9d437f8594
Problem: The MCP tool previously handled project root acquisition and path resolution within its method, leading to potential inconsistencies and repetition. Solution: Refactored the tool () to utilize the new Higher-Order Function (HOF) from . Specific Changes: - Imported HOF. - Updated the Zod schema for the parameter to be optional, as the HOF handles deriving it from the session if not provided. - Wrapped the entire function body with the HOF. - Removed the manual call to from within the function body. - Destructured the from the object received by the wrapped function, ensuring it's the normalized path provided by the HOF. - Used the normalized variable when calling and when passing arguments to . This change standardizes project root handling for the tool, simplifies its method, and ensures consistent path normalization. This serves as the pattern for refactoring other MCP tools.
12 lines
3.1 KiB
Plaintext
12 lines
3.1 KiB
Plaintext
# Task ID: 75
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# Title: Integrate Google Search Grounding for Research Role
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# Status: pending
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# Dependencies: None
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# Priority: medium
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# Description: Update the AI service layer to enable Google Search Grounding specifically when a Google model is used in the 'research' role.
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# Details:
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**Goal:** Conditionally enable Google Search Grounding based on the AI role.\n\n**Implementation Plan:**\n\n1. **Modify `ai-services-unified.js`:** Update `generateTextService`, `streamTextService`, and `generateObjectService`.\n2. **Conditional Logic:** Inside these functions, check if `providerName === 'google'` AND `role === 'research'`.\n3. **Construct `providerOptions`:** If the condition is met, create an options object:\n ```javascript\n let providerSpecificOptions = {};\n if (providerName === 'google' && role === 'research') {\n log('info', 'Enabling Google Search Grounding for research role.');\n providerSpecificOptions = {\n google: {\n useSearchGrounding: true,\n // Optional: Add dynamic retrieval for compatible models\n // dynamicRetrievalConfig: { mode: 'MODE_DYNAMIC' } \n }\n };\n }\n ```\n4. **Pass Options to SDK:** Pass `providerSpecificOptions` to the Vercel AI SDK functions (`generateText`, `streamText`, `generateObject`) via the `providerOptions` parameter:\n ```javascript\n const { text, ... } = await generateText({\n // ... other params\n providerOptions: providerSpecificOptions \n });\n ```\n5. **Update `supported-models.json`:** Ensure Google models intended for research (e.g., `gemini-1.5-pro-latest`, `gemini-1.5-flash-latest`) include `'research'` in their `allowed_roles` array.\n\n**Rationale:** This approach maintains the clear separation between 'main' and 'research' roles, ensuring grounding is only activated when explicitly requested via the `--research` flag or when the research model is invoked.\n\n**Clarification:** The Search Grounding feature is specifically designed to provide up-to-date information from the web when using Google models. This implementation ensures that grounding is only activated in research contexts where current information is needed, while preserving normal operation for standard tasks. The `useSearchGrounding: true` flag instructs the Google API to augment the model's knowledge with recent web search results relevant to the query.
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# Test Strategy:
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1. Configure a Google model (e.g., gemini-1.5-flash-latest) as the 'research' model in `.taskmasterconfig`.\n2. Run a command with the `--research` flag (e.g., `task-master add-task --prompt='Latest news on AI SDK 4.2' --research`).\n3. Verify logs show 'Enabling Google Search Grounding'.\n4. Check if the task output incorporates recent information.\n5. Configure the same Google model as the 'main' model.\n6. Run a command *without* the `--research` flag.\n7. Verify logs *do not* show grounding being enabled.\n8. Add unit tests to `ai-services-unified.test.js` to verify the conditional logic for adding `providerOptions`. Ensure mocks correctly simulate different roles and providers.
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