Description

This PR optimizes the sync generate() method in BaseChatModel to improve throughput when processing multiple prompts by parallelizing LLM calls using a thread-pool executor.

Changes

• Core Optimization: Replaced sequential loop with thread-pool executor mapping for multi-input processing in chat_models.py:904-947
• Performance: Added fast path for single input to avoid unnecessary overhead
• Compatibility: Preserved original ordering, callback behavior, and error propagation
• Resource Management: Used get_executor_for_config context manager for proper thread pool lifecycle

Technical Details

The refactor maintains the same API while significantly improving performance for batch processing scenarios:

• Before: Sequential processing of each message list
• After: Parallel processing using thread-pool executor with proper error handling
• Error Handling: Preserved existing error propagation with on_llm_error callbacks
• Ordering: Results are returned in the same order as input messages

Performance Impact

This change will improve throughput when processing multiple prompts simultaneously, especially beneficial for:

• Batch inference scenarios
• Multi-prompt workflows
• Applications processing multiple conversations in parallel

Testing

The changes preserve all existing behavior:

• ✅ Error handling and callback invocation
• ✅ Result ordering and structure
• ✅ Single input fast path optimization
• ✅ Resource cleanup via context manager

Related

Part of the broader LLM optimization initiative documented in /Users/bytedance/langchain/langchain/.trae/documents/Optimize LLM Calls Across Codebase.md

Checklist

• Code follows project conventions
• Maintains backward compatibility
• Preserves error handling behavior
• Uses existing utilities (get_executor_for_config)
• No breaking changes to public API