Ankit/add xai #83
Ankit/add xai #83
Conversation
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For agents where xAI Grok is not optimized (e.g., those using JSON mode), instead of default LLM we can configure it as OpenAI if available, or VideoDB Proxy if OpenAI is not available. |
WalkthroughThe changes introduce a new environment variable for LLM integrations, enhance error handling and support for specific LLM types across multiple agent classes, and implement a new XAI model. Notable modifications include the addition of a Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Agent
participant LLM
participant XAI
User->>Agent: Send request
Agent->>Agent: _check_supported_llm()
alt LLM Supported
Agent->>LLM: Process request
LLM->>Agent: Return response
Agent->>User: Send response
else LLM Not Supported
Agent->>User: Send error message
end
User->>XAI: Send request
XAI->>XAI: Validate config
XAI->>User: Return response or error
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Actionable comments posted: 10
🧹 Outside diff range and nitpick comments (12)
backend/.env.sample (1)
7-7: Consider adding documentation about xAI integration.Based on the PR comments about fallback mechanisms, consider adding a comment above the key to document:
- The purpose of this key (xAI/Grok integration)
- The fallback behavior when this key is not configured (using OpenAI or VideoDB Proxy)
Example addition:
# LLM Integrations OPENAI_API_KEY= ANTHROPIC_API_KEY= +# Required for xAI/Grok integration. If not configured, system will fallback to OpenAI (if available) or VideoDB Proxy XAI_API_KEY=backend/director/constants.py (1)
22-22: Consider renaming XAI to GROK for claritySince we're specifically integrating xAI's Grok model (as indicated in the PR comments about "xAI Grok"), consider using
GROK = "grok"instead ofXAI = "xai"to be more specific and consistent with the actual model name. This would make it clearer for developers which specific model is being referenced.- XAI = "xai" + GROK = "grok"backend/director/llm/__init__.py (1)
16-16: Simplify boolean conversion using bool()Consider using the more concise
bool()function instead of the ternary operator.- xai = True if os.getenv("XAI_API_KEY") else False + xai = bool(os.getenv("XAI_API_KEY"))🧰 Tools
🪛 Ruff (0.8.0)
16-16: Use
bool(...)instead ofTrue if ... else FalseReplace with `bool(...)
(SIM210)
backend/director/agents/base.py (2)
44-57: Consider adding type hints and improving documentationThe method could benefit from additional improvements for better maintainability.
Consider these enhancements:
- def _check_supported_llm(self): + def _check_supported_llm(self) -> None: """Check if supported_llm is present and validate LLM. + + Raises: + Exception: If the configured LLM is not supported by this agent. + """🧰 Tools
🪛 Ruff (0.8.0)
46-47: Use a single
ifstatement instead of nestedifstatements(SIM102)
44-57: Consider implementing LLM fallback mechanismBased on the PR discussion, some agents aren't optimized for xAI Grok, especially in JSON mode. Instead of failing immediately, consider implementing a fallback mechanism that:
- Attempts to use OpenAI if available when xAI is not supported
- Falls back to VideoDB Proxy if OpenAI is not accessible
This would improve system resilience and align with the suggested configuration in the PR comments.
