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Add GitHub Actions workflow for docs deployment and remove outdated markdown files

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.github/workflows/docs-ci.yaml vendored Normal file
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name: docs-ci
on:
push:
branches:
- main
paths:
- docs/**
permissions:
contents: write
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Configure Git Credentials
run: |
git config user.name github-actions[bot]
git config user.email 41898282+github-actions[bot]@users.noreply.github.com
- uses: actions/setup-python@v5
with:
python-version: 3.x
- run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
- uses: actions/cache@v4
with:
key: mkdocs-material-${{ env.cache_id }}
path: .cache
restore-keys: |
mkdocs-material-
- run: pip install mkdocs-material
- run: pip install "mkdocs-material[imaging]"
- run: mkdocs gh-deploy --force

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INSTALL.md
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## Installation
To get started with PR-Agent quickly, you first need to acquire two tokens:
1. An OpenAI key from [here](https://platform.openai.com/api-keys), with access to GPT-4.
2. A GitHub\GitLab\BitBucket personal access token (classic), with the repo scope. [GitHub from [here](https://github.com/settings/tokens)]
There are several ways to use PR-Agent:
**Locally**
- [Using Docker image (no installation required)](INSTALL.md#use-docker-image-no-installation-required)
- [Run from source](INSTALL.md#run-from-source)
**GitHub specific methods**
- [Run as a GitHub Action](INSTALL.md#run-as-a-github-action)
- [Run as a GitHub App](INSTALL.md#run-as-a-github-app)
- [Deploy as a Lambda Function](INSTALL.md#deploy-as-a-lambda-function)
- [AWS CodeCommit](INSTALL.md#aws-codecommit-setup)
**GitLab specific methods**
- [Run a GitLab webhook server](INSTALL.md#run-a-gitlab-webhook-server)
**BitBucket specific methods**
- [Run as a Bitbucket Pipeline](INSTALL.md#run-as-a-bitbucket-pipeline)
- [Run on a hosted app](INSTALL.md#run-on-a-hosted-bitbucket-app)
- [Bitbucket server and data center](INSTALL.md#bitbucket-server-and-data-center)
---
### Use Docker image (no installation required)
A list of the relevant tools can be found in the [tools guide](./docs/TOOLS_GUIDE.md).
To invoke a tool (for example `review`), you can run directly from the Docker image. Here's how:
- For GitHub:
```
docker run --rm -it -e OPENAI.KEY=<your key> -e GITHUB.USER_TOKEN=<your token> codiumai/pr-agent:latest --pr_url <pr_url> review
```
- For GitLab:
```
docker run --rm -it -e OPENAI.KEY=<your key> -e CONFIG.GIT_PROVIDER=gitlab -e GITLAB.PERSONAL_ACCESS_TOKEN=<your token> codiumai/pr-agent:latest --pr_url <pr_url> review
```
Note: If you have a dedicated GitLab instance, you need to specify the custom url as variable:
```
docker run --rm -it -e OPENAI.KEY=<your key> -e CONFIG.GIT_PROVIDER=gitlab -e GITLAB.PERSONAL_ACCESS_TOKEN=<your token> -e GITLAB.URL=<your gitlab instance url> codiumai/pr-agent:latest --pr_url <pr_url> review
```
- For BitBucket:
```
docker run --rm -it -e CONFIG.GIT_PROVIDER=bitbucket -e OPENAI.KEY=$OPENAI_API_KEY -e BITBUCKET.BEARER_TOKEN=$BITBUCKET_BEARER_TOKEN codiumai/pr-agent:latest --pr_url=<pr_url> review
```
For other git providers, update CONFIG.GIT_PROVIDER accordingly, and check the `pr_agent/settings/.secrets_template.toml` file for the environment variables expected names and values.
---
If you want to ensure you're running a specific version of the Docker image, consider using the image's digest:
```bash
docker run --rm -it -e OPENAI.KEY=<your key> -e GITHUB.USER_TOKEN=<your token> codiumai/pr-agent@sha256:71b5ee15df59c745d352d84752d01561ba64b6d51327f97d46152f0c58a5f678 --pr_url <pr_url> review
```
Or you can run a [specific released versions](./RELEASE_NOTES.md) of pr-agent, for example:
```
codiumai/pr-agent@v0.9
```
---
### Run from source
1. Clone this repository:
```
git clone https://github.com/Codium-ai/pr-agent.git
```
2. Navigate to the `/pr-agent` folder and install the requirements in your favorite virtual environment:
```
pip install -e .
```
*Note: If you get an error related to Rust in the dependency installation then make sure Rust is installed and in your `PATH`, instructions: https://rustup.rs*
3. Copy the secrets template file and fill in your OpenAI key and your GitHub user token:
```
cp pr_agent/settings/.secrets_template.toml pr_agent/settings/.secrets.toml
chmod 600 pr_agent/settings/.secrets.toml
# Edit .secrets.toml file
```
4. Run the cli.py script:
```
python3 -m pr_agent.cli --pr_url <pr_url> review
python3 -m pr_agent.cli --pr_url <pr_url> ask <your question>
python3 -m pr_agent.cli --pr_url <pr_url> describe
python3 -m pr_agent.cli --pr_url <pr_url> improve
python3 -m pr_agent.cli --pr_url <pr_url> add_docs
python3 -m pr_agent.cli --pr_url <pr_url> generate_labels
python3 -m pr_agent.cli --issue_url <issue_url> similar_issue
...
