Merge pull request #746 from Codium-ai/tr/claude3

Tr/claude3

Usage.md

@@ -265,21 +265,16 @@ inline_code_comments = true
 Each time you invoke a `/review` tool, it will use inline code comments.
 
 #### BitBucket Self-Hosted App automatic tools
-You can configure in your local `.pr_agent.toml` file which tools will **run automatically** when a new PR is opened.
+to control which commands will run automatically when a new PR is opened, you can set the `pr_commands` parameter in the configuration file:
-
+```
-Specifically, set the following values:
-```yaml
 [bitbucket_app]
-auto_review = true    # set as config var in .pr_agent.toml
+pr_commands = [
-auto_describe = true  # set as config var in .pr_agent.toml
+    "/review --pr_reviewer.num_code_suggestions=0",
-auto_improve = true   # set as config var in .pr_agent.toml
+    "/improve --pr_code_suggestions.summarize=false",
+]
 ```
 
-`bitbucket_app.auto_review`, `bitbucket_app.auto_describe` and `bitbucket_app.auto_improve` are used to enable/disable automatic tools.
+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.
-If not set, the default option is that only the `review` tool will run automatically when a new PR is opened.
-
-Note that due to limitations of the bitbucket platform, the `auto_describe` tool will be able to publish a PR description only as a comment. 
-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.
 
 ### Azure DevOps provider
 
@@ -469,6 +464,20 @@ 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.
 
+##### 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:

pr_agent/algo/__init__.py

@@ -19,7 +19,8 @@ MAX_TOKENS = {
     'vertex_ai/codechat-bison-32k': 32000,
     'codechat-bison': 6144,
     'codechat-bison-32k': 32000,
-    'anthropic.claude-v2': 100000,
     'anthropic.claude-instant-v1': 100000,
     'anthropic.claude-v1': 100000,
+    'anthropic.claude-v2': 100000,
+    'anthropic/claude-3-opus-20240229': 100000,
 }

pr_agent/algo/ai_handlers/litellm_ai_handler.py

@@ -4,7 +4,8 @@ import boto3
 import litellm
 import openai
 from litellm import acompletion
-from openai.error import APIError, RateLimitError, Timeout, TryAgain
+# from openai.error import APIError, RateLimitError, Timeout, TryAgain
+from openai import APIError, RateLimitError, Timeout
 from retry import retry
 from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
 from pr_agent.config_loader import get_settings
@@ -77,28 +78,9 @@ class LiteLLMAIHandler(BaseAiHandler):
         """
         return get_settings().get("OPENAI.DEPLOYMENT_ID", None)
 
-    @retry(exceptions=(APIError, Timeout, TryAgain, AttributeError, RateLimitError),
+    @retry(exceptions=(APIError, Timeout, 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):
-        """
-        Performs a chat completion using the OpenAI ChatCompletion API.
-        Retries in case of API errors or timeouts.
-        
-        Args:
-            model (str): The model to use for chat completion.
-            temperature (float): The temperature parameter for chat completion.
-            system (str): The system message for chat completion.
-            user (str): The user message for chat completion.
-        
-        Returns:
-            tuple: A tuple containing the response and finish reason from the API.
-        
-        Raises:
-            TryAgain: If the API response is empty or there are no choices in the response.
-            APIError: If there is an error during OpenAI inference.
-            Timeout: If there is a timeout during OpenAI inference.
-            TryAgain: If there is an attribute error during OpenAI inference.
-        """
         try:
             resp, finish_reason = None, None
             deployment_id = self.deployment_id
@@ -117,7 +99,7 @@ class LiteLLMAIHandler(BaseAiHandler):
 
             get_logger().debug("Prompts", artifact={"system": system, "user": user})
             response = await acompletion(**kwargs)
-        except (APIError, Timeout, TryAgain) as e:
+        except (APIError, Timeout) as e:
             get_logger().error("Error during OpenAI inference: ", e)
             raise
         except (RateLimitError) as e:
@@ -125,9 +107,9 @@ class LiteLLMAIHandler(BaseAiHandler):
             raise
         except (Exception) as e:
             get_logger().error("Unknown error during OpenAI inference: ", e)
-            raise TryAgain from e
+            raise APIError from e
         if response is None or len(response["choices"]) == 0:
