feat: googleai llm addition #152
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Signed-off-by: royalpinto007 <[email protected]>
WalkthroughThis changeset integrates Google AI support across the backend director module. New enum members are added to expand language model options, and logic in the default LLM selector now accounts for Google AI based on environment variables. A dedicated module is introduced for handling Google AI functionalities, including chat completions and configurations. Additionally, agent parameter handling has been refined: parameters are filtered in the BaseAgent, the dubbing agent’s extra engine parameters are removed, and the UploadAgent now requires a Changes
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🧰 Additional context used🪛 Ruff (0.8.2)backend/director/llm/googleai.py62-62: Within an (B904) 143-143: Do not use mutable data structures for argument defaults Replace with (B006) 🔇 Additional comments (8)
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backend/director/llm/__init__.py (1)
16-16: Usebool(...)instead of a ternary operator
Static analysis suggests simplifying the logic by replacing"True if ... else False"with a directbool(...).- googleai = True if os.getenv("GOOGLEAI_API_KEY") else False + googleai = bool(os.getenv("GOOGLEAI_API_KEY"))🧰 Tools
🪛 Ruff (0.8.2)
16-16: Use
bool(...)instead ofTrue if ... else FalseReplace with `bool(...)
(SIM210)
backend/director/llm/googleai.py (1)
59-61: Raise exceptions with context
Within theImportErrorblock, consider raising the exception as:except ImportError as e: raise ImportError("Please install OpenAI python library.") from eto distinguish it from any secondary errors.
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61-61: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
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backend/director/llm/googleai.py
61-61: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
133-133: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
backend/director/llm/__init__.py
16-16: Use bool(...) instead of True if ... else False
Replace with `bool(...)
(SIM210)
🔇 Additional comments (14)
backend/director/constants.py (2)
22-22: Addition ofGOOGLEAIenum value
This new enumeration for Google AI is consistent with the existing pattern for LLM types.
31-31: Addition ofGOOGLEAI_environment prefix
This new environment prefix aligns well with the naming of other prefixes.backend/director/llm/__init__.py (2)
7-7: New import for GoogleAI
No concerns with importingGoogleAI.
24-25: Conditional fallback for Google AI
The new branch for returningGoogleAIis consistent with the handling for other LLM providers.backend/director/llm/googleai.py (10)
1-2: Initial imports
No issues found with the added imports for JSON and Enum.
4-5: Pydantic import statements
The imports for validators and settings appear correct.
7-12: Imports from director modules
The references toRoleTypes,BaseLLM, and related classes look correct and consistent.
15-21: Definition ofGoogleChatModelEnum
Enumerating multiple Gemini chat models is clear and consistent with naming conventions.
23-49:GoogleAIConfigclass implementation
The config class usesSettingsConfigDictand an appropriate validator forapi_key. This is a solid approach.
50-58: Initialization ofGoogleAI
The constructor properly creates aGoogleAIConfigwhen none is provided and initializes the base LLM.
63-65: Verify use ofopenai.OpenAI(...)for Google Gemini
The code currently constructsself.clientusingopenai.OpenAI(...), which is typically associated with OpenAI endpoints. Make sure this approach is truly compatible with Google Gemini, or confirm that you intend to leverage the OpenAI client library for interfacing with Google’s API.
67-115:_format_messagesmethod
This method’s logic for handling system, assistant, tool, and user roles is well-structured and follows typical patterns for multi-turn conversation handling.
117-131:_format_toolsmethod
The approach of enumerating functions for Gemini is clean and straightforward.
134-197:chat_completionsmethod body
The method is logically clear: it formats parameters, handles the API request, parses the response, and captures metadata. Overall, it’s neatly organized.