```
[Optional] Add the pr_agent folder to your PYTHONPATH
```
export PYTHONPATH=$PYTHONPATH:<PATH to pr_agent folder>
```
---
### Run as a GitHub Action
You can use our pre-built Github Action Docker image to run PR-Agent as a Github Action.
1. Add the following file to your repository under `.github/workflows/pr_agent.yml`:
```yaml
on:
pull_request:
types:
- opened
- reopened
- ready_for_review
- review_requested
issue_comment:
types:
- created
- edited
jobs:
pr_agent_job:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
contents: write
name: Run pr agent on every pull request, respond to user comments
steps:
- name: PR Agent action step
id: pragent
uses: Codium-ai/pr-agent@main
env:
OPENAI_KEY: ${{ secrets.OPENAI_KEY }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
```
** if you want to pin your action to a specific release (v0.7 for example) for stability reasons, use:
```yaml
on:
pull_request:
types:
- opened
- reopened
- ready_for_review
- review_requested
issue_comment:
types:
- created
- edited
jobs:
pr_agent_job:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
contents: write
name: Run pr agent on every pull request, respond to user comments
steps:
- name: PR Agent action step
id: pragent
uses: Codium-ai/pr-agent@v0.7
env:
OPENAI_KEY: ${{ secrets.OPENAI_KEY }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
```
2. Add the following secret to your repository under `Settings > Secrets and variables > Actions > New repository secret > Add secret`:
```
Name = OPENAI_KEY
Secret = <your key>
```
The GITHUB_TOKEN secret is automatically created by GitHub.
3. Merge this change to your main branch.
When you open your next PR, you should see a comment from `github-actions` bot with a review of your PR, and instructions on how to use the rest of the tools.
4. You may configure PR-Agent by adding environment variables under the env section corresponding to any configurable property in the [configuration](pr_agent/settings/configuration.toml) file. Some examples:
```yaml
env:
# ... previous environment values
OPENAI.ORG: "<Your organization name under your OpenAI account>"
PR_REVIEWER.REQUIRE_TESTS_REVIEW: "false" # Disable tests review
PR_CODE_SUGGESTIONS.NUM_CODE_SUGGESTIONS: 6 # Increase number of code suggestions
```
---
### Run as a GitHub App
Allowing you to automate the review process on your private or public repositories.
1. Create a GitHub App from the [Github Developer Portal](https://docs.github.com/en/developers/apps/creating-a-github-app).
- Set the following permissions:
- Pull requests: Read & write
- Issue comment: Read & write
- Metadata: Read-only
- Contents: Read-only
- Set the following events:
- Issue comment
- Pull request
- Push (if you need to enable triggering on PR update)
2. Generate a random secret for your app, and save it for later. For example, you can use:
```
WEBHOOK_SECRET=$(python -c "import secrets; print(secrets.token_hex(10))")
```
3. Acquire the following pieces of information from your app's settings page:
- App private key (click "Generate a private key" and save the file)
- App ID
4. Clone this repository:
```
git clone https://github.com/Codium-ai/pr-agent.git
```
5. Copy the secrets template file and fill in the following:
```
cp pr_agent/settings/.secrets_template.toml pr_agent/settings/.secrets.toml
# Edit .secrets.toml file
```
- Your OpenAI key.
- Copy your app's private key to the private_key field.
- Copy your app's ID to the app_id field.
- Copy your app's webhook secret to the webhook_secret field.
- Set deployment_type to 'app' in [configuration.toml](./pr_agent/settings/configuration.toml)
> The .secrets.toml file is not copied to the Docker image by default, and is only used for local development.
> If you want to use the .secrets.toml file in your Docker image, you can add remove it from the .dockerignore file.
> In most production environments, you would inject the secrets file as environment variables or as mounted volumes.
> For example, in order to inject a secrets file as a volume in a Kubernetes environment you can update your pod spec to include the following,
> assuming you have a secret named `pr-agent-settings` with a key named `.secrets.toml`:
```
volumes:
- name: settings-volume
secret:
secretName: pr-agent-settings
// ...
containers:
// ...
volumeMounts:
- mountPath: /app/pr_agent/settings_prod
name: settings-volume
```
> Another option is to set the secrets as environment variables in your deployment environment, for example `OPENAI.KEY` and `GITHUB.USER_TOKEN`.
6. Build a Docker image for the app and optionally push it to a Docker repository. We'll use Dockerhub as an example:
```
docker build . -t codiumai/pr-agent:github_app --target github_app -f docker/Dockerfile
docker push codiumai/pr-agent:github_app # Push to your Docker repository
```
7. Host the app using a server, serverless function, or container environment. Alternatively, for development and
debugging, you may use tools like smee.io to forward webhooks to your local machine.
You can check [Deploy as a Lambda Function](#deploy-as-a-lambda-function)
8. Go back to your app's settings, and set the following:
- Webhook URL: The URL of your app's server or the URL of the smee.io channel.
- Webhook secret: The secret you generated earlier.