Would you like help implementing this fallback mechanism in the
_check_supported_llmmethod?🧰 Tools
🪛 Ruff (0.8.0)
46-47: Use a single
ifstatement instead of nestedifstatements(SIM102)
backend/director/handler.py (2)
Line range hint
156-167: Update configuration check for xAI integrationThe
checkmethod currently returns a hardcodedTrueforllm_configured. This should be updated to properly verify the xAI configuration.Consider this implementation:
def check(self): """Check the configuration of the server.""" videodb_configured = True if os.getenv("VIDEO_DB_API_KEY") else False + xai_configured = True if os.getenv("XAI_API_KEY") else False + openai_configured = True if os.getenv("OPENAI_API_KEY") else False db = load_db(os.getenv("SERVER_DB_TYPE", "sqlite")) db_configured = db.health_check() return { "videodb_configured": videodb_configured, - "llm_configured": True, + "llm_configured": { + "xai": xai_configured, + "openai": openai_configured + }, "db_configured": db_configured, }
Line range hint
77-104: Add LLM validation in ChatHandlerGiven the PR's focus on xAI integration and the suggestion to use OpenAI as fallback, the
chatmethod should validate LLM configuration before proceeding.Consider adding LLM validation before agent execution:
def chat(self, message): logger.info(f"ChatHandler input message: {message}") session = Session(db=self.db, **message) session.create() input_message = InputMessage(db=self.db, **message) input_message.publish() try: + # Validate LLM configuration + if not self._validate_llm_config(): + raise ValueError("No valid LLM configuration found. Please configure either xAI or OpenAI.") + self.add_videodb_state(session) agents = [agent(session=session) for agent in self.agents]backend/director/llm/openai.py (1)
159-159: LGTM! Consider some minor improvements.The additional condition correctly ensures that
response_formatis only set for the official OpenAI API endpoint, which is necessary for proper integration with other LLM providers.Consider these improvements:
- Extract the API URL to a constant to avoid magic strings:
+ OPENAI_API_BASE = "https://api.openai.com/v1" ... - if response_format and self.config.api_base == "https://api.openai.com/v1": + if response_format and self.config.api_base == OPENAI_API_BASE:
- Add a comment explaining why this condition exists:
# Only set response_format for official OpenAI API as other endpoints (e.g., xAI) # may not support this parameter if response_format and self.config.api_base == OPENAI_API_BASE:backend/director/agents/subtitle.py (2)
115-115: Correct LLM configuration for JSON responses.The supported LLM configuration correctly excludes xAI, as this agent requires structured JSON responses. This aligns with the PR comment about configuring agents not optimized for xAI Grok to use OpenAI or VideoDB Proxy instead.
Consider adding a code comment explaining why xAI is not supported (JSON response requirement) to help future maintainers understand this design decision.
LLM compatibility check exists but JSON error handling needs improvement
The verification shows that:
- BaseAgent implements
_check_supported_llm()which validates LLM compatibility- SubtitleAgent inherits from BaseAgent and calls
chat_completionswith JSON format- JSON parsing is done without error handling as suspected
The review comment's concerns are partially valid. While LLM compatibility checking is properly implemented through inheritance, the JSON parsing needs the suggested error handling improvements.
backend/director/agents/subtitle.py: Add try-catch block aroundjson.loads(llm_response.content)as suggested in the review🔗 Analysis chain
Line range hint
208-211: Verify LLM compatibility check and enhance error handling.While the supported LLMs are correctly configured, we should ensure robust error handling for LLM responses:
- Verify that BaseAgent._check_supported_llm is called before using chat_completions
- Add specific error handling for JSON parsing failures
Consider adding specific error handling for JSON parsing:
- translated_subtitles = json.loads(llm_response.content) + try: + translated_subtitles = json.loads(llm_response.content) + if "subtitles" not in translated_subtitles: + raise ValueError("Missing 'subtitles' key in LLM response") + except json.JSONDecodeError as e: + logger.error(f"Failed to parse LLM response: {e}") + raise ValueError(f"LLM response was not valid JSON: {e}")🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check if BaseAgent._check_supported_llm is called in the inheritance chain ast-grep --pattern 'class BaseAgent { $$$ def _check_supported_llm($$$) { $$$ } $$$ }' # Check if the method is called before chat_completions ast-grep --pattern 'def run($$$): $$$ _check_supported_llm($$$) $$$ chat_completions($$$) $$$'Length of output: 207
