* feat: support minimax model with XML wrapping fix
- Add wrapWithMxFile utility to properly wrap XML for draw.io
- Fix 'Not a diagram file' error when model generates raw <root> XML
- Add supportsPromptCaching check for conditional caching
- Only enable Bedrock prompt caching for Claude models
* docs: update model mention to minimax-m2 across About pages and READMEs
- Update tooltip in chat-panel.tsx to mention minimax-m2 model change
- Update English, Chinese, and Japanese About pages with model change info
- Update English, Chinese, and Japanese READMEs with demo site model note
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Co-authored-by: dayuan.jiang <jiangdy@amazon.co.jp>
* feat: add daily token limit with actual usage tracking
- Add DAILY_TOKEN_LIMIT env var for configurable daily token limit
- Track actual tokens from Bedrock API response metadata (not estimates)
- Server sends inputTokens + cachedInputTokens + outputTokens via messageMetadata
- Client increments token count in onFinish callback with actual usage
- Add NaN guards to prevent corrupted localStorage values
- Add token limit toast notification with quota display
- Remove client-side token estimation (was blocking legitimate requests)
- Switch to js-tiktoken for client compatibility (pure JS, no WASM)
* feat: add TPM (tokens per minute) rate limiting
- Add 50k tokens/min client-side rate limit
- Track tokens per minute with automatic minute rollover
- Check TPM limit after daily limits pass
- Show toast when rate limit reached
- NaN guards for localStorage values
* feat: make TPM limit configurable via TPM_LIMIT env var
* chore: restore cache debug logs
* fix: prevent race condition in TPM tracking
checkTPMLimit was resetting TPM count to 0 when checking, which
overwrote the count saved by incrementTPMCount. Now checkTPMLimit
only reads and incrementTPMCount handles all writes.
* chore: improve TPM limit error message clarity
- Save and restore chat messages, XML snapshots, session ID, and diagram XML to localStorage
- Restore diagram when DrawIO becomes ready (using new onLoad callback)
- Change close protection default to false since auto-save handles persistence
- Clear localStorage when clearing chat
- Handle edge cases: undefined edit fields, empty chartXML, missing access code header
- Add validation for orphaned mxPoint elements in validateMxCellStructure()
- Add cleanup of orphaned mxPoint elements in convertToLegalXml()
- Orphaned mxPoints cause 'Could not add object mxPoint' errors in draw.io
- mxPoint elements must have 'as' attribute or be inside <Array as="points">
Co-authored-by: dayuan.jiang <jiangdy@amazon.co.jp>
- Add 6th strategy: match by value attribute (label text)
- Add 7th strategy: normalized whitespace match
- Remove lastProcessedIndex tracking - always search from beginning
- Pairs may not be in document order, so sequential tracking was unreliable
Co-authored-by: dayuan.jiang <jiangdy@amazon.co.jp>
- Add Biome as formatter and linter (replaces Prettier)
- Configure Husky + lint-staged for pre-commit hooks
- Add VS Code settings for format on save
- Ignore components/ui/ (shadcn generated code)
- Remove semicolons, use 4-space indent
- Reformat all files to new style
- Add App Context section describing the left/right panel layout
- Add App Features section with icon locations (history, theme, upload, export, clear)
- Dynamically inject model name into system prompt via {{MODEL_NAME}} placeholder
- Expand edit_diagram tool description with usage guidelines
When images are included in chat messages, the AI SDK telemetry with
recordInputs: true sends base64 image data to Langfuse. Langfuse then
attempts to upload these images to media storage, causing 1m31s timeouts.
Setting recordInputs: false prevents this while still capturing user
text input via setTraceInput().
Bedrock streaming responses don't auto-report token usage to OpenTelemetry.
This fix manually sets span attributes (ai.usage.promptTokens, gen_ai.usage.input_tokens)
from the AI SDK onFinish callback to ensure Langfuse captures token counts.
