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TidGi-Desktop/features/vectorSearch.feature
lin onetwo 6384fd8bd1
Fix/misc bug (#677) 
* fix: not removed

* Optimize tidgi.config.json writes for workspace updates

Update logic to write tidgi.config.json only for the modified workspace instead of all wiki workspaces on each update. This reduces redundant file operations and improves performance during workspace updates.

* Refactor workspace saving and UI update logic

Introduced a private saveWorkspacesToSettings method to centralize logic for saving workspaces and removing syncable fields from wiki workspaces. The set and setWorkspaces methods now support skipping UI updates for batch operations, improving performance. Fixed minor issues in legacy migration and error messages.

* Add 'Ask AI' context menu and wiki embed split view

Introduces an 'Ask AI' option to the wiki context menu, enabling users to send selected text to an agent chat in a split view with the wiki embedded. Implements new tab type WIKI_EMBED, updates tab and channel types, adds localization, manages BrowserView bounds for embedding, and ensures persistence and IPC wiring for the new workflow.

* Update wiki

* electron chrome mcp mode sometimes wont show browser view

Clarified troubleshooting steps in docs/MCP.md regarding browser view issues and updated the instructions. Reordered the 'start:dev:mcp' script in package.json for better organization.

* Add agent selection to 'Talk with AI' context menu

Replaces the 'Ask AI' context menu with 'Talk with AI' and adds a submenu for selecting different agent definitions. Updates translations for all supported languages, modifies the askAIWithSelection channel to support agentDefId, and refactors tab creation logic to support split view with agent selection. Improves robustness in view management by handling case-insensitive workspace IDs and custom bounds logic.

* Add e2e test and refactor 'Talk with AI' split view logic

Introduces a new Cucumber feature for 'Talk with AI' from wiki selection, adds a step definition to trigger the workflow via IPC, and refactors split view tab creation to reuse existing tabs when possible. Updates the agent browser service to support finding or creating the appropriate split view tab, and adjusts menu and view services for improved robustness and code clarity. Also adds test IDs to relevant components for more reliable UI testing.

* Update defaultWiki.feature

* Add config error handling and i18n for agent errors

Introduces a new feature test for configuration error handling, adds step definition to remove AI settings for testing, and updates error message rendering to support new error types. Internationalized error messages and button labels for configuration issues are added in both English and Chinese locales. The error message renderer now uses a data-testid for easier testing and recognizes additional error types as fixable in settings.

* Refactor feature files to use two-column selector tables

Updated all feature files to use a standardized two-column format for selector tables, with explicit 'element description' and 'selector' columns. Step definitions in ui.ts were refactored to support this format, improving readability and maintainability of test steps and error handling.

* Delete tiddlywiki

* test: allow parallel

* test: implement scenario isolation for E2E tests

- Isolate each test scenario in test-artifacts/{scenarioSlug}/ directory
- Use dynamic ports for mock OpenAI server to avoid port conflicts
- Log VIEW_LOADED event via did-finish-load in main process (more reliable)
- Search all .log files when waiting for log markers
- Increase timeout for log marker steps to 15 seconds
- Fix ts-node cache issues by clearing cache before tests
- Move application launch to individual scenarios (required for mock server setup)

All 45 E2E test scenarios now pass consistently.

* refactor: optimize agent.feature by moving common steps to Background

- Add MockOpenAIServer.addRules() method to append responses dynamically
- Add 'I have started the mock OpenAI server without rules' step for Background
- Add 'I add mock OpenAI responses:' step to inject responses per scenario
- Move application launch and navigation to Background (shared by all scenarios)
- Keep scenario-specific mock responses in individual scenarios

This improves test maintainability by reducing duplication while keeping
scenario-specific configuration flexible.

* lint

* Refactor scenario path helpers into shared module

Moved scenario-specific path helper functions from individual step definition files to a centralized 'features/supports/paths.ts' module. Updated imports in step definitions to use the shared helpers, improving code reuse and maintainability. Also enhanced test for ContextService to skip optional runtime keys.

* Refactor slug generation to use shared slugify helper

Introduced a new src/helpers/slugify.ts utility for consistent slug generation across the codebase. Updated appPaths.ts to use the shared slugify function, improving maintainability and ensuring identical behavior for test scenario slugs. Added documentation and clarified slugification rules in relevant files. Minor comments and clarifications were added to E2E and mock server code.