9. Install the app by navigating to the "Install App" tab and selecting your desired repositories.
> **Note:** When running PR-Agent from GitHub App, the default configuration file (configuration.toml) will be loaded.<br>
> However, you can override the default tool parameters by uploading a local configuration file `.pr_agent.toml`<br>
> For more information please check out the [USAGE GUIDE](./Usage.md#working-with-github-app)
---
### Deploy as a Lambda Function
1. Follow steps 1-5 of [Method 5](#run-as-a-github-app).
2. Build a docker image that can be used as a lambda function
```shell
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: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.
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 [Method 5](#run-as-a-github-app) with the function url as your Webhook URL.
The Webhook URL would look like `https://<LAMBDA_FUNCTION_URL>/api/v1/github_webhooks`
---
### AWS CodeCommit Setup
Not all features have been added to CodeCommit yet. As of right now, CodeCommit has been implemented to run the pr-agent CLI on the command line, using AWS credentials stored in environment variables. (More features will be added in the future.) The following is a set of instructions to have pr-agent do a review of your CodeCommit pull request from the command line:
1. Create an IAM user that you will use to read CodeCommit pull requests and post comments
* Note: That user should have CLI access only, not Console access
2. Add IAM permissions to that user, to allow access to CodeCommit (see IAM Role example below)
3. Generate an Access Key for your IAM user
4. Set the Access Key and Secret using environment variables (see Access Key example below)
5. Set the `git_provider` value to `codecommit` in the `pr_agent/settings/configuration.toml` settings file
6. Set the `PYTHONPATH` to include your `pr-agent` project directory
* Option A: Add `PYTHONPATH="/PATH/TO/PROJECTS/pr-agent` to your `.env` file
* Option B: Set `PYTHONPATH` and run the CLI in one command, for example:
* `PYTHONPATH="/PATH/TO/PROJECTS/pr-agent python pr_agent/cli.py [--ARGS]`
##### AWS CodeCommit IAM Role Example
Example IAM permissions to that user to allow access to CodeCommit:
* Note: The following is a working example of IAM permissions that has read access to the repositories and write access to allow posting comments
* Note: If you only want pr-agent to review your pull requests, you can tighten the IAM permissions further, however this IAM example will work, and allow the pr-agent to post comments to the PR
* Note: You may want to replace the `"Resource": "*"` with your list of repos, to limit access to only those repos
```
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"codecommit:BatchDescribe*",
"codecommit:BatchGet*",
"codecommit:Describe*",
"codecommit:EvaluatePullRequestApprovalRules",
"codecommit:Get*",
"codecommit:List*",
"codecommit:PostComment*",
"codecommit:PutCommentReaction",
"codecommit:UpdatePullRequestDescription",
"codecommit:UpdatePullRequestTitle"
],
"Resource": "*"
}
]
}
```
##### AWS CodeCommit Access Key and Secret
Example setting the Access Key and Secret using environment variables
```sh
export AWS_ACCESS_KEY_ID="XXXXXXXXXXXXXXXX"
export AWS_SECRET_ACCESS_KEY="XXXXXXXXXXXXXXXX"
export AWS_DEFAULT_REGION="us-east-1"
```
##### AWS CodeCommit CLI Example
After you set up AWS CodeCommit using the instructions above, here is an example CLI run that tells pr-agent to **review** a given pull request.
(Replace your specific PYTHONPATH and PR URL in the example)
```sh
PYTHONPATH="/PATH/TO/PROJECTS/pr-agent" python pr_agent/cli.py \
--pr_url https://us-east-1.console.aws.amazon.com/codesuite/codecommit/repositories/MY_REPO_NAME/pull-requests/321 \
review
```
---
### Run a GitLab webhook server
1. From the GitLab workspace or group, create an access token. Enable the "api" scope only.
2. Generate a random secret for your app, and save it for later. For example, you can use:
```
WEBHOOK_SECRET=$(python -c "import secrets; print(secrets.token_hex(10))")
```
3. Follow the instructions to build the Docker image, setup a secrets file and deploy on your own server from [Method 5](#run-as-a-github-app) steps 4-7. Be sure to set the target to `gitlab_webhook` instead of `github_app` when building the Docker image.
4. In the secrets file, fill in the following:
- Your OpenAI key.
- In the [gitlab] section, fill in personal_access_token and shared_secret. The access token can be a personal access token, or a group or project access token.
- Set deployment_type to 'gitlab' in [configuration.toml](./pr_agent/settings/configuration.toml)
5. Create a webhook in GitLab. Set the URL to the URL of your app's server with the path `/webhook` (e.g. `http://pr-agent.example.com:3000/webhook`). Set the secret token to the generated secret from step 2.
In the "Trigger" section, check the ‘comments’ and ‘merge request events’ boxes.
6. Test your installation by opening a merge request or commenting on a merge request using one of CodiumAI's commands.
### Run as a Bitbucket Pipeline
You can use the Bitbucket Pipeline system to run PR-Agent on every pull request open or update.
1. Add the following file in your repository bitbucket_pipelines.yml
```yaml
pipelines:
pull-requests:
'**':
- step:
name: PR Agent Review
image: python:3.10
services:
- docker
script:
- docker run -e CONFIG.GIT_PROVIDER=bitbucket -e OPENAI.KEY=$OPENAI_API_KEY -e BITBUCKET.BEARER_TOKEN=$BITBUCKET_BEARER_TOKEN codiumai/pr-agent:latest --pr_url=https://bitbucket.org/$BITBUCKET_WORKSPACE/$BITBUCKET_REPO_SLUG/pull-requests/$BITBUCKET_PR_ID review
```
2. Add the following secure variables to your repository under Repository settings > Pipelines > Repository variables.
OPENAI_API_KEY: <your key>
BITBUCKET_BEARER_TOKEN: <your token>
You can get a Bitbucket token for your repository by following Repository Settings -> Security -> Access Tokens.
