Merge pull request #1927 from abhinav-1305/minor-fix

docs: improve installation for various providers

docs/docs/ai_search/index.md

@@ -19,7 +19,6 @@
 </div>
 
 <style>
-Untitled
 .search-section {
     max-width: 800px;
     margin: 0 auto;
@@ -305,9 +304,8 @@ window.addEventListener('load', function() {
             spinner.style.display = 'none';
             const errorDiv = document.createElement('div');
             errorDiv.className = 'error-message';
-            errorDiv.textContent = `${error}`;
-            resultsContainer.value = "";
-            resultsContainer.appendChild(errorDiv);
+            errorDiv.textContent = error instanceof Error ? error.message : String(error);
+            resultsContainer.replaceChildren(errorDiv);
         }
     }
 

docs/docs/faq/index.md

@@ -66,7 +66,7 @@ ___
 ___
 
 ??? note "Q: Can Qodo Merge review draft/offline PRs?"
-    #### Answer:<span style="display:none;">5</span>
+    #### Answer:<span style="display:none;">6</span>
 
     Yes. While Qodo Merge won't automatically review draft PRs, you can still get feedback by manually requesting it through [online commenting](https://qodo-merge-docs.qodo.ai/usage-guide/automations_and_usage/#online-usage).
 
@@ -74,7 +74,7 @@ ___
 ___
 
 ??? note "Q: Can the 'Review effort' feedback be calibrated or customized?"
-    #### Answer:<span style="display:none;">5</span>
+    #### Answer:<span style="display:none;">7</span>
 
     Yes, you can customize review effort estimates using the `extra_instructions` configuration option (see [documentation](https://qodo-merge-docs.qodo.ai/tools/review/#configuration-options)).
     

docs/docs/installation/azure.md

@@ -1,7 +1,7 @@
 ## Azure DevOps Pipeline
 
 You can use a pre-built Action Docker image to run PR-Agent as an Azure devops pipeline.
-add the following file to your repository under `azure-pipelines.yml`:
+Add the following file to your repository under `azure-pipelines.yml`:
 
 ```yaml
 # Opt out of CI triggers
@@ -71,7 +71,7 @@ git_provider="azure"
 ```
 
 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 expiration date, and will use the user identity for API calls.
+PAT is faster to create, but has built-in expiration 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 separate ADO user identity (via AAD) to the agent.
 
 If PAT was chosen, you can assign the value in .secrets.toml.

docs/docs/installation/bitbucket.md

@@ -50,7 +50,7 @@ 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
+python cli.py --pr_url https://git.on-prem-instance-of-bitbucket.com/projects/PROJECT/repos/REPO/pull-requests/1 review
 ```
 
 ### Run it as service
@@ -63,6 +63,6 @@ docker push codiumai/pr-agent:bitbucket_server_webhook  # Push to your Docker re
 ```
 
 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.
+Fill in the name and URL. For Authentication, select 'None'. Select the 'Pull Request Opened' checkbox to receive that event as a webhook.
 
 The URL should end with `/webhook`, for example: https://domain.com/webhook

docs/docs/installation/gitea.md

@@ -17,12 +17,11 @@ 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 "gitea"
-
-2. In the secrets file/variables:
-    - Set your AI model key in the respective section
-    - In the [Gitea] section, set `personal_access_token` (with token from step 2) and `webhook_secret` (with secret from step 3)
+    - In the configuration file/variables:
+        - Set `config.git_provider` to "gitea"
+    - In the secrets file/variables:
+        - Set your AI model key in the respective section
+        - In the [Gitea] section, set `personal_access_token` (with token from step 2) and `webhook_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:
 

docs/docs/installation/gitlab.md

@@ -46,7 +46,7 @@ Note that if your base branches are not protected, don't set the variables as `p
 
 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. For the user from step 1. generate a `personal_access_token` with `api` access.
+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:
 
@@ -111,7 +111,7 @@ For example: `GITLAB.PERSONAL_ACCESS_TOKEN` --> `GITLAB__PERSONAL_ACCESS_TOKEN`
 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.
+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

docs/docs/installation/locally.md

@@ -12,7 +12,7 @@ To invoke a tool (for example `review`), you can run PR-Agent directly from the
 - For GitHub:
 
     ```bash
-    docker run --rm -it -e OPENAI.KEY=<your key> -e GITHUB.USER_TOKEN=<your token> codiumai/pr-agent:latest --pr_url <pr_url> review
+    docker run --rm -it -e OPENAI.KEY=<your_openai_key> -e GITHUB.USER_TOKEN=<your_github_token> codiumai/pr-agent:latest --pr_url <pr_url> review
     ```
 
     If you are using GitHub enterprise server, you need to specify the custom url as variable.

pr_agent/git_providers/gerrit_provider.py

@@ -103,7 +103,7 @@ def prepare_repo(url: urllib3.util.Url, project, refspec):
     repo_url = (f"{url.scheme}://{url.auth}@{url.host}:{url.port}/{project}")
 
     directory = pathlib.Path(mkdtemp())
-    clone(repo_url, directory),
+    clone(repo_url, directory)
     fetch(repo_url, refspec, cwd=directory)
     checkout(cwd=directory)
     return directory

pr_agent/settings/code_suggestions/pr_code_suggestions_reflect_prompts.toml

@@ -5,7 +5,7 @@ In addition to evaluating the suggestion correctness and importance, another sub
 
 Examine each suggestion meticulously, assessing its quality, relevance, and accuracy within the context of PR. Keep in mind that the suggestions may vary in their correctness, accuracy and impact.
 Consider the following components of each suggestion:
-    1. 'one_sentence_summary' - A one-liner summary summary of the suggestion's purpose
+    1. 'one_sentence_summary' - A one-liner summary of the suggestion's purpose
     2. 'suggestion_content' - The suggestion content, explaining the proposed modification
     3. 'existing_code' - a code snippet from a __new hunk__ section in the PR code diff that the suggestion addresses
     4. 'improved_code' - a code snippet demonstrating how the 'existing_code' should be after the suggestion is applied

pr_agent/settings/configuration.toml

@@ -8,7 +8,7 @@
 # models
 model="o4-mini"
 fallback_models=["gpt-4.1"]
-#model_reasoning="o4-mini" # dedictated reasoning model for self-reflection
+#model_reasoning="o4-mini" # dedicated reasoning model for self-reflection
 #model_weak="gpt-4o" # optional, a weaker model to use for some easier tasks
 # CLI
 git_provider="github"