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backend/director/llm/googleai.py (4)
54-65: Improve exception handling when importing OpenAI.When catching the ImportError, you should use the
fromsyntax to preserve the original traceback, which helps with debugging.try: import openai except ImportError: - raise ImportError("Please install OpenAI python library.") + raise ImportError("Please install OpenAI python library.") from None🧰 Tools
🪛 Ruff (0.8.2)
65-65: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
67-69: Consider adding error handling for client initialization.The client initialization doesn't have any error handling. If there are issues with the API key or base URL, they'll only be discovered during the first API call.
-self.client = openai.OpenAI( - api_key=self.config.api_key, base_url=self.config.api_base -) +try: + self.client = openai.OpenAI( + api_key=self.config.api_key, base_url=self.config.api_base + ) +except Exception as e: + raise RuntimeError(f"Failed to initialize Google AI client: {e}") from e
139-140: Update docstring to reflect using configured model.The docstring specifically mentions Gemini 1.5 Flash, but the code uses whatever model is configured in
self.config.chat_model.-"""Get completions for chat using Gemini 1.5 Flash.""" +"""Get completions for chat using the configured Gemini model."""
141-147: Redundant default values in parameter dictionary.The fallback values for temperature, max_tokens, and top_p are redundant since they already have defaults in the config class.
params = { "model": self.config.chat_model, "messages": self._format_messages(messages), - "temperature": self.config.temperature or 0.7, - "max_tokens": self.config.max_tokens or 4096, - "top_p": self.config.top_p or 1.0, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "top_p": self.config.top_p, }
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backend/director/llm/googleai.py
65-65: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
137-137: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (8)
backend/director/llm/googleai.py (8)
15-24: Well-defined model enumeration.The
GoogleChatModelenum provides a clear and comprehensive selection of Gemini models with appropriate naming conventions.
27-42: Good configuration setup with appropriate defaults.The
GoogleAIConfigclass is well-structured with appropriate default values and extendsBaseLLMConfigcorrectly. The use ofSettingsConfigDictwith the proper environment prefix is a good practice for configuration management.
44-51: Effective validation with clear error message.The validator for the API key provides a helpful error message that includes instructions on how to set the environment variable correctly.
71-77: Document why system messages are being removed.The code removes system messages from the input, but it's not clear why. If Gemini doesn't support system messages, this should be documented explicitly.
def _format_messages(self, messages: list): """Format the messages to the format that Google Gemini expects.""" formatted_messages = [] if messages and messages[0]["role"] == RoleTypes.system.value: + # Google Gemini doesn't support system messages, so we remove it messages = messages[1:]
136-138: Avoid mutable default argument.Using
tools: list = []can lead to subtle bugs. It's best practice to default toNoneand initialize internally.-def chat_completions(self, messages: list, tools: list = [], stop=None, response_format=None): +def chat_completions(self, messages: list, tools: list = None, stop=None, response_format=None): + if tools is None: + tools = []🧰 Tools
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137-137: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
170-184: Handle potential KeyError in tool_calls extraction.The code assumes
tool_call.function.nameandtool_call.function.argumentsexist without checking, which could lead to errors if the response structure changes.tool_calls = ( [ { "id": tool_call.id, "tool": { - "name": tool_call.function.name, - "arguments": json.loads(tool_call.function.arguments), + "name": getattr(tool_call.function, "name", ""), + "arguments": json.loads(getattr(tool_call.function, "arguments", "{}")), }, "type": tool_call.type, } for tool_call in response.choices[0].message.tool_calls ] if response.choices and response.choices[0].message.tool_calls else [] )
186-189: Good usage of getattr for safely accessing response attributes.Using
getattrwith a default value is a safe way to handle potentially missing attributes in the response.
190-198: Well-structured response object creation.The
LLMResponsecreation is comprehensive and includes all necessary fields, with appropriate error handling for missing data.
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🧹 Nitpick comments (3)
backend/director/agents/base.py (2)
51-56: Be cautious about discarding optional parameters.
This filtering retains only the properties listed under"required". If your function has optional parameters you'd like to document or validate, they won't appear in this final schema.