Note that comments on a PR are not supported in Bitbucket Pipeline.
### Run using CodiumAI-hosted Bitbucket app
Please contact <support@codium.ai> or visit [CodiumAI pricing page](https://www.codium.ai/pricing/) if you're interested in a hosted BitBucket app solution that provides full functionality including PR reviews and comment handling. It's based on the [bitbucket_app.py](https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/git_providers/bitbucket_provider.py) implementation.
### Bitbucket Server and Data Center
Login into your on-prem instance of Bitbucket with your service account username and password.
Navigate to `Manage account`, `HTTP Access tokens`, `Create Token`.
Generate the token and add it to .secret.toml under `bitbucket_server` section
```toml
[bitbucket_server]
bearer_token = "<your key>"
```
#### Run it as CLI
Modify `configuration.toml`:
```toml
git_provider="bitbucket_server"
```
and pass the Pull request URL:
```shell
python cli.py --pr_url https://git.onpreminstanceofbitbucket.com/projects/PROJECT/repos/REPO/pull-requests/1 review
```
#### Run it as service
To run pr-agent as webhook, build the docker image:
```
docker build . -t codiumai/pr-agent:bitbucket_server_webhook --target bitbucket_server_webhook -f docker/Dockerfile
docker push codiumai/pr-agent:bitbucket_server_webhook # Push to your Docker repository
```
Navigate to `Projects` or `Repositories`, `Settings`, `Webhooks`, `Create Webhook`.
Fill the name and URL, Authentication None select the Pull Request Opened checkbox to receive that event as webhook.
The URL should end with `/webhook`, for example: https://domain.com/webhook
=======

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PR_COMPRESSION.md
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# PR Compression Strategy
There are two scenarios:
1. The PR is small enough to fit in a single prompt (including system and user prompt)
2. The PR is too large to fit in a single prompt (including system and user prompt)
For both scenarios, we first use the following strategy
#### Repo language prioritization strategy
We prioritize the languages of the repo based on the following criteria:
1. Exclude binary files and non code files (e.g. images, pdfs, etc)
2. Given the main languages used in the repo
2. We sort the PR files by the most common languages in the repo (in descending order):
* ```[[file.py, file2.py],[file3.js, file4.jsx],[readme.md]]```
## Small PR
In this case, we can fit the entire PR in a single prompt:
1. Exclude binary files and non code files (e.g. images, pdfs, etc)
2. We Expand the surrounding context of each patch to 3 lines above and below the patch
## Large PR
### Motivation
Pull Requests can be very long and contain a lot of information with varying degree of relevance to the pr-agent.
We want to be able to pack as much information as possible in a single LMM prompt, while keeping the information relevant to the pr-agent.
#### Compression strategy
We prioritize additions over deletions:
- Combine all deleted files into a single list (`deleted files`)
- File patches are a list of hunks, remove all hunks of type deletion-only from the hunks in the file patch
#### Adaptive and token-aware file patch fitting
We use [tiktoken](https://github.com/openai/tiktoken) to tokenize the patches after the modifications described above, and we use the following strategy to fit the patches into the prompt:
1. Within each language we sort the files by the number of tokens in the file (in descending order):
* ```[[file2.py, file.py],[file4.jsx, file3.js],[readme.md]]```
2. Iterate through the patches in the order described above
2. Add the patches to the prompt until the prompt reaches a certain buffer from the max token length
3. If there are still patches left, add the remaining patches as a list called `other modified files` to the prompt until the prompt reaches the max token length (hard stop), skip the rest of the patches.
4. If we haven't reached the max token length, add the `deleted files` to the prompt until the prompt reaches the max token length (hard stop), skip the rest of the patches.
### Example
<kbd><img src=https://codium.ai/images/git_patch_logic.png width="768"></kbd>

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Usage.md
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## Usage Guide
### Table of Contents
- [Introduction](#introduction)
- [Configuration Options](#configuration-options)
- [Managing Mail Notifications](#managing-mail-notifications)
- [Usage and Automation](#usage-and-automation)
- [Local Repo (CLI)](#working-from-a-local-repo-cli)
- [Online Usage](#online-usage)
- [GitHub App](#working-with-github-app)
- [GitHub Action](#working-with-github-action)
- [GitLab Webhook](#working-with-gitlab-webhook)
- [BitBucket App](#working-with-bitbucket-self-hosted-app)
- [Azure DevOps Provider](#azure-devops-provider)
- [Additional Configurations Walkthrough](#appendix---additional-configurations-walkthrough)
- [Ignoring files from analysis](#ignoring-files-from-analysis)
- [Extra instructions](#extra-instructions)
- [Working with large PRs](#working-with-large-prs)
- [Changing a model](#changing-a-model)
- [Patch Extra Lines](#patch-extra-lines)
- [Editing the prompts](#editing-the-prompts)
## Introduction
After [installation](/INSTALL.md), there are three basic ways to invoke CodiumAI PR-Agent:
1. Locally running a CLI command
2. Online usage - by [commenting](https://github.com/Codium-ai/pr-agent/pull/229#issuecomment-1695021901) on a PR
3. Enabling PR-Agent tools to run automatically when a new PR is opened
Specifically, CLI commands can be issued by invoking a pre-built [docker image](/INSTALL.md#running-from-source), or by invoking a [locally cloned repo](INSTALL.md#method-2-run-from-source).