Script:
#!/bin/bash # Let's try a broader search for BaseAgent class and its methods rg -A 10 "class BaseAgent" # Search for _check_supported_llm usage rg "_check_supported_llm" # Search for SubtitleAgent class definition and inheritance rg -A 5 "class SubtitleAgent" # Look for chat_completions usage in subtitle.py rg -l "chat_completions" backend/director/agents/subtitle.pyLength of output: 1776
Script:
#!/bin/bash # Let's check the implementation of _check_supported_llm in base.py rg -A 10 "def _check_supported_llm" backend/director/agents/base.py # Let's see how chat_completions is used in subtitle.py rg -B 2 -A 2 "chat_completions" backend/director/agents/subtitle.py # Check if there's any error handling around JSON parsing rg -B 2 -A 2 "json.loads" backend/director/agents/subtitle.pyLength of output: 1292
backend/director/agents/meme_maker.py (1)
Line range hint
91-106: Enhance error handling for LLM-specific failures.The current error handling in
_prompt_runnercould be enhanced to handle LLM-specific failures more gracefully. Consider:
- Adding specific error handling for LLM-related exceptions
- Logging the LLM type when errors occur
- Implementing retry logic for transient failures
def _prompt_runner(self, prompts): """Run the prompts in parallel.""" clip_timestamps = [] image_timestamps = [] with concurrent.futures.ThreadPoolExecutor() as executor: future_to_index = { executor.submit( self.llm.chat_completions, [ContextMessage(content=prompt, role=RoleTypes.user).to_llm_msg()], response_format={"type": "json_object"}, ): i for i, prompt in enumerate(prompts) } for future in concurrent.futures.as_completed(future_to_index): try: llm_response = future.result() if not llm_response.status: - logger.error(f"LLM failed with {llm_response.content}") + logger.error(f"LLM ({self.llm.__class__.__name__}) failed with {llm_response.content}") continue output = json.loads(llm_response.content) clip_timestamps.extend(output.get("clip_timestamps", [])) image_timestamps.extend(output.get("image_timestamps", [])) - except Exception as e: - logger.exception(f"Error in getting matches: {e}") + except json.JSONDecodeError as e: + logger.error(f"Invalid JSON response from LLM ({self.llm.__class__.__name__}): {e}") + continue + except Exception as e: + logger.exception(f"Error in getting matches from LLM ({self.llm.__class__.__name__}): {e}") continuebackend/director/agents/prompt_clip.py (1)
51-51: Supported LLM configuration aligns with JSON format requirementsThe configuration of
supported_llmto only includeOPENAIandVIDEODB_PROXYis appropriate given that this agent heavily relies on structured JSON responses. This aligns with the PR comment about agents not being optimized for xAI Grok in JSON mode.Consider documenting the JSON format dependency in the class docstring to make it clear why certain LLMs are supported while others are not.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (13)
backend/.env.sample(1 hunks)backend/director/agents/base.py(3 hunks)backend/director/agents/meme_maker.py(2 hunks)backend/director/agents/profanity_remover.py(2 hunks)backend/director/agents/prompt_clip.py(2 hunks)backend/director/agents/subtitle.py(2 hunks)backend/director/constants.py(2 hunks)backend/director/handler.py(2 hunks)backend/director/llm/__init__.py(2 hunks)backend/director/llm/openai.py(1 hunks)backend/director/llm/xai.py(1 hunks)docs/llm/xai.md(1 hunks)mkdocs.yml(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- docs/llm/xai.md
🧰 Additional context used
🪛 Ruff (0.8.0)
backend/director/agents/base.py
46-47: Use a single if statement instead of nested if statements
(SIM102)
backend/director/llm/__init__.py
16-16: Use bool(...) instead of True if ... else False
Replace with `bool(...)
(SIM210)
backend/director/llm/xai.py
56-56: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
136-136: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (14)
backend/.env.sample (1)
7-7: LGTM! Appropriate placement and format.
The XAI_API_KEY is correctly placed in the LLM Integrations section and follows the established pattern of empty values in the sample file.
backend/director/constants.py (2)
31-31: LGTM! Environment prefix follows the established pattern
The new environment prefix follows the consistent pattern used for other LLM types.
33-34: LGTM! Formatting changes only
backend/director/llm/__init__.py (1)
7-7: LGTM! Import follows established pattern
The XAI import follows the same organization pattern as other LLM provider imports.
backend/director/agents/base.py (2)
8-8: LGTM: Import changes are well-organized
The new imports are appropriately grouped with other session-related imports and are necessary for the enhanced error handling functionality.
