QA Agent Interface #208
QA Agent Interface #208
Conversation
WalkthroughThis change removes the file validation step from GitHub workflows and deletes the validation script. It refactors the document QA agent to delegate answering to a new RAG-based function with strict schema validation and error handling. A new prompt classification function is added. The search API is enhanced to integrate AI-generated answers with related documents and fallback logic. A new React AI-powered search dialog and logo component are introduced. Environment variable validation is added, along with updates to dependencies and configuration. Changes
Sequence Diagram(s)AI-Powered Search FlowsequenceDiagram
participant User
participant CustomSearchDialog
participant /api/search
participant Agent API (doc-qa)
participant Retriever
participant RAG Answer Generator
User->>CustomSearchDialog: Enters search query
CustomSearchDialog->>/api/search: GET /api/search?query=...
/api/search->>Agent API (doc-qa): POST /agents/doc-qa (with query)
Agent API (doc-qa)->>Retriever: retrieveRelevantDocs(prompt)
Retriever->>Retriever: Vector search, reconstruct docs
Retriever-->>Agent API (doc-qa): Relevant docs
Agent API (doc-qa)->>RAG Answer Generator: answerQuestion(ctx, prompt)
RAG Answer Generator-->>Agent API (doc-qa): JSON answer + document paths
Agent API (doc-qa)-->>/api/search: JSON answer + docs
/api/search-->>CustomSearchDialog: AI answer + docs
CustomSearchDialog-->>User: Displays AI answer and sources
Fallback Search FlowsequenceDiagram
participant User
participant CustomSearchDialog
participant /api/search
participant Fumadocs Search
User->>CustomSearchDialog: Enters search query
CustomSearchDialog->>/api/search: GET /api/search?query=...
/api/search->>Fumadocs Search: Fallback search (on agent error)
Fumadocs Search-->>/api/search: Fallback results
/api/search-->>CustomSearchDialog: Fallback results with notice
CustomSearchDialog-->>User: Displays fallback results
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| Status | Name | Latest Commit | Preview URL | Updated (UTC) |
|---|---|---|---|---|
| ✅ Deployment successful! View logs |
docs | abe37e9 | Commit Preview URL | Jun 30 2025, 01:50 PM |
| set -euo pipefail | ||
| ./bin/collect-all-files.sh | \ | ||
| ./bin/validate-files.sh > all-files.txt | ||
| ./bin/collect-all-files.sh > all-files.txt |
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Actionable comments posted: 7
🧹 Nitpick comments (10)
.github/workflows/sync-docs.yml (1)
18-23: Step label & logic diverge after droppingvalidate-files.shThe step is still named “Collect and validate files”, yet the validation phase was removed (
validate-files.shno longer invoked). This can confuse future maintainers and reviewers, and makes it easy to overlook that no safety checks are happening on the file list.- - name: Collect and validate files + - name: Collect changed filesIf you still need basic sanity-checks (e.g. path traversal, size limits), consider adding lightweight validation inside
collect-changed-files.shor a follow-up step..github/workflows/sync-docs-full.yml (1)
12-16: Rename step or reinstate validation to avoid misleading workflow docsSame issue as in the incremental workflow: the label advertises validation that no longer occurs. Either rename the step or add minimal validation logic.
- - name: Collect and validate files + - name: Collect all filesEnsure
collect-all-files.shguards against unexpected entries now that the dedicated validator is gone.agent-docs/src/agents/doc-qa/prompt.ts (1)
14-60: Well-implemented prompt classification with room for minor improvements.The function follows Agentuity agent guidelines excellently with proper TypeScript usage, structured error handling, and context.logger usage. The classification criteria are clear and the conservative fallback approach is sound.
Consider these optional enhancements:
- Make model configurable:
export async function getPromptType(ctx: AgentContext, input: string, model = 'gpt-4o-mini'): Promise<PromptType> { // ... const result = await generateObject({ - model: openai('gpt-4o-mini'), + model: openai(model),
- Add retry logic for robustness:
// Add retry wrapper around generateObject call const maxRetries = 2; for (let attempt = 0; attempt <= maxRetries; attempt++) { try { const result = await generateObject(/* ... */); return result.object.type as PromptType; } catch (error) { if (attempt === maxRetries) { ctx.logger.error('Error classifying prompt after %d attempts, defaulting to Normal: %o', maxRetries + 1, error); return 'Normal' as PromptType; } ctx.logger.warn('Prompt classification attempt %d failed, retrying: %o', attempt + 1, error); } }agent-docs/src/agents/doc-qa/retriever.ts (1)
17-28: Add validation for metadata structure.The type checking for
vec.metadata.pathis good, but consider validating the entire metadata structure for better robustness.vectors.forEach(vec => { if (!vec.metadata) { ctx.logger.warn('Vector missing metadata'); return; } + // Validate metadata structure + if (typeof vec.metadata !== 'object') { + ctx.logger.warn('Vector metadata is not an object'); + return; + } const path = typeof vec.metadata.path === 'string' ? vec.metadata.path : undefined;agent-docs/src/agents/doc-qa/index.ts (1)
21-23: Improve prompt validation.The current check allows empty strings to pass through. Consider checking for meaningful content.
- if (prompt === undefined || prompt === null) { + if (!prompt || !prompt.trim()) { return resp.text("How can I help you?"); }agent-docs/src/agents/doc-qa/rag.ts (1)
86-94: Consider increasing token limit for complex responses.The current limit of 2048 tokens might be insufficient for detailed technical documentation answers with code examples and structured formatting.
const result = await generateObject({ model: openai('gpt-4o'), system: systemPrompt, prompt: prompt, schema: AnswerSchema, - maxTokens: 2048, + maxTokens: 4096, // Increased for detailed technical responses });components/CustomSearchDialog.tsx (3)
51-66: Add error boundary for localStorage failures.The current error handling logs warnings but doesn't provide user feedback when localStorage fails.