For online usage, you will need to setup either a [GitHub App](INSTALL.md#method-5-run-as-a-github-app), or a [GitHub Action](INSTALL.md#method-3-run-as-a-github-action).
GitHub App and GitHub Action also enable to run PR-Agent specific tool automatically when a new PR is opened.
### git provider
The [git_provider](pr_agent/settings/configuration.toml#L4) field in the configuration file determines the GIT provider that will be used by PR-Agent. Currently, the following providers are supported:
`
"github", "gitlab", "bitbucket", "azure", "codecommit", "local", "gerrit"
`
## Configuration Options
The different tools and sub-tools used by CodiumAI PR-Agent are adjustable via the **[configuration file](pr_agent/settings/configuration.toml)**.
In addition to general configuration options, each tool has its own configurations. For example, the `review` tool will use parameters from the [pr_reviewer](/pr_agent/settings/configuration.toml#L16) section in the configuration file.
See the [Tools Guide](./docs/TOOLS_GUIDE.md) for a detailed description of the different tools and their configurations.
There are three ways to set persistent configurations:
1. Wiki configuration page 💎
2. Local configuration file
3. Global configuration file 💎
In terms of precedence, wiki configurations will override local configurations, and local configurations will override global configurations.
### Wiki configuration file 💎
Specifically for GitHub, with PR-Agent-Pro you can set configurations by creating a page called `.pr_agent.toml` in the [wiki](https://github.com/Codium-ai/pr-agent/wiki/pr_agent.toml) of the repo.
The advantage of this method is that it allows to set configurations without needing to commit new content to the repo - just edit the wiki page and **save**.
<kbd><img src="https://codium.ai/images/pr_agent/wiki_configuration.png" width="512"></kbd>
Click [here](https://codium.ai/images/pr_agent/wiki_configuration_pr_agent.mp4) to see a short instructional video. We recommend surrounding the configuration content with triple-quotes, to allow better presentation when displayed in the wiki as markdown.
An example content:
\`\`\`<br>
[pr_description] # /describe #<br>
keep_original_user_title=false<br>
\`\`\`
PR-Agent will know to remove the triple-quotes when reading the configuration content.
### Local configuration file
By uploading a local `.pr_agent.toml` file to the root of the repo's main branch, you can edit and customize any configuration parameter. Note that you need to upload `.pr_agent.toml` prior to creating a PR, in order for the configuration to take effect.
For example, if you set in `.pr_agent.toml`:
```
[pr_reviewer]
extra_instructions="""\
- instruction a
- instruction b
...
"""
```
Then you can give a list of extra instructions to the `review` tool.
### Global configuration file 💎
If you create a repo called `pr-agent-settings` in your **organization**, it's configuration file `.pr_agent.toml` will be used as a global configuration file for any other repo that belongs to the same organization.
Parameters from a local `.pr_agent.toml` file, in a specific repo, will override the global configuration parameters.
For example, in the GitHub organization `Codium-ai`:
- The repo [`https://github.com/Codium-ai/pr-agent-settings`](https://github.com/Codium-ai/pr-agent-settings/blob/main/.pr_agent.toml) contains a `.pr_agent.toml` file that serves as a global configuration file for all the repos in the GitHub organization `Codium-ai`.
- The repo [`https://github.com/Codium-ai/pr-agent`](https://github.com/Codium-ai/pr-agent/blob/main/.pr_agent.toml) inherits the global configuration file from `pr-agent-settings`.
## Managing mail notifications
Unfortunately, it is not possible in GitHub to disable mail notifications from a specific user.
If you are subscribed to notifications for a repo with PR-Agent, we recommend turning off notifications for PR comments, to avoid lengthy emails:
<kbd><img src="https://codium.ai/images/pr_agent/notifications.png" width="512"></kbd>
As an alternative, you can filter in your mail provider the notifications specifically from the PR-Agent bot:
https://www.quora.com/How-can-you-filter-emails-for-specific-people-in-Gmail#:~:text=On%20the%20Filters%20and%20Blocked,the%20body%20of%20the%20email
<kbd><img src="https://codium.ai/images/pr_agent/filter_mail_notifications.png" width="512"></kbd>
## Usage and Automation
### Working from a local repo (CLI)
When running from your local repo (CLI), your local configuration file will be used.
Examples of invoking the different tools via the CLI:
- **Review**: `python -m pr_agent.cli --pr_url=<pr_url> review`
- **Describe**: `python -m pr_agent.cli --pr_url=<pr_url> describe`
- **Improve**: `python -m pr_agent.cli --pr_url=<pr_url> improve`
- **Ask**: `python -m pr_agent.cli --pr_url=<pr_url> ask "Write me a poem about this PR"`
- **Reflect**: `python -m pr_agent.cli --pr_url=<pr_url> reflect`
- **Update Changelog**: `python -m pr_agent.cli --pr_url=<pr_url> update_changelog`
`<pr_url>` is the url of the relevant PR (for example: https://github.com/Codium-ai/pr-agent/pull/50).