76-76: LGTM: Appropriate placement of LLM validation
The LLM validation is correctly placed before the agent execution, ensuring early failure if an unsupported LLM is configured.
mkdocs.yml (1)
77-77: LGTM! The XAI documentation is correctly placed under LLM integrations.
The addition aligns well with the PR objective of integrating xAI LLM into the system.
Let's verify the existence of the referenced documentation file:
✅ Verification successful
Documentation file exists and is correctly referenced in mkdocs.yml ✓
The referenced XAI documentation file docs/llm/xai.md exists in the codebase, confirming that the navigation entry in mkdocs.yml is valid and properly configured.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify the existence of XAI documentation file
# Expected: File should exist since it's referenced in navigation
if [ -f "docs/llm/xai.md" ]; then
echo "✓ XAI documentation file exists"
else
echo "✗ Missing documentation file: docs/llm/xai.md"
fi
Length of output: 101
backend/director/handler.py (1)
23-23: LGTM: Import addition is well-placed
The addition of TextContent import aligns with the enhanced error handling implementation.
backend/director/agents/profanity_remover.py (2)
16-16: LGTM: Import addition is appropriate
The addition of the LLMType import aligns with the PR's objective of integrating new LLM support.
38-38: Verify JSON response compatibility across LLM providers
The supported LLM configuration aligns with the PR comment about using OpenAI/VideoDB Proxy for agents not optimized for xAI. However, since this agent relies on JSON responses (used in llm.chat_completions with response_format={"type": "json_object"}), we should verify JSON support across providers.
✅ Verification successful
JSON response format is properly handled across supported LLM providers
The verification shows that:
- The JSON response format is consistently used across multiple agents (profanity_remover, prompt_clip, meme_maker, subtitle) that support the same LLM providers
- The base agent implements proper LLM type validation through
_check_supported_llm() - The profanity remover correctly handles JSON responses with
json.loads(llm_response.content)
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other agents using JSON response format to ensure consistent handling
ast-grep --pattern 'response_format={"type": "json_object"}'
# Check if there's any special handling for different LLM types
rg -A 5 "chat_completions.*response_format.*json" backend/
Length of output: 123
Script:
#!/bin/bash
# Search for chat_completions usage with response_format in a more flexible way
rg -A 5 "response_format.*=.*{.*type.*:.*json" backend/
# Search for JSON response handling patterns
rg -A 5 "chat_completions.*response_format" backend/
# Look for the profanity_remover agent implementation
rg -A 10 "class ProfanityRemover" backend/
Length of output: 1413
Script:
#!/bin/bash
# Search for response_format usage with proper escaping
rg -A 5 'response_format.*type.*json' backend/
# Look for the execute method in profanity remover
rg -A 15 'def execute' backend/director/agents/profanity_remover.py
# Search for LLM type handling in the base agent
rg -A 10 'supported_llm' backend/director/agents/
Length of output: 7686
backend/director/agents/subtitle.py (1)
14-14: LGTM!
The import of LLMType is correctly placed and aligns with the new LLM support functionality.
backend/director/agents/meme_maker.py (2)
14-14: LGTM!
The import of LLMType is correctly placed and necessary for the supported_llm attribute.
46-46: Verify the intentional exclusion of XAI from supported LLMs.
The supported_llm list only includes OPENAI and VIDEODB_PROXY, which aligns with the PR comment about using these as fallbacks for agents not optimized for xAI in JSON mode. However, since this PR's main goal is xAI integration, please:
- Confirm if excluding xAI from this agent is intentional
- Consider adding a code comment explaining why xAI is not supported (if the exclusion is intentional)
Let's verify if other agents support xAI:
✅ Verification successful
Let me gather more context about the XAI integration and JSON mode support.