* Enforce strict timeout rules in E2E test steps

Added and clarified critical warnings for AI agents regarding timeout modifications in application, cleanup, and wiki step definitions. All timeouts are now strictly limited to 5s local and 10s CI, with explicit comments and environment-based values. Updated documentation and code comments to reinforce that timeouts indicate real bugs and should not be increased.

* Update features/stepDefinitions/application.ts

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Refactor E2E helpers, improve test reliability and cleanup

Centralizes data table parsing for UI step definitions, refactors mock OpenAI server setup, and improves workspace settings path handling for tests. Adjusts timeouts for window and app closing to better reflect real-world performance. Fixes type usage in workspace ID lookups and adds error handling for resize observer and cleanup in WikiEmbedTabContent. Enhances agent browser tab logic and view service cleanup to prevent memory leaks and catch workspace ID casing issues.

* Update agent.ts

* fix: resolve all E2E test timeout issues

* fix: improve CI test reliability with better timing and cleanup

- Use exponential-backoff library for agent creation retry logic
- Extend agent cancel delay to 1000ms for CI environments
- Fix git log refresh marker timing with queueMicrotask
- Improve cleanup timeout handling with force close strategy

All E2E tests passing locally including previously failing CI tests.

* Improve Git log E2E signal and add debug logging

Renames the test artifact in the CI workflow for clarity. Moves the E2E test timing log in useGitLogData to after entries are rendered, using a more reliable signal. Adds a debug log to notifyGitStateChange for better traceability.

* test-artifacts-ci

* Optimize test artifact handling and Git log logging

Update CI workflow to clean up large cache folders in test artifacts and only upload logs, settings, and screenshots to reduce artifact size. Refactor useGitLogData to log immediately after state updates for improved E2E test reliability, removing unnecessary setTimeout.

* Update useGitLogData.ts

* Improve Git log E2E test logging and .gitignore

Added 'test-artifacts-ci.zip' to .gitignore. Moved the '[test-id-git-log-refreshed]' log to immediately after data load for more reliable E2E test detection, and removed redundant logging from the render effect in useGitLogData.ts.

* Update useGitLogData.ts

* Update useGitLogData.ts

* Fix git log refresh marker not appearing in CI

- Move git-log-refreshed marker before RAF to ensure it's recorded
- RAF callbacks may not execute reliably in headless CI environments
- Add debug logging to track loadGitLog execution
- Add try-catch around log call to catch any errors
- Keep git-log-data-rendered in useEffect for UI tracking

* Update useGitLogData.ts

* Update useGitLogData.ts

* Add comprehensive logging to diagnose git-log-refreshed issue

- Log before RAF and inside RAF to pinpoint exact failure location
- Add try-catch to capture any errors
- Two log markers: before-raf and in-raf
- This will definitively show where the logging fails in CI

* Fix race condition: prevent concurrent loadGitLog calls

Root cause: commit triggers 2 refreshes (gitStateChange$ + handleCommitSuccess)
- First loadGitLog (refreshTrigger=1) succeeds
- Second loadGitLog (refreshTrigger=2) starts but never completes
- Add loadGitLogInProgress guard to prevent concurrent execution
- Log when loadGitLog is skipped due to in-progress call

This ensures git-log-refreshed is always logged after commit.

* Remove redundant triggerRefresh calls causing race condition

- handleCommitSuccess/Revert/Undo no longer call triggerRefresh
- gitStateChange\$ observable already triggers refresh for these operations
- Redundant calls caused 2 concurrent loadGitLog, causing CI test failures
- Local tests passed because both completed; CI failed because 2nd never completed

This ensures only 1 loadGitLog runs per git operation.

* Remove unused triggerRefresh parameter from useCommitSelection

- triggerRefresh no longer used in handlers
- Remove from interface and call site
- Clean up lint errors

* Remove triggerRefresh completely - no longer needed

- Observable subscription handles all git state changes
- Remove function definition and exports
- Fix all lint errors

Root cause resolved: commit triggered double refresh causing race condition.
Now only single refresh via observable.

