Solvro Knowledge Graph RAG FAQs

Name: Solvro Knowledge Graph RAG
Author: Solvro

Question 1

Does Solvro Knowledge Graph RAG offer observability for tracing queries?

Accepted Answer

Yes, it includes comprehensive observability and tracing integration with Langfuse, providing full trace tracking, session monitoring, performance metrics, and visual insights into the entire RAG pipeline execution.

Question 2

What kind of data can be processed and loaded into the knowledge graph?

Accepted Answer

The system features a multi-threaded ETL data pipeline with robust PDF support and dynamic graph schema management, allowing you to load and structure diverse document-based information into Neo4j.

Question 3

Is the system configurable for different knowledge domains?

Accepted Answer

Absolutely. The graph schema is dynamically configurable via JSON, allowing users to define custom nodes and relationships to suit various knowledge domains beyond the initial Wroclaw University of Science and Technology example.

Question 4

How does the Solvro RAG system ensure accurate answers?

Accepted Answer

It employs an intelligent query routing system with guardrails, a multi-stage RAG pipeline built with LangGraph, and a dual LLM strategy (one for routing, one for precise Cypher generation) to ensure high accuracy and relevance.

Question 5

What is Solvro Knowledge Graph RAG?

Accepted Answer

It's a production-ready system that transforms natural language queries into Cypher, retrieves relevant information from a Neo4j knowledge graph, and provides contextual, AI-generated answers.

Solvro Knowledge Graph RAG

소개

주요 기능

사용 사례