Harrison/fix caching bug

[korbit-ai]

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Description by Korbit AI

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What change is being made?

Fix a caching bug in langchain/llms/base.py by ensuring llm_output is properly initialized when there are no missing prompts.

Why are these changes being made?

Previously, the code did not handle the case where missing_prompts is empty, leading to potential errors when accessing new_results.llm_output. This fix ensures llm_output is initialized to an empty dictionary in such cases, preventing runtime errors and improving code robustness.

Summary by CodeRabbit

• New Features
  • Improved handling of missing prompts, enhancing performance by avoiding unnecessary operations.
• Bug Fixes
  • Resolved potential errors by ensuring the callback manager is only invoked when there are prompts to process.

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PR Type

Bug fix

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Description

• Fixed a caching bug in langchain/llms/base.py by ensuring llm_output is properly initialized when there are no missing prompts.
• Added a condition to check if missing_prompts is non-empty before invoking the callback manager, preventing unnecessary operations.
• Improved code robustness by ensuring LLMResult is returned with the correct llm_output.

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Changes walkthrough 📝

	Relevant files
Bug fix	base.py Fix caching bug by handling empty missing prompts                langchain/llms/base.py Added condition to check if missing_prompts is non-empty before invoking callback manager. Initialized llm_output to an empty dictionary when there are no missing prompts. Ensured LLMResult is returned with the correct llm_output. +18/-14

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[korbit-ai]

Clone of the PR langchain-ai/langchain#788

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I have reviewed your code and did not find any issues!

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[furwellness]

@coderabbitai review

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Walkthrough

This change enhances the logic of the callback manager in the LLM processing by introducing a condition that checks for missing prompts before invoking the callback methods. If no prompts are missing, it sets the output to an empty dictionary, thus preventing unnecessary processing and improving performance. This ensures that only relevant operations are executed, increasing the overall robustness of the function.

Changes

Files	Change Summary
langchain/llms/base.py	Added a conditional check for missing_prompts before callback manager execution. If empty, sets llm_output to an empty dictionary, improving control flow and performance.

Poem

🐇 In the land where prompts reside,
A check was made, with joy and pride.
If none were missing, we took a pause,
An empty output earned applause!
Now swift and sure, our code will run,
A happy rabbit job well done! 🥕✨

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[furwellness]

/review

[codiumai-pr-agent-free]

PR Reviewer Guide 🔍

(Review updated until commit 443e405)

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

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Conditional Execution The new code introduces a conditional check for non-empty missing_prompts before processing, which may change the behavior of the function. Verify if this change is intended and doesn't break existing functionality. Default LLM Output The code now sets llm_output to an empty dictionary when there are no missing prompts. Ensure this doesn't affect any downstream processes that expect specific content in llm_output.

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Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 248c297 and 443e405.

Files selected for processing (1)

• langchain/llms/base.py (1 hunks)

Additional comments not posted (3)

langchain/llms/base.py (3)

95-98: Conditional check for missing prompts is a good improvement.

The logic now ensures the callback manager is only invoked when necessary, enhancing efficiency.

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109-111: Initialization of llm_output is a robust enhancement.

This change prevents runtime errors by ensuring llm_output is always initialized, even when no prompts are missing.

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113-113: Return statement update maintains integrity.

The return statement now correctly uses the updated llm_output, ensuring the integrity of the LLMResult.

[furwellness]

Zero comments from coderabbit

[codiumai-pr-agent-free]

Persistent review updated to latest commit 443e405

[codiumai-pr-agent-free]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible issue	Initialize the variable before the conditional block to avoid potential errors Consider initializing llm_output as an empty dictionary at the beginning of the method to avoid potential UnboundLocalError if missing_prompts is empty. langchain/llms/base.py [95-111] +llm_output = {} if len(missing_prompts) > 0: self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, missing_prompts, verbose=self.verbose ) try: new_results = self._generate(missing_prompts, stop=stop) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_llm_error(e, verbose=self.verbose) raise e self.callback_manager.on_llm_end(new_results, verbose=self.verbose) for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] langchain.llm_cache.update(prompt, llm_string, result) llm_output = new_results.llm_output -else: - llm_output = {} Apply this suggestion Suggestion importance[1-10]: 9 Why: Initializing llm_output at the beginning of the method prevents potential UnboundLocalError and ensures robustness, addressing a possible issue effectively.	9
Best practice	Use a more descriptive variable name for the caught exception Consider using a more descriptive variable name instead of e for the caught exception. This improves code readability and maintainability. langchain/llms/base.py [101-103] -except (KeyboardInterrupt, Exception) as e: - self.callback_manager.on_llm_error(e, verbose=self.verbose) - raise e +except (KeyboardInterrupt, Exception) as exception: + self.callback_manager.on_llm_error(exception, verbose=self.verbose) + raise exception Apply this suggestion Suggestion importance[1-10]: 7 Why: Using a more descriptive variable name for exceptions improves code readability and maintainability, but it is a minor improvement.	7
Enhancement	Add logging for better visibility of caching behavior Consider adding a log or debug statement when there are no missing prompts to provide more visibility into the caching behavior. langchain/llms/base.py [110-111] else: llm_output = {} + if self.verbose: + print("All prompts found in cache, no LLM call required.") Apply this suggestion Suggestion importance[1-10]: 6 Why: Adding a log statement when there are no missing prompts can enhance debugging and monitoring, but it is not critical for functionality.	6
Performance	Use a more efficient approach to update the dictionary Consider using a more efficient approach to update existing_prompts by using a dictionary comprehension instead of a loop. langchain/llms/base.py [105-108] +existing_prompts.update({ + missing_prompt_idxs[i]: result + for i, result in enumerate(new_results.generations) +}) for i, result in enumerate(new_results.generations): - existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] langchain.llm_cache.update(prompt, llm_string, result) Apply this suggestion Suggestion importance[1-10]: 5 Why: While using a dictionary comprehension can be more efficient, the current loop is straightforward and the performance gain may be negligible in this context.	5