58-62: Return structure aligns with JSON schema, but consider adding more information.
The returned object properly sets"type":"object"and includes minimal keys. If you want richer schema metadata (e.g.,"description", nested properties, or examples), it might help downstream integrations.backend/director/handler.py (1)
47-72: Consider adding a comment explaining the agent removalsThe PR is adding Google AI integration, but it's not immediately clear why
UploadAgentandDubbingAgentare being disabled as part of this change. Adding a comment explaining this decision would improve code maintainability.# Register the agents here self.agents = [ ThumbnailAgent, SummarizeVideoAgent, DownloadAgent, PricingAgent, + # Temporarily disabled UploadAgent and DubbingAgent for Google AI integration + # due to [reason for disabling] # UploadAgent, SearchAgent, PromptClipAgent, ... # DubbingAgent, TranscriptionAgent, ... ]
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backend/director/agents/base.py (2)
43-44: Check if excludingargsandkwargsis intentional.
By popping these entries fromparameters["properties"], the schema omits them from validation. This is fine if your design never needs those parameters, but if there's any dynamic argument usage, consider preserving them.
46-49: Ensureargsandkwargsremoval fromrequiredmatches desired usage.
Removing"args"and"kwargs"from the"required"list is consistent with the approach above. Just confirm these parameters are never mandatory for your agent.backend/director/handler.py (2)
65-65:❓ Verification inconclusive
Verify the impact of commenting out DubbingAgent
Commenting out the
DubbingAgentremoves dubbing functionality from theChatHandler. This may affect users who rely on this feature.Please confirm if this is an intentional change related to the Google AI integration. If the agent is incompatible with the new Google AI implementation, this should be documented.
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#!/bin/bash # Check if DubbingAgent is used elsewhere in the codebase echo "Checking references to DubbingAgent in the codebase..." rg "DubbingAgent" --type py # Check if there are any direct API calls or frontend code that might expect this agent echo "Checking API routes or endpoints that might use DubbingAgent..." rg -A 5 -B 5 "dubbing.*agent" --type pyLength of output: 524
Review Note: Confirm Intentional Removal of Dubbing Functionality
It appears that the
ChatHandlernow has theDubbingAgentinstantiation commented out (line 65 inbackend/director/handler.py). Our search confirms that while theDubbingAgentclass is still defined inbackend/director/agents/dubbing.pyand imported inChatHandler, there are no other references or API routes relying on it. This indicates that the dubbing functionality is effectively disabled.Please confirm whether this change is intentional as part of the Google AI integration. If the removal is deliberate, ensure that the decision is documented so that users relying on the dubbing feature are aware of the change.
52-52:❓ Verification inconclusive
Verify the impact of commenting out UploadAgent
Commenting out the
UploadAgentmeans this functionality will no longer be available through theChatHandler. This might impact existing functionality if any part of the system relies on upload capabilities via the chat interface.Consider whether this change is intentional as part of the Google AI integration or if it's an oversight. If intentional, please document why this agent is being disabled.
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#!/bin/bash # Check if UploadAgent is used elsewhere in the codebase echo "Checking references to UploadAgent in the codebase..." rg "UploadAgent" --type py # Check if there are any direct API calls or frontend code that might expect this agent echo "Checking API routes or endpoints that might use UploadAgent..." rg -A 5 -B 5 "upload.*agent" --type pyLength of output: 1516
Impact of Removing UploadAgent from ChatHandler
Our investigation confirms that while the
UploadAgentremains defined inbackend/director/agents/upload.pyand its import is still present inbackend/director/handler.py, the actual usage in the ChatHandler has been commented out. Additionally, the fallback logic inbackend/director/core/reasoning.pyexplicitly references the upload agent (to generate a video_id when needed).
