semantic_search

Semantic search matches candidates with job descriptions by generating vector embeddings for both, upserting them into a vector database, and executing similarity queries to rank applicants based on semantic relevance.

The best way to rank candidates beyond exact keyword matching is applying a hybrid algorithm that combines semantic similarity, experience matching, and skills overlap, returning results filtered by metadata like location or years of experience.

AWS Bedrock and OpenAI are supported interchangeably for resume parsing. The semantic search approach supports dynamic embedding dimensions, allowing you to switch between AWS Bedrock Titan and OpenAI providers for generating vectors.

You filter semantic search results by attaching metadata to vectors during upsert, then applying metadata filters during similarity queries to restrict candidates by specific location or experience criteria.

Pinecone vector database supports batch upserts for large volumes of candidate resumes. It includes chunking for long documents, namespace management, and approximate nearest neighbor options to handle scale.

Limitations of semantic search for high-volume applicant tracking include embedding generation cost and performance overhead. These can be managed using built-in caching strategies and approximate nearest neighbor configurations to optimize queries.