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v0.30
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mrT23-patc
| Author | SHA1 | Date | |
|---|---|---|---|
| 4f049ab586 | |||
| 0672189412 |
@ -1,4 +1,4 @@
|
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<div align="center">
|
||||
|
||||
|
||||
<div align="center">
|
||||
|
||||
@ -22,6 +22,7 @@ PR-Agent aims to help efficiently review and handle pull requests, by providing
|
||||
[](https://github.com/apps/qodo-merge-pro/)
|
||||
[](https://github.com/apps/qodo-merge-pro-for-open-source/)
|
||||
[](https://discord.com/invite/SgSxuQ65GF)
|
||||
<!-- TODO: add badge also for twitter -->
|
||||
<a href="https://github.com/Codium-ai/pr-agent/commits/main">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/last-commit/Codium-ai/pr-agent/main?style=for-the-badge" height="20">
|
||||
</a>
|
||||
@ -44,7 +45,7 @@ PR-Agent aims to help efficiently review and handle pull requests, by providing
|
||||
## Getting Started
|
||||
|
||||
### Try it Instantly
|
||||
Test PR-Agent on any public GitHub repository by commenting `@CodiumAI-Agent /improve`
|
||||
Test PR-Agent on any public GitHub repository by commenting `@CodiumAI-Agent /improve`. The bot will reply with code suggestions
|
||||
|
||||
### GitHub Action
|
||||
Add automated PR reviews to your repository with a simple workflow file using [GitHub Action setup guide](https://qodo-merge-docs.qodo.ai/installation/github/#run-as-a-github-action)
|
||||
@ -186,7 +187,7 @@ ___
|
||||
|
||||
## Try It Now
|
||||
|
||||
Try the Claude Sonnet powered PR-Agent instantly on _your public GitHub repository_. Just mention `@CodiumAI-Agent` and add the desired command in any PR comment. The agent will generate a response based on your command.
|
||||
Try PR-Agent instantly on _your public GitHub repository_. Just mention `@CodiumAI-Agent` and add the desired command in any PR comment. The agent will generate a response based on your command.
|
||||
For example, add a comment to any pull request with the following text:
|
||||
|
||||
```
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
FROM public.ecr.aws/lambda/python:3.12 AS base
|
||||
FROM public.ecr.aws/lambda/python:3.12
|
||||
|
||||
RUN dnf update -y && \
|
||||
dnf install -y gcc python3-devel git && \
|
||||
@ -9,10 +9,4 @@ RUN pip install --no-cache-dir . && rm pyproject.toml
|
||||
RUN pip install --no-cache-dir mangum==0.17.0
|
||||
COPY pr_agent/ ${LAMBDA_TASK_ROOT}/pr_agent/
|
||||
|
||||
FROM base AS github_lambda
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CMD ["pr_agent.servers.github_lambda_webhook.lambda_handler"]
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|
||||
FROM base AS gitlab_lambda
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||||
CMD ["pr_agent.servers.gitlab_lambda_webhook.lambda_handler"]
|
||||
|
||||
FROM github_lambda
|
||||
CMD ["pr_agent.servers.serverless.serverless"]
|
||||
|
||||
@ -65,7 +65,7 @@ There are two possible paths leading to this auto-approval - one via the `review
|
||||
ensure_ticket_compliance = true # Default is false
|
||||
```
|
||||
|
||||
If `ensure_ticket_compliance` is set to `true`, auto-approval for the `review` toll path will be disabled if no ticket is linked to the PR, or if the PR is not fully compliant with a linked ticket. This ensures that PRs are only auto-approved if their associated tickets are properly resolved.
|
||||
If `ensure_ticket_compliance` is set to `true`, auto-approval will be disabled if no ticket is linked to the PR, or if the PR is not fully compliant with a linked ticket. This ensures that PRs are only auto-approved if their associated tickets are properly resolved.
|
||||
|
||||
You can also prevent auto-approval if the PR exceeds the ticket's scope (see [here](https://qodo-merge-docs.qodo.ai/core-abilities/fetching_ticket_context/#configuration-options)).