Consider adding user-visible feedback for localStorage issues:
} catch (error) { console.warn('Failed to load chat history:', error); + // Optionally show a toast notification to user about failed history loading }
82-87: Improve input focus timing.The setTimeout for focusing the input is a workaround that might not be reliable across all browsers and devices.
useEffect(() => { if (open) { - setTimeout(() => inputRef.current?.focus(), 100); + // Use requestAnimationFrame for more reliable timing + requestAnimationFrame(() => { + inputRef.current?.focus(); + }); } }, [open]);
302-318: Add input validation and rate limiting.Consider adding client-side validation and rate limiting to prevent spam queries.
<input ref={inputRef} type="text" value={currentInput} onChange={(e) => setCurrentInput(e.target.value)} onKeyDown={handleKeyDown} placeholder="Ask about Agentuity..." + maxLength={500} // Prevent extremely long queries className="flex-1 px-3 py-2 text-sm border border-gray-200 dark:border-gray-700 rounded-lg focus:outline-none focus:ring-1 focus:ring-gray-300 dark:focus:ring-gray-600 focus:border-transparent bg-white dark:bg-gray-800 text-gray-800 dark:text-gray-200 placeholder-gray-400 dark:placeholder-gray-500 transition-all" disabled={loading} />app/api/search/route.ts (1)
119-119: Remove debug logging for production.Consider removing or conditionally enabling this log:
- console.log('Returning results:', results.length, 'items'); + if (process.env.NODE_ENV === 'development') { + console.log('Returning results:', results.length, 'items'); + }
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package-lock.jsonis excluded by!**/package-lock.json
📒 Files selected for processing (14)
.github/workflows/sync-docs-full.yml(1 hunks).github/workflows/sync-docs.yml(1 hunks)agent-docs/src/agents/doc-processing/docs-orchestrator.ts(1 hunks)agent-docs/src/agents/doc-qa/index.ts(1 hunks)agent-docs/src/agents/doc-qa/prompt.ts(1 hunks)agent-docs/src/agents/doc-qa/rag.ts(1 hunks)agent-docs/src/agents/doc-qa/retriever.ts(1 hunks)agent-docs/src/agents/doc-qa/types.ts(1 hunks)app/api/search/route.ts(1 hunks)app/layout.tsx(2 hunks)bin/validate-files.sh(0 hunks)components/CustomSearchDialog.tsx(1 hunks)components/icons/AgentuityLogo.tsx(1 hunks)package.json(2 hunks)
💤 Files with no reviewable changes (1)
- bin/validate-files.sh
🧰 Additional context used
📓 Path-based instructions (2)
`agent-docs/src/agents/**/*.ts`: Use TypeScript for better type safety and IDE s...
agent-docs/src/agents/**/*.ts: Use TypeScript for better type safety and IDE support.
Import types from '@agentuity/sdk'.
Use structured error handling with try/catch blocks.
Leverage the provided logger (context.logger) for consistent logging.
Use the storage APIs (context.kv and context.vector) for persisting data.
Consider agent communication (response.handoff, context.getAgent) for complex workflows.
📄 Source: CodeRabbit Inference Engine (agent-docs/.cursor/rules/sdk.mdc)
List of files the instruction was applied to:
agent-docs/src/agents/doc-processing/docs-orchestrator.tsagent-docs/src/agents/doc-qa/prompt.tsagent-docs/src/agents/doc-qa/retriever.tsagent-docs/src/agents/doc-qa/index.tsagent-docs/src/agents/doc-qa/rag.tsagent-docs/src/agents/doc-qa/types.ts
`agent-docs/**/src/agents/**/index.ts`: Prefer using the `agentuity agent create...
agent-docs/**/src/agents/**/index.ts: Prefer using theagentuity agent createcommand to create a new Agent.
Prefer loading types from the node modules package@agentuity/sdkin the node_modules folder.
The file should export a default function.
Prefer naming the default function Agent or the name of the Agent based on the context of the Agent description.
All code should be in Typescript format.
Use the provided logger from theAgentContextinterface such asctx.logger.info("my message: %s", "hello").
📄 Source: CodeRabbit Inference Engine (agent-docs/.cursor/rules/agent.mdc)
List of files the instruction was applied to:
agent-docs/src/agents/doc-qa/index.ts
🧠 Learnings (12)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
agent-docs/src/agents/doc-processing/docs-orchestrator.ts (5)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentContext` interface in Agentuity supplies request-specific information and helper methods for accessing AI services such as KeyValue storage and Vector storage.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentContext interface exposes logging (context.logger), key-value storage (context.kv), vector storage (context.vector), remote agent access (context.getAgent), and tracing (context.tracer). Utilize these capabilities for building robust and observable agents.
package.json (2)
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/mdx.mdc:0-0
Timestamp: 2025-06-24T02:01:12.231Z
Learning: In MDX files (*.mdx), React components can be embedded and used within the markdown content.
components/icons/AgentuityLogo.tsx (2)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
app/layout.tsx (2)
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/mdx.mdc:0-0
Timestamp: 2025-06-24T02:01:12.231Z
Learning: In MDX files (*.mdx), React components can be embedded and used within the markdown content.
agent-docs/src/agents/doc-qa/prompt.ts (3)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
agent-docs/src/agents/doc-qa/retriever.ts (2)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
agent-docs/src/agents/doc-qa/index.ts (7)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentResponse` interface in Agentuity provides helper methods for responding with various data formats from the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentResponse interface provides methods for creating responses in multiple formats (json, text, binary, html, empty) and for redirecting to another agent with response.handoff. Use these methods to standardize agent responses.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
agent-docs/src/agents/doc-qa/rag.ts (3)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
components/CustomSearchDialog.tsx (4)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/mdx.mdc:0-0
Timestamp: 2025-06-24T02:01:12.231Z
Learning: In MDX files (*.mdx), React components can be embedded and used within the markdown content.
app/api/search/route.ts (4)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
agent-docs/src/agents/doc-qa/types.ts (1)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
🧬 Code Graph Analysis (3)
app/layout.tsx (1)
components/CustomSearchDialog.tsx (1)
CustomSearchDialog(42-354)
agent-docs/src/agents/doc-qa/prompt.ts (1)
agent-docs/src/agents/doc-qa/types.ts (2)
PromptType(24-24)PromptClassificationSchema(15-19)
components/CustomSearchDialog.tsx (2)
components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)lib/source.ts (1)
source(5-8)
🪛 Biome (1.9.4)
app/api/search/route.ts
[error] 86-86: Change to an optional chain.
