `sample.png`
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appears in the composer
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- [ ] Type "Describe this image" and click send
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- [ ] Within 90 seconds, an assistant response renders that references
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the CopilotKit / logo / image content
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### 3. Sample PDF round-trip
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- [ ] Click "Try with sample PDF"
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- [ ] Within 10 seconds, an attachment chip labelled `sample.pdf`
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appears in the composer
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- [ ] Type "Summarize this document" and click send
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- [ ] Within 90 seconds, an assistant response renders that references
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the document contents (should mention "CopilotKit")
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## Known Limitations vs. langgraph-python port
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- PydanticAI's AG-UI bridge does not expose a LangChain-style
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`before_model` middleware. The equivalent behaviour is implemented via
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a PydanticAI `history_processors` hook that rewrites incoming binary
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parts into `image_url` (for GPT-4o vision) or extracted text (for
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PDFs via `pypdf`) before each model call. Functionally equivalent to
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the langgraph-python reference.
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- The frontend's `onRunInitialized` shim (rewriting modern
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image/document parts into legacy `binary` parts) is framework-agnostic
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and kept intact so the Python-side history processor sees the same
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wire shape the langgraph-python reference does.
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## Expected Results
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- Images flow through to GPT-4o vision natively.
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- PDFs flatten to inline text; the agent can answer questions about the
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document contents.
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- Bundled samples work without mic / file-picker permission dialogs.
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