**Notes:**
(1) in addition to editing your local configuration file, you can also change any configuration value by adding it to the command line:
```
python -m pr_agent.cli --pr_url=<pr_url> /review --pr_reviewer.extra_instructions="focus on the file: ..."
```
(2) You can print results locally, without publishing them, by setting in `configuration.toml`:
```
[config]
publish_output=false
verbosity_level=2
```
This is useful for debugging or experimenting with different tools.
### Online usage
Online usage means invoking PR-Agent tools by [comments](https://github.com/Codium-ai/pr-agent/pull/229#issuecomment-1695021901) on a PR.
Commands for invoking the different tools via comments:
- **Review**: `/review`
- **Describe**: `/describe`
- **Improve**: `/improve`
- **Ask**: `/ask "..."`
- **Reflect**: `/reflect`
- **Update Changelog**: `/update_changelog`
To edit a specific configuration value, just add `--config_path=<value>` to any command.
For example, if you want to edit the `review` tool configurations, you can run:
```
/review --pr_reviewer.extra_instructions="..." --pr_reviewer.require_score_review=false
```
Any configuration value in [configuration file](pr_agent/settings/configuration.toml) file can be similarly edited. Comment `/config` to see the list of available configurations.
### Working with GitHub App
#### GitHub app automatic tools when a new PR is opened
The [github_app](pr_agent/settings/configuration.toml#L108) section defines GitHub app specific configurations.
The configuration parameter `pr_commands` defines the list of tools that will be **run automatically** when a new PR is opened.
```
[github_app]
pr_commands = [
"/describe --pr_description.add_original_user_description=true --pr_description.keep_original_user_title=true",
"/review --pr_reviewer.num_code_suggestions=0",
"/improve",
]
```
This means that when a new PR is opened/reopened or marked as ready for review, PR-Agent will run the `describe`, `review` and `improve` tools.
For the `describe` tool, for example, the `add_original_user_description` and `keep_original_user_title` parameters will be set to true.
You can override the default tool parameters by using one the three options for a [configuration file](#configuration-options): **wiki**, **local**, or **global**.
For example, if your local `.pr_agent.toml` file contains:
```
[pr_description]
add_original_user_description = false
keep_original_user_title = false
```
When a new PR is opened, PR-Agent will run the `describe` tool with the above parameters.
To cancel the automatic run of all the tools, set:
```
[github_app]
handle_pr_actions = []
```
You can also disable automatic runs for PRs with specific titles, by setting the `ignore_pr_titles` parameter with the relevant regex. For example:
```
[github_app]
ignore_pr_title = ["^[Auto]", ".*ignore.*"]
```
will ignore PRs with titles that start with "Auto" or contain the word "ignore".
#### GitHub app automatic tools for push actions (commits to an open PR)
In addition to running automatic tools when a PR is opened, the GitHub app can also respond to new code that is pushed to an open PR.
The configuration toggle `handle_push_trigger` can be used to enable this feature.
The configuration parameter `push_commands` defines the list of tools that will be **run automatically** when new code is pushed to the PR.
```
[github_app]
handle_push_trigger = true
push_commands = [
"/describe --pr_description.add_original_user_description=true --pr_description.keep_original_user_title=true --pr_description.final_update_message=false",
"/review --pr_reviewer.num_code_suggestions=0 --pr_reviewer.final_update_message=false",
]
```
This means that when new code is pushed to the PR, the PR-Agent will run the `describe` and `review` tools, with the specified parameters.
### Working with GitHub Action
`GitHub Action` is a different way to trigger PR-Agent tools, and uses a different configuration mechanism than `GitHub App`.
You can configure settings for `GitHub Action` by adding environment variables under the env section in `.github/workflows/pr_agent.yml` file.
Specifically, start by setting the following environment variables:
```yaml
env:
OPENAI_KEY: ${{ secrets.OPENAI_KEY }} # Make sure to add your OpenAI key to your repo secrets
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Make sure to add your GitHub token to your repo secrets
github_action_config.auto_review: "true" # enable\disable auto review
github_action_config.auto_describe: "true" # enable\disable auto describe
github_action_config.auto_improve: "true" # enable\disable auto improve
```
`github_action_config.auto_review`, `github_action_config.auto_describe` and `github_action_config.auto_improve` are used to enable/disable automatic tools that run when a new PR is opened.
If not set, the default configuration is for all three tools to run automatically when a new PR is opened.
Note that you can give additional config parameters by adding environment variables to `.github/workflows/pr_agent.yml`, or by using a `.pr_agent.toml` file in the root of your repo, similar to the GitHub App usage.
For example, you can set an environment variable: `pr_description.add_original_user_description=false`, or add a `.pr_agent.toml` file with the following content:
```
[pr_description]
add_original_user_description = false
```
### Working with GitLab Webhook
After setting up a GitLab webhook, to control which commands will run automatically when a new PR is opened, you can set the `pr_commands` parameter in the configuration file, similar to the GitHub App:
```
[gitlab]
pr_commands = [
"/describe --pr_description.add_original_user_description=true --pr_description.keep_original_user_title=true",
"/review --pr_reviewer.num_code_suggestions=0",
"/improve",
]
```
### Working with BitBucket Self-Hosted App
Similar to GitHub app, when running PR-Agent from BitBucket App, the default [configuration file](pr_agent/settings/configuration.toml) from a pre-built docker will be initially loaded.
