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Merge remote-tracking branch 'origin/main' into tr/help_fixes
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mrT23
@ -86,18 +86,49 @@ code_suggestions_self_review_text = "... (your text here) ..."
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{width=512}
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💎 In addition, by setting:
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!!! tip "Tip - demanding self-review from the PR author 💎"
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By setting:
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```
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[pr_code_suggestions]
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approve_pr_on_self_review = true
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```
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the tool can automatically approve the PR when the user checks the self-review checkbox.
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the tool can automatically add an approval when the PR author clicks the self-review checkbox.
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!!! tip "Tip - demanding self-review from the PR author"
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If you set the number of required reviewers for a PR to 2, this effectively means that the PR author must click the self-review checkbox before the PR can be merged (in addition to a human reviewer).
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- If you set the number of required reviewers for a PR to 2, this effectively means that the PR author must click the self-review checkbox before the PR can be merged (in addition to a human reviewer).
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{width=512}
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- If you keep the number of required reviewers for a PR to 1 and enable this configuration, this effectively means that the PR author can approve the PR by actively clicking the self-review checkbox.
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To prevent unauthorized approvals, this configuration defaults to false, and cannot be altered through online comments; enabling requires a direct update to the configuration file and a commit to the repository. This ensures that utilizing the feature demands a deliberate documented decision by the repository owner.
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### How many code suggestions are generated?
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PR-Agent uses a dynamic strategy to generate code suggestions based on the size of the pull request (PR). Here's how it works:
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1) Chunking large PRs:
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- PR-Agent divides large PRs into 'chunks'.
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- Each chunk contains up to `pr_code_suggestions.max_context_tokens` tokens (default: 14,000).
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2) Generating suggestions:
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- For each chunk, PR-Agent generates up to `pr_code_suggestions.num_code_suggestions_per_chunk` suggestions (default: 4).
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This approach has two main benefits:
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- Scalability: The number of suggestions scales with the PR size, rather than being fixed.
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- Quality: By processing smaller chunks, the AI can maintain higher quality suggestions, as larger contexts tend to decrease AI performance.
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Note: Chunking is primarily relevant for large PRs. For most PRs (up to 500 lines of code), PR-Agent will be able to process the entire code in a single call.
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### 'Extra instructions' and 'best practices'
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#### Extra instructions
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@ -170,18 +201,10 @@ Using a combination of both can help the AI model to provide relevant and tailor
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??? example "General options"
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<table>
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<tr>
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<td><b>num_code_suggestions</b></td>
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<td>Number of code suggestions provided by the 'improve' tool. Default is 4 for CLI, 0 for auto tools.</td>
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</tr>
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<tr>
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<td><b>extra_instructions</b></td>
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<td>Optional extra instructions to the tool. For example: "focus on the changes in the file X. Ignore change in ...".</td>
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</tr>
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<tr>
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<td><b>rank_suggestions</b></td>
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<td>If set to true, the tool will rank the suggestions, based on importance. Default is false.</td>
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</tr>
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<tr>
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<td><b>commitable_code_suggestions</b></td>
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<td>If set to true, the tool will display the suggestions as commitable code comments. Default is false.</td>
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@ -212,29 +235,25 @@ Using a combination of both can help the AI model to provide relevant and tailor
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</tr>
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</table>
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??? example "params for 'extended' mode"
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??? example "Params for number of suggestions and AI calls"
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<table>
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<tr>
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<td><b>auto_extended_mode</b></td>
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<td>Enable extended mode automatically (no need for the --extended option). Default is true.</td>
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<td>Enable chunking the PR code and running the tool on each chunk. Default is true.</td>
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</tr>
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<tr>
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<td><b>num_code_suggestions_per_chunk</b></td>
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<td>Number of code suggestions provided by the 'improve' tool, per chunk. Default is 5.</td>
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<td>Number of code suggestions provided by the 'improve' tool, per chunk. Default is 4.</td>
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</tr>
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<tr>
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<td><b>max_number_of_calls</b></td>
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<td>Maximum number of chunks. Default is 3.</td>
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</tr>
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<tr>
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<td><b>rank_extended_suggestions</b></td>
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<td>If set to true, the tool will rank the suggestions, based on importance. Default is true.</td>
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</tr>
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<tr>
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<td><b>max_number_of_calls</b></td>
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<td>Maximum number of chunks. Default is 5.</td>
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</tr>
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<tr>
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<td><b>final_clip_factor</b></td>
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<td>Factor to remove suggestions with low confidence. Default is 0.9.</td>
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</tr>
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</table>
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## A note on code suggestions quality
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@ -175,10 +175,10 @@ By uploading a local `.pr_agent.toml` file to the root of the repo's main branch
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For example, if your local `.pr_agent.toml` file contains:
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```
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[pr_reviewer]
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inline_code_comments = true
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extra_instructions = "Answer in japanese"
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```
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Each time you invoke a `/review` tool, it will use inline code comments.
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Each time you invoke a `/review` tool, it will use the extra instructions you set in the local configuration file.
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Note that among other limitations, BitBucket provides relatively low rate-limits for applications (up to 1000 requests per hour), and does not provide an API to track the actual rate-limit usage.
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@ -108,7 +108,6 @@ enable_help_text=false
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[pr_code_suggestions] # /improve #
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max_context_tokens=14000
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num_code_suggestions=4
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commitable_code_suggestions = false
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extra_instructions = ""
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rank_suggestions = false
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@ -44,10 +44,8 @@ class PRCodeSuggestions:
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self.is_extended = self._get_is_extended(args or [])
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except:
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self.is_extended = False
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if self.is_extended:
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions_per_chunk
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else:
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions
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self.ai_handler = ai_handler()
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self.ai_handler.main_pr_language = self.main_language
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@ -601,7 +599,6 @@ class PRCodeSuggestions:
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if get_settings().pr_code_suggestions.final_clip_factor != 1:
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max_len = max(
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len(data_sorted),
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get_settings().pr_code_suggestions.num_code_suggestions,
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get_settings().pr_code_suggestions.num_code_suggestions_per_chunk,
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)
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new_len = int(0.5 + max_len * get_settings().pr_code_suggestions.final_clip_factor)
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