|
||||
|
||||
|
||||
@ -187,15 +187,14 @@ For example: `GITHUB.WEBHOOK_SECRET` --> `GITHUB__WEBHOOK_SECRET`
|
||||
2. Build a docker image that can be used as a lambda function
|
||||
|
||||
```shell
|
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# Note: --target github_lambda is optional as it's the default target
|
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docker buildx build --platform=linux/amd64 . -t codiumai/pr-agent:github_lambda --target github_lambda -f docker/Dockerfile.lambda
|
||||
docker buildx build --platform=linux/amd64 . -t codiumai/pr-agent:serverless -f docker/Dockerfile.lambda
|
||||
```
|
||||
|
||||
3. Push image to ECR
|
||||
|
||||
```shell
|
||||
docker tag codiumai/pr-agent:github_lambda <AWS_ACCOUNT>.dkr.ecr.<AWS_REGION>.amazonaws.com/codiumai/pr-agent:github_lambda
|
||||
docker push <AWS_ACCOUNT>.dkr.ecr.<AWS_REGION>.amazonaws.com/codiumai/pr-agent:github_lambda
|
||||
docker tag codiumai/pr-agent:serverless <AWS_ACCOUNT>.dkr.ecr.<AWS_REGION>.amazonaws.com/codiumai/pr-agent:serverless
|
||||
docker push <AWS_ACCOUNT>.dkr.ecr.<AWS_REGION>.amazonaws.com/codiumai/pr-agent:serverless
|
||||
```
|
||||
|
||||
4. Create a lambda function that uses the uploaded image. Set the lambda timeout to be at least 3m.
|
||||
|
||||
@ -61,12 +61,12 @@ git clone https://github.com/qodo-ai/pr-agent.git
|
||||
```
|
||||
|
||||
5. Prepare variables and secrets. Skip this step if you plan on setting these as environment variables when running the agent:
|
||||
1. In the configuration file/variables:
|
||||
- Set `config.git_provider` to "gitlab"
|
||||
1. In the configuration file/variables:
|
||||
- Set `config.git_provider` to "gitlab"
|
||||
|
||||
2. In the secrets file/variables:
|
||||
- Set your AI model key in the respective section
|
||||
- In the [gitlab] section, set `personal_access_token` (with token from step 2) and `shared_secret` (with secret from step 3)
|
||||
2. In the secrets file/variables:
|
||||
- Set your AI model key in the respective section
|
||||
- In the [gitlab] section, set `personal_access_token` (with token from step 2) and `shared_secret` (with secret from step 3)
|
||||
|
||||
6. Build a Docker image for the app and optionally push it to a Docker repository. We'll use Dockerhub as an example:
|
||||
|
||||
@ -88,63 +88,3 @@ OPENAI__KEY=<your_openai_api_key>
|
||||
8. Create a webhook in your GitLab project. Set the URL to `http[s]://<PR_AGENT_HOSTNAME>/webhook`, the secret token to the generated secret from step 3, and enable the triggers `push`, `comments` and `merge request events`.
|
||||
|
||||
9. Test your installation by opening a merge request or commenting on a merge request using one of PR Agent's commands.
|
||||
|
||||
## Deploy as a Lambda Function
|
||||
|
||||
Note that since AWS Lambda env vars cannot have "." in the name, you can replace each "." in an env variable with "__".<br>
|
||||
For example: `GITLAB.PERSONAL_ACCESS_TOKEN` --> `GITLAB__PERSONAL_ACCESS_TOKEN`
|
||||
|
||||
1. Follow steps 1-5 from [Run a GitLab webhook server](#run-a-gitlab-webhook-server).
|
||||
2. Build a docker image that can be used as a lambda function
|
||||
|
||||
```shell
|
||||
docker buildx build --platform=linux/amd64 . -t codiumai/pr-agent:gitlab_lambda --target gitlab_lambda -f docker/Dockerfile.lambda
|
||||
```
|
||||
|
||||
3. Push image to ECR
|
||||
|
||||
```shell
|
||||
docker tag codiumai/pr-agent:gitlab_lambda <AWS_ACCOUNT>.dkr.ecr.<AWS_REGION>.amazonaws.com/codiumai/pr-agent:gitlab_lambda
|
||||
docker push <AWS_ACCOUNT>.dkr.ecr.<AWS_REGION>.amazonaws.com/codiumai/pr-agent:gitlab_lambda
|
||||
```
|
||||
|
||||
4. Create a lambda function that uses the uploaded image. Set the lambda timeout to be at least 3m.
|
||||
5. Configure the lambda function to have a Function URL.
|
||||
6. In the environment variables of the Lambda function, specify `AZURE_DEVOPS_CACHE_DIR` to a writable location such as /tmp. (see [link](https://github.com/Codium-ai/pr-agent/pull/450#issuecomment-1840242269))
|
||||
7. Go back to steps 8-9 of [Run a GitLab webhook server](#run-a-gitlab-webhook-server) with the function url as your Webhook URL.