Unsafe fix: Change to an optional chain.
(lint/complexity/useOptionalChain)
⏰ Context from checks skipped due to timeout of 90000ms (1)
- GitHub Check: Workers Builds: docs
🔇 Additional comments (22)
package.json (1)
17-17: Dependencies are up-to-date – approvedVerified via
npm viewthat all three packages in package.json match the latest stable releases on npm:
- cmdk: 1.1.1
- react-markdown: 10.1.0
- remark-gfm: 4.0.1
No further version changes are needed; ready to merge.
components/icons/AgentuityLogo.tsx (1)
1-30: Excellent accessible SVG component implementation.The component follows React and accessibility best practices with proper TypeScript interfaces, ARIA attributes, and flexible styling options.
agent-docs/src/agents/doc-processing/docs-orchestrator.ts (1)
3-3: LGTM: Clean import path correction.The import path update aligns with the codebase restructuring and maintains consistency with the new QA agent modules.
app/layout.tsx (1)
5-5: Clean integration of AI-powered search dialog.The CustomSearchDialog integration properly extends the RootProvider configuration without disrupting existing functionality, following Fumadocs patterns correctly.
Also applies to: 86-91
agent-docs/src/agents/doc-qa/retriever.ts (1)
7-11: LGTM: Well-structured function signature and query setup.The function correctly uses Agentuity SDK types and properly structures the vector search query.
agent-docs/src/agents/doc-qa/index.ts (2)
2-2: LGTM: Proper import of the answerQuestion function.Clean import from the rag module following the established pattern.
25-26: LGTM: Clean delegation to RAG module.The simplified flow correctly delegates to the answerQuestion function and returns the structured response.
agent-docs/src/agents/doc-qa/rag.ts (3)
9-10: LGTM: Proper function structure and integration.The function correctly integrates with the retriever module and follows TypeScript best practices.
12-84: Excellent system prompt design with comprehensive instructions.The system prompt is well-structured with clear rules, formatting guidelines, and examples. The MDX formatting instructions and JSON output specification are particularly well-thought-out.
95-114: LGTM: Comprehensive error handling with helpful fallback.The error handling provides a well-formatted fallback response that maintains the expected output structure while giving users actionable guidance.
components/CustomSearchDialog.tsx (3)
1-17: LGTM: Proper imports and setup.Good use of modern React patterns and appropriate external libraries for the chat interface functionality.
19-40: Well-designed TypeScript interfaces.The Message and SearchResult interfaces are comprehensive and properly typed for the chat functionality.
227-280: LGTM: Excellent message rendering with proper accessibility.The message rendering logic is well-structured with proper styling, accessibility features, and responsive design.
agent-docs/src/agents/doc-qa/types.ts (4)
1-6: LGTM: Clean Zod schema for document structure.The RelevantDocSchema properly defines the expected structure for documents with appropriate string validation.
8-11: LGTM: Well-defined answer schema.The AnswerSchema correctly captures the expected AI response structure with an answer string and document array.
13-19: Excellent schema design for prompt classification.The schemas provide proper validation for prompt types and classification results with confidence scoring and reasoning.
21-25: LGTM: Proper type inference pattern.The exported TypeScript types correctly use Zod's infer utility to generate types from the schemas, enabling both runtime validation and compile-time type checking.
app/api/search/route.ts (5)
8-10: LGTM!Clean utility function for path-to-URL conversion.
13-29: Well-structured metadata retrieval with proper fallbacks.Good error handling and fallback logic for missing metadata.
31-37: Clean title formatting logic.The path-to-title conversion provides good readability.
39-59: Robust snippet generation with smart fallbacks.Good layered approach to snippet generation with contextual fallbacks.
122-164: Excellent error handling with graceful fallbacks.The multi-level fallback strategy ensures search functionality remains available even when the AI service is down. Good UX consideration with clear error messages.
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Actionable comments posted: 2
♻️ Duplicate comments (1)
components/CustomSearchDialog.tsx (1)
187-194: Security validation implemented correctly.The URL validation addresses the security concern from previous reviews by checking for safe URL patterns.
🧹 Nitpick comments (3)
agent-docs/src/agents/doc-qa/index.ts (1)
9-21: Consider simplifying the input parsing logic.The current implementation is robust but could be streamlined. The type annotations and logic can be cleaner.
- let jsonRequest: any = null; - let prompt: string; + let prompt: string; try { - jsonRequest = await req.data.json(); - if (typeof jsonRequest === 'object' && jsonRequest !== null && 'message' in jsonRequest) { - prompt = String(jsonRequest.message || ''); - } else { - prompt = JSON.stringify(jsonRequest); - } + const jsonRequest = await req.data.json(); + prompt = (typeof jsonRequest === 'object' && jsonRequest?.message) + ? String(jsonRequest.message) + : JSON.stringify(jsonRequest); } catch { prompt = await req.data.text(); }components/CustomSearchDialog.tsx (2)
42-361: Consider component decomposition for better maintainability.This component handles multiple responsibilities (state management, API calls, UI rendering) and is quite large (320+ lines). Consider extracting some functionality into custom hooks or smaller components.
Consider extracting:
- A
useSearchHistorycustom hook for localStorage management- A
useSearchAPIcustom hook for the fetch logic- A
MessageListcomponent for rendering messages- A
SearchInputcomponent for the input areaThis would improve readability, testability, and reusability while maintaining the same functionality.
250-254: Consider memoizing ReactMarkdown for performance.The ReactMarkdown component re-renders on every message update. Consider memoizing it for better performance.
