Merge branch 'qodo-ai:main' into main

docs/docs/installation/gitlab.md

@@ -43,36 +43,47 @@ Note that if your base branches are not protected, don't set the variables as `p
 
 ## Run a GitLab webhook server
 
-1. From the GitLab workspace or group, create an access token with "Reporter" role ("Developer" if using Pro version of the agent) and "api" scope.
+1. In GitLab create a new user and give it "Reporter" role ("Developer" if using Pro version of the agent) for the intended group or project.
 
-2. Generate a random secret for your app, and save it for later. For example, you can use:
+2. For the user from step 1. generate a `personal_access_token` with `api` access.
+
+3. Generate a random secret for your app, and save it for later (`shared_secret`). For example, you can use:
 
 ```
-WEBHOOK_SECRET=$(python -c "import secrets; print(secrets.token_hex(10))")
+SHARED_SECRET=$(python -c "import secrets; print(secrets.token_hex(10))")
 ```
 
-3. Clone this repository:
+4. Clone this repository:
 
 ```
-git clone https://github.com/Codium-ai/pr-agent.git
+git clone https://github.com/qodo-ai/pr-agent.git
 ```
 
-4. Prepare variables and secrets. Skip this step if you plan on settings these as environment variables when running the agent:
+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 `deployment_type` to "gitlab"
+    - 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 1) and `shared_secret` (with secret from step 2)
+    - In the [gitlab] section, set `personal_access_token` (with token from step 2) and `shared_secret` (with secret from step 3)
 
