Update setup.py

docs/docs/installation/bitbucket.md

@@ -3,7 +3,7 @@
 
 You can use the Bitbucket Pipeline system to run Qodo Merge on every pull request open or update.
 
-1. Add the following file in your repository bitbucket-pipelines.yml
+1. Add the following file in your repository bitbucket_pipelines.yml
 
 ```yaml
 pipelines:

docs/docs/installation/gitlab.md

@@ -42,36 +42,21 @@ 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. 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 [here](https://qodo-merge-docs.qodo.ai/installation/github/#run-as-a-github-app) steps 4-7.
 
-3. Clone this repository:
+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](https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/settings/configuration.toml)
 
-```
-git clone https://github.com/Codium-ai/pr-agent.git
-```
+5. Create a webhook in GitLab. Set the URL to ```http[s]://<PR_AGENT_HOSTNAME>/webhook```. Set the secret token to the generated secret from step 2.
+In the "Trigger" section, check the ‘comments’ and ‘merge request events’ boxes.
 
-4. Prepare variables and secrets. Skip this step if you plan on settings these as environment variables when running the agent:
-  1. In the configuration file/variables:
-    - Set `deployment_type` 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)
-
-
-5. 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. Test your installation by opening a merge request or commenting on a merge request using one of CodiumAI's commands.
-boxes
+6. Test your installation by opening a merge request or commenting or a merge request using one of CodiumAI's commands.

docs/docs/usage-guide/changing_a_model.md

@@ -133,26 +133,9 @@ Your [application default credentials](https://cloud.google.com/docs/authenticat
 
 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.
 
-### Google AI Studio
-
-To use [Google AI Studio](https://aistudio.google.com/) models, set the relevant models in the configuration section of the configuration file:
-
-```toml
-[config] # in configuration.toml
-model="google_ai_studio/gemini-1.5-flash"
-model_turbo="google_ai_studio/gemini-1.5-flash"
-fallback_models=["google_ai_studio/gemini-1.5-flash"]
-
-[google_ai_studio] # in .secrets.toml
-gemini_api_key = "..."
-```
-
-If you don't want to set the API key in the .secrets.toml file, you can set the `GOOGLE_AI_STUDIO.GEMINI_API_KEY` environment variable.
-
 ### Anthropic
 
 To use Anthropic models, set the relevant models in the configuration section of the configuration file:
-
 ```
 [config]
 model="anthropic/claude-3-opus-20240229"

pr_agent/algo/__init__.py

@@ -38,8 +38,6 @@ MAX_TOKENS = {
     'vertex_ai/gemini-1.5-pro': 1048576,
     'vertex_ai/gemini-1.5-flash': 1048576,
     'vertex_ai/gemma2': 8200,
-    'gemini/gemini-1.5-pro': 1048576,
-    'gemini/gemini-1.5-flash': 1048576,
     'codechat-bison': 6144,
     'codechat-bison-32k': 32000,
     'anthropic.claude-instant-v1': 100000,

pr_agent/algo/ai_handlers/litellm_ai_handler.py

@@ -83,11 +83,6 @@ class LiteLLMAIHandler(BaseAiHandler):
             litellm.vertex_location = get_settings().get(
                 "VERTEXAI.VERTEX_LOCATION", None
             )
-        # Google AI Studio
-        # SEE https://docs.litellm.ai/docs/providers/gemini
-        if get_settings().get("GOOGLE_AI_STUDIO.GEMINI_API_KEY", None):
-          os.environ["GEMINI_API_KEY"] = get_settings().google_ai_studio.gemini_api_key
-
     def prepare_logs(self, response, system, user, resp, finish_reason):
         response_log = response.dict().copy()
         response_log['system'] = system

pr_agent/algo/ai_handlers/openai_ai_handler.py

@@ -1,7 +1,6 @@
-from os import environ
 from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
 import openai
-from openai import APIError, AsyncOpenAI, RateLimitError, Timeout
+from openai.error import APIError, RateLimitError, Timeout, TryAgain
 from retry import retry
 
 from pr_agent.config_loader import get_settings
@@ -15,7 +14,7 @@ class OpenAIHandler(BaseAiHandler):
         # Initialize OpenAIHandler specific attributes here
         try:
             super().__init__()
-            environ["OPENAI_API_KEY"] = get_settings().openai.key
+            openai.api_key = get_settings().openai.key
             if get_settings().get("OPENAI.ORG", None):
                 openai.organization = get_settings().openai.org
             if get_settings().get("OPENAI.API_TYPE", None):
@@ -25,7 +24,7 @@ class OpenAIHandler(BaseAiHandler):
             if get_settings().get("OPENAI.API_VERSION", None):
                 openai.api_version = get_settings().openai.api_version
             if get_settings().get("OPENAI.API_BASE", None):
-                environ["OPENAI_BASE_URL"] = get_settings().openai.api_base
+                openai.api_base = get_settings().openai.api_base
 
         except AttributeError as e:
             raise ValueError("OpenAI key is required") from e
@@ -37,7 +36,7 @@ class OpenAIHandler(BaseAiHandler):
         """
         return get_settings().get("OPENAI.DEPLOYMENT_ID", None)
 
