Cosdata Structured Search
Next-Gen Knowledge Graph
Innovative knowledge graph purpose-built for structured search and vector search integration, optimized for Graph-Based RAG applications with unprecedented scalability and ease of use.
Innovative knowledge graph purpose-built for structured search and vector search integration, optimized for Graph-Based RAG applications with unprecedented scalability and ease of use.
Cosdata Structured Search is an innovative knowledge graph solution designed specifically for AI applications. It bridges the gap between traditional structured data and vector embeddings, enabling more powerful and contextual retrieval for advanced AI systems.
Explore the documentationSeamlessly integrate structured data with vector embeddings to create a powerful knowledge graph that enhances retrieval accuracy and context awareness.
Purpose-built for Graph Retrieval Augmented Generation, enabling more contextual, accurate, and comprehensive responses for complex queries.
Designed to handle billions of entities and relationships with efficient query performance, maintaining speed even as your knowledge base grows.
Intuitive APIs and comprehensive documentation make it easy to integrate structured search capabilities into your existing applications.
Combine structured queries with vector similarity search for more precise and contextually relevant results.
Works natively with Cosdata HNSW and Cosdata Serverless for a complete vector database ecosystem.
Define and evolve your knowledge graph schema without downtime or complex migrations.
Powerful yet intuitive query language for traversing and retrieving information from your knowledge graph.
Graph-based RAG is an advanced approach that enhances traditional RAG systems by incorporating structured relationships between data points. This enables AI systems to understand context, follow complex reasoning paths, and provide more accurate and comprehensive responses.
Unlike traditional RAG which treats documents as independent entities, Graph-based RAG understands the connections between information, allowing for more nuanced and contextually aware retrieval.
Read Microsoft's research on Graph-based RAGEnable AI agents to build and maintain structured memory of interactions, entities, and relationships for more coherent and contextual responses over time.
Create comprehensive knowledge graphs from enterprise data, enabling more accurate information retrieval and contextual understanding for corporate AI assistants.
Support multi-hop reasoning and inference by traversing relationship paths in the knowledge graph, enabling AI systems to answer complex questions requiring multiple steps of logic.
Join the waitlist for Cosdata Structured Search and be among the first to experience the next generation of knowledge graph technology.