-            raise TryAgain
+            raise APIError
         else:
             resp = response["choices"][0]['message']['content']
             finish_reason = response["choices"][0]["finish_reason"]

pr_agent/servers/bitbucket_app.py

@@ -16,6 +16,7 @@ from starlette_context import context
 from starlette_context.middleware import RawContextMiddleware
 
 from pr_agent.agent.pr_agent import PRAgent
+from pr_agent.algo.utils import update_settings_from_args
 from pr_agent.config_loader import get_settings, global_settings
 from pr_agent.git_providers.utils import apply_repo_settings
 from pr_agent.identity_providers import get_identity_provider
@@ -72,6 +73,24 @@ async def handle_manifest(request: Request, response: Response):
     manifest_obj = json.loads(manifest)
     return JSONResponse(manifest_obj)
 
+
+async def _perform_commands_bitbucket(commands_conf: str, agent: PRAgent, api_url: str, log_context: dict):
+    apply_repo_settings(api_url)
+    commands = get_settings().get(f"bitbucket_app.{commands_conf}", {})
+    for command in commands:
+        try:
+            split_command = command.split(" ")
+            command = split_command[0]
+            args = split_command[1:]
+            other_args = update_settings_from_args(args)
+            new_command = ' '.join([command] + other_args)
+            get_logger().info(f"Performing command: {new_command}")
+            with get_logger().contextualize(**log_context):
+                await agent.handle_request(api_url, new_command)
+        except Exception as e:
+            get_logger().error(f"Failed to perform command {command}: {e}")
+
+
 @router.post("/webhook")
 async def handle_github_webhooks(background_tasks: BackgroundTasks, request: Request):
     log_context = {"server_type": "bitbucket_app"}
@@ -119,8 +138,11 @@ async def handle_github_webhooks(background_tasks: BackgroundTasks, request: Req
                         apply_repo_settings(pr_url)
                         if get_identity_provider().verify_eligibility("bitbucket",
                                                         sender_id, pr_url) is not Eligibility.NOT_ELIGIBLE:
+                            if get_settings().get("bitbucket_app.pr_commands"):
+                                await _perform_commands_bitbucket("pr_commands", PRAgent(), pr_url, log_context)
+                            else: # backwards compatibility
                                 auto_review = get_setting_or_env("BITBUCKET_APP.AUTO_REVIEW", None)
-                            if auto_review is None or is_true(auto_review):  # by default, auto review is enabled
+                                if is_true(auto_review):  # by default, auto review is disabled
                                     await PRReviewer(pr_url).run()
                                 auto_improve = get_setting_or_env("BITBUCKET_APP.AUTO_IMPROVE", None)
                                 if is_true(auto_improve):  # by default, auto improve is disabled
@@ -128,8 +150,6 @@ async def handle_github_webhooks(background_tasks: BackgroundTasks, request: Req
                                 auto_describe = get_setting_or_env("BITBUCKET_APP.AUTO_DESCRIBE", None)
                                 if is_true(auto_describe):  # by default, auto describe is disabled
                                     await PRDescription(pr_url).run()
-                # with get_logger().contextualize(**log_context):
-                #     await agent.handle_request(pr_url, "review")
             elif event == "pullrequest:comment_created":
                 pr_url = data["data"]["pullrequest"]["links"]["html"]["href"]
                 log_context["api_url"] = pr_url

pr_agent/settings/configuration.toml

@@ -165,9 +165,10 @@ pr_commands = [
 ]
 
 [bitbucket_app]
-#auto_review = true    # set as config var in .pr_agent.toml
+pr_commands = [
-#auto_describe = true  # set as config var in .pr_agent.toml
+    "/review --pr_reviewer.num_code_suggestions=0",
-#auto_improve = true   # set as config var in .pr_agent.toml
+    "/improve --pr_code_suggestions.summarize=false",
+]
 
 
 [local]

requirements.txt

@@ -9,10 +9,10 @@ GitPython==3.1.32
 google-cloud-aiplatform==1.35.0
 google-cloud-storage==2.10.0
 Jinja2==3.1.2
-litellm==0.12.5
+litellm==1.29.1
 loguru==0.7.2
 msrest==0.7.1
-openai==0.27.8
+openai==1.13.3
 pinecone-client
 pinecone-datasets @ git+https://github.com/mrT23/pinecone-datasets.git@main
 lancedb==0.5.1