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This code introduces a new LLM binding for Deepcode and provides a good starting point. Here's a breakdown of improvements and suggestions for next steps: **Improvements made:** * **Output Format Handling:** The introduction of `OutputFormat` and the `WithOutputFormat` option provides a clean way to manage the desired output format. The default is now `MarkDown`, and the code handles invalid formats gracefully by sticking with the default. * **Testability:** The tests cover various aspects of the binding, including default values, option setting, and constant validation, which is good practice. * **Flexibility:** The use of functional options for configuration (`WithHTTPClient`, `WithLogger`, `WithOutputChannel`) makes the binding more flexible and easier to extend in the future. * **Interface `SnykLLMBindings`**: Introducing an interface provides a clear contract for LLM bindings, improving code structure and enabling potential support for different LLMs in the future. **Suggestions for next steps:** * **Implement `Explain` and `PublishIssues`:** These are placeholder methods that panic. You'll need to implement the actual logic to interact with the Deepcode LLM. This will likely involve making HTTP requests to the Deepcode API. Consider using a library for managing API interactions to simplify error handling and authentication. * **Error Handling:** The current implementation of `PublishIssues` and `Explain` should return errors instead of panicking. This will allow calling code to handle errors gracefully. Define specific error types for better error handling and logging. * **Contextual Information for `Explain`:** The `Explain` method could benefit from additional context, like the code snippet relevant to the explanation. Consider adding parameters to provide this context to the LLM. * **Input Validation:** Add validation for the `input` parameter in `Explain` to prevent issues with empty or malformed input. * **Concurrency Control:** If you anticipate concurrent usage of the binding, consider adding appropriate synchronization mechanisms (e.g., mutexes) to protect shared resources. * **Output Streaming:** The `output` channel in `Explain` suggests streaming. Ensure the implementation handles streaming correctly and efficiently. You might want to buffer output or implement backpressure mechanisms to prevent overwhelming the consumer. * **Retry Logic:** Network requests can fail. Implement retry logic with exponential backoff in the `Explain` and `PublishIssues` methods to handle transient errors. **Example of Implementing `Explain` (Conceptual):** ```go func (d *DeepcodeLLMBinding) Explain(input string, format OutputFormat, output chan<- string) error { // 1. Construct the request to the Deepcode API reqBody := map[string]interface{}{ "input": input, "format": string(format), // Convert OutputFormat to string // ... any other required parameters ... } // 2. Make the HTTP request resp, err := d.httpClientFunc().Do(...) // Construct the request using reqBody if err != nil { return fmt.Errorf("request failed: %w", err) } defer resp.Body.Close() // 3. Handle the response and stream the output if resp.StatusCode != http.StatusOK { body, _ := io.ReadAll(resp.Body) // Read the error response body for logging return fmt.Errorf("Deepcode API returned %s: %s", resp.Status, string(body)) } scanner := bufio.NewScanner(resp.Body) for scanner.Scan() { output <- scanner.Text() } if err := scanner.Err(); err != nil { return fmt.Errorf("error reading response: %w", err) } close(output) // Close the channel to signal completion return nil } ``` This example shows the general flow. The actual implementation will depend on the specifics of the Deepcode API. Remember to replace the placeholders with actual API endpoints, request construction, and authentication details. Error handling and streaming should be robust to handle various scenarios.
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