Clean up duplicate Story 18 files (already archived)

This commit is contained in:
Dave
2025-12-27 18:51:11 +00:00
parent 50e2c2cd70
commit 1baf3fa728
2 changed files with 0 additions and 188 deletions

View File

@@ -1,66 +0,0 @@
# Story: Token-by-Token Streaming Responses
## User Story
**As a** User
**I want** to see the model's response appear token-by-token as it generates
**So that** I get immediate feedback and can see the model is working, rather than waiting for the entire response to complete.
## Acceptance Criteria
* [ ] Model responses should appear token-by-token in real-time as Ollama generates them
* [ ] The streaming should feel smooth and responsive (like ChatGPT's typing effect)
* [ ] Tool calls should still work correctly with streaming enabled
* [ ] The user should see partial responses immediately, not wait for full completion
* [ ] Streaming should work for both text responses and responses that include tool calls
* [ ] Error handling should gracefully handle streaming interruptions
* [ ] The UI should auto-scroll to follow new tokens as they appear
## Out of Scope
* Configurable streaming speed/throttling
* Showing thinking/reasoning process separately (that could be a future enhancement)
* Streaming for tool outputs (tool outputs can remain non-streaming)
## Implementation Notes
### Backend (Rust)
* Change `stream: false` to `stream: true` in Ollama request
* Parse streaming JSON response from Ollama (newline-delimited JSON)
* Emit `chat:token` events for each token received
* Handle both streaming text and tool call responses
* Use `reqwest` with streaming body support
* Consider using `futures::StreamExt` for async stream processing
### Frontend (TypeScript)
* Listen for `chat:token` events
* Append tokens to the current assistant message in real-time
* Update the UI state without full re-renders (performance)
* Maintain smooth auto-scroll as tokens arrive
* Handle the transition from streaming text to tool calls
### Ollama Streaming Format
Ollama returns newline-delimited JSON when streaming:
```json
{"message":{"role":"assistant","content":"Hello"},"done":false}
{"message":{"role":"assistant","content":" world"},"done":false}
{"message":{"role":"assistant","content":"!"},"done":true}
```
### Challenges
* Parsing streaming JSON (each line is a separate JSON object)
* Maintaining state between streaming chunks
* Handling tool calls that interrupt streaming text
* Performance with high token throughput
* Error recovery if stream is interrupted
## Related Functional Specs
* Functional Spec: UI/UX (specifically mentions streaming as deferred)
## Dependencies
* Story 13 (interruption) should work with streaming
* May need `tokio-stream` or similar for stream utilities
## Testing Considerations
* Test with long responses to verify smooth streaming
* Test with responses that include tool calls
* Test interruption during streaming
* Test error cases (network issues, Ollama crashes)
* Test performance with different token rates

View File

@@ -1,122 +0,0 @@
# Story 18: Streaming Responses - Testing Notes
## Manual Testing Checklist
### Setup
1. Start Ollama: `ollama serve`
2. Ensure a model is running: `ollama list`
3. Build and run the app: `npm run tauri dev`
### Test Cases
#### TC1: Basic Streaming
- [ ] Send a simple message: "Hello, how are you?"
- [ ] Verify tokens appear one-by-one in real-time
- [ ] Verify smooth streaming with no lag
- [ ] Verify message appears in the chat history after streaming completes
#### TC2: Long Response Streaming
- [ ] Send: "Write a long explanation of how React hooks work"
- [ ] Verify streaming continues smoothly for long responses
- [ ] Verify auto-scroll keeps the latest token visible
- [ ] Verify no UI stuttering or performance issues
#### TC3: Code Block Streaming
- [ ] Send: "Show me a simple Python function"
- [ ] Verify code blocks stream correctly
- [ ] Verify syntax highlighting appears after streaming completes
- [ ] Verify code formatting is preserved
#### TC4: Tool Calls During Streaming
- [ ] Send: "Read the package.json file"
- [ ] Verify streaming stops when tool call is detected
- [ ] Verify tool execution begins immediately
- [ ] Verify tool output appears in chat
- [ ] Verify conversation can continue after tool execution
#### TC5: Multiple Turns
- [ ] Have a 3-4 turn conversation
- [ ] Verify each response streams correctly
- [ ] Verify message history is maintained
- [ ] Verify context is preserved across turns
#### TC6: Stop Button During Streaming
- [ ] Send a request for a long response
- [ ] Click the Stop button mid-stream
- [ ] Verify streaming stops immediately
- [ ] Verify partial response is preserved in chat
- [ ] Verify can send new messages after stopping
#### TC7: Network Interruption
- [ ] Send a request
- [ ] Stop Ollama during streaming (simulate network error)
- [ ] Verify graceful error handling
- [ ] Verify partial content is preserved
- [ ] Verify error message is shown
#### TC8: Fast Streaming
- [ ] Use a fast model (e.g., llama3.1:8b)
- [ ] Send: "Count from 1 to 20"
- [ ] Verify UI can keep up with fast token rate
- [ ] Verify no dropped tokens
## Expected Behavior
### Streaming Flow
1. User sends message
2. Message appears in chat immediately
3. "Thinking..." indicator appears briefly
4. Tokens start appearing in real-time in assistant message bubble
5. Auto-scroll keeps latest token visible
6. When streaming completes, `chat:update` event finalizes the message
7. Message is added to history
8. UI returns to ready state
### Events
- `chat:token`: Emitted for each token (payload: `string`)
- `chat:update`: Emitted when streaming completes (payload: `Message[]`)
### UI States
- **Idle**: Input enabled, no loading indicator
- **Streaming**: Input disabled, streaming content visible, auto-scrolling
- **Tool Execution**: Input disabled, tool output visible
- **Error**: Error message visible, input re-enabled
## Debugging
### Backend Logs
Check terminal for Rust logs:
- Look for "=== Ollama Request ===" to verify streaming is enabled
- Check for streaming response parsing logs
### Frontend Console
Open DevTools console:
- Look for `chat:token` events
- Look for `chat:update` events
- Check for any JavaScript errors
### Ollama Logs
Check Ollama logs:
```bash
journalctl -u ollama -f # Linux
tail -f /var/log/ollama.log # If configured
```
## Known Issues / Limitations
1. **Streaming is Ollama-only**: Other providers (Claude, GPT) not yet supported
2. **Tool outputs don't stream**: Tools execute and return results all at once
3. **No streaming animations**: Just simple text append, no typing effects
4. **Token buffering**: Very fast streaming might batch tokens slightly
## Success Criteria
All acceptance criteria from Story 18 must pass:
- [x] Backend emits `chat:token` events
- [x] Frontend listens and displays tokens in real-time
- [ ] Tokens appear smoothly without lag (manual verification required)
- [ ] Auto-scroll works during streaming (manual verification required)
- [ ] Tool calls work correctly with streaming (manual verification required)
- [ ] Stop button cancels streaming (manual verification required)
- [ ] Error handling works (manual verification required)
- [ ] Multi-turn conversations work (manual verification required)