By uploading a local `.pr_agent.toml` file to the root of the repo's main branch, you can edit and customize any configuration parameter. Note that you need to upload `.pr_agent.toml` prior to creating a PR, in order for the configuration to take effect.
For example, if your local `.pr_agent.toml` file contains:
```
[pr_reviewer]
inline_code_comments = true
```
Each time you invoke a `/review` tool, it will use inline code comments.
#### BitBucket Self-Hosted App automatic tools
to control which commands will run automatically when a new PR is opened, you can set the `pr_commands` parameter in the configuration file:
```
[bitbucket_app]
pr_commands = [
"/review --pr_reviewer.num_code_suggestions=0",
"/improve --pr_code_suggestions.summarize=false",
]
```
Note that due to limitations of the bitbucket platform, not all tools or sub-options, are supported. See [here](./README.md#Overview) for an overview of the supported tools for bitbucket.
### Azure DevOps provider
To use Azure DevOps provider use the following settings in configuration.toml:
```
[config]
git_provider="azure"
use_repo_settings_file=false
```
Azure DevOps provider supports [PAT token](https://learn.microsoft.com/en-us/azure/devops/organizations/accounts/use-personal-access-tokens-to-authenticate?view=azure-devops&tabs=Windows) or [DefaultAzureCredential](https://learn.microsoft.com/en-us/azure/developer/python/sdk/authentication-overview#authentication-in-server-environments) authentication.
PAT is faster to create, but has build in experation date, and will use the user identity for API calls.
Using DefaultAzureCredential you can use managed identity or Service principle, which are more secure and will create seperate ADO user identity (via AAD) to the agent.
If PAT was choosen, you can assign the value in .secrets.toml.
If DefaultAzureCredential was choosen, you can assigned the additional env vars like AZURE_CLIENT_SECRET directly,
or use managed identity/az cli (for local develpment) without any additional configuration.
in any case, 'org' value must be assigned in .secrets.toml:
```
[azure_devops]
org = "https://dev.azure.com/YOUR_ORGANIZATION/"
# pat = "YOUR_PAT_TOKEN" needed only if using PAT for authentication
```
##### Azure DevOps Webhook
To trigger from an Azure webhook, you need to manually [add a webhook](https://learn.microsoft.com/en-us/azure/devops/service-hooks/services/webhooks?view=azure-devops).
Use the "Pull request created" type to trigger a review, or "Pull request commented on" to trigger any supported comment with /<command> <args> comment on the relevant PR. Note that for the "Pull request commented on" trigger, only API v2.0 is supported.
To control which commands will run automatically when a new PR is opened, you can set the `pr_commands` parameter in the configuration file, similar to the GitHub App:
```
[azure_devops_server]
pr_commands = [
"/describe --pr_description.add_original_user_description=true --pr_description.keep_original_user_title=true",
"/review --pr_reviewer.num_code_suggestions=0",
"/improve",
]
```
For webhook security, create a sporadic username/password pair and configure the webhook username and password on both the server and Azure DevOps webhook. These will be sent as basic Auth data by the webhook with each request:
```
[azure_devops_server]
webhook_username = "<basic auth user>"
webhook_password = "<basic auth password>"
```
> :warning: **Ensure that the webhook endpoint is only accessible over HTTPS** to mitigate the risk of credential interception when using basic authentication.
## Appendix - additional configurations walkthrough
#### Ignoring files from analysis
In some cases, you may want to exclude specific files or directories from the analysis performed by CodiumAI PR-Agent. This can be useful, for example, when you have files that are generated automatically or files that shouldn't be reviewed, like vendored code.
To ignore files or directories, edit the **[ignore.toml](/pr_agent/settings/ignore.toml)** configuration file. This setting also exposes the following environment variables:
- `IGNORE.GLOB`
- `IGNORE.REGEX`
For example, to ignore python files in a PR with online usage, comment on a PR:
`/review --ignore.glob=['*.py']`
To ignore python files in all PRs, set in a configuration file:
```
[ignore]
glob = ['*.py']
```
#### Extra instructions
All PR-Agent tools have a parameter called `extra_instructions`, that enables to add free-text extra instructions. Example usage:
```
/update_changelog --pr_update_changelog.extra_instructions="Make sure to update also the version ..."
```
#### Working with large PRs
The default mode of CodiumAI is to have a single call per tool, using GPT-4, which has a token limit of 8000 tokens.
This mode provide a very good speed-quality-cost tradeoff, and can handle most PRs successfully.
When the PR is above the token limit, it employs a [PR Compression strategy](./PR_COMPRESSION.md).
However, for very large PRs, or in case you want to emphasize quality over speed and cost, there are 2 possible solutions:
1) [Use a model](#changing-a-model) with larger context, like GPT-32K, or claude-100K. This solution will be applicable for all the tools.
2) For the `/improve` tool, there is an ['extended' mode](./docs/IMPROVE.md) (`/improve --extended`),
which divides the PR to chunks, and process each chunk separately. With this mode, regardless of the model, no compression will be done (but for large PRs, multiple model calls may occur)
#### Changing a model
See [here](pr_agent/algo/__init__.py) for the list of available models.
To use a different model than the default (GPT-4), you need to edit [configuration file](pr_agent/settings/configuration.toml#L2).
For models and environments not from OPENAI, you might need to provide additional keys and other parameters. See below for instructions.
