How are embeddings used in document retrieval?

Embeddings are used in document retrieval to convert text into numerical vectors that capture semantic meaning, enabling systems to find relevant documents based on conceptual similarity rather than exact keyword matches. When a document or query is processed, an embedding model generates a high-dimensional vector representing its content. During retrieval, the system compares the query’s vector to precomputed document vectors in a database, returning documents whose vectors are “closest” to the query vector using metrics like cosine similarity. This approach works because embeddings place semantically similar texts (e.g., “cat” and “feline”) closer in vector space, even if they don’t share exact words.

For example, a developer might use a pre-trained model like BERT or Sentence-BERT to generate embeddings for all documents in a corpus. These embeddings are stored in a vector database such as FAISS or Elasticsearch. When a user searches for “how to troubleshoot network latency,” the system converts the query into an embedding and searches the database for document vectors with the smallest distance to it. This might retrieve articles about “fixing slow internet connections” even if they don’t mention “troubleshoot” or “latency.” Tools like cosine similarity or approximate nearest neighbor algorithms efficiently handle these comparisons, making retrieval scalable for large datasets.

Key considerations include choosing the right embedding model for the domain (e.g., clinical text vs. tech blogs) and balancing accuracy with computational cost. For instance, a support portal might use lightweight models like Word2Vec for fast but less nuanced retrieval, while a research tool could prioritize OpenAI’s text-embedding-3-small for higher accuracy. Developers must also preprocess data (e.g., chunking long documents) and manage trade-offs: dense vectors improve accuracy but require more storage. This method outperforms keyword-based systems by handling synonyms and context, though it relies on quality training data and may struggle with rare domain-specific terms.