// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Semantic Search
A search method that understands the meaning and context of your query, rather than just matching keywords, to find more relevant results.
TECHNICAL DEFINITION
Semantic Search is an information retrieval paradigm that leverages natural language processing (NLP) and vector embeddings to understand the contextual meaning and intent behind a user's query, rather than relying solely on keyword matching, thereby returning more conceptually relevant documents or data.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Vector Search
- Meaning-Based Search
- Contextual Search
- Neural Search
USAGE NOTE
Semantic search powers RAG systems and improves the relevance of search results in knowledge bases and applications.
DEVELOPERS
Organizations developing technology related to Semantic Search.
Google AI develops cutting-edge research and technologies in natural language understanding, including foundational models like BERT and MUM, which are critical for powering semantic search capabilities across Google's products and for external developers.
OpenAI provides advanced language models and embedding APIs that are extensively used for semantic search. Their embedding models allow developers to convert text into vectors, enabling efficient similarity search and information retrieval.
Hugging Face is a hub for open-source AI models, datasets, and tools, offering a vast ecosystem of transformer models and libraries (like 'transformers' and 'sentence-transformers') essential for building and deploying semantic search systems.
Pinecone is a leading vector database company that provides a managed service for storing, indexing, and querying billions of high-dimensional vectors in real-time. This technology is foundational for scalable semantic search applications.
Weaviate is an open-source vector database that allows developers to store data objects and vector embeddings, enabling semantic search, recommendation engines, and other AI-powered applications through GraphQL or RESTful APIs.
Cohere offers powerful language AI models, including state-of-the-art embedding models that are optimized for semantic search and information retrieval tasks, allowing businesses to build more intelligent search experiences.
Elastic, known for Elasticsearch, has significantly evolved its search capabilities to include vector search, allowing users to combine traditional keyword search with semantic similarity for more relevant and contextual results.
Zilliz is the company behind Milvus, an open-source vector database, and offers Zilliz Cloud, a managed service for Milvus. Their products are specifically designed for efficient similarity search and AI applications, including semantic search.
Meta AI conducts extensive research in AI and develops foundational open-source tools like Faiss (Facebook AI Similarity Search), a library for efficient similarity search and clustering of dense vectors, widely used in semantic search.