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

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Tr/claude3
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Tal
2024-03-06 01:08:41 -08:00
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parent 3016d5d1ed 26fb2a4164
commit 8083a6e74a
6 changed files with 67 additions and 54 deletions
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33
Usage.md
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@ -265,21 +265,16 @@ inline_code_comments = true
Each time you invoke a `/review` tool, it will use inline code comments. Each time you invoke a `/review` tool, it will use inline code comments.
#### BitBucket Self-Hosted App automatic tools #### 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] [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 ### 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. 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 ##### Amazon Bedrock
To use Amazon Bedrock and its foundational models, add the below configuration: To use Amazon Bedrock and its foundational models, add the below configuration:

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

30
pr_agent/algo/ai_handlers/litellm_ai_handler.py
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@ -4,7 +4,8 @@ import boto3
import litellm import litellm
import openai import openai
from litellm import acompletion 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 retry import retry
from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
from pr_agent.config_loader import get_settings from pr_agent.config_loader import get_settings
@ -77,28 +78,9 @@ class LiteLLMAIHandler(BaseAiHandler):
""" """
return get_settings().get("OPENAI.DEPLOYMENT_ID", None) 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)) 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): 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: try:
resp, finish_reason = None, None resp, finish_reason = None, None
deployment_id = self.deployment_id deployment_id = self.deployment_id
@ -117,7 +99,7 @@ class LiteLLMAIHandler(BaseAiHandler):
get_logger().debug("Prompts", artifact={"system": system, "user": user}) get_logger().debug("Prompts", artifact={"system": system, "user": user})
response = await acompletion(**kwargs) response = await acompletion(**kwargs)
except (APIError, Timeout, TryAgain) as e: except (APIError, Timeout) as e:
get_logger().error("Error during OpenAI inference: ", e) get_logger().error("Error during OpenAI inference: ", e)
raise raise
except (RateLimitError) as e: except (RateLimitError) as e:
@ -125,9 +107,9 @@ class LiteLLMAIHandler(BaseAiHandler):
raise raise
except (Exception) as e: except (Exception) as e:
get_logger().error("Unknown error during OpenAI inference: ", 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: if response is None or len(response["choices"]) == 0:
raise TryAgain raise APIError
else: else:
resp = response["choices"][0]['message']['content'] resp = response["choices"][0]['message']['content']
finish_reason = response["choices"][0]["finish_reason"] finish_reason = response["choices"][0]["finish_reason"]

44
pr_agent/servers/bitbucket_app.py
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@ -16,6 +16,7 @@ from starlette_context import context
from starlette_context.middleware import RawContextMiddleware from starlette_context.middleware import RawContextMiddleware
from pr_agent.agent.pr_agent import PRAgent 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.config_loader import get_settings, global_settings
from pr_agent.git_providers.utils import apply_repo_settings from pr_agent.git_providers.utils import apply_repo_settings
from pr_agent.identity_providers import get_identity_provider 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) manifest_obj = json.loads(manifest)
return JSONResponse(manifest_obj) 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") @router.post("/webhook")
async def handle_github_webhooks(background_tasks: BackgroundTasks, request: Request): async def handle_github_webhooks(background_tasks: BackgroundTasks, request: Request):
log_context = {"server_type": "bitbucket_app"} log_context = {"server_type": "bitbucket_app"}
@ -118,18 +137,19 @@ async def handle_github_webhooks(background_tasks: BackgroundTasks, request: Req
with get_logger().contextualize(**log_context): with get_logger().contextualize(**log_context):
apply_repo_settings(pr_url) apply_repo_settings(pr_url)
if get_identity_provider().verify_eligibility("bitbucket", if get_identity_provider().verify_eligibility("bitbucket",
sender_id, pr_url) is not Eligibility.NOT_ELIGIBLE: sender_id, pr_url) is not Eligibility.NOT_ELIGIBLE:
auto_review = get_setting_or_env("BITBUCKET_APP.AUTO_REVIEW", None) if get_settings().get("bitbucket_app.pr_commands"):
if auto_review is None or is_true(auto_review): # by default, auto review is enabled await _perform_commands_bitbucket("pr_commands", PRAgent(), pr_url, log_context)
await PRReviewer(pr_url).run() else: # backwards compatibility
auto_improve = get_setting_or_env("BITBUCKET_APP.AUTO_IMPROVE", None) auto_review = get_setting_or_env("BITBUCKET_APP.AUTO_REVIEW", None)
if is_true(auto_improve): # by default, auto improve is disabled if is_true(auto_review): # by default, auto review is disabled
await PRCodeSuggestions(pr_url).run() await PRReviewer(pr_url).run()
auto_describe = get_setting_or_env("BITBUCKET_APP.AUTO_DESCRIBE", None) auto_improve = get_setting_or_env("BITBUCKET_APP.AUTO_IMPROVE", None)
if is_true(auto_describe): # by default, auto describe is disabled if is_true(auto_improve): # by default, auto improve is disabled
await PRDescription(pr_url).run() await PRCodeSuggestions(pr_url).run()
# with get_logger().contextualize(**log_context): auto_describe = get_setting_or_env("BITBUCKET_APP.AUTO_DESCRIBE", None)
# await agent.handle_request(pr_url, "review") if is_true(auto_describe): # by default, auto describe is disabled
await PRDescription(pr_url).run()
elif event == "pullrequest:comment_created": elif event == "pullrequest:comment_created":
pr_url = data["data"]["pullrequest"]["links"]["html"]["href"] pr_url = data["data"]["pullrequest"]["links"]["html"]["href"]
log_context["api_url"] = pr_url log_context["api_url"] = pr_url

7
pr_agent/settings/configuration.toml
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@ -165,9 +165,10 @@ pr_commands = [
] ]
[bitbucket_app] [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] [local]

4
requirements.txt
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@ -9,10 +9,10 @@ GitPython==3.1.32
google-cloud-aiplatform==1.35.0 google-cloud-aiplatform==1.35.0
google-cloud-storage==2.10.0 google-cloud-storage==2.10.0
Jinja2==3.1.2 Jinja2==3.1.2
litellm==0.12.5 litellm==1.29.1
loguru==0.7.2 loguru==0.7.2
msrest==0.7.1 msrest==0.7.1
openai==0.27.8 openai==1.13.3
pinecone-client pinecone-client
pinecone-datasets @ git+https://github.com/mrT23/pinecone-datasets.git@main pinecone-datasets @ git+https://github.com/mrT23/pinecone-datasets.git@main
lancedb==0.5.1 lancedb==0.5.1