Based on the gathered information, I can now generate the final response:
Intentional exclusion of XAI is likely correct for this agent
The exclusion of XAI from supported_llm appears to be intentional and correct because:
- The agent heavily relies on strict JSON format output (as seen in the code)
- Other similar agents (profanity_remover, subtitle, prompt_clip) also only support OPENAI and VIDEODB_PROXY
- This aligns with the PR comment about agents not being optimized for xAI in JSON mode
However, a code comment explaining this limitation would improve maintainability.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check xAI support patterns across agents
# Expected: Find other agents that might support xAI
# Search for supported_llm initialization patterns
echo "Checking supported_llm patterns across agents:"
rg "supported_llm.*=.*\[.*\]" "backend/director/agents/"
# Search for XAI related constants
echo -e "\nChecking XAI related constants:"
rg "XAI" "backend/director/constants.py"
Length of output: 745
Script:
#!/bin/bash
# Check for XAI-related code and JSON mode patterns
# Search for XAI in LLMType enum
echo "Checking LLMType enum for XAI:"
rg "class LLMType" -A 10 backend/director/
# Search for JSON mode related patterns
echo -e "\nChecking JSON mode patterns:"
rg "json_mode|json format" -i backend/director/agents/
# Search for any XAI documentation or comments
echo -e "\nChecking for XAI documentation:"
rg "xai|grok" -i "backend/director/**/*.py" -g "!*.pyc"
Length of output: 1694
backend/director/agents/prompt_clip.py (1)
Line range hint 96-117: Verify JSON response format compatibility across supported LLMs
The _prompt_runner method enforces JSON response format using response_format={"type": "json_object"}. While this works with OpenAI, we should verify compatibility with VideoDB_Proxy.
✅ Verification successful
Let me gather more information about the VideoDB_Proxy implementation.
Let me search specifically for the VideoDBProxy's chat_completions implementation.
Based on the findings, I can now generate the final response:
The JSON response format is compatible with VideoDB_Proxy
The VideoDBProxy implementation uses OpenAI's client library and directly passes through the response_format parameter to the underlying API calls. Since VideoDB_Proxy is configured to use GPT-4 by default and routes requests through OpenAI's API format, the JSON response format enforcement will work correctly.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other instances of response_format usage with VideoDB_Proxy
rg -A 2 "response_format.*json_object"
# Search for VideoDB_Proxy implementation to verify JSON format support
ast-grep --pattern 'class $PROXY {
$$$
chat_completions($$$) {
$$$
}
$$$
}'
Length of output: 1166
Script:
#!/bin/bash
# Search for VideoDB_Proxy class implementation
rg -l "class VideoDB_Proxy"
# Search for any proxy or LLM interface implementations
rg -A 10 "class.*Proxy|class.*LLM"
# Search for any chat_completions method implementations
ast-grep --pattern 'chat_completions($$$) {
$$$
}'
Length of output: 9405
Script:
#!/bin/bash
# Search for chat_completions implementation in videodb_proxy.py
rg -A 20 "def chat_completions" backend/director/llm/videodb_proxy.py
# Check if VideoDBProxy uses OpenAI's client
rg -A 5 "import openai" backend/director/llm/videodb_proxy.py
Length of output: 1037
| @@ -12,12 +13,15 @@ def get_default_llm(): | |||
|
|
|||
| openai = True if os.getenv("OPENAI_API_KEY") else False | |||
| anthropic = True if os.getenv("ANTHROPIC_API_KEY") else False | |||
| xai = True if os.getenv("XAI_API_KEY") else False | |||
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding API key validation
The current implementation doesn't validate the XAI_API_KEY format or value. Consider adding validation to fail fast if the API key is invalid.
- xai = True if os.getenv("XAI_API_KEY") else False
+ xai_key = os.getenv("XAI_API_KEY")
+ if xai_key and not xai_key.strip():
+ raise ValueError("XAI_API_KEY is empty or contains only whitespace")
+ xai = bool(xai_key)📝 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.
| xai = True if os.getenv("XAI_API_KEY") else False | |
| xai_key = os.getenv("XAI_API_KEY") | |
| if xai_key and not xai_key.strip(): | |
| raise ValueError("XAI_API_KEY is empty or contains only whitespace") | |
| xai = bool(xai_key) |
🧰 Tools
🪛 Ruff (0.8.0)
16-16: Use bool(...) instead of True if ... else False
Replace with `bool(...)
(SIM210)
| elif xai or default_llm == LLMType.XAI: | ||
| return XAI() |
There was a problem hiding this comment.
