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docs: improve /implement tool documentation and update news section
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README.md
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README.md
@ -41,6 +41,17 @@ Qode Merge PR-Agent aims to help efficiently review and handle pull requests, by
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## News and Updates
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### Jan 2, 2025
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New tool: [/Implement 💎](https://qodo-merge-docs.qodo.ai/tools/implement/), which converts human code review discussions and feedback into ready-to-commit code changes.
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<kbd><img src="https://codium.ai/images/pr_agent/implement1.png" width="512"></kbd>
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### Jan 1, 2025
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Update logic and [documentation](https://qodo-merge-docs.qodo.ai/usage-guide/changing_a_model/#ollama) for running local models via Ollama.
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### December 30, 2024
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Following [feedback](https://research.kudelskisecurity.com/2024/08/29/careful-where-you-code-multiple-vulnerabilities-in-ai-powered-pr-agent/) from the community, we have addressed two vulnerabilities identified in the open-source PR-Agent project. The fixes are now included in the newly released version (v0.26), available as of today.
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@ -79,12 +90,6 @@ Focused mode
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<kbd><img src="https://qodo.ai/images/pr_agent/code_suggestions_focused_mode.png" width="512"></kbd>
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### November 4, 2024
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Qodo Merge PR Agent will now leverage context from Jira or GitHub tickets to enhance the PR Feedback. Read more about this feature
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[here](https://qodo-merge-docs.qodo.ai/core-abilities/fetching_ticket_context/)
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## Overview
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<div style="text-align:left;">
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@ -158,7 +163,7 @@ ___
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\
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‣ **Similar Code 💎 ([`/find_similar_component`](https://pr-agent-docs.codium.ai/tools/similar_code/))**: Retrieves the most similar code components from inside the organization's codebase, or from open-source code.
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\
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‣ **Implement 💎 ([`/implement`](https://pr-agent-docs.codium.ai/tools/implement/))**: Generates implementation code from review suggestions.
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‣ **Implement 💎 ([`/implement`](https://qodo-merge-docs.qodo.ai/tools/implement/))**: Generates implementation code from review suggestions.
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___
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## Example results
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@ -1,12 +1,12 @@
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## Overview
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The `implement` tool automatically generates implementation code based on PR review suggestions.
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It combines LLM capabilities with PR review suggestions to help developers implement code changes quickly and with confidence.
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The `implement` tool converts human code review discussions and feedback into ready-to-commit code changes.
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It leverages LLM technology to transform PR comments and review suggestions into concrete implementation code, helping developers quickly turn feedback into working solutions.
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## Usage Scenarios
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### 1. For Reviewers
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### For Reviewers
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Reviewers can request code changes by: <br>
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1. Selecting the code block to be modified. <br>
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@ -15,10 +15,10 @@ Reviewers can request code changes by: <br>
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/implement <code-change-description>
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```
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{width=512}
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{width=640}
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### 2. For PR Authors
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### For PR Authors
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PR authors can implement suggested changes by replying to a review comment using either: <br>
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1. Add specific implementation details as described above
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@ -30,16 +30,16 @@ PR authors can implement suggested changes by replying to a review comment using
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/implement
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```
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{width=512}
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{width=640}
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### 3. For Referencing Comments
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### For Referencing Comments
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You can reference and implement changes from any comment by:
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```
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/implement <link-to-review-comment>
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```
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{width=512}
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{width=640}
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Note that the implementation will occur within the review discussion thread.
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@ -40,7 +40,7 @@ E.g. to use a new model locally via Ollama, set in `.secrets.toml` or in a confi
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model = "ollama/qwen2.5-coder:32b"
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fallback_models=["ollama/qwen2.5-coder:32b"]
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custom_model_max_tokens=128000 # set the maximal input tokens for the model
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duplicate_examples=true # will duplicate the examples in the prompt, to help the model to output structured output
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duplicate_examples=true # will duplicate the examples in the prompt, to help the model to generate structured output
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[ollama]
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api_base = "http://localhost:11434" # or whatever port you're running Ollama on
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@ -48,11 +48,14 @@ api_base = "http://localhost:11434" # or whatever port you're running Ollama on
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!!! note "Local models vs commercial models"
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Qodo Merge is compatible with almost any AI model, but analyzing complex code repositories and pull requests requires a model specifically optimized for code analysis.
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Commercial models such as GPT-4, Claude Sonnet, and Gemini have demonstrated robust capabilities in generating structured output for code analysis tasks with large input. In contrast, most open-source models currently available (as of January 2025) face challenges with these complex tasks.
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Based on our testing, local open-source models are suitable for experimentation and learning purposes, but they are not suitable for production-level code analysis tasks.
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Hence, for production workflows and real-world usage, we recommend using commercial models.
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### Hugging Face Inference Endpoints
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### Hugging Face
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To use a new model with Hugging Face Inference Endpoints, for example, set:
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```
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