Background
The core