|
||||
The Webhook URL would look like `https://<LAMBDA_FUNCTION_URL>/webhook`
|
||||
|
||||
### Using AWS Secrets Manager
|
||||
|
||||
For production Lambda deployments, use AWS Secrets Manager instead of environment variables:
|
||||
|
||||
1. Create individual secrets for each GitLab webhook with this JSON format (e.g., secret name: `project-webhook-secret-001`)
|
||||
|
||||
```json
|
||||
{
|
||||
"gitlab_token": "glpat-xxxxxxxxxxxxxxxxxxxxxxxx",
|
||||
"token_name": "project-webhook-001"
|
||||
}
|
||||
```
|
||||
|
||||
2. Create a main configuration secret for common settings (e.g., secret name: `pr-agent-main-config`)
|
||||
|
||||
```json
|
||||
{
|
||||
"openai.key": "sk-proj-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
|
||||
}
|
||||
```
|
||||
|
||||
3. Set these environment variables in your Lambda:
|
||||
|
||||
```bash
|
||||
CONFIG__SECRET_PROVIDER=aws_secrets_manager
|
||||
AWS_SECRETS_MANAGER__SECRET_ARN=arn:aws:secretsmanager:us-east-1:123456789012:secret:pr-agent-main-config-AbCdEf
|
||||
```
|
||||
|
||||
4. In your GitLab webhook configuration, set the **Secret Token** to the **Secret name** created in step 1:
|
||||
- Example: `project-webhook-secret-001`
|
||||
|
||||
**Important**: When using Secrets Manager, GitLab's webhook secret must be the Secrets Manager secret name.
|
||||
|
||||
5. Add IAM permission `secretsmanager:GetSecretValue` to your Lambda execution role
|
||||
@ -98,11 +98,6 @@ extra_instructions = "..."
|
||||
<tr>
|
||||
<td><b>require_security_review</b></td>
|
||||
<td>If set to true, the tool will add a section that checks if the PR contains a possible security or vulnerability issue. Default is true.</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><b>require_todo_scan</b></td>
|
||||
<td>If set to true, the tool will add a section that lists TODO comments found in the PR code changes. Default is false.
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><b>require_ticket_analysis_review</b></td>
|
||||
|
||||
@ -90,7 +90,7 @@ duplicate_examples=true # will duplicate the examples in the prompt, to help the
|
||||
api_base = "http://localhost:11434" # or whatever port you're running Ollama on
|
||||
```
|
||||
|
||||
By default, Ollama uses a context window size of 2048 tokens. In most cases this is not enough to cover pr-agent prompt and pull-request diff. Context window size can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context length to 8K, use: `OLLAMA_CONTEXT_LENGTH=8192 ollama serve`. More information you can find on the [official ollama faq](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size).
|
||||
By default, Ollama uses a context window size of 2048 tokens. In most cases this is not enough to cover pr-agent promt and pull-request diff. Context window size can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context length to 8K, use: `OLLAMA_CONTEXT_LENGTH=8192 ollama serve`. More information you can find on the [official ollama faq](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size).
|
||||
|
||||
Please note that the `custom_model_max_tokens` setting should be configured in accordance with the `OLLAMA_CONTEXT_LENGTH`. Failure to do so may result in unexpected model output.