+ import { memo } from 'react'; + + const MarkdownContent = memo(({ content }: { content: string }) => ( + <div className="prose prose-sm max-w-none dark:prose-invert prose-gray text-sm"> + <ReactMarkdown remarkPlugins={[remarkGfm]}> + {content} + </ReactMarkdown> + </div> + )); {message.type === 'ai' ? ( - <div className="prose prose-sm max-w-none dark:prose-invert prose-gray text-sm"> - <ReactMarkdown remarkPlugins={[remarkGfm]}> - {message.content} - </ReactMarkdown> - </div> + <MarkdownContent content={message.content} /> ) : ( <p className="text-sm">{message.content}</p> )}
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agent-docs/src/agents/doc-qa/index.ts(1 hunks)agent-docs/src/agents/doc-qa/retriever.ts(1 hunks)app/api/search/route.ts(1 hunks)components/CustomSearchDialog.tsx(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- agent-docs/src/agents/doc-qa/retriever.ts
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📓 Path-based instructions (2)
`agent-docs/**/src/agents/**/index.ts`: Prefer using the `agentuity agent create...
agent-docs/**/src/agents/**/index.ts: Prefer using theagentuity agent createcommand to create a new Agent.
Prefer loading types from the node modules package@agentuity/sdkin the node_modules folder.
The file should export a default function.
Prefer naming the default function Agent or the name of the Agent based on the context of the Agent description.
All code should be in Typescript format.
Use the provided logger from theAgentContextinterface such asctx.logger.info("my message: %s", "hello").
📄 Source: CodeRabbit Inference Engine (agent-docs/.cursor/rules/agent.mdc)
List of files the instruction was applied to:
agent-docs/src/agents/doc-qa/index.ts
`agent-docs/src/agents/**/*.ts`: Use TypeScript for better type safety and IDE s...
agent-docs/src/agents/**/*.ts: Use TypeScript for better type safety and IDE support.
Import types from '@agentuity/sdk'.
Use structured error handling with try/catch blocks.
Leverage the provided logger (context.logger) for consistent logging.
Use the storage APIs (context.kv and context.vector) for persisting data.
Consider agent communication (response.handoff, context.getAgent) for complex workflows.
📄 Source: CodeRabbit Inference Engine (agent-docs/.cursor/rules/sdk.mdc)
List of files the instruction was applied to:
agent-docs/src/agents/doc-qa/index.ts
🧠 Learnings (3)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
agent-docs/src/agents/doc-qa/index.ts (7)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentResponse` interface in Agentuity provides helper methods for responding with various data formats from the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentResponse interface provides methods for creating responses in multiple formats (json, text, binary, html, empty) and for redirecting to another agent with response.handoff. Use these methods to standardize agent responses.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
components/CustomSearchDialog.tsx (4)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
🧬 Code Graph Analysis (1)
components/CustomSearchDialog.tsx (2)
components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)lib/source.ts (1)
source(5-8)
⏰ Context from checks skipped due to timeout of 90000ms (1)
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🔇 Additional comments (3)
components/CustomSearchDialog.tsx (3)
42-47: Well-structured component state.The state management is clean and follows React best practices with proper TypeScript typing.
51-87: Excellent lifecycle management.The useEffect hooks properly handle localStorage persistence, auto-scrolling, and focus management with appropriate cleanup and error handling.
108-113: Request timeout implementation looks good.The AbortController implementation follows the pattern discussed in previous reviews and provides proper timeout handling.
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Actionable comments posted: 1
🧹 Nitpick comments (1)
components/CustomSearchDialog.tsx (1)
196-362: Consider splitting into smaller components for better maintainability.The UI implementation is comprehensive with good accessibility and styling, but the component is quite large (360+ lines). Consider extracting sub-components for better maintainability:
Consider refactoring into smaller components:
// Extract message rendering const MessageItem = ({ message, onSourceClick }: { message: Message; onSourceClick: (url: string) => void }) => { // Message rendering logic }; // Extract header const DialogHeader = ({ onClose }: { onClose: () => void }) => { // Header rendering logic }; // Extract input area const InputArea = ({ currentInput, setCurrentInput, sendMessage, loading, messages, onRetry, onClear }: InputAreaProps) => { // Input area rendering logic };This would improve readability, testability, and maintainability of the main component.
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components/CustomSearchDialog.tsx(1 hunks)
🧰 Additional context used
🧠 Learnings (2)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
components/CustomSearchDialog.tsx (4)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
🧬 Code Graph Analysis (1)
components/CustomSearchDialog.tsx (2)
components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)lib/source.ts (1)
source(5-8)
⏰ Context from checks skipped due to timeout of 90000ms (1)
- GitHub Check: Workers Builds: docs
🔇 Additional comments (4)
components/CustomSearchDialog.tsx (4)
1-41: Well-structured imports and type definitions.The imports are comprehensive and the
MessageandSearchResultinterfaces provide good type safety for the chat functionality and API integration.
42-49: Clean state management and ref usage.The component state is well-organized and the useRef usage for DOM manipulation is appropriate for the auto-scroll and focus functionality.
50-87: Well-implemented effects with proper error handling.The localStorage persistence logic correctly handles Date object serialization/deserialization, and the UX effects (auto-scroll, focus) enhance the user experience appropriately.
159-194: Well-implemented event handlers with proper security.The event handlers provide good UX with keyboard shortcuts, and the URL validation in
handleSourceClickproperly addresses security concerns by validating URL formats before opening them.