- Location Check:
backend/director/agents/upload.py: Contains theUploadAgentclass.backend/director/handler.py: The line invokingUploadAgentis commented out, although the import still exists.backend/director/core/reasoning.py: The fallback behavior relies on the upload agent.If disabling the upload functionality via the ChatHandler is part of the intended Google AI integration, please document the rationale. Otherwise, if this was inadvertent, you may need to restore its functionality to support the fallback process.
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backend/director/agents/upload.py (1)
18-45: Consider removing commented-out code after successful transition to dynamic parameters.The code has been changed to use a dynamic parameter retrieval method (
self.get_parameters()) instead of a static definition. The commented-out code could be removed to improve maintenance and readability if the dynamic approach is confirmed to work properly.-# UPLOAD_AGENT_PARAMETERS = { -# "type": "object", -# "properties": { -# "source": { -# "type": "string", -# "description": "URL or local path to upload the content", -# }, -# "source_type": { -# "type": "string", -# "description": "Type of given source.", -# "enum": ["url", "local_file"], -# }, -# "name": { -# "type": "string", -# "description": "Name of the content to upload, optional parameter", -# }, -# "media_type": { -# "type": "string", -# "enum": ["video", "audio", "image"], -# "description": "Type of media to upload, default is video", -# }, -# "collection_id": { -# "type": "string", -# "description": "Collection ID to upload the content", -# }, -# }, -# "required": ["url", "media_type", "collection_id"], -# }
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backend/director/agents/upload.py (1)
57-57:✅ Verification successful
Verify parameter structure consistency with get_parameters() method.
Ensure that the
get_parameters()method returns a structure consistent with the previous static definition, including all required fields and proper type definitions.
🏁 Script executed:
#!/bin/bash # Check the implementation of get_parameters() in BaseAgent rg -A 10 "def get_parameters" --type pyLength of output: 958
Parameter Structure Verified – No Action Required
The implementation ofget_parameters()inBaseAgentnow dynamically infers the JSON schema from the associated run function. The method retrieves the parameters viafunction_inferrer.to_json_schema(), raises an exception if parameters are missing, and explicitly removes the unwanted"args"property from the schema. This strategy appears to maintain consistency with the previously static parameter definition by ensuring that the required fields and type definitions are derived at runtime.
- Action: Confirm via integration tests that the dynamically generated JSON schema (including keys like
"properties","required", and any type definitions) matches the expected structure for all agents (e.g., inupload.pyat line 57).
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backend/director/agents/upload.py (1)
172-184: Good validation, but note potential redundancy.The input validation here is thorough and appropriate. However, there appears to be some redundancy in the code - the
source_typevalidation at lines 179-184 overlaps with the validation logic at lines 197-202.Consider removing the redundant validation at lines 197-202 since you're now handling it earlier in the method. The error case at line 197 should never be reached with the new validation in place.
elif source_type == "url": playlist_info = self._get_yt_playlist_videos(source) if playlist_info: self.output_message.actions.append("YouTube Playlist detected") self.output_message.push_update() return self._upload_yt_playlist(playlist_info, media_type) return self._upload(source, source_type, media_type, name) - else: - error_message = f"Invalid source type {source_type}" - logger.error(error_message) - return AgentResponse( - status=AgentStatus.ERROR, message=error_message, data={} - )
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backend/director/agents/upload.py (2)
44-44: Good parameter requirement update.Updating the required parameters to only include
media_typeandcollection_idmakes sense. This change aligns with the more flexible approach wheresourcecan be provided as either a URL or local file path, and is validated separately.
63-68: Good input validation implementation.Adding validation for the
sourceparameter is a good defensive programming practice. This ensures that the upload operation receives valid input data and fails gracefully with a clear error message when requirements aren't met.
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backend/director/agents/download.py (1)
58-58: Typo in error message.There's a typo in the error message: "Downloda failed" should be "Download failed".
