Semantic Search
Semantic Search uses AI-driven vector search to retrieve context-aware results by understanding query intent. With RAG and a vector database, it ensures precise, structured responses. The system processes queries via API Request & Vector Search Nodes, refining results for fast, scalable, and intelligent retrieval.
Key Features
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- RAG Implementation – Enhances search accuracy by retrieving relevant data before generating responses.
- Vector Database – Stores data as high-dimensional vectors for efficient similarity searches.
- Structured Responses – Organizes search results into clear formats like lists, tables, or sections.
Architecture
Semantic Search utilizes AI-driven Vector Search to retrieve contextually relevant information by understanding the intent behind queries rather than relying solely on keyword matching.
This approach ensures more accurate and intelligent search results, significantly reducing manual effort while improving efficiency. By leveraging Retrieval-Augmented Generation (RAG) and a vector database, the system processes search queries with high precision, delivering structured responses.
The architecture starts with chunking and indexing data into a vector database, making it easily searchable. The API Request Node acts as the trigger, where the Vector Search Node performs the search using an embedding model to find the most relevant results.
With adjustable parameters like certainty level and search limits, the system ensures optimal retrieval of information. Finally, the API Response Node structures the output, enabling seamless integration with various applications for quick and accurate AI-powered responses.
How it Works?
Tools used
- API Request Node
- Vector Search Node
Benefits
- Context-Aware Retrieval – Understands intent beyond exact keyword matching.
- Fast and Scalable Search – Enables quick and efficient information retrieval.
- RAG Response – Provides well-informed, context-aware answers backed by external data.