-    @retry(exceptions=(APIError, Timeout, AttributeError, RateLimitError),
+    @retry(exceptions=(APIError, Timeout, TryAgain, AttributeError, RateLimitError),
            tries=OPENAI_RETRIES, delay=2, backoff=2, jitter=(1, 3))
     async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2):
         try:
@@ -45,19 +44,20 @@ class OpenAIHandler(BaseAiHandler):
             get_logger().info("System: ", system)
             get_logger().info("User: ", user)
             messages = [{"role": "system", "content": system}, {"role": "user", "content": user}]
-            client = AsyncOpenAI()
-            chat_completion = await client.chat.completions.create(
+
+            chat_completion = await openai.ChatCompletion.acreate(
                 model=model,
+                deployment_id=deployment_id,
                 messages=messages,
                 temperature=temperature,
             )
-            resp = chat_completion.choices[0].message.content
-            finish_reason = chat_completion.choices[0].finish_reason
-            usage = chat_completion.usage
+            resp = chat_completion["choices"][0]['message']['content']
+            finish_reason = chat_completion["choices"][0]["finish_reason"]
+            usage = chat_completion.get("usage")
             get_logger().info("AI response", response=resp, messages=messages, finish_reason=finish_reason,
                               model=model, usage=usage)
             return resp, finish_reason
-        except (APIError, Timeout) as e:
+        except (APIError, Timeout, TryAgain) as e:
             get_logger().error("Error during OpenAI inference: ", e)
             raise
         except (RateLimitError) as e:
@@ -65,4 +65,4 @@ class OpenAIHandler(BaseAiHandler):
             raise
         except (Exception) as e:
             get_logger().error("Unknown error during OpenAI inference: ", e)
-            raise
+            raise TryAgain from e

pr_agent/algo/utils.py

@@ -43,10 +43,6 @@ class PRReviewHeader(str, Enum):
     INCREMENTAL = "## Incremental PR Reviewer Guide"
 
 
-class PRDescriptionHeader(str, Enum):
-    CHANGES_WALKTHROUGH = "### **Changes walkthrough** 📝"
-
-
 def get_setting(key: str) -> Any:
     try:
         key = key.upper()
@@ -1028,7 +1024,8 @@ def process_description(description_full: str) -> Tuple[str, List]:
     if not description_full:
         return "", []
 
-    description_split = description_full.split(PRDescriptionHeader.CHANGES_WALKTHROUGH.value)
+    split_str = "### **Changes walkthrough** 📝"
+    description_split = description_full.split(split_str)
     base_description_str = description_split[0]
     changes_walkthrough_str = ""
     files = []
@@ -1063,9 +1060,6 @@ def process_description(description_full: str) -> Tuple[str, List]:
                     if not res or res.lastindex != 4:
                         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 hypen ('- ')
-                        res = re.search(pattern_back, file_data, re.DOTALL)
                     if res and res.lastindex == 4:
                         short_filename = res.group(1).strip()
                         short_summary = res.group(2).strip()

pr_agent/git_providers/azuredevops_provider.py

@@ -5,7 +5,7 @@ from urllib.parse import urlparse
 from ..algo.file_filter import filter_ignored
 from ..log import get_logger
 from ..algo.language_handler import is_valid_file
-from ..algo.utils import clip_tokens, find_line_number_of_relevant_line_in_file, load_large_diff, PRDescriptionHeader
+from ..algo.utils import clip_tokens, find_line_number_of_relevant_line_in_file, load_large_diff
 from ..config_loader import get_settings
 from .git_provider import GitProvider
 from pr_agent.algo.types import EDIT_TYPE, FilePatchInfo
@@ -404,7 +404,7 @@ class AzureDevopsProvider(GitProvider):
                 pr_body = pr_body[:ind]
 
             if len(pr_body) > MAX_PR_DESCRIPTION_AZURE_LENGTH:
-                changes_walkthrough_text = PRDescriptionHeader.CHANGES_WALKTHROUGH.value
+                changes_walkthrough_text = '## **Changes walkthrough**'
                 ind = pr_body.find(changes_walkthrough_text)
                 if ind != -1:
                     pr_body = pr_body[:ind]

pr_agent/settings/.secrets_template.toml

@@ -43,9 +43,6 @@ api_base = "" # the base url for your local Llama 2, Code Llama, and other model
 vertex_project = "" # the google cloud platform project name for your vertexai deployment
 vertex_location = "" # the google cloud platform location for your vertexai deployment
 