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Actionable comments posted: 0
🧹 Nitpick comments (1)
lib/env.ts (1)
37-52: Consider environment-aware logging for production.The validation function provides helpful console output, but consider making the logging conditional based on environment to avoid cluttering production logs.
export const validateEnv = (): boolean => { try { const config = getAgentConfig(); - console.log('✓ Environment variables validated'); - console.log('✓ Agent URL:', config.url); - console.log('✓ Bearer token:', config.bearerToken ? 'configured' : 'not set'); + if (process.env.NODE_ENV !== 'production') { + console.log('✓ Environment variables validated'); + console.log('✓ Agent URL:', config.url); + console.log('✓ Bearer token:', config.bearerToken ? 'configured' : 'not set'); + } return true; } catch (error) { console.error('❌ Environment validation failed:', error);
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📒 Files selected for processing (7)
.env.example(1 hunks).gitignore(1 hunks)app/api/search/route.ts(1 hunks)app/layout.tsx(2 hunks)lib/env.ts(1 hunks)package.json(2 hunks)wrangler.jsonc(1 hunks)
✅ Files skipped from review due to trivial changes (3)
- .gitignore
- wrangler.jsonc
- .env.example
🚧 Files skipped from review as they are similar to previous changes (2)
- app/layout.tsx
- package.json
🧰 Additional context used
🧠 Learnings (3)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
lib/env.ts (2)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
app/api/search/route.ts (4)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
⏰ Context from checks skipped due to timeout of 90000ms (1)
- GitHub Check: Workers Builds: docs
🔇 Additional comments (11)
lib/env.ts (3)
5-8: LGTM: Clean interface definition.The
AgentConfiginterface is well-defined with appropriate types for URL and optional bearer token.
13-32: LGTM: Robust environment variable validation.The function correctly validates environment variables with clear error messaging and flexible URL construction. The logic properly handles both AGENT_FULL_URL and AGENT_BASE_URL + AGENT_ID combinations.
58-68: LGTM: Comprehensive type declarations.The global type declarations properly extend the NodeJS ProcessEnv interface with all required environment variables.
app/api/search/route.ts (8)
9-11: LGTM: Clean URL conversion utility.The function correctly converts document paths to URLs by removing file extensions and adding leading slash.
14-30: LGTM: Robust metadata extraction with fallback.The function properly handles errors when extracting document metadata and provides a reasonable fallback title format.
40-60: LGTM: Smart snippet generation with multiple fallbacks.The function provides intelligent content snippets with proper truncation and multiple fallback strategies when data is unavailable.
71-88: Excellent improvement: Environment-based configuration.This addresses the previous concern about hardcoded URLs. The code now properly uses environment configuration from
getAgentConfig()and includes conditional bearer token authentication.
97-105: Good fix: Optional chaining addressed.The code now uses
data?.answer?.trim()which properly addresses the previous static analysis concern about safe property access.
108-128: LGTM: Robust document processing with deduplication.The code properly deduplicates documents, handles processing errors gracefully, and generates appropriate metadata for each document reference.
133-175: Excellent error handling and fallback strategy.The comprehensive fallback mechanism ensures the search functionality remains available even when the AI agent fails, with clear user communication about the fallback state.
62-176: AI agent configuration and fallback logic validatedEnvironment variable validation (
validateEnvin lib/env.ts and invoked in app/layout.tsx), thegetAgentConfigimplementation, AI error handling in app/api/search/route.ts, and the fallback todefaultSearchHandlerare all in place. No further action is needed.
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Actionable comments posted: 0
♻️ Duplicate comments (1)
agent-docs/src/agents/doc-qa/index.ts (1)
27-28: Add logging to improve observability.While the
answerQuestionfunction handles errors internally (as confirmed by retrieved learnings), the coding guidelines recommend using the provided logger for better observability and debugging.+ ctx.logger.info("Processing question: %s", prompt); const answer = await answerQuestion(ctx, prompt); + ctx.logger.info("Generated answer successfully"); return resp.json(answer);
🧹 Nitpick comments (1)
lib/env.ts (1)
57-66: Remove the unnecessary empty export.The static analysis tool correctly identified that the empty export on line 66 is unnecessary since the file already has named exports.
Apply this fix:
-export {};
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📓 Path-based instructions (2)
`agent-docs/**/src/agents/**/index.ts`: Prefer using the `agentuity agent create...
agent-docs/**/src/agents/**/index.ts: Prefer using theagentuity agent createcommand to create a new Agent.
Prefer loading types from the node modules package@agentuity/sdkin the node_modules folder.
The file should export a default function.
Prefer naming the default function Agent or the name of the Agent based on the context of the Agent description.
All code should be in Typescript format.
Use the provided logger from theAgentContextinterface such asctx.logger.info("my message: %s", "hello").
📄 Source: CodeRabbit Inference Engine (agent-docs/.cursor/rules/agent.mdc)
List of files the instruction was applied to:
agent-docs/src/agents/doc-qa/index.ts
`agent-docs/src/agents/**/*.ts`: Use TypeScript for better type safety and IDE s...
agent-docs/src/agents/**/*.ts: Use TypeScript for better type safety and IDE support.
Import types from '@agentuity/sdk'.
Use structured error handling with try/catch blocks.
Leverage the provided logger (context.logger) for consistent logging.
Use the storage APIs (context.kv and context.vector) for persisting data.
Consider agent communication (response.handoff, context.getAgent) for complex workflows.
📄 Source: CodeRabbit Inference Engine (agent-docs/.cursor/rules/sdk.mdc)
List of files the instruction was applied to:
agent-docs/src/agents/doc-qa/index.ts
🧠 Learnings (3)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
agent-docs/src/agents/doc-qa/index.ts (8)
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentResponse` interface in Agentuity provides helper methods for responding with various data formats from the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentResponse interface provides methods for creating responses in multiple formats (json, text, binary, html, empty) and for redirecting to another agent with response.handoff. Use these methods to standardize agent responses.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
lib/env.ts (2)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
🪛 Biome (1.9.4)
lib/env.ts
[error] 66-66: This empty export is useless because there's another export or import.
This export makes useless the empty export.
Safe fix: Remove this useless empty export.
(lint/complexity/noUselessEmptyExport)
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🔇 Additional comments (7)
lib/env.ts (3)
4-7: LGTM! Clean interface design.The
AgentConfiginterface is well-designed with appropriate typing - required URL and optional bearer token.
12-31: LGTM! Robust environment validation logic.The
getAgentConfigfunction implements a flexible configuration approach supporting both full URL and base URL + agent ID modes. The validation logic is sound and error messages are clear and actionable.
