Fix LangChainOpenAIHandler for Azure

[MarkRx]

User description

Refactored to support delayed ChatOpenAI instantiation for Azure

Fixes #1101

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PR Type

Enhancement, Bug fix

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Description

• Refactored LangChainOpenAIHandler to improve Azure support and efficiency:
  • Introduced a new _get_chat method with @functools.cache for lazy and efficient chat instance creation
  • Updated chat method to use the new _get_chat method, supporting delayed ChatOpenAI instantiation
  • Removed initialization of _chat in the constructor and added early validation
• Updated LangChain dependencies in requirements.txt:
  • Upgraded langchain to version 0.2.0
  • Added new dependencies: langchain-core 0.2.28 and langchain-openai 0.1.20
• These changes improve the handling of Azure OpenAI deployments and ensure compatibility with the latest LangChain versions

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Changes walkthrough 📝

	Relevant files
Enhancement	langchain_ai_handler.py Refactor LangChainOpenAIHandler for Azure support                pr_agent/algo/ai_handlers/langchain_ai_handler.py Refactored LangChainOpenAIHandler to support delayed ChatOpenAI instantiation for Azure Introduced a new _get_chat method with @functools.cache for efficient chat instance creation Updated chat method to use the new _get_chat method Removed initialization of _chat in the constructor and added early validation +31/-27
Dependencies	requirements.txt Update LangChain dependencies                                                        requirements.txt Updated langchain dependency to version 0.2.0 Added new dependencies: langchain-core 0.2.28 and langchain-openai 0.1.20 +3/-1

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PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🏅 Score: 92

🧪 No relevant tests

🔒 No security concerns identified

🔀 Multiple PR themes Sub-PR theme: Refactor LangChainOpenAIHandler for improved Azure support Relevant files: pr_agent/algo/ai_handlers/langchain_ai_handler.py Sub-PR theme: Update LangChain dependencies Relevant files: requirements.txt

⚡ Key issues to review Error Handling The new _get_chat method might benefit from more specific error handling, especially for network-related issues when interacting with Azure or OpenAI APIs.

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PR Code Suggestions ✨

Category	Suggestion	Score
Error handling	Add error handling for chat class initialization in the constructor Consider adding error handling for the case where _get_chat() fails to initialize the chat class. This could prevent potential runtime errors if the initialization fails. pr_agent/algo/ai_handlers/langchain_ai_handler.py [20-25] def __init__(self): # Initialize OpenAIHandler specific attributes here super().__init__() self.azure = get_settings().get("OPENAI.API_TYPE", "").lower() == "azure" # Initialize the primary chat class for early validation - self._get_chat(self.deployment_id) + try: + self._get_chat(self.deployment_id) + except Exception as e: + get_logger().error(f"Failed to initialize chat class: {e}") + raise Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion improves error handling and provides better logging, which is crucial for debugging and maintaining the code.	8
	Add error checking and exception handling in the chat method The chat method is not handling the case where _get_chat might return None or raise an exception. Consider adding a check or try-except block to handle potential errors. pr_agent/algo/ai_handlers/langchain_ai_handler.py [27-29] def chat(self, messages: list, model: str, temperature: float): chat = self._get_chat(self.deployment_id) - return chat.invoke(input = messages, model=model, temperature=temperature) + if not chat: + raise ValueError("Failed to initialize chat instance") + try: + return chat.invoke(input=messages, model=model, temperature=temperature) + except Exception as e: + get_logger().error(f"Error during chat invocation: {e}") + raise Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion adds important error checking and exception handling, which can prevent silent failures and improve the robustness of the code.	8
Performance	Replace unlimited caching with LRU cache for better handling of dynamic parameters The _get_chat method is using functools.cache, which might cause issues if the deployment_id changes dynamically. Consider using lru_cache with a small maxsize instead to allow for some caching while still being able to handle changes in deployment_id. pr_agent/algo/ai_handlers/langchain_ai_handler.py [53-54] -@functools.cache +@functools.lru_cache(maxsize=8) def _get_chat(self, deployment_id = None): Apply this suggestion Suggestion importance[1-10]: 7 Why: Using LRU cache instead of unlimited caching is a good practice for handling potentially changing parameters, improving memory management.	7
Dependency management	Pin langchain package versions and uncomment them in requirements.txt Consider pinning the versions of the langchain packages to specific versions rather than using the latest versions. This can help ensure consistency and prevent unexpected issues from package updates. requirements.txt [33-35] -# langchain==0.2.0 -# langchain-core==0.2.28 -# langchain-openai==0.1.20 +langchain==0.2.0 +langchain-core==0.2.28 +langchain-openai==0.1.20 Apply this suggestion Suggestion importance[1-10]: 6 Why: Pinning package versions is a good practice for maintaining consistency, but the suggestion to uncomment the lines may not align with the intent of the original code.	6

[mrT23]

can you explain these lines ?
why do you add cache ? whats the benefit ? can't it cause problems when running things in parallel ?

I would recommend removing the cache line. Seems like a false optimization to me, since I don't see a real use case where it would actually benefit

[MarkRx]

It was a quick way to ensure a single chat object existed per LLM deployment. I assumed chat initialization and first use could have some I/O overhead though thinking more about it I agree I'll remove it.

[Nichebiche]

PR-Agent was enabled for this repository. To continue using it, please link your git user with your CodiumAI identity here.

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🏅 Score: 92

🧪 No relevant tests

🔒 No security concerns identified

🔀 Multiple PR themes Sub-PR theme: Refactor LangChainOpenAIHandler for improved Azure support Relevant files: pr_agent/algo/ai_handlers/langchain_ai_handler.py Sub-PR theme: Update LangChain dependencies Relevant files: requirements.txt

⚡ Key issues to review Error Handling The new _get_chat method might benefit from more specific error handling, especially for network-related issues when interacting with Azure or OpenAI APIs.