-[google_ai_studio]
-gemini_api_key = "" # the google AI Studio API key
-
 [github]
 # ---- Set the following only for deployment type == "user"
 user_token = ""  # A GitHub personal access token with 'repo' scope.
@@ -63,7 +60,6 @@ webhook_secret = "<WEBHOOK SECRET>"  # Optional, may be commented out.
 [gitlab]
 # Gitlab personal access token
 personal_access_token = ""
-shared_secret = ""  # webhook secret
 
 [bitbucket]
 # For Bitbucket personal/repository bearer token

pr_agent/tools/pr_code_suggestions.py

@@ -367,18 +367,6 @@ class PRCodeSuggestions:
                                                          "code_suggestions_feedback": code_suggestions_feedback[i]})
                             suggestion["score"] = 7
                             suggestion["score_why"] = ""
-
-                        # if the before and after code is the same, clear one of them
-                        try:
-                            if suggestion['existing_code'] == suggestion['improved_code']:
-                                get_logger().debug(
-                                    f"edited improved suggestion {i + 1}, because equal to existing code: {suggestion['existing_code']}")
-                                if get_settings().pr_code_suggestions.commitable_code_suggestions:
-                                    suggestion['improved_code'] = ""  # we need 'existing_code' to locate the code in the PR
-                                else:
-                                    suggestion['existing_code'] = ""
-                        except Exception as e:
-                            get_logger().error(f"Error processing suggestion {i + 1}, error: {e}")
             else:
                 # get_logger().error(f"Could not self-reflect on suggestions. using default score 7")
                 for i, suggestion in enumerate(data["code_suggestions"]):
@@ -434,6 +422,13 @@ class PRCodeSuggestions:
                     continue
 
                 if ('existing_code' in suggestion) and ('improved_code' in suggestion):
+                    if suggestion['existing_code'] == suggestion['improved_code']:
+                        get_logger().debug(
+                            f"edited improved suggestion {i + 1}, because equal to existing code: {suggestion['existing_code']}")
+                        if get_settings().pr_code_suggestions.commitable_code_suggestions:
+                            suggestion['improved_code'] = ""  # we need 'existing_code' to locate the code in the PR
+                        else:
+                            suggestion['existing_code'] = ""
                     suggestion = self._truncate_if_needed(suggestion)
                     one_sentence_summary_list.append(suggestion['one_sentence_summary'])
                     suggestion_list.append(suggestion)

pr_agent/tools/pr_description.py

@@ -12,7 +12,7 @@ from pr_agent.algo.ai_handlers.litellm_ai_handler import LiteLLMAIHandler
 from pr_agent.algo.pr_processing import get_pr_diff, retry_with_fallback_models, get_pr_diff_multiple_patchs, \
     OUTPUT_BUFFER_TOKENS_HARD_THRESHOLD
 from pr_agent.algo.token_handler import TokenHandler
-from pr_agent.algo.utils import set_custom_labels, PRDescriptionHeader
+from pr_agent.algo.utils import set_custom_labels
 from pr_agent.algo.utils import load_yaml, get_user_labels, ModelType, show_relevant_configurations, get_max_tokens, \
     clip_tokens
 from pr_agent.config_loader import get_settings
@@ -501,7 +501,7 @@ extra_file_yaml =
                     pr_body += "</details>\n"
             elif 'pr_files' in key.lower() and get_settings().pr_description.enable_semantic_files_types:
                 changes_walkthrough, pr_file_changes = self.process_pr_files_prediction(changes_walkthrough, value)
-                changes_walkthrough = f"{PRDescriptionHeader.CHANGES_WALKTHROUGH.value}\n{changes_walkthrough}"
+                changes_walkthrough = f"### **Changes walkthrough** 📝\n{changes_walkthrough}"
             else:
                 # if the value is a list, join its items by comma
                 if isinstance(value, list):

requirements.txt

@@ -4,12 +4,10 @@ atlassian-python-api==3.41.4
 azure-devops==7.1.0b3
 azure-identity==1.15.0
 boto3==1.33.6
-certifi==2024.8.30
 dynaconf==3.2.4
 fastapi==0.111.0
 GitPython==3.1.41
 google-cloud-aiplatform==1.38.0
-google-generativeai==0.8.3
 google-cloud-storage==2.10.0
 Jinja2==3.1.2
 litellm==1.50.2

setup.py

@@ -3,3 +3,4 @@
 from setuptools import setup
 
 setup()
+print("aaa")

tests/e2e_tests/e2e_utils.py

@@ -5,7 +5,7 @@ REVIEW_START_WITH = '## PR Reviewer Guide 🔍\n\n<table>\n<tr><td>⏱️&nbsp;<
 IMPROVE_START_WITH_REGEX_PATTERN = r'^## PR Code Suggestions ✨\n\n<!-- [a-z0-9]+ -->\n\n<table><thead><tr><td>Category</td>'
 
 NUM_MINUTES = 5
-print("aaa")
+
 NEW_FILE_CONTENT = """\
 from pr_agent import cli
 from pr_agent.config_loader import get_settings