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.
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.
- The [Tools Guide](./docs/TOOLS_GUIDE.md) provides a detailed description of the different tools and their configurations.
- 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.
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`.
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:
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:
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.
When running PR-Agent from GitHub 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.
Much like the configurations for `pr_commands`, you can override the default tool parameters by uploading a local configuration file to the root of your repo.
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:
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).
`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.
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:
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.
In addition, some subsections like `PR changes walkthrough` will not appear, since they require the usage of collapsible sections, which are not supported by bitbucket.
To use Azure DevOps provider use the following settings in configuration.toml:
```
[config]
git_provider="azure"
use_repo_settings_file=false
```
And use the following settings (you have to replace the values) in .secrets.toml:
```
[azure_devops]
org = "https://dev.azure.com/YOUR_ORGANIZATION/"
pat = "YOUR_PAT_TOKEN"
```
##### 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:
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 = "<basicauthuser>"
webhook_password = "<basicauthpassword>"
```
> :warning: **Ensure that the webhook endpoint is only accessible over HTTPS** to mitigate the risk of credential interception when using basic authentication.
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)
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):
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:
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.
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.
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:
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.
