Handling very long documents with embedding models requires addressing token limits and maintaining contextual relevance. Most embedding models, like BERT or OpenAI’s text-embedding-ada, have maximum input lengths (often 512 or 8192 tokens). When documents exceed this limit, you need to split the text into smaller chunks, embed each chunk, and then combine or select results intelligently. The key challenges are preserving meaningful context across chunks and ensuring the final output reflects the document’s overall intent. For example, a 10,000-word research paper can’t be processed in one go, so breaking it into sections or paragraphs while retaining relationships between ideas is critical.
To implement this, start by choosing a chunking strategy. Simple methods include splitting by fixed token counts (e.g., 512 tokens) or natural boundaries like paragraphs. Overlapping chunks (e.g., sliding a window with 25% overlap) can help maintain context between segments. For instance, if a sentence spans two chunks, overlap ensures it’s fully captured in at least one. Tools like LangChain’s text splitters or custom Python logic (using libraries like tiktoken for token counting) can automate this. After chunking, embed each segment independently. If the task requires a single document-level embedding (e.g., for search), average the chunk embeddings or use a pooling method. However, averaging might dilute nuanced information, so consider alternatives like storing all chunk vectors and retrieving the most relevant during queries.
Architectural choices also matter. For applications like semantic search, store all chunk embeddings in a vector database (e.g., FAISS, Pinecone) and include metadata like chunk position or document ID. This lets you reconstruct the original context during retrieval. For summarization or Q&A, use a hybrid approach: embed chunks to find relevant sections, then process those with a language model. For example, in a document Q&A system, first retrieve top chunks using embeddings, then pass those to an LLM like GPT-4 to generate answers. Always test chunk sizes and overlap ratios with your specific documents—technical manuals might need smaller chunks than novels. Monitor performance to balance accuracy and computational cost, as processing hundreds of chunks per document adds latency.