Multi-vector embedding approaches represent data by splitting it into smaller segments and generating distinct embeddings for each part, rather than using a single vector for the entire input. This method is useful for capturing fine-grained details or diverse aspects of complex data. For example, a long document might be divided into paragraphs or sections, each encoded into its own vector. These vectors collectively provide a richer representation than a single embedding, enabling systems to better handle tasks like retrieval, where matching specific sub-components improves accuracy. The core idea is to break data into meaningful units and embed them separately, balancing depth and flexibility.
A practical application of multi-vector embeddings is in retrieval-augmented systems. Suppose a search engine needs to find relevant passages from a large corpus. Instead of embedding entire documents as single vectors, the system might split each document into chunks (e.g., sentences or paragraphs) and generate embeddings for each chunk. During a query, the system compares the query’s embedding against all chunk embeddings, identifying the most relevant sections rather than entire documents. This approach reduces noise and improves precision. Similarly, in e-commerce, a product description with multiple attributes (price, features, reviews) could use separate embeddings for each attribute, allowing searches to target specific aspects of the product without conflating unrelated details.
However, multi-vector approaches introduce trade-offs. Storing and processing numerous vectors increases computational and memory costs. For instance, a 10-page document split into 50 paragraphs requires 50 embeddings instead of one, scaling storage needs linearly with data volume. Retrieval also becomes more complex: searching across thousands of vectors per item demands efficient indexing, like approximate nearest neighbor algorithms (e.g., FAISS) to avoid latency. Developers must balance granularity with practicality—choosing segment sizes (e.g., sentences vs. paragraphs) that suit the task. While multi-vector embeddings add overhead, their ability to capture nuanced data often justifies the cost in scenarios where precision outweighs resource constraints.