To implement multi-vector representations for complex documents, start by breaking the document into meaningful components and generating embeddings for each part. This approach captures the nuances of different sections, such as paragraphs, tables, images, or headings, which a single vector might miss. For example, a legal contract could be split into clauses, definitions, and annexures, each processed separately. Use embedding models like BERT for text, CLIP for images, or specialized encoders for structured data (e.g., tables). Store these vectors in a database that supports multi-vector indexing, such as FAISS, Pinecone, or Elasticsearch, and include metadata to track their source (e.g., “Section 2.1” or “Figure 3”).
A practical way to structure this is to preprocess the document into chunks using rules or models. For text, libraries like spaCy or NLTK can split content into sentences or paragraphs. For tables, extract structured data with tools like Camelot or Tabula and convert rows/columns into JSON. Images can be processed with vision transformers. Each chunk is then encoded into a vector. For instance, a research paper might have separate vectors for the abstract (encoded with a text model), methodology diagrams (encoded with CLIP), and results tables (encoded as key-value pairs). Metadata like “document_id: 123, chunk_type: table, page: 5” helps during retrieval to reconstruct context.
The main challenges are balancing granularity and computational cost. Smaller chunks (e.g., sentences) capture fine details but increase storage and query complexity. Larger chunks (e.g., sections) provide context but may dilute specific information. A hybrid approach often works: store fine-grained vectors for search and coarser ones for context. For example, retrieve top-matching sentence vectors first, then use their parent section’s vector to refine results. Tools like LangChain’s multi-vector retriever or custom pipelines in Python can manage this. Ensure consistency by testing retrieval quality—if queries about “payment terms” in contracts return both clause text and related tables, your setup is working. Regularly update embeddings if document structures change to maintain accuracy.