|
||||
|
||||
|
||||
@ -131,7 +131,7 @@ class LiteLLMAIHandler(BaseAiHandler):
|
||||
self.api_base = openrouter_api_base
|
||||
litellm.api_base = openrouter_api_base
|
||||
|
||||
# Models that only use user message
|
||||
# Models that only use user meessage
|
||||
self.user_message_only_models = USER_MESSAGE_ONLY_MODELS
|
||||
|
||||
# Model that doesn't support temperature argument
|
||||
@ -212,7 +212,7 @@ class LiteLLMAIHandler(BaseAiHandler):
|
||||
|
||||
return kwargs
|
||||
|
||||
def add_litellm_callbacks(self, kwargs) -> dict:
|
||||
def add_litellm_callbacks(selfs, kwargs) -> dict:
|
||||
captured_extra = []
|
||||
|
||||
def capture_logs(message):
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import copy
|
||||
import difflib
|
||||
import hashlib
|
||||
@ -15,7 +14,7 @@ import traceback
|
||||
from datetime import datetime
|
||||
from enum import Enum
|
||||
from importlib.metadata import PackageNotFoundError, version
|
||||
from typing import Any, List, Tuple, TypedDict
|
||||
from typing import Any, List, Tuple
|
||||
|
||||
import html2text
|
||||
import requests
|
||||
@ -38,31 +37,21 @@ def get_model(model_type: str = "model_weak") -> str:
|
||||
return get_settings().config.model_reasoning
|
||||
return get_settings().config.model
|
||||
|
||||
|
||||
class Range(BaseModel):
|
||||
line_start: int # should be 0-indexed
|
||||
line_end: int
|
||||
column_start: int = -1
|
||||
column_end: int = -1
|
||||
|
||||
|
||||
class ModelType(str, Enum):
|
||||
REGULAR = "regular"
|
||||
WEAK = "weak"
|
||||
REASONING = "reasoning"
|
||||
|
||||
|
||||
class TodoItem(TypedDict):
|
||||
relevant_file: str
|
||||
line_range: Tuple[int, int]
|
||||
content: str
|
||||
|
||||
|
||||
class PRReviewHeader(str, Enum):
|
||||
REGULAR = "## PR Reviewer Guide"
|
||||
INCREMENTAL = "## Incremental PR Reviewer Guide"
|
||||
|
||||
|
||||
class ReasoningEffort(str, Enum):
|
||||
HIGH = "high"
|
||||
MEDIUM = "medium"
|
||||
@ -120,7 +109,6 @@ def unique_strings(input_list: List[str]) -> List[str]:
|
||||
seen.add(item)
|
||||
return unique_list
|
||||
|
||||
|
||||
def convert_to_markdown_v2(output_data: dict,
|
||||
gfm_supported: bool = True,
|
||||
incremental_review=None,
|
||||
@ -143,7 +131,6 @@ def convert_to_markdown_v2(output_data: dict,
|
||||
"Focused PR": "✨",
|
||||
"Relevant ticket": "🎫",
|
||||
"Security concerns": "🔒",
|
||||
"Todo sections": "📝",
|
||||
"Insights from user's answers": "📝",
|
||||
"Code feedback": "🤖",
|
||||
"Estimated effort to review [1-5]": "⏱️",
|
||||
@ -164,7 +151,6 @@ def convert_to_markdown_v2(output_data: dict,
|
||||
if gfm_supported:
|
||||
markdown_text += "<table>\n"
|
||||
|
||||
todo_summary = output_data['review'].pop('todo_summary', '')
|
||||
for key, value in output_data['review'].items():
|
||||
if value is None or value == '' or value == {} or value == []:
|
||||
if key.lower() not in ['can_be_split', 'key_issues_to_review']:
|
||||
@ -223,23 +209,6 @@ def convert_to_markdown_v2(output_data: dict,
|
||||
markdown_text += f"### {emoji} Security concerns\n\n"
|
||||
value = emphasize_header(value.strip(), only_markdown=True)
|
||||
markdown_text += f"{value}\n\n"
|
||||
elif 'todo sections' in key_nice.lower():
|
||||
if gfm_supported:
|
||||
markdown_text += "<tr><td>"
|
||||
if is_value_no(value):
|
||||
markdown_text += f"✅ <strong>No TODO sections</strong>"
|
||||
else:
|
||||
markdown_todo_items = format_todo_items(value, git_provider, gfm_supported)
|
||||
markdown_text += f"{emoji} <strong>TODO sections</strong>\n<br><br>\n"
|
||||
markdown_text += markdown_todo_items
|
||||
markdown_text += "</td></tr>\n"
|
||||
else:
|
||||
if is_value_no(value):
|
||||
markdown_text += f"### ✅ No TODO sections\n\n"
|
||||
else:
|
||||
markdown_todo_items = format_todo_items(value, git_provider, gfm_supported)