##### Azure
To use Azure, set in your `.secrets.toml` (working from CLI), or in the GitHub `Settings > Secrets and variables` (working from GitHub App or GitHub Action):
```
[openai]
key = "" # your azure api key
api_type = "azure"
api_version = '2023-05-15' # Check Azure documentation for the current API version
api_base = "" # The base URL for your Azure OpenAI resource. e.g. "https://<your resource name>.openai.azure.com"
deployment_id = "" # The deployment name you chose when you deployed the engine
```
and set in your configuration file:
```
[config]
model="" # the OpenAI model you've deployed on Azure (e.g. gpt-3.5-turbo)
```
##### Huggingface
**Local**
You can run Huggingface models locally through either [VLLM](https://docs.litellm.ai/docs/providers/vllm) or [Ollama](https://docs.litellm.ai/docs/providers/ollama)
E.g. to use a new Huggingface model locally via Ollama, set:
```
[__init__.py]
MAX_TOKENS = {
"model-name-on-ollama": <max_tokens>
}
e.g.
MAX_TOKENS={
...,
"ollama/llama2": 4096
}
[config] # in configuration.toml
model = "ollama/llama2"
[ollama] # in .secrets.toml
api_base = ... # the base url for your huggingface inference endpoint
# e.g. if running Ollama locally, you may use:
api_base = "http://localhost:11434/"
```
**Inference Endpoints**
To use a new model with Huggingface Inference Endpoints, for example, set:
```
[__init__.py]
MAX_TOKENS = {
"model-name-on-huggingface": <max_tokens>
}
e.g.
MAX_TOKENS={
...,
"meta-llama/Llama-2-7b-chat-hf": 4096
}
[config] # in configuration.toml
model = "huggingface/meta-llama/Llama-2-7b-chat-hf"
[huggingface] # in .secrets.toml
key = ... # your huggingface api key
api_base = ... # the base url for your huggingface inference endpoint
```
(you can obtain a Llama2 key from [here](https://replicate.com/replicate/llama-2-70b-chat/api))
##### Replicate
To use Llama2 model with Replicate, for example, set:
```
[config] # in configuration.toml
model = "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1"
[replicate] # in .secrets.toml
key = ...
```
(you can obtain a Llama2 key from [here](https://replicate.com/replicate/llama-2-70b-chat/api))
Also review the [AiHandler](pr_agent/algo/ai_handler.py) file for instruction how to set keys for other models.
##### Vertex AI
To use Google's Vertex AI platform and its associated models (chat-bison/codechat-bison) set:
```
[config] # in configuration.toml
model = "vertex_ai/codechat-bison"
fallback_models="vertex_ai/codechat-bison"
[vertexai] # in .secrets.toml
vertex_project = "my-google-cloud-project"
vertex_location = ""
```
Your [application default credentials](https://cloud.google.com/docs/authentication/application-default-credentials) will be used for authentication so there is no need to set explicit credentials in most environments.
If you do want to set explicit credentials then you can use the `GOOGLE_APPLICATION_CREDENTIALS` environment variable set to a path to a json credentials file.
##### Anthropic
To use Anthropic models, set the relevant models in the configuration section of the configuration file:
```
[config]
model="anthropic/claude-3-opus-20240229"
model_turbo="anthropic/claude-3-opus-20240229"
fallback_models=["anthropic/claude-3-opus-20240229"]
```
And also set the api key in the .secrets.toml file:
```
[anthropic]
KEY = "..."
```
##### Amazon Bedrock
To use Amazon Bedrock and its foundational models, add the below configuration:
```
[config] # in configuration.toml
model = "anthropic.claude-v2"
fallback_models="anthropic.claude-instant-v1"
[aws] # in .secrets.toml
bedrock_region = "us-east-1"
```
Note that you have to add access to foundational models before using them. Please refer to [this document](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) for more details.
AWS session is automatically authenticated from your environment, but you can also explicitly set `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables.
#### Patch Extra Lines
By default, around any change in your PR, git patch provides 3 lines of context above and below the change.
```
@@ -12,5 +12,5 @@ def func1():
code line that already existed in the file...
code line that already existed in the file...
code line that already existed in the file....
-code line that was removed in the PR
+new code line added in the PR
code line that already existed in the file...
code line that already existed in the file...
code line that already existed in the file...
```
For the `review`, `describe`, `ask` and `add_docs` tools, if the token budget allows, PR-Agent tries to increase the number of lines of context, via the parameter:
```
[config]
patch_extra_lines=3
```
Increasing this number provides more context to the model, but will also increase the token budget.
If the PR is too large (see [PR Compression strategy](./PR_COMPRESSION.md)), PR-Agent automatically sets this number to 0, using the original git patch.
#### Editing the prompts
The prompts for the various PR-Agent tools are defined in the `pr_agent/settings` folder.
In practice, the prompts are loaded and stored as a standard setting object.
Hence, editing them is similar to editing any other configuration value - just place the relevant key in `.pr_agent.toml`file, and override the default value.
For example, if you want to edit the prompts of the [describe](./pr_agent/settings/pr_description_prompts.toml) tool, you can add the following to your `.pr_agent.toml` file:
```
[pr_description_prompt]
system="""
...
"""
user="""
...
"""
```
Note that the new prompt will need to generate an output compatible with the relevant [post-process function](./pr_agent/tools/pr_description.py#L137).