* Remove fixed time waits from gitLog.feature and fix race condition

- Remove all fixed time wait steps from gitLog.feature (14 instances)
- Remove redundant triggerRefresh calls in handleCommitSuccess/Revert/Undo
- Add loadGitLogInProgress guard to prevent concurrent loadGitLog
- Root cause: commit triggered 2 refreshes causing race condition
- Only gitStateChange\$ observable now triggers refresh
- All 4 gitLog tests pass locally

* Fix clear timing: clear log BEFORE commit, not after

Root cause: test cleared git-log-refreshed AFTER commit completed
- But commit already triggered refresh and logged git-log-refreshed
- Clear deleted it, then test waited for new log that would never come
- Solution: clear BEFORE clicking commit button
- This way commit's git-log-refreshed is the first one after clear

Test now passes locally.

* Update cleanup.ts

* Initial commit when init a new git.

* Refactor feature steps for multi-element and log marker tables

Updated multiple feature files and step definitions to support table-driven steps for clicking and asserting multiple elements, and for waiting for multiple log markers in sequence. This reduces redundant waits, improves test reliability, and streamlines Gherkin syntax for multi-element actions and assertions. Also removed unnecessary manual wait steps where content or element checks now handle waiting automatically.

* Minor code cleanup and formatting improvements

Reordered imports in browserView.ts, fixed whitespace in cleanup.ts and useGitLogData.ts, and improved line formatting in GitLog/index.tsx for better readability and consistency.

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: tidgi <tiddlygit@gmail.com>
2026-01-20 11:11:28 +08:00

