Understanding Vector Search and Distance Metrics in Vector Search
Retrieval Strategies Dense Retrieval Dense retrieval uses continuous, fixed-dimensional vectors (embeddings) to represent documents and queries. These dense vectors capture semantic meaning and contextual information. Key Features: Represents text as dense vectors (typically 768-4096 dimensions) Captures semantic relationships well Uses neural models like BERT, Sentence-BERT, or OpenAI’s text-embedding models Excels at understanding synonyms and related concepts Advantages: Strong semantic understanding Handles paraphrasing effectively Good at capturing document meaning Works well for concept-based queries Disadvantages: ...