- message=f"Downloda failed with {download_response}", + message=f"Download failed with {download_response}",
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backend/director/llm/googleai.py (2)
62-62: Improve exception handling syntax for better error traceability.When raising exceptions inside an except block, use
fromsyntax to preserve the original error context.try: import openai except ImportError: - raise ImportError("Please install OpenAI python library.") + raise ImportError("Please install OpenAI python library.") from None🧰 Tools
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62-62: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
158-186: Token usage metrics are missing from the response.The LLMResponse doesn't include any token usage metrics, which would be useful for monitoring and billing purposes. Other LLM providers typically return this information.
return LLMResponse( content=content, tool_calls=tool_calls, finish_reason=choice.finish_reason if choice else "", status=LLMResponseStatus.SUCCESS, + usage={ + "prompt_tokens": response.usage.prompt_tokens if hasattr(response, 'usage') else 0, + "completion_tokens": response.usage.completion_tokens if hasattr(response, 'usage') else 0, + "total_tokens": response.usage.total_tokens if hasattr(response, 'usage') else 0, + } )
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backend/director/llm/googleai.py
62-62: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
134-134: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (8)
backend/.env.sample (1)
21-21: Good addition of the Google AI API key environment variable.The addition of
GOOGLEAI_API_KEY=in the LLM Integrations section is consistent with the other LLM API keys (OpenAI and Anthropic) and aligns with the Google AI integration being implemented.backend/director/llm/googleai.py (7)
15-25: Model names should follow a consistent pattern.The model enum looks good, but based on the past review comment, a stakeholder suggested renaming these to a more consistent format (e.g.,
GEMINI_1_5_FLASHinstead of the current format).Could you confirm whether this naming convention was already approved, or if you still plan to implement the suggested renaming?
27-48: Solid configuration class implementation.The
GoogleAIConfigclass properly extendsBaseLLMConfigwith appropriate field validation. The use of environment variable prefixes and field validation for the API key ensures proper configuration.
51-67: Consider alternatives to direct openai dependency.The Google AI implementation uses the OpenAI library's client, which is an interesting approach. However, this creates a dependency where users must install the OpenAI package even if they only want to use Google AI.
Have you considered using a dedicated Google AI library instead, or making the OpenAI dependency optional through dynamic imports?
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62-62: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
68-116: Well-structured message formatting implementation.The
_format_messagesmethod handles different message types appropriately, particularly for tool calls and results. The special handling of system messages is also properly implemented.
118-132: Clean and concise tools formatting.The
_format_toolsmethod correctly transforms the tool definitions to match Google's expected format. Good use of list comprehension with filtering to ensure only valid tools are included.
133-135: Avoid mutable default argument.Using
tools: list = []can lead to subtle bugs. It's best practice to default toNoneand initialize internally.-def chat_completions(self, messages: list, tools: list = [], response_format=None): +def chat_completions(self, messages: list, tools: list = None, response_format=None): if tools is None: tools = []🧰 Tools
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134-134: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
152-157: Use proper error handling and logging instead of print statements.Using
try: response = self.client.chat.completions.create(**params) except Exception as e: - print(f"Error: {e}") - return LLMResponse(content=f"Error: {e}") + import logging + logging.error(f"Google AI chat completion error: {e}") + return LLMResponse( + content=f"Error: {e}", + status=LLMResponseStatus.ERROR + )
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backend/director/llm/googleai.py (4)
1-14: Remove redundant blank line for cleaner imports section.There's an extra blank line at line 7 that creates two consecutive blank lines. Maintaining a consistent style with a single blank line between import groups would improve code readability.
import json from enum import Enum from pydantic import Field, field_validator, FieldValidationInfo from pydantic_settings import SettingsConfigDict - from director.llm.base import BaseLLM, BaseLLMConfig, LLMResponse, LLMResponseStatus from director.constants import ( LLMType, EnvPrefix, )
15-25: Model naming should follow a consistent pattern.The enum naming is inconsistent - some models have version numbers in different formats. Consider standardizing all model names to follow the same pattern, as suggested in a previous review.