💡 Codebase verification
Consider implementing JSON response format support in XAI class
The verification revealed that several agents (subtitle.py, profanity_remover.py, prompt_clip.py, meme_maker.py) require JSON response format, but the XAI implementation doesn't handle the response_format parameter in its chat_completions method, unlike other LLM providers. This could lead to failures in these agents when using XAI.
- Add
response_formathandling inchat_completionsmethod ofbackend/director/llm/xai.pyto support JSON mode - If XAI's Grok model doesn't support JSON mode natively, implement a fallback to another provider that does (like OpenAI)
🔗 Analysis chain
Consider implementing fallback mechanism for non-optimized agents
As mentioned in the PR comments, some agents might not be optimized for xAI Grok, particularly in JSON mode. Consider implementing a fallback mechanism to use OpenAI or VideoDB Proxy in such cases.
Let's verify if there are any JSON mode agents that might need this fallback:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for agent configurations that specify JSON mode
rg -l "json_mode|output_format.*json" --type python
# Search for existing fallback implementations we can reference
rg -l "fallback.*llm|backup.*llm" --type python
Length of output: 165
Script:
#!/bin/bash
# Search for agent configurations that specify JSON mode without file type filter
rg -l "json_mode|output_format.*json"
# Search for existing fallback implementations we can reference
rg -l "fallback.*llm|backup.*llm"
# Look for agent configurations and LLM settings
fd -e py -e yaml -e yml . | xargs rg -l "output_format|json_mode|response_format"
Length of output: 484
Script:
#!/bin/bash
# Check the content of agent files to understand their JSON requirements
rg -A 5 "output_format|json_mode|response_format" backend/director/agents/
# Check XAI implementation for JSON handling
cat backend/director/llm/xai.py
Length of output: 8229
| def _check_supported_llm(self): | ||
| """Check if supported_llm is present and validate LLM.""" | ||
| if hasattr(self, "supported_llm") and hasattr(self, "llm"): | ||
| if self.llm.llm_type not in self.supported_llm: | ||
| error = f"`@{self.agent_name}` Agent does not support the configured LLM `{self.llm.llm_type.upper()}`, To use this Agent, please configure one of the following LLMs: {[llm.upper() for llm in self.supported_llm]}." | ||
| self.output_message.content.append( | ||
| TextContent( | ||
| status_message="LLM not supported.", | ||
| text=error, | ||
| status=MsgStatus.error, | ||
| ) | ||
| ) | ||
| raise Exception(error) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Simplify conditional checks and enhance error handling
The method implementation can be improved for better readability and maintainability.
Apply this diff to simplify the nested conditions and improve error message construction:
def _check_supported_llm(self):
"""Check if supported_llm is present and validate LLM."""
- if hasattr(self, "supported_llm") and hasattr(self, "llm"):
- if self.llm.llm_type not in self.supported_llm:
+ if hasattr(self, "supported_llm") and hasattr(self, "llm") and self.llm.llm_type not in self.supported_llm:
+ supported_llms = ", ".join(llm.upper() for llm in self.supported_llm)
+ error = (
+ f"`@{self.agent_name}` Agent does not support the configured LLM `{self.llm.llm_type.upper()}`.\n"
+ f"To use this Agent, please configure one of the following LLMs: {supported_llms}."
+ )
- error = f"`@{self.agent_name}` Agent does not support the configured LLM `{self.llm.llm_type.upper()}`, To use this Agent, please configure one of the following LLMs: {[llm.upper() for llm in self.supported_llm]}."
self.output_message.content.append(
TextContent(
status_message="LLM not supported.",
text=error,
status=MsgStatus.error,
)
)
raise Exception(error)📝 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.