|
||||
markdown_text += f"### {emoji} TODO sections\n\n"
|
||||
markdown_text += markdown_todo_items
|
||||
elif 'can be split' in key_nice.lower():
|
||||
if gfm_supported:
|
||||
markdown_text += f"<tr><td>"
|
||||
@ -1320,7 +1289,7 @@ def process_description(description_full: str) -> Tuple[str, List]:
|
||||
pattern_back = r'<details>\s*<summary><strong>(.*?)</strong><dd><code>(.*?)</code>.*?</summary>\s*<hr>\s*(.*?)\n\n\s*(.*?)</details>'
|
||||
res = re.search(pattern_back, file_data, re.DOTALL)
|
||||
if not res or res.lastindex != 4:
|
||||
pattern_back = r'<details>\s*<summary><strong>(.*?)</strong>\s*<dd><code>(.*?)</code>.*?</summary>\s*<hr>\s*(.*?)\s*-\s*(.*?)\s*</details>' # looking for hyphen ('- ')
|
||||
pattern_back = r'<details>\s*<summary><strong>(.*?)</strong>\s*<dd><code>(.*?)</code>.*?</summary>\s*<hr>\s*(.*?)\s*-\s*(.*?)\s*</details>' # looking for hypen ('- ')
|
||||
res = re.search(pattern_back, file_data, re.DOTALL)
|
||||
if res and res.lastindex == 4:
|
||||
short_filename = res.group(1).strip()
|
||||
@ -1398,47 +1367,3 @@ def set_file_languages(diff_files) -> List[FilePatchInfo]:
|
||||
get_logger().exception(f"Failed to set file languages: {e}")
|
||||
|
||||
return diff_files
|
||||
|
||||
def format_todo_item(todo_item: TodoItem, git_provider, gfm_supported) -> str:
|
||||
relevant_file = todo_item.get('relevant_file', '').strip()
|
||||
line_number = todo_item.get('line_number', '')
|
||||
content = todo_item.get('content', '')
|
||||
reference_link = git_provider.get_line_link(relevant_file, line_number, line_number)
|
||||
file_ref = f"{relevant_file} [{line_number}]"
|
||||
if reference_link:
|
||||
if gfm_supported:
|
||||
file_ref = f"<a href='{reference_link}'>{file_ref}</a>"
|
||||
else:
|
||||
file_ref = f"[{file_ref}]({reference_link})"
|
||||
|
||||
if content:
|
||||
return f"{file_ref}: {content.strip()}"
|
||||
else:
|
||||
# if content is empty, return only the file reference
|
||||
return file_ref
|
||||
|
||||
|
||||
def format_todo_items(value: list[TodoItem] | TodoItem, git_provider, gfm_supported) -> str:
|
||||
markdown_text = ""
|
||||
MAX_ITEMS = 5 # limit the number of items to display
|
||||
if gfm_supported:
|
||||
if isinstance(value, list):
|
||||
markdown_text += "<ul>\n"
|
||||
if len(value) > MAX_ITEMS:
|
||||
get_logger().debug(f"Truncating todo items to {MAX_ITEMS} items")
|
||||
value = value[:MAX_ITEMS]
|
||||
for todo_item in value:
|
||||
markdown_text += f"<li>{format_todo_item(todo_item, git_provider, gfm_supported)}</li>\n"
|
||||
markdown_text += "</ul>\n"
|
||||
else:
|
||||
markdown_text += f"<p>{format_todo_item(value, git_provider, gfm_supported)}</p>\n"
|
||||
else:
|
||||
if isinstance(value, list):
|
||||
if len(value) > MAX_ITEMS:
|
||||
get_logger().debug(f"Truncating todo items to {MAX_ITEMS} items")
|
||||
value = value[:MAX_ITEMS]
|
||||
for todo_item in value:
|
||||
markdown_text += f"- {format_todo_item(todo_item, git_provider, gfm_supported)}\n"
|
||||
else:
|
||||
markdown_text += f"- {format_todo_item(value, git_provider, gfm_supported)}\n"
|
||||
return markdown_text
|
||||
@ -86,7 +86,7 @@ class BitbucketServerProvider(GitProvider):
|
||||
|
||||
def get_repo_settings(self):
|
||||
try:
|
||||
content = self.bitbucket_client.get_content_of_file(self.workspace_slug, self.repo_slug, ".pr_agent.toml")
|
||||
content = self.bitbucket_client.get_content_of_file(self.workspace_slug, self.repo_slug, ".pr_agent.toml", self.get_pr_branch())
|
||||
|
||||
return content
|
||||
except Exception as e:
|
||||
|
||||
@ -3,6 +3,8 @@ import json
|
||||
import os
|
||||
from typing import Union
|
||||
|
||||
from dynaconf.utils import DynaBox
|
||||
|
||||
from pr_agent.agent.pr_agent import PRAgent
|
||||
from pr_agent.config_loader import get_settings