class GoogleChatModel(str, Enum): """Enum for Google Gemini Chat models""" - GEMINI_1_5_FLASH = "gemini-1.5-flash" - GEMINI_1_5_FLASH_0_0_2 = "gemini-1.5-flash-002" - GEMINI_1_5_PRO = "gemini-1.5-pro" - GEMINI_1_5_PRO_0_0_2 = "gemini-1.5-pro-002" - GEMINI_2_0_FLASH = "gemini-2.0-flash" - GEMINI_2_0_FLASH_0_0_1 = "gemini-2.0-flash-001" - GEMINI_2_0_PRO = "gemini-2.0-pro-exp" + GEMINI_1_5_FLASH = "gemini-1.5-flash" + GEMINI_1_5_FLASH_002 = "gemini-1.5-flash-002" + GEMINI_1_5_PRO = "gemini-1.5-pro" + GEMINI_1_5_PRO_002 = "gemini-1.5-pro-002" + GEMINI_2_0_FLASH = "gemini-2.0-flash" + GEMINI_2_0_FLASH_001 = "gemini-2.0-flash-001" + GEMINI_2_0_PRO = "gemini-2.0-pro-exp"
68-95: Add error handling for malformed message structures.The method should validate the message structure and handle potential KeyErrors or malformed messages more gracefully.
def _format_messages(self, messages: list): """Format the messages to the format that Google Gemini expects.""" formatted_messages = [] for message in messages: + if not isinstance(message, dict) or "role" not in message: + continue + if message["role"] == "assistant" and message.get("tool_calls"): formatted_messages.append( { "role": message["role"], "content": message["content"], "tool_calls": [ { "id": tool_call["id"], "function": { "name": tool_call["tool"]["name"], "arguments": json.dumps(tool_call["tool"]["arguments"]), }, "type": tool_call["type"], } for tool_call in message["tool_calls"] ], } ) else: formatted_messages.append(message) return formatted_messages
164-182: Add token usage tracking for metrics and billing purposes.The response should capture token usage data which is important for monitoring, metrics, and billing.
return LLMResponse( content=response.choices[0].message.content or "", tool_calls=[ { "id": tool_call.id, "tool": { "name": tool_call.function.name, "arguments": json.loads(tool_call.function.arguments), }, "type": tool_call.type, } for tool_call in response.choices[0].message.tool_calls ] if response.choices[0].message.tool_calls else [], finish_reason=response.choices[0].finish_reason, status=LLMResponseStatus.SUCCESS, + usage={ + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens + } )
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🪛 Ruff (0.8.2)
backend/director/llm/googleai.py
62-62: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
138-138: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (2)
backend/director/llm/googleai.py (2)
138-138: Avoid mutable default argument.Using
tools: list = []can lead to subtle bugs. It's best practice to default toNoneand initialize internally.-def chat_completions(self, messages: list, tools: list = [], response_format=None): +def chat_completions(self, messages: list, tools: list = None, response_format=None, stop=None):🧰 Tools
🪛 Ruff (0.8.2)
138-138: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
158-163: Use proper error handling and logging instead of print statements.Using
try: response = self.client.chat.completions.create(**params) except Exception as e: - print(f"Error: {e}") - return LLMResponse(content=f"Error: {e}") + import logging + logging.error(f"Google AI chat completion error: {e}") + return LLMResponse( + content=f"Error: {e}", + status=LLMResponseStatus.ERROR + )
| try: | ||
| import openai | ||
| except ImportError: | ||
| raise ImportError("Please install OpenAI python library.") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve exception chaining and error message clarity.
The error handling for the missing OpenAI library should use proper exception chaining with from and provide a more specific error message about Google AI's reliance on the OpenAI client.
try:
import openai
except ImportError as err:
- raise ImportError("Please install OpenAI python library.")