- Add Zod schema validation for log-feedback and log-save endpoints
- Create singleton LangfuseClient to avoid per-request instantiation
- Simplify log-save to only flag trace (no XML content sent)
- Use generic error messages to prevent info leakage
- Update log-feedback API to find existing chat trace by sessionId and attach score to it
- Update log-save API to create span on existing chat trace instead of standalone trace
- Add thumbs up/down feedback buttons on assistant messages
- Add message regeneration and edit functionality
- Add save dialog with format selection (drawio, png, svg)
- Pass sessionId through components for Langfuse linking
- Remove default bedrock provider requirement
- Auto-detect provider when only one API key is configured
- Show helpful error when no keys or multiple keys without AI_PROVIDER
- Fixes#73
- Extract system prompts to dedicated lib/system-prompts.ts module
- Add extended system prompt (~4000 tokens) for models with higher cache minimums (Opus 4.5, Haiku 4.5)
- Clean up debug logs while preserving informational and cache-related logs
- Improve code formatting and organization in chat route
* feat: add trace-level input/output to Langfuse observability
- Add @langfuse/client and @langfuse/tracing dependencies
- Wrap POST handler with observe() for proper tracing
- Use updateActiveTrace() to set trace input, output, sessionId, userId
- Filter Next.js HTTP spans in shouldExportSpan so AI SDK spans become root traces
- Enable recordInputs/recordOutputs in experimental_telemetry
* refactor: extract Langfuse logic to separate lib/langfuse.ts module
- Add essential draw.io XML structure rules to system prompt
- Include critical rules about mxCell nesting (all must be direct children of root)
- Add shape/vertex and connector/edge examples with proper structure
- Improve tool description for display_diagram with validation rules
- Update xml_guide.md with better swimlane examples showing flat structure
- Add client-side XML validation to catch nested mxCell errors early
Helps address issues #40 (local Ollama models not working) and #39 (mxCell nesting errors)
- Install @ai-sdk/deepseek package
- Add DeepSeek provider support to lib/ai-providers.ts
- Add DeepSeek configuration to env.example
- Update README.md with DeepSeek in provider list
- Support both default and custom base URL for DeepSeek
* fix: add custom anthropic baseURL
* feat: add baseURL support for all AI providers
- Add GOOGLE_BASE_URL for Google Generative AI
- Add AZURE_BASE_URL for Azure OpenAI
- Add OLLAMA_BASE_URL support (was documented but not implemented)
- Add OPENROUTER_BASE_URL for OpenRouter
- Fix missing semicolon in Anthropic case
- Update env.example with new environment variables
Closes#20
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Co-authored-by: dayuan.jiang <jdy.toh@gmail.com>
- Add lib/cached-responses.ts with pre-generated XML for 4 example prompts
- Modify chat API route to check cache before calling AI
- Cache returns instant response (~0.26s) vs AI generation (~20-25s)
- Add "(cached for instant response)" text to example panel
- Cache only activates for first message with empty diagram
* fix: correct anthropic beta header config for fine-grained tool streaming
- Use bedrock.anthropicBeta for Bedrock provider (not additionalModelRequestFields)
- Use top-level headers for direct Anthropic API
- Update @ai-sdk/amazon-bedrock to 3.0.62
- Add headers support to ModelConfig interface
* fix: update @ai-sdk/amazon-bedrock to 3.0.62 for tool streaming support
- Add OpenRouter provider support with @openrouter/ai-sdk-provider
- Fix input not disabling during 'submitted' state for fast providers
- Apply disable logic to all interactive elements (textarea, buttons, handlers)
- Clean up env.example by removing model examples and separator blocks
- Upgrade zod to v4.1.12 for compatibility with ollama-ai-provider-v2
- Add debug logging for status changes in chat components
- Add formatXML function to format single-line XML with proper indentation
- Format chartXml after fetching to ensure consistency
- Update replaceXMLParts to handle single-line XML with substring fallback
- Improve edit_diagram tool guidance with SEARCH/REPLACE best practices
- Add concrete examples to help AI use minimal, targeted edits