|
||||
from pr_agent.git_providers import get_git_provider
|
||||
@ -91,7 +93,7 @@ async def run_action():
|
||||
|
||||
for key in get_settings():
|
||||
setting = get_settings().get(key)
|
||||
if str(type(setting)) == "<class 'dynaconf.utils.boxing.DynaBox'>":
|
||||
if isinstance(setting, DynaBox):
|
||||
if key.lower() in ['pr_description', 'pr_code_suggestions', 'pr_reviewer']:
|
||||
if hasattr(setting, 'extra_instructions'):
|
||||
extra_instructions = setting.extra_instructions
|
||||
|
||||
@ -1,27 +0,0 @@
|
||||
from fastapi import FastAPI
|
||||
from mangum import Mangum
|
||||
from starlette.middleware import Middleware
|
||||
from starlette_context.middleware import RawContextMiddleware
|
||||
|
||||
from pr_agent.servers.gitlab_webhook import router
|
||||
|
||||
try:
|
||||
from pr_agent.config_loader import apply_secrets_manager_config
|
||||
apply_secrets_manager_config()
|
||||
except Exception as e:
|
||||
try:
|
||||
from pr_agent.log import get_logger
|
||||
get_logger().debug(f"AWS Secrets Manager initialization failed, falling back to environment variables: {e}")
|
||||
except:
|
||||
# Fail completely silently if log module is not available
|
||||
pass
|
||||
|
||||
middleware = [Middleware(RawContextMiddleware)]
|
||||
app = FastAPI(middleware=middleware)
|
||||
app.include_router(router)
|
||||
|
||||
handler = Mangum(app, lifespan="off")
|
||||
|
||||
|
||||
def lambda_handler(event, context):
|
||||
return handler(event, context)
|
||||
@ -23,5 +23,5 @@ app.include_router(router)
|
||||
handler = Mangum(app, lifespan="off")
|
||||
|
||||
|
||||
def lambda_handler(event, context):
|
||||
return handler(event, context)
|
||||
def serverless(event, context):
|
||||
return handler(event, context)
|
||||
@ -78,7 +78,6 @@ require_tests_review=true
|
||||
require_estimate_effort_to_review=true
|
||||
require_can_be_split_review=false
|
||||
require_security_review=true
|
||||
require_todo_scan=false
|
||||
require_ticket_analysis_review=true
|
||||
# general options
|
||||
publish_output_no_suggestions=true # Set to "false" if you only need the reviewer's remarks (not labels, not "security audit", etc.) and want to avoid noisy "No major issues detected" comments.
|
||||
|
||||
@ -1,12 +1,11 @@
|
||||
[pr_description_prompt]
|
||||
system="""You are PR-Reviewer, a language model designed to review a Git Pull Request (PR).
|
||||
Your task is to provide a full description for the PR content: type, description, title, and files walkthrough.
|
||||
Your task is to provide a full description for the PR content - type, description, title and files walkthrough.
|
||||
- Focus on the new PR code (lines starting with '+' in the 'PR Git Diff' section).
|
||||
- Keep in mind that the 'Previous title', 'Previous description' and 'Commit messages' sections may be partial, simplistic, non-informative or out of date. Hence, compare them to the PR diff code, and use them only as a reference.
|
||||
- The generated title and description should prioritize the most significant changes.
|
||||
- If needed, each YAML output should be in block scalar indicator ('|')
|
||||
- When quoting variables, names or file paths from the code, use backticks (`) instead of single quote (').
|
||||
- When needed, use '- ' as bullets
|
||||
|
||||
{%- if extra_instructions %}
|
||||
|
||||
@ -182,4 +181,4 @@ pr_files:
|
||||
|
||||
Response (should be a valid YAML, and nothing else):
|
||||
```yaml
|
||||
"""
|
||||
"""
|
||||
@ -1,12 +1,12 @@
|
||||
[pr_help_prompts]
|
||||
system="""You are Doc-helper, a language models designed to answer questions about a documentation website for an open-soure project called "PR-Agent" (recently renamed to "Qodo Merge").
|
||||
You will receive a question, and the full documentation website content.