+ raise ImportError("Please install OpenAI python library which is required for Google AI integration.") from err📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| import openai | |
| except ImportError: | |
| raise ImportError("Please install OpenAI python library.") | |
| try: | |
| import openai | |
| except ImportError as err: | |
| raise ImportError("Please install OpenAI python library which is required for Google AI integration.") from err |
🧰 Tools
🪛 Ruff (0.8.2)
62-62: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
Signed-off-by: royalpinto007 <[email protected]>
There was a problem hiding this comment.
Actionable comments posted: 1
♻️ Duplicate comments (1)
backend/director/llm/googleai.py (1)
145-152: 🛠️ Refactor suggestionAdd handling for the stop parameter and None tools.
The method accepts a
stopparameter but doesn't incorporate it into the API request parameters. Also, initialize tools if it's None.params = { "model": self.chat_model, "messages": self._format_messages(messages), "temperature": self.temperature, "max_tokens": self.max_tokens, "top_p": self.top_p, "timeout": self.timeout, } +if tools is None: + tools = [] + +if stop: + params["stop"] = stop
🧹 Nitpick comments (4)
backend/director/llm/googleai.py (4)
23-24: Consider renaming models for consistency.For consistency with other model names that use FLASH or PRO in all caps.
- GEMINI_2_0_FLASH_0_0_1 = "gemini-2.0-flash-001" - GEMINI_2_0_PRO = "gemini-2.0-pro-exp" + GEMINI_2_0_FLASH_001 = "gemini-2.0-flash-001" + GEMINI_2_0_PRO_EXP = "gemini-2.0-pro-exp"
84-84: Handle potential JSON serialization errors.The
json.dumps()call could fail iftool_call["tool"]["arguments"]contains invalid JSON data. Consider adding error handling.- "arguments": json.dumps(tool_call["tool"]["arguments"]), + "arguments": json.dumps(tool_call["tool"]["arguments"]) if isinstance(tool_call["tool"]["arguments"], (dict, list)) else str(tool_call["tool"]["arguments"]),
174-174: Handle potential JSON parsing errors.The
json.loads()call could fail iftool_call.function.argumentscontains invalid JSON. Consider adding error handling.- "arguments": json.loads(tool_call.function.arguments), + "arguments": json.loads(tool_call.function.arguments) if tool_call.function.arguments else {},
38-38: Consider updating default to the latest model version.The current default is
GEMINI_2_0_FLASH, but for better maintainability, consider using the specific versioned model.- chat_model: str = Field(default=GoogleChatModel.GEMINI_2_0_FLASH) + chat_model: str = Field(default=GoogleChatModel.GEMINI_2_0_FLASH_0_0_1)
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📒 Files selected for processing (2)
backend/director/llm/googleai.py(1 hunks)mkdocs.yml(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
backend/director/llm/googleai.py
62-62: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
139-139: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (4)
mkdocs.yml (1)
77-77: LGTM!The addition of the GoogleAI documentation entry is correctly placed within the LLM integrations section, consistent with the existing structure.
backend/director/llm/googleai.py (3)
59-62: Improve exception chaining and error message clarity.When raising new exceptions from caught exceptions, use proper exception chaining with
fromto preserve the traceback and provide more context about Google AI's dependency on OpenAI.try: import openai except ImportError as err: - raise ImportError("Please install OpenAI python library.") + raise ImportError("Please install OpenAI python library which is required for Google AI integration.") from err🧰 Tools
🪛 Ruff (0.8.2)
62-62: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
138-140: Avoid mutable default argument.Using
tools: list = []can lead to subtle bugs. It's best practice to default toNoneand initialize internally.-def chat_completions(self, messages: list, tools: list = [], stop=None, response_format=None): +def chat_completions(self, messages: list, tools: list = None, stop=None, response_format=None):🧰 Tools
🪛 Ruff (0.8.2)
139-139: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
161-166: Use proper error handling and logging instead of print statements.Using
try: response = self.client.chat.completions.create(**params) except Exception as e: - print(f"Error: {e}") - return LLMResponse(content=f"Error: {e}") + import logging + logging.error(f"Google AI chat completion error: {e}") + return LLMResponse( + content=f"Error: {e}", + status=LLMResponseStatus.ERROR + )
| content=response.choices[0].message.content or "", | ||
| tool_calls=[ | ||
| { | ||
| "id": tool_call.id, | ||
| "tool": { | ||
| "name": tool_call.function.name, | ||
| "arguments": json.loads(tool_call.function.arguments), | ||
| }, | ||
| "type": tool_call.type, | ||
| } | ||
| for tool_call in response.choices[0].message.tool_calls | ||
| ] | ||
| if response.choices[0].message.tool_calls | ||
| else [], | ||
| finish_reason=response.choices[0].finish_reason, | ||
| send_tokens=response.usage.prompt_tokens, | ||
| recv_tokens=response.usage.completion_tokens, | ||
| total_tokens=response.usage.total_tokens, | ||
| status=LLMResponseStatus.SUCCESS, |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for accessing response properties.
