To handle token limits and optimize performance in LangChain, focus on three key areas: input management, processing efficiency, and model selection. First, break large inputs into smaller chunks using LangChain’s text splitters, which maintain context while avoiding overflow. For example, the RecursiveCharacterTextSplitter splits text at natural boundaries (paragraphs, sentences) with overlap to preserve meaning. When using chains like RetrievalQA, pair this with a vector store to retrieve only relevant document sections, reducing input size. Always truncate or summarize unnecessary content before sending to the model.
Next, optimize processing by caching repeated requests and streamlining prompts. LangChain’s Memory component stores previous interactions, avoiding redundant API calls for similar queries. Simplify prompts by removing filler text—e.g., instead of lengthy explanations, use direct instructions like “Summarize this in 3 sentences.” For complex tasks, use the MapReduceChain to split work into parallelizable sub-tasks (map step) and combine results efficiently (reduce step). Asynchronous processing (via async/await) can further speed up batch operations by avoiding blocking calls.
Finally, choose models strategically. Smaller models like gpt-3.5-turbo handle basic tasks with lower token costs and latency compared to larger models like gpt-4. For repetitive tasks, fine-tune a smaller model to reduce reliance on expensive APIs. When streaming responses, use LangChain’s callback system to process outputs incrementally, improving perceived performance. Always monitor token usage via built-in callbacks or logging to identify bottlenecks. For example, track the token_usage metadata in OpenAI responses to audit costs and adjust chunk sizes or model parameters accordingly.