|
||||
You will recieve a question, and the full documentation website content.
|
||||
Your goal is to provide the best answer to the question using the documentation provided.
|
||||
|
||||
Additional instructions:
|
||||
- Try to be short and concise in your answers. Try to give examples if needed.
|
||||
- The main tools of PR-Agent are 'describe', 'review', 'improve'. If there is ambiguity to which tool the user is referring to, prioritize snippets of these tools over others.
|
||||
- If the question has ambiguity and can relate to different tools or platforms, provide the best answer possible based on what is available, but also state in your answer what additional information would be needed to give a more accurate answer.
|
||||
- If the question has ambiguity and can relate to different tools or platfroms, provide the best answer possible based on what is available, but also state in your answer what additional information would be needed to give a more accurate answer.
|
||||
|
||||
|
||||
The output must be a YAML object equivalent to type $DocHelper, according to the following Pydantic definitions:
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
system="""You are PR-Reviewer, a language model designed to review a Git Pull Request (PR).
|
||||
Given the PR Info and the PR Git Diff, generate 3 short questions about the PR code for the PR author.
|
||||
The goal of the questions is to help the language model understand the PR better, so the questions should be insightful, informative, non-trivial, and relevant to the PR.
|
||||
You should prefer asking yes/no questions, or multiple choice questions. Also add at least one open-ended question, but make sure they are not too difficult, and can be answered in a sentence or two.
|
||||
You should prefer asking yes\\no questions, or multiple choice questions. Also add at least one open-ended question, but make sure they are not too difficult, and can be answered in a sentence or two.
|
||||
|
||||
|
||||
Example output:
|
||||
|
||||
@ -37,9 +37,9 @@ __new hunk__
|
||||
======
|
||||
|
||||
- In the format above, the diff is organized into separate '__new hunk__' and '__old hunk__' sections for each code chunk. '__new hunk__' contains the updated code, while '__old hunk__' shows the removed code. If no code was removed in a specific chunk, the __old hunk__ section will be omitted.
|
||||
- We also added line numbers for the '__new hunk__' code, to help you refer to the code lines in your suggestions. These line numbers are not part of the actual code, and should only be used for reference.
|
||||
- We also added line numbers for the '__new hunk__' code, to help you refer to the code lines in your suggestions. These line numbers are not part of the actual code, and should only used for reference.
|
||||
- Code lines are prefixed with symbols ('+', '-', ' '). The '+' symbol indicates new code added in the PR, the '-' symbol indicates code removed in the PR, and the ' ' symbol indicates unchanged code. \
|
||||
The review should address new code added in the PR code diff (lines starting with '+').
|
||||
The review should address new code added in the PR code diff (lines starting with '+')
|
||||
{%- if is_ai_metadata %}
|
||||
- If available, an AI-generated summary will appear and provide a high-level overview of the file changes. Note that this summary may not be fully accurate or complete.
|
||||
{%- endif %}
|
||||
@ -72,13 +72,6 @@ class KeyIssuesComponentLink(BaseModel):
|
||||
start_line: int = Field(description="The start line that corresponds to this issue in the relevant file")
|
||||
end_line: int = Field(description="The end line that corresponds to this issue in the relevant file")
|
||||
|
||||
{%- if require_todo_scan %}
|
||||
class TodoSection(BaseModel):
|
||||
relevant_file: str = Field(description="The full path of the file containing the TODO comment")
|
||||
line_number: int = Field(description="The line number where the TODO comment starts")
|
||||
content: str = Field(description="The content of the TODO comment. Only include actual TODO comments within code comments (e.g., comments starting with '#', '//', '/*', '<!--', ...). Remove leading 'TODO' prefixes. If more than 10 words, summarize the TODO comment to a single short sentence up to 10 words.")
|
||||
{%- endif %}
|
||||
|
||||
{%- if related_tickets %}
|
||||
|
||||
class TicketCompliance(BaseModel):
|
||||
@ -100,17 +93,14 @@ class Review(BaseModel):
|
||||
score: str = Field(description="Rate this PR on a scale of 0-100 (inclusive), where 0 means the worst possible PR code, and 100 means PR code of the highest quality, without any bugs or performance issues, that is ready to be merged immediately and run in production at scale.")
|
||||
{%- endif %}
|
||||
{%- if require_tests %}
|
||||
relevant_tests: str = Field(description="yes/no question: does this PR have relevant tests added or updated ?")
