Choose between semantic Vector Search for similarity-based retrieval or Knowledge Graph Search for relationship-aware understanding.
Semantic similarity search powered by embeddings. Find documents that match the meaning of your query, not just keywords.
Knowledge graph with entity extraction. Understand relationships between people, organizations, and concepts.
Both approaches have their strengths. Choose based on your use case.
| Feature | Vector Search | Graph Search |
|---|---|---|
| Best For | "Find documents about X" | "How is X related to Y?" |
| Query Style | Natural questions | Relationship questions |
| Retrieval | Similar chunks | Connected entities |
| Visualization | Document list | Interactive graph |
| Ingestion Speed | Fast (embedding) | Slower (LLM extraction) |
| Example | "What are the key findings?" | "Who works at Company X?" |
Built with enterprise-grade infrastructure and cutting-edge AI.
Powered by Google's latest Gemini models for accurate understanding and generation.
Scalable document storage with vector search and graph traversal capabilities.
Enterprise-grade search with grounding and citation support.
Ingest PDFs, Word docs, text files, and more. Automatic processing and chunking.
Natural chat interface with context retention and follow-up questions.
Parallel processing for fast document ingestion. See results in seconds.