Handling large input sizes in LangChain workflows requires strategies to manage token limits, maintain context, and optimize processing. Most language models (LMs) have maximum token constraints—for example, GPT-3.5 Turbo processes up to 4,096 tokens. To work within these limits, break inputs into smaller chunks using LangChain’s text splitters. The RecursiveCharacterTextSplitter is a common tool that splits text by iterating through separators (like paragraphs, sentences, or words) to preserve logical structure. For instance, splitting a 10,000-word document into 1,000-token chunks ensures each piece fits the model’s input size. Overlap between chunks (e.g., 10% of the chunk size) helps retain context across sections, reducing fragmentation of ideas. This approach is essential for tasks like summarization or question answering over lengthy documents.
Another method involves combining summarization with iterative processing. For example, use LangChain’s MapReduceChain to first summarize individual chunks (the “map” step) and then consolidate those summaries into a final output (the “reduce” step). This reduces the total tokens sent to the LM while preserving key information. Alternatively, the RefineChain iteratively builds a response by processing each chunk and refining the output incrementally. For a 50-page PDF, you might summarize each page separately and then merge those summaries hierarchically. This balances efficiency with depth, though it requires careful tuning to avoid losing critical details during aggregation.
For real-time workflows, consider integrating retrieval-augmented techniques. LangChain’s RetrievalQA chain pairs a vector database (like FAISS or Pinecone) with LM queries. Instead of processing the entire input, you index the data and retrieve only relevant snippets for each query. For example, embedding a large FAQ document allows the system to fetch the top three most relevant answers before generating a final response. This minimizes token usage and latency while maintaining accuracy. Additionally, using memory modules like ConversationBufferWindowMemory helps track essential context across interactions without storing entire histories. By combining chunking, summarization, and retrieval, you can scale LangChain workflows effectively while managing computational costs and model limitations.