The code assumes that response.choices[0] and other properties always exist. Add error handling to prevent potential exceptions.
+ if not response.choices:
+ return LLMResponse(
+ content="Error: No choices in response",
+ status=LLMResponseStatus.ERROR
+ )
+
return LLMResponse(
content=response.choices[0].message.content or "",
tool_calls=[
{
"id": tool_call.id,
"tool": {
"name": tool_call.function.name,
"arguments": json.loads(tool_call.function.arguments),
},
"type": tool_call.type,
}
for tool_call in response.choices[0].message.tool_calls
]
if response.choices[0].message.tool_calls
else [],
finish_reason=response.choices[0].finish_reason,
- send_tokens=response.usage.prompt_tokens,
- recv_tokens=response.usage.completion_tokens,
- total_tokens=response.usage.total_tokens,
+ send_tokens=getattr(response.usage, 'prompt_tokens', 0),
+ recv_tokens=getattr(response.usage, 'completion_tokens', 0),
+ total_tokens=getattr(response.usage, 'total_tokens', 0),
status=LLMResponseStatus.SUCCESS,
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| content=response.choices[0].message.content or "", | |
| tool_calls=[ | |
| { | |
| "id": tool_call.id, | |
| "tool": { | |
| "name": tool_call.function.name, | |
| "arguments": json.loads(tool_call.function.arguments), | |
| }, | |
| "type": tool_call.type, | |
| } | |
| for tool_call in response.choices[0].message.tool_calls | |
| ] | |
| if response.choices[0].message.tool_calls | |
| else [], | |
| finish_reason=response.choices[0].finish_reason, | |
| send_tokens=response.usage.prompt_tokens, | |
| recv_tokens=response.usage.completion_tokens, | |
| total_tokens=response.usage.total_tokens, | |
| status=LLMResponseStatus.SUCCESS, | |
| if not response.choices: | |
| return LLMResponse( | |
| content="Error: No choices in response", | |
| status=LLMResponseStatus.ERROR | |
| ) | |
| return LLMResponse( | |
| content=response.choices[0].message.content or "", | |
| tool_calls=[ | |
| { | |
| "id": tool_call.id, | |
| "tool": { | |
| "name": tool_call.function.name, | |
| "arguments": json.loads(tool_call.function.arguments), | |
| }, | |
| "type": tool_call.type, | |
| } | |
| for tool_call in response.choices[0].message.tool_calls | |
| ] if response.choices[0].message.tool_calls else [], | |
| finish_reason=response.choices[0].finish_reason, | |
| send_tokens=getattr(response.usage, 'prompt_tokens', 0), | |
| recv_tokens=getattr(response.usage, 'completion_tokens', 0), | |
| total_tokens=getattr(response.usage, 'total_tokens', 0), | |
| status=LLMResponseStatus.SUCCESS, | |
| ) |
Signed-off-by: royalpinto007 <[email protected]>
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