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Feature: Vector Search - Embedding Generation and Semantic Search
As a user
I want to use vector database to perform semantic search in my wiki
So that I can find relevant content based on meaning rather than exact keywords
Background:
Given I add test ai settings
@vectorSearch @mockOpenAI
Scenario: Agent workflow - Create notes, update embeddings, then search
Given I have started the mock OpenAI server
| response | stream | embedding |
| <tool_use name="wiki-operation">{"workspaceName":"wiki","operation":"wiki-add-tiddler","title":"AI Agent Guide","text":"智能体是一种可以执行任务的AI系统，它可以使用工具、搜索信息并与用户交互。"}</tool_use> | false | |
| 已成功在工作区 wiki 中创建条目 "AI Agent Guide"。 | false | |
| <tool_use name="wiki-operation">{"workspaceName":"wiki","operation":"wiki-add-tiddler","title":"Vector Database Tutorial","text":"向量数据库用于存储和检索高维向量数据，支持语义搜索和相似度匹配。"}</tool_use> | false | |
| 已成功在工作区 wiki 中创建条目 "Vector Database Tutorial"。 | false | |
| <tool_use name="wiki-update-embeddings">{"workspaceName":"wiki","forceUpdate":false}</tool_use> | false | |
| | false | note1 |
| | false | note2 |
| 已成功为工作区 wiki 生成向量嵌入索引。总计2个笔记，2个嵌入向量。 | false | |
| <tool_use name="wiki-search">{"workspaceName":"wiki","searchType":"vector","query":"如何使用AI智能体","limit":5,"threshold":0.7}</tool_use> | false | |
| | false | query-note1 |
| 根据向量搜索结果，在工作区 wiki 中找到以下相关内容：\n\n**Tiddler: AI Agent Guide** (Similarity: 95.0%)\n这篇笔记介绍了AI智能体的基本概念和使用方法。 | false | |
# Launch application after mock server is ready
Then I launch the TidGi application
And I wait for the page to load completely
And I should see a "page body" element with selector "body"
# Ensure we are in the agent workspace (not wiki workspace) for agent interaction
When I click on an "agent workspace button" element with selector "[data-testid='workspace-agent']"
And I should see a "new tab button" element with selector "[data-tab-id='new-tab-button']"
# Step 1: Open agent chat interface
When I click on a "new tab button" element with selector "[data-tab-id='new-tab-button']"
And I should see a "search interface" element with selector ".aa-Autocomplete"
When I click on a "search input box" element with selector ".aa-Input"
And I should see an "autocomplete panel" element with selector ".aa-Panel"
When I click on an "agent suggestion" element with selector '[data-autocomplete-source-id="agentsSource"] .aa-ItemWrapper'
# Step 2: Create first note
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "在 wiki 工作区创建一个名为 AI Agent Guide 的笔记，内容是：智能体是一种可以执行任务的AI系统，它可以使用工具、搜索信息并与用户交互。" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 4 messages in chat history
# Step 3: Create second note
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "再创建一个名为 Vector Database Tutorial 的笔记，内容是：向量数据库用于存储和检索高维向量数据，支持语义搜索和相似度匹配。" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 8 messages in chat history
# Step 4: Update vector embeddings using agent tool
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "为 wiki 工作区更新向量索引" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 12 messages in chat history
# Step 5: Perform vector search
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "使用向量搜索在 wiki 中查找关于如何使用AI智能体的内容" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 16 messages in chat history
# Verify the last message contains vector search results
And I should see "search result in last message" elements with selectors:
| element description | selector |
| search result in last message| [data-testid='message-bubble']:last-child:has-text('Tiddler: AI Agent Guide') |
@vectorSearch @mockOpenAI
Scenario: UI workflow - Generate embeddings via preferences, then search
Given I have started the mock OpenAI server
| response | stream | embedding |
| <tool_use name="wiki-operation">{"workspaceName":"wiki","operation":"wiki-add-tiddler","title":"Machine Learning Basics","text":"机器学习是人工智能的一个分支，通过算法让计算机从数据中学习规律。"}</tool_use> | false | |
| 已成功在工作区 wiki 中创建条目 "Machine Learning Basics"。 | false | |
| | false | note3 |
| <tool_use name="wiki-search">{"workspaceName":"wiki","searchType":"vector","query":"机器学习","limit":5,"threshold":0.7}</tool_use> | false | |
| | false | query-note3 |
| 根据向量搜索结果，在工作区 wiki 中找到以下相关内容：\n\n**Tiddler: Machine Learning Basics** (Similarity: 98.0%)\n这篇笔记介绍了机器学习的基本概念。 | false | |
# Launch application after mock server is ready
Then I launch the TidGi application
And I wait for the page to load completely
And I should see a "page body" element with selector "body"
# Ensure we are in the agent workspace (not wiki workspace) for agent interaction
When I click on an "agent workspace button" element with selector "[data-testid='workspace-agent']"
And I should see a "new tab button" element with selector "[data-tab-id='new-tab-button']"
# Step 1: Create a test note via agent
When I click on "new tab button and create default agent button" elements with selectors:
| element description | selector |
| new tab button | [data-tab-id='new-tab-button'] |
| create default agent button| [data-testid='create-default-agent-button'] |
And I should see a "message input box" element with selector "[data-testid='agent-message-input']"
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "在 wiki 工作区创建一个名为 Machine Learning Basics 的笔记，内容是：机器学习是人工智能的一个分支，通过算法让计算机从数据中学习规律。" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 4 messages in chat history
# Step 2: Open preferences and navigate to Search section to generate embeddings
When I click on a "settings button" element with selector "#open-preferences-button"
When I switch to "preferences" window
When I click on a "search section" element with selector "[data-testid='preference-section-search']"
# Wait for workspace list to load
# The Search.tsx renders workspace cards with name, status, and buttons
And I should see a "wiki workspace card" element with selector "*:has-text('wiki')"