| def _check_supported_llm(self): | |
| """Check if supported_llm is present and validate LLM.""" | |
| if hasattr(self, "supported_llm") and hasattr(self, "llm"): | |
| if self.llm.llm_type not in self.supported_llm: | |
| error = f"`@{self.agent_name}` Agent does not support the configured LLM `{self.llm.llm_type.upper()}`, To use this Agent, please configure one of the following LLMs: {[llm.upper() for llm in self.supported_llm]}." | |
| self.output_message.content.append( | |
| TextContent( | |
| status_message="LLM not supported.", | |
| text=error, | |
| status=MsgStatus.error, | |
| ) | |
| ) | |
| raise Exception(error) | |
| def _check_supported_llm(self): | |
| """Check if supported_llm is present and validate LLM.""" | |
| if hasattr(self, "supported_llm") and hasattr(self, "llm") and self.llm.llm_type not in self.supported_llm: | |
| supported_llms = ", ".join(llm.upper() for llm in self.supported_llm) | |
| error = ( | |
| f"`@{self.agent_name}` Agent does not support the configured LLM `{self.llm.llm_type.upper()}`.\n" | |
| f"To use this Agent, please configure one of the following LLMs: {supported_llms}." | |
| ) | |
| self.output_message.content.append( | |
| TextContent( | |
| status_message="LLM not supported.", | |
| text=error, | |
| status=MsgStatus.error, | |
| ) | |
| ) | |
| raise Exception(error) |
🧰 Tools
🪛 Ruff (0.8.0)
46-47: Use a single if statement instead of nested if statements
(SIM102)
| session.output_message.content.append( | ||
| TextContent(text=f"{e}", status=MsgStatus.error) | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling for LLM-specific scenarios
While the structured error reporting is good, consider these improvements:
- Sanitize error messages before exposing them to output
- Add specific error handling for LLM-related issues
- Include more context in error messages for better debugging
Consider this implementation:
- session.output_message.content.append(
- TextContent(text=f"{e}", status=MsgStatus.error)
- )
+ error_msg = str(e)
+ if isinstance(e, LLMError): # Add this class for LLM-specific errors
+ error_msg = f"LLM Error: {error_msg}. Please verify LLM configuration and API keys."
+ session.output_message.content.append(
+ TextContent(
+ text=sanitize_error_message(error_msg), # Add this helper function
+ status=MsgStatus.error,
+ error_type=getattr(e, '__class__.__name__', 'Unknown')
+ )
+ )Committable suggestion skipped: line range outside the PR's diff.
| self.llm = get_default_llm() | ||
| self.supported_llm = [LLMType.OPENAI, LLMType.VIDEODB_PROXY] | ||
| super().__init__(session=session, **kwargs) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add robust LLM validation and error handling
The current implementation could be enhanced with:
- Explicit validation of the default LLM against supported_llm
- Error handling for LLM-specific JSON parsing failures
- Clear error messages for unsupported LLM configurations
Consider adding this validation in the constructor:
def __init__(self, session: Session, **kwargs):
self.agent_name = "profanity_remover"
self.description = (
"Agent to beep the profanities in the given video and return the clean stream."
"if user has not given those optional param of beep_audio_id always try with sending it as None so that defaults are picked from env"
)
self.parameters = self.get_parameters()
+ self.supported_llm = [LLMType.OPENAI, LLMType.VIDEODB_PROXY]
self.llm = get_default_llm()
- self.supported_llm = [LLMType.OPENAI, LLMType.VIDEODB_PROXY]
+ if not self._check_supported_llm():
+ raise ValueError(f"Default LLM is not supported. Supported LLMs: {self.supported_llm}")
super().__init__(session=session, **kwargs)And add explicit error handling for JSON responses:
llm_response = self.llm.chat_completions(
[profanity_llm_message.to_llm_msg()],
response_format={"type": "json_object"},
)
- profanity_timeline_response = json.loads(llm_response.content)
+ try:
+ profanity_timeline_response = json.loads(llm_response.content)
+ if not isinstance(profanity_timeline_response, dict) or "timestamps" not in profanity_timeline_response:
+ raise ValueError("Invalid response format from LLM")
+ except json.JSONDecodeError as e:
+ logger.error(f"Failed to parse LLM response: {e}")
+ raise ValueError(f"LLM response is not valid JSON: {llm_response.content[:100]}...")Committable suggestion skipped: line range outside the PR's diff.
| @@ -11,6 +11,7 @@ | |||
| VideoContent, | |||
| VideoData, | |||
| ) | |||
| from director.constants import LLMType | |||
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding error handling for LLM-specific JSON parsing
The code assumes all supported LLMs will return properly formatted JSON. While BaseAgent._check_supported_llm validates LLM support, we should add specific error handling for JSON parsing failures.
