Enhance retrieval with deterministic relationships and user context
Upgrade your Retrieval-Augmented Generation pipelines by combining vector search with a deterministic knowledge graph for accurate and personalized context.
The Challenge
Standard RAG relies purely on vector similarity, which can miss nuanced relationships between concepts or fail to incorporate user-specific conversational history.
The Solution
MemorySync fuses vector embeddings with an Entity Knowledge Graph. This hybrid approach allows your system to retrieve exact factual relationships alongside semantic similarities, resulting in highly accurate RAG outputs.
How It Works
Ingest
Documents are processed to extract both embeddings and relational entities.
Graph
A knowledge graph is constructed to map exact relationships.
Retrieve
Queries perform both vector and graph searches in parallel.
Synthesize
The retrieved context provides a factual basis for LLM generation.
Key Benefits
- Reduced generation errors
- Factual grounding via knowledge graphs
- User-specific context inclusion
- Dynamic data updates
- Low-latency hybrid retrieval
Supported Integrations
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MemorySync provides the infrastructure layer for persistent memory, adaptive retrieval, and enterprise AI intelligence.