# Click the generate button - use button text "生成" instead of data-testid
# The button shows "生成" for initial generation, "更新嵌入" after generation
When I click on a "generate button with text" element with selector "button:has-text('生成')"
# Verify generation completed with detailed status information
# Should show: workspace name, embedding count, note count, last updated time and action buttons
Then I should see "workspace name in status and embedding count status and embedding word and last updated label and update button after generation and delete button after generation" elements with selectors:
| element description | selector |
| workspace name in status | *:has-text('wiki') |
| embedding count status | *:has-text('个笔记') |
| embedding word | *:has-text('嵌入') |
| last updated label | *:has-text('最后更新') |
| update button after generation | button:has-text('更新嵌入') |
| delete button after generation | button:has-text('删除') |
# Close preferences
When I close "preferences" window
And I switch to "main" window
# Step 3: Perform vector search and verify results match agent workflow
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "使用向量搜索在 wiki 中查找关于机器学习的内容" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 8 messages in chat history
# Verify the last message contains vector search results
And I should see a "ML search result in last message" element with selector "[data-testid='message-bubble']:last-child:has-text('Tiddler: Machine Learning Basics')"
@vectorSearch @mockOpenAI
Scenario: Vector search with low similarity - No results below threshold, then lower threshold
Given I have started the mock OpenAI server
| response | stream | embedding |
| <tool_use name="wiki-operation">{"workspaceName":"wiki","operation":"wiki-add-tiddler","title":"AI Technology","text":"人工智能技术正在改变世界。"}</tool_use> | false | |
| 已成功在工作区 wiki 中创建条目 "AI Technology"。 | false | |
| <tool_use name="wiki-operation">{"workspaceName":"wiki","operation":"wiki-add-tiddler","title":"Machine Learning","text":"机器学习算法和应用。"}</tool_use> | false | |
| 已成功在工作区 wiki 中创建条目 "Machine Learning"。 | false | |
| <tool_use name="wiki-update-embeddings">{"workspaceName":"wiki","forceUpdate":false}</tool_use> | false | |
| | false | note4 |
| | false | note5 |
| 已成功为工作区 wiki 生成向量嵌入索引。总计2个笔记，2个嵌入向量。 | false | |
| <tool_use name="wiki-search">{"workspaceName":"wiki","searchType":"vector","query":"天气预报","limit":5,"threshold":0.7}</tool_use> | false | |
| | false | unrelated |
| 在Wiki工作空间"wiki"中未找到符合条件的向量搜索结果（相似度阈值：0.7）。 | false | |
| <tool_use name="wiki-search">{"workspaceName":"wiki","searchType":"vector","query":"天气预报","limit":5,"threshold":0.1}</tool_use> | false | |
| | false | unrelated |
| 根据向量搜索结果，在工作区 wiki 中找到以下相关内容：\n\n**Tiddler: AI Technology** (Similarity: 15.0%)\n低相似度结果。 | false | |
# Launch application after mock server is ready
Then I launch the TidGi application
And I wait for the page to load completely
And I should see a "page body" element with selector "body"
# Ensure we are in the agent workspace (not wiki workspace) for agent interaction
When I click on an "agent workspace button" element with selector "[data-testid='workspace-agent']"
And I should see a "new tab button" element with selector "[data-tab-id='new-tab-button']"
# Step 1: Open agent chat interface
When I click on "new tab button and create default agent button" elements with selectors:
| element description | selector |
| new tab button | [data-tab-id='new-tab-button'] |
| create default agent button| [data-testid='create-default-agent-button'] |
And I should see a "message input box" element with selector "[data-testid='agent-message-input']"
# Step 2: Create first note about AI
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "在 wiki 工作区创建一个名为 AI Technology 的笔记，内容是：人工智能技术正在改变世界。" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 4 messages in chat history
# Step 3: Create second note about ML
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "再创建一个名为 Machine Learning 的笔记，内容是：机器学习算法和应用。" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 8 messages in chat history
# Step 4: Update vector embeddings
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "为 wiki 工作区更新向量索引" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 12 messages in chat history
# Step 5: Search for unrelated content with high threshold (should find nothing)
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "使用向量搜索在 wiki 中查找关于天气预报的内容，阈值设为0.7" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 16 messages in chat history
# Verify the 16th message contains "no results found" with threshold info
Then I should see "no results in 16th message and threshold 0.7 in 16th message" elements with selectors:
| element description | selector |
| no results in 16th message | [data-testid='message-bubble']:nth-child(16):has-text('未找到符合条件') |
| threshold 0.7 in 16th message| [data-testid='message-bubble']:nth-child(16):has-text('0.7') |
# Step 6: Lower threshold and search again (should find low-similarity results)
When I click on a "message input textarea" element with selector "[data-testid='agent-message-input']"
When I type "再次搜索天气预报，但这次把阈值降低到0.1" in "chat input" element with selector "[data-testid='agent-message-input']"
And I press "Enter" key
Then I should see 20 messages in chat history
# Verify the 20th message contains low-similarity result
Then I should see "AI Technology and low similarity in 20th message" elements with selectors:
| element description | selector |
| AI Technology in 20th message | [data-testid='message-bubble']:nth-child(20):has-text('Tiddler: AI Technology') |
| low similarity in 20th message | [data-testid='message-bubble']:nth-child(20):has-text('15') |
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