Add LLM-specific error handling:
def _prompt_runner(self, prompts):
matches = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_index = {
executor.submit(
self.llm.chat_completions,
[ContextMessage(content=prompt, role=RoleTypes.user).to_llm_msg()],
response_format={"type": "json_object"},
): i
for i, prompt in enumerate(prompts)
}
for future in concurrent.futures.as_completed(future_to_index):
try:
llm_response = future.result()
if not llm_response.status:
logger.error(f"LLM failed with {llm_response.content}")
continue
+ try:
output = json.loads(llm_response.content)
+ except json.JSONDecodeError as e:
+ logger.error(f"Failed to parse JSON from {self.llm.__class__.__name__}: {e}")
+ logger.debug(f"Raw response: {llm_response.content}")
+ continue
matches.extend(output["sentences"])
except Exception as e:
logger.exception(f"Error in getting matches: {e}")
continueAlso applies to: 96-117
| import openai | ||
| except ImportError: | ||
| raise ImportError("Please install OpenAI python library.") | ||
|
|
||
| self.client = openai.OpenAI(api_key=self.api_key, base_url=self.api_base) |
There was a problem hiding this comment.
Correct the usage of the OpenAI API client
The OpenAI Python library does not have an OpenAI class. You should set the API key and base URL directly on the openai module and then use the openai.ChatCompletion.create() method.
Apply this diff to fix the issue:
try:
import openai
except ImportError as e:
raise ImportError("Please install OpenAI python library.") from e
-self.client = openai.OpenAI(api_key=self.api_key, base_url=self.api_base)
+openai.api_key = self.api_key
+openai.api_base = self.api_baseUpdate the chat_completions method:
try:
- response = self.client.chat.completions.create(**params)
+ response = openai.ChatCompletion.create(**params)
except Exception as e:
logging.error(f"Error in chat_completions: {e}")
return LLMResponse(content=f"Error: {e}")Also applies to: 156-157
🧰 Tools
🪛 Ruff (0.8.0)
56-56: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
| try: | ||
| import openai | ||
| except ImportError: | ||
| raise ImportError("Please install OpenAI python library.") |
There was a problem hiding this comment.
Use 'raise ... from e' when re-raising exceptions in except blocks
When raising an exception within an except clause, include the original exception using from e to preserve the traceback and provide more context.
Apply this diff to fix the issue:
try:
import openai
-except ImportError:
+except ImportError as e:
- raise ImportError("Please install OpenAI python library.")
+ raise ImportError("Please install OpenAI python library.") from e📝 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 e: | |
| raise ImportError("Please install OpenAI python library.") from e |
🧰 Tools
🪛 Ruff (0.8.0)
56-56: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
| print(f"Error: {e}") | ||
| return LLMResponse(content=f"Error: {e}") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use logging instead of print statements for error handling
Using the logging module instead of print allows for better control over log levels and integration with logging systems.
Apply this diff to fix the issue:
+import logging
# ... rest of the code ...
try:
response = openai.ChatCompletion.create(**params)
except Exception as e:
- print(f"Error: {e}")
+ logging.error(f"Error in chat_completions: {e}")
return LLMResponse(content=f"Error: {e}")Committable suggestion skipped: line range outside the PR's diff.
| return formatted_tools | ||
|
|
||
| def chat_completions( | ||
| self, messages: list, tools: list = [], stop=None, response_format=None |
There was a problem hiding this comment.
Avoid mutable default arguments
Using a mutable default argument like tools: list = [] can lead to unexpected behavior because the same list is shared across function calls. Instead, set the default to None and initialize it within the function.
Apply this diff to fix the issue:
def chat_completions(
- self, messages: list, tools: list = [], stop=None, response_format=None
+ self, messages: list, tools: list = None, stop=None, response_format=None
):
+ if tools is None:
+ tools = []Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.0)
136-136: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
|
I've added a Sample error message 
|
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