SAG-RAG
What it is
A complex Retrieval-Augmented Generation (RAG) backend that implements a multi-stage pipeline including speculative query planning, multi-agent retrieval (vector, lexical, and structured), and graph-based reasoning. It uses a combination of Qdrant, Elasticsearch, and Neo4j to provide high-fidelity answers with provenance and confidence scores.
The problem it solves
Standard RAG systems often struggle with complex queries, noise in retrieved evidence, and lack of structured reasoning or source attribution.
What you could use it for
- Complex document Q&A with source attribution
- Retrieving information across multiple formats (vector/lexical/structured)
- Knowledge graph-based reasoning for identifying contradictions and entity relations
- Domain-specific information retrieval with author-aware bias
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.