36-51: LGTM! Excellent startup validation with helpful logging.The
validateEnvfunction provides comprehensive validation with user-friendly console output and detailed error guidance. The boolean return value makes it easy to integrate into application startup.agent-docs/src/agents/doc-qa/index.ts (4)
2-2: LGTM - Clean import statement.The import follows proper ES module syntax and correctly imports the answerQuestion function from the local RAG module.
9-10: LGTM - Appropriate variable declarations.The variable declarations properly type the JSON request and prompt variables for the parsing logic.
13-21: LGTM - Robust request parsing implementation.The parsing logic properly handles JSON extraction with appropriate fallbacks and addresses the previous review feedback about input robustness.
23-25: LGTM - Appropriate empty prompt handling.The condition correctly uses
!prompt.trim()to handle empty or whitespace-only prompts, addressing the previous unreachable condition concern.
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Actionable comments posted: 0
🧹 Nitpick comments (4)
components/CustomSearchDialog.tsx (4)
94-94: Consider using a more reliable focus mechanism.The hardcoded 100ms setTimeout for focusing the input may not be reliable across different devices or when the dialog animation takes longer than expected.
- setTimeout(() => inputRef.current?.focus(), 100); + const timeoutId = setTimeout(() => inputRef.current?.focus(), 150); + return () => clearTimeout(timeoutId);Alternatively, consider using
requestAnimationFramefor better timing synchronization with browser rendering.
42-382: Consider breaking down this large component for better maintainability.This component has grown to 382 lines with multiple responsibilities. Consider extracting some parts into smaller, focused components:
MessageListcomponent for rendering the messages (lines 256-308)MessageInputcomponent for the input area (lines 327-377)- Custom hooks for message management and localStorage persistence
This would improve testability, reusability, and make the code easier to maintain.
256-307: Extract message rendering into a separate component.The message rendering logic is complex and could benefit from being extracted into a dedicated
MessageItemcomponent. This would improve readability and make it easier to test the message rendering logic independently.interface MessageItemProps { message: Message; onSourceClick: (url: string) => void; } function MessageItem({ message, onSourceClick }: MessageItemProps) { // Extract the current message rendering logic here }
134-134: Consider adding response content-type validation.While the response status is validated, consider also validating the content-type to ensure the response is actually JSON before parsing.
+ const contentType = response.headers.get('content-type'); + if (!contentType?.includes('application/json')) { + throw new Error('Invalid response format'); + } const data: SearchResult[] = await response.json();
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🚧 Files skipped from review as they are similar to previous changes (1)
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🧰 Additional context used
🧠 Learnings (2)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
components/CustomSearchDialog.tsx (5)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
🧬 Code Graph Analysis (1)
components/CustomSearchDialog.tsx (2)
components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)lib/source.ts (1)
source(5-8)
⏰ Context from checks skipped due to timeout of 90000ms (1)
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🔇 Additional comments (3)
components/CustomSearchDialog.tsx (3)
117-123: Good implementation of AbortController management.The abort controller implementation properly handles cleanup and prevents overlapping requests. The ref-based approach ensures proper cleanup even if the component unmounts during a request.
207-214: Excellent URL validation implementation.The URL validation properly restricts to safe patterns and includes appropriate logging for debugging. This addresses the security concerns raised in previous reviews.
60-75: Robust localStorage error handling.The localStorage operations include proper error handling with console warnings, which is appropriate for this non-critical functionality. The timestamp restoration logic is well-implemented.
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Actionable comments posted: 2
🧹 Nitpick comments (6)
components/CustomSearchDialog/hooks/useMessages.tsx (2)
68-68: Consider reducing the timeout duration for better UX.A 30-second timeout may feel too long for users expecting near-instant search results. Consider reducing to 10-15 seconds for better perceived performance.
- const timeoutId = setTimeout(() => controller.abort(), 30000); + const timeoutId = setTimeout(() => controller.abort(), 15000);
30-36: Optimize localStorage saves for performance.Saving to localStorage on every message change could impact performance with large message histories. Consider debouncing or only saving on significant changes.
- useEffect(() => { - try { - localStorage.setItem(STORAGE_KEY, JSON.stringify(messages)); - } catch (error) { - console.warn('Failed to save chat history:', error); - } - }, [messages]); + const debouncedSave = useMemo( + () => debounce((msgs: Message[]) => { + try { + localStorage.setItem(STORAGE_KEY, JSON.stringify(msgs)); + } catch (error) { + console.warn('Failed to save chat history:', error); + } + }, 500), + [] + ); + + useEffect(() => { + debouncedSave(messages); + }, [messages, debouncedSave]);components/CustomSearchDialog/SearchInput.tsx (1)
57-61: Consider showing hint only when appropriate.The "Press Enter to send" hint appears whenever there's any input. Consider showing it only when the input is ready to send (trimmed and non-empty).
- {currentInput.trim() && ( + {currentInput.trim() && !loading && ( <div className="absolute right-2 bottom-2 text-xs text-gray-400"> Press Enter to send </div> )}components/CustomSearchDialog/index.tsx (2)
15-15: Remove unused inputRef.The
inputRefis defined but never used since the SearchInput component manages its own ref internally.- const inputRef = useRef<HTMLTextAreaElement>(null);Also remove the focus attempt on line 29:
- setTimeout(() => inputRef.current?.focus(), 100);
83-86: Consider preserving input on send for better UX.Clearing the input immediately on send might interfere with user expectations if they want to retry the same query. Consider clearing only on successful response.