-
-5. Build a Docker image for the app and optionally push it to a Docker repository. We'll use Dockerhub as an example:
+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 gitlab_pr_agent --target gitlab_webhook -f docker/Dockerfile
 docker push codiumai/pr-agent:gitlab_webhook  # Push to your Docker repository
 ```
 
-6. Create a webhook in GitLab. Set the URL to ```http[s]://<PR_AGENT_HOSTNAME>/webhook```, the secret token to the generated secret from step 2, and enable the triggers `push`, `comments` and `merge request events`.
+7. Set the environmental variables, the method depends on your docker runtime. Skip this step if you included your secrets/configuration directly in the Docker image.
+
+```
+"CONFIG.GIT_PROVIDER": "gitlab"
+"GITLAB.PERSONAL_ACCESS_TOKEN": "<personal_access_token>"
+"GITLAB.SHARED_SECRET": "<shared_secret>"
+"GITLAB.URL": "https://gitlab.com"
+"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.
 
-7. Test your installation by opening a merge request or commenting on a merge request using one of CodiumAI's commands.
-boxes

docs/docs/usage-guide/configuration_options.md

@@ -69,3 +69,28 @@ For example, in the GitHub organization `Codium-ai`:
 - The file [`https://github.com/Codium-ai/pr-agent-settings/.pr_agent.toml`](https://github.com/Codium-ai/pr-agent-settings/blob/main/.pr_agent.toml)  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`.
+
+### Bitbucket Organization level configuration file 💎
+`Relevant platforms: Bitbucket Cloud, Bitbucket Data Center`
+
+In Bitbucket, there are two levels where you can define a global configuration file:
+
+* Project-level global configuration:
+
+Create a repository named `pr-agent-settings` within a specific project. The configuration file in this repository will apply to all repositories under the same project.
+
+* Organization-level global configuration:
+
+Create a dedicated project to hold a global configuration file that affects all repositories across all projects in your organization.
+
+**Setting up organization-level global configuration:**
+
+1.	Create a new project with both the name and key: PR_AGENT_SETTINGS.
+2.	Inside the PR_AGENT_SETTINGS project, create a repository named pr-agent-settings.
+3.	In this repository, add a .pr_agent.toml configuration file—structured similarly to the global configuration file described above.
+
+Repositories across your entire Bitbucket organization will inherit the configuration from this file.
+
+!!! note "Note"
+    If both organization-level and project-level global settings are defined, the project-level settings will take precedence over the organization-level configuration. Additionally, parameters from a repository’s local .pr_agent.toml file will always override both global settings.
+

pr_agent/algo/ai_handlers/litellm_ai_handler.py

@@ -41,11 +41,6 @@ class LiteLLMAIHandler(BaseAiHandler):
             os.environ["AWS_ACCESS_KEY_ID"] = get_settings().aws.AWS_ACCESS_KEY_ID
             os.environ["AWS_SECRET_ACCESS_KEY"] = get_settings().aws.AWS_SECRET_ACCESS_KEY
             os.environ["AWS_REGION_NAME"] = get_settings().aws.AWS_REGION_NAME
-        if get_settings().get("litellm.use_client"):
-            litellm_token = get_settings().get("litellm.LITELLM_TOKEN")
-            assert litellm_token, "LITELLM_TOKEN is required"
-            os.environ["LITELLM_TOKEN"] = litellm_token
-            litellm.use_client = True
         if get_settings().get("LITELLM.DROP_PARAMS", None):
             litellm.drop_params = get_settings().litellm.drop_params
         if get_settings().get("LITELLM.SUCCESS_CALLBACK", None):

pr_agent/algo/cli_args.py

@@ -1,4 +1,4 @@
-from base64 import b64decode, encode
+from base64 import b64decode, encode, b64encode
 import hashlib
 
 class CliArgs:
@@ -9,7 +9,9 @@ class CliArgs:
                 return True, ""
 
             # decode forbidden args
-            _encoded_args = 'ZW5hYmxlX2NvbW1lbnRfYXBwcm92YWw=:ZW5hYmxlX21hbnVhbF9hcHByb3ZhbA==:ZW5hYmxlX2F1dG9fYXBwcm92YWw=:YXBwcm92ZV9wcl9vbl9zZWxmX3Jldmlldw==:YmFzZV91cmw=:dXJs:YXBwX25hbWU=:c2VjcmV0X3Byb3ZpZGVy:Z2l0X3Byb3ZpZGVy:c2tpcF9rZXlz:b3BlbmFpLmtleQ==:QU5BTFlUSUNTX0ZPTERFUg==:dXJp:YXBwX2lk:d2ViaG9va19zZWNyZXQ=:YmVhcmVyX3Rva2Vu:UEVSU09OQUxfQUNDRVNTX1RPS0VO:b3ZlcnJpZGVfZGVwbG95bWVudF90eXBl:cHJpdmF0ZV9rZXk=:bG9jYWxfY2FjaGVfcGF0aA==:ZW5hYmxlX2xvY2FsX2NhY2hl:amlyYV9iYXNlX3VybA==:YXBpX2Jhc2U=:YXBpX3R5cGU=:YXBpX3ZlcnNpb24=:c2tpcF9rZXlz'
+            # b64encode('word'.encode()).decode()
+            _encoded_args = 'c2hhcmVkX3NlY3JldA==:dXNlcg==:c3lzdGVt:ZW5hYmxlX2NvbW1lbnRfYXBwcm92YWw=:ZW5hYmxlX21hbnVhbF9hcHByb3ZhbA==:ZW5hYmxlX2F1dG9fYXBwcm92YWw=:YXBwcm92ZV9wcl9vbl9zZWxmX3Jldmlldw==:YmFzZV91cmw=:dXJs:YXBwX25hbWU=:c2VjcmV0X3Byb3ZpZGVy:Z2l0X3Byb3ZpZGVy:c2tpcF9rZXlz:b3BlbmFpLmtleQ==:QU5BTFlUSUNTX0ZPTERFUg==:dXJp:YXBwX2lk:d2ViaG9va19zZWNyZXQ=:YmVhcmVyX3Rva2Vu:UEVSU09OQUxfQUNDRVNTX1RPS0VO:b3ZlcnJpZGVfZGVwbG95bWVudF90eXBl:cHJpdmF0ZV9rZXk=:bG9jYWxfY2FjaGVfcGF0aA==:ZW5hYmxlX2xvY2FsX2NhY2hl:amlyYV9iYXNlX3VybA==:YXBpX2Jhc2U=:YXBpX3R5cGU=:YXBpX3ZlcnNpb24=:c2tpcF9rZXlz'
+
             forbidden_cli_args = []
             for e in _encoded_args.split(':'):
                 forbidden_cli_args.append(b64decode(e).decode())

pr_agent/settings/.secrets_template.toml

@@ -78,9 +78,6 @@ webhook_secret = ""
 app_key = ""
 base_url = ""
 
-[litellm]
-LITELLM_TOKEN = "" # see https://docs.litellm.ai/docs/debugging/hosted_debugging for details and instructions on how to get a token
-
 [azure_devops]
 # For Azure devops personal access token
 org = ""