|
||||
relevant_tests: str = Field(description="yes\\no question: does this PR have relevant tests added or updated ?")
|
||||
{%- endif %}
|
||||
{%- if question_str %}
|
||||
insights_from_user_answers: str = Field(description="shortly summarize the insights you gained from the user's answers to the questions")
|
||||
{%- endif %}
|
||||
key_issues_to_review: List[KeyIssuesComponentLink] = Field("A short and diverse list (0-{{ num_max_findings }} issues) of high-priority bugs, problems or performance concerns introduced in the PR code, which the PR reviewer should further focus on and validate during the review process.")
|
||||
{%- if require_security_review %}
|
||||
security_concerns: str = Field(description="Does this PR code introduce vulnerabilities such as exposure of sensitive information (e.g., API keys, secrets, passwords), or security concerns like SQL injection, XSS, CSRF, and others ? Answer 'No' (without explaining why) if there are no possible issues. If there are security concerns or issues, start your answer with a short header, such as: 'Sensitive information exposure: ...', 'SQL injection: ...', etc. Explain your answer. Be specific and give examples if possible")
|
||||
{%- endif %}
|
||||
{%- if require_todo_scan %}
|
||||
todo_sections: Union[List[TodoSection], str] = Field(description="A list of TODO comments found in the PR code. Return 'No' (as a string) if there are no TODO comments in the PR")
|
||||
security_concerns: str = Field(description="Does this PR code introduce possible vulnerabilities such as exposure of sensitive information (e.g., API keys, secrets, passwords), or security concerns like SQL injection, XSS, CSRF, and others ? Answer 'No' (without explaining why) if there are no possible issues. If there are security concerns or issues, start your answer with a short header, such as: 'Sensitive information exposure: ...', 'SQL injection: ...' etc. Explain your answer. Be specific and give examples if possible")
|
||||
{%- endif %}
|
||||
{%- if require_can_be_split_review %}
|
||||
can_be_split: List[SubPR] = Field(min_items=0, max_items=3, description="Can this PR, which contains {{ num_pr_files }} changed files in total, be divided into smaller sub-PRs with distinct tasks that can be reviewed and merged independently, regardless of the order ? Make sure that the sub-PRs are indeed independent, with no code dependencies between them, and that each sub-PR represent a meaningful independent task. Output an empty list if the PR code does not need to be split.")
|
||||
@ -158,10 +148,6 @@ review:
|
||||
- ...
|
||||
security_concerns: |
|
||||
No
|
||||
{%- if require_todo_scan %}
|
||||
todo_sections: |
|
||||
No
|
||||
{%- endif %}
|
||||
{%- if require_can_be_split_review %}
|
||||
can_be_split:
|
||||
- relevant_files:
|
||||
@ -280,10 +266,6 @@ review:
|
||||
- ...
|
||||
security_concerns: |
|
||||
No
|
||||
{%- if require_todo_scan %}
|
||||
todo_sections: |
|
||||
No
|
||||
{%- endif %}
|
||||
{%- if require_can_be_split_review %}
|
||||
can_be_split:
|
||||
- relevant_files:
|
||||
|
||||
@ -21,7 +21,7 @@ from pr_agent.servers.help import HelpMessage
|
||||
|
||||
#Common code that can be called from similar tools:
|
||||
def modify_answer_section(ai_response: str) -> str | None:
|
||||
# Gets the model's answer and relevant sources section, replacing the heading of the answer section with:
|
||||
# Gets the model's answer and relevant sources section, repacing the heading of the answer section with:
|
||||
# :bulb: Auto-generated documentation-based answer:
|
||||
"""
|
||||
For example: The following input:
|
||||
|
||||
@ -87,7 +87,6 @@ class PRReviewer:
|
||||
"require_estimate_effort_to_review": get_settings().pr_reviewer.require_estimate_effort_to_review,
|
||||
'require_can_be_split_review': get_settings().pr_reviewer.require_can_be_split_review,
|
||||
'require_security_review': get_settings().pr_reviewer.require_security_review,
|
||||
'require_todo_scan': get_settings().pr_reviewer.get("require_todo_scan", False),
|
||||
'question_str': question_str,
|
||||
'answer_str': answer_str,
|
||||
"extra_instructions": get_settings().pr_reviewer.extra_instructions,
|
||||
|
||||
Reference in New Issue
Block a user