The current implementation clears input optimistically. If this behavior is intentional for UX reasons, consider adding this to the useMessages hook instead for better state management.
components/CustomSearchDialog/MessageList.tsx (1)
116-118: Consider consistent locale formatting.Using
toLocaleTimeString()without a specific locale may show inconsistent formatting across users. Consider specifying a locale for consistency.- {message.timestamp.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' })} + {message.timestamp.toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit' })}
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📒 Files selected for processing (7)
components/CustomSearchDialog.tsx(1 hunks)components/CustomSearchDialog/MessageList.tsx(1 hunks)components/CustomSearchDialog/SearchInput.tsx(1 hunks)components/CustomSearchDialog/hooks/useMessages.tsx(1 hunks)components/CustomSearchDialog/index.tsx(1 hunks)components/CustomSearchDialog/types.ts(1 hunks)package.json(2 hunks)
✅ Files skipped from review due to trivial changes (1)
- components/CustomSearchDialog/types.ts
🚧 Files skipped from review as they are similar to previous changes (2)
- components/CustomSearchDialog.tsx
- package.json
🧰 Additional context used
🧠 Learnings (4)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
components/CustomSearchDialog/MessageList.tsx (2)
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/mdx.mdc:0-0
Timestamp: 2025-06-24T02:01:12.231Z
Learning: In MDX files (*.mdx), React components can be embedded and used within the markdown content.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
components/CustomSearchDialog/hooks/useMessages.tsx (1)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
components/CustomSearchDialog/index.tsx (1)
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
🧬 Code Graph Analysis (4)
components/CustomSearchDialog/MessageList.tsx (2)
components/CustomSearchDialog/types.ts (2)
MessageListProps(26-30)Message(3-14)components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)
components/CustomSearchDialog/hooks/useMessages.tsx (1)
components/CustomSearchDialog/types.ts (2)
Message(3-14)SearchResult(16-22)
components/CustomSearchDialog/index.tsx (5)
components/CustomSearchDialog/types.ts (1)
CustomSearchDialogProps(24-24)components/CustomSearchDialog/hooks/useMessages.tsx (1)
useMessages(8-157)components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)components/CustomSearchDialog/MessageList.tsx (1)
MessageList(10-35)components/CustomSearchDialog/SearchInput.tsx (1)
SearchInput(7-74)
components/CustomSearchDialog/SearchInput.tsx (1)
components/CustomSearchDialog/types.ts (1)
SearchInputProps(32-37)
⏰ Context from checks skipped due to timeout of 90000ms (1)
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🔇 Additional comments (6)
components/CustomSearchDialog/hooks/useMessages.tsx (1)
140-147: Good URL validation implementation.The URL validation logic properly checks for relative and absolute URLs while warning about potentially unsafe URLs. This prevents security issues from malicious source URLs.
components/CustomSearchDialog/SearchInput.tsx (1)
10-21: Good auto-resize implementation.The textarea auto-resize logic is well implemented with a sensible max height constraint and proper height reset before calculating new dimensions.
components/CustomSearchDialog/index.tsx (1)
42-120: Well-structured dialog implementation.The component properly uses Radix UI Dialog primitives for accessibility, implements proper keyboard shortcuts, and integrates the child components cleanly with appropriate prop passing.
components/CustomSearchDialog/MessageList.tsx (3)
37-51: Excellent empty state and accessibility.The empty state provides clear guidance to users and the component properly handles the no-messages case with appropriate visual hierarchy.
74-128: Well-implemented message differentiation.The MessageItem component effectively distinguishes between user and AI messages with appropriate styling, icons, and layout. The source handling integration is clean and functional.
89-93: No sanitization needed for ReactMarkdown usageReactMarkdown escapes any raw HTML by default and only parses standard markdown (we’re not using
rehype-raw), so there is no risk of HTML/script injection. Sincemessage.contentcomes from our own AI agent and we haven’t enabled raw HTML parsing, no additional sanitization is required.
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Actionable comments posted: 0
🧹 Nitpick comments (3)
components/CustomSearchDialog/MessageList.tsx (2)
88-93: Consider security implications of markdown rendering.ReactMarkdown is generally safe, but ensure user input is properly sanitized upstream. The remarkGfm plugin adds GitHub Flavored Markdown support which is appropriate for documentation.
For additional security, consider adding a
linkTarget="_blank"andtransformLinkUrito control link behavior:<ReactMarkdown remarkPlugins={[remarkGfm]} + linkTarget="_blank" + transformLinkUri={(uri) => uri.startsWith('http') ? uri : '#'} > {message.content} </ReactMarkdown>
116-118: Consider internationalization for timestamp format.The timestamp formatting uses browser defaults which is good, but consider if you need consistent formatting across different locales.
For consistent formatting regardless of locale:
-{message.timestamp.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' })} +{message.timestamp.toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit' })}app/api/search/route.ts (1)
131-131: Consider removing debug logging in production.The console.log statement may not be needed in production environments.
Consider using a proper logging library or environment-based logging:
-console.log('Returning results:', results.length, 'items'); +if (process.env.NODE_ENV === 'development') { + console.log('Returning results:', results.length, 'items'); +}
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agent-docs/src/agents/doc-qa/rag.ts(1 hunks)app/api/search/route.ts(1 hunks)components/CustomSearchDialog/MessageList.tsx(1 hunks)components/CustomSearchDialog/index.tsx(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- components/CustomSearchDialog/index.tsx
- agent-docs/src/agents/doc-qa/rag.ts
🧰 Additional context used
🧠 Learnings (3)
📓 Common learnings
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: The `AgentRequest` interface in Agentuity provides helper methods and public variables for handling incoming data to the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
components/CustomSearchDialog/MessageList.tsx (2)
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/mdx.mdc:0-0
Timestamp: 2025-06-24T02:01:12.231Z
Learning: In MDX files (*.mdx), React components can be embedded and used within the markdown content.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
app/api/search/route.ts (7)
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentHandler function type in the Agentuity JavaScript SDK is defined as: (request: AgentRequest, response: AgentResponse, context: AgentContext) => Promise<AgentResponseType>. This pattern should be followed for agent handler implementations in TypeScript.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: When developing Agentuity AI Agents in TypeScript, import types (`AgentRequest`, `AgentResponse`, `AgentContext`) from the `@agentuity/sdk` package to ensure type safety and consistency.
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/sdk.mdc:0-0
Timestamp: 2025-06-24T02:01:32.957Z
Learning: The AgentRequest interface provides methods for accessing trigger type, metadata, and request payload in various formats (JSON, text, ArrayBuffer, typed object). Use these methods to robustly handle incoming agent requests.
Learnt from: afterrburn
PR: agentuity/docs#195
File: agent-docs/.editorconfig:0-0
Timestamp: 2025-06-18T04:05:53.918Z
Learning: User afterrburn does not want EditorConfig-related suggestions or fixes for the agent-docs project, as they indicated they "don't care about that file" when referring to .editorconfig.
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
🧬 Code Graph Analysis (1)
components/CustomSearchDialog/MessageList.tsx (2)
components/CustomSearchDialog/types.ts (2)
MessageListProps(26-30)Message(3-14)components/icons/AgentuityLogo.tsx (1)
AgentuityLogo(6-30)
⏰ Context from checks skipped due to timeout of 90000ms (1)
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🔇 Additional comments (8)
components/CustomSearchDialog/MessageList.tsx (2)
13-16: Auto-scroll implementation looks good.The useEffect properly triggers on message changes and uses smooth scrolling behavior. The optional chaining ensures it won't error if the ref is null.
74-127: Excellent message rendering implementation.The component properly handles different message types with appropriate styling, markdown rendering for AI messages, and clickable source links. The conditional rendering logic is clean and the accessibility considerations with proper ARIA attributes are well implemented.
app/api/search/route.ts (6)
9-12: Document path to URL conversion looks correct.The regex properly removes .md/.mdx extensions while preserving any hash fragments. This handles both simple paths and those with anchors.
15-31: Robust metadata extraction with proper error handling.The function correctly attempts to get page metadata from the source and falls back to a formatted title. The error handling prevents crashes when pages don't exist.
41-61: Smart snippet generation with multiple fallback strategies.The function tries to use the page description first, then generates a contextual description based on the path structure. The error handling ensures a meaningful snippet is always returned.
72-94: Well-implemented AI agent integration.The code properly uses environment configuration, handles optional bearer tokens, and includes appropriate error handling for the API call. The fetch configuration is correct.
108-129: Excellent document processing with duplicate handling.The code properly deduplicates documents using Set, handles errors gracefully when processing individual documents, and generates meaningful metadata for each result.
134-176: Comprehensive fallback strategy with user-friendly messaging.The error handling is excellent - it falls back to the original search handler when AI fails, provides clear messaging to users about the fallback, and has a final safety net for complete failures. The error messages are informative and user-friendly.
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This is what it looks like -- a one turn QA. Next step will likely make it more chat-like. |
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Actionable comments posted: 0
🧹 Nitpick comments (3)
README.md (3)
11-17: Clarify project root vs.agent-docspathIf this README already sits inside the
agent-docsdirectory, the first step (cd agent-docs) is redundant and may mislead newcomers who clone only the docs repo. Consider re-phrasing to:-1. **Navigate to the Agent Directory:** - ```bash - cd agent-docs - ``` +1. **Ensure you are in the `agent-docs` directory** + (skip if this README is already at that path): + ```bash + cd agent-docs # only if you cloned the monorepo root + ```
20-22: Mention CLI prerequisite foragentuity dev
agentuity devpresupposes the global/PNPM-local CLI is installed (pnpm dlx agentuity@latest devor similar). Without noting this, users may hit “command not found”.-2. **Start the Agent:** - ```bash - agentuity dev - ``` +2. **Start the Agent (requires Agentuity CLI):** + ```bash + # install once, if not already available + pnpm dlx agentuity@latest dev # or: npx agentuity dev + ```
24-29: Leverage.env.exampleand remind about.gitignorePointing users to copy the shipped example prevents accidental commits of secrets and matches the new
env.tsvalidation.-3. **Update `.env.local`:** - Add your agent's URL and ID: - ```plaintext - AGENT_BASE_URL=YOUR_AGENT_URL - AGENT_ID=YOUR_DOC_QA_AGENT_ID - ``` +3. **Create your environment file** + ```bash + cp .env.example .env.local + ``` + Then edit `.env.local` with your agent details: + ```dotenv + AGENT_BASE_URL=https://my-agent.example.com + AGENT_ID=YOUR_DOC_QA_AGENT_ID + ``` + _(Ensure `.env.local` stays in `.gitignore`.)_
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🧠 Learnings (2)
📓 Common learnings
Learnt from: afterrburn
PR: agentuity/docs#208
File: agent-docs/src/agents/doc-qa/index.ts:27-28
Timestamp: 2025-06-28T18:06:02.296Z
Learning: The answerQuestion function in agent-docs/src/agents/doc-qa/rag.ts handles errors internally by logging them and returning a fallback JSON answer, so it doesn't require external try-catch blocks at the call site.
README.md (3)
Learnt from: CR
PR: agentuity/docs#0
File: .cursor/rules/overview.mdc:0-0
Timestamp: 2025-06-24T02:01:16.849Z
Learning: This documentation app is a NextJS application built on top of Fumadocs, which may influence documentation structure, component usage, and best practices for extending or customizing the documentation site.
Learnt from: afterrburn
PR: agentuity/docs#195
File: agent-docs/src/agents/doc-processing/docs-processor.ts:7-15
Timestamp: 2025-06-18T04:07:57.583Z
Learning: In the agent-docs project, the ChunkMetadata type requires title and description fields to be mandatory strings rather than optional, indicating that the system ensures these fields are always populated during document processing.
Learnt from: CR
PR: agentuity/docs#0
File: agent-docs/.cursor/rules/agent.mdc:0-0
Timestamp: 2025-06-24T02:01:24.554Z
Learning: In Agentuity AI Agent files (`**/src/agents/**/index.ts`), always use TypeScript and export a default function, preferably named `Agent` or contextually after the Agent.
⏰ Context from checks skipped due to timeout of 90000ms (1)
- GitHub Check: Workers Builds: docs
Just tried the preview url on this. i assume it's not wired up in preview so no worries on that ... but however this is what it looks like when the agent cant be communicated with. |
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Yes - it falls back to the default search result. I forgot to make sure those default links are clickable. |
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Looks like the links still work -- we're good. I will merge first thing in the morning tomorrow! |
Summary by CodeRabbit
New Features
Enhancements
Bug Fixes
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