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Integrate Cohere for Semantic Code Search

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Linked challenge: Accelerated Code Dev & Review Agent

Format
Code-aware
Lines
21
Sections
4
Linked challenge
Accelerated Code Dev & Review Agent

Prompt source

Original prompt text with formatting preserved for inspection.

21 lines
4 sections
No variables
1 code block
Enhance your 'CodeReviewer' agent or create a new 'ContextAgent'. Implement a tool within Mastra AI that uses Cohere's embedding API to perform semantic search over a small, pre-indexed set of 'best practice' code snippets or internal documentation. The agent should use this tool to retrieve relevant context *before* performing a code review, thereby improving the quality and relevance of its feedback. Provide code for initializing the Cohere client and creating the embedding tool.

```typescript
// Example Cohere integration (pseudo-code)
import cohere from 'cohere-ai';

const cohereClient = new cohere.CohereClient({ token: process.env.COHERE_API_KEY });

const semanticSearchTool = createTool({
  id: 'semantic_code_search',
  description: 'Searches for relevant code examples or documentation based on a query.',
  schema: { "type": "object", "properties": { "query": { "type": "string" } }, "required": ["query"] },
  async execute({ query }) {
    const response = await cohereClient.embed({
      texts: [query],
      model: 'embed-english-v3.0',
      inputType: 'search_query',
    });
    // ... then search Qdrant/vector store with embeddings (mocked here)
    return 'Relevant code snippets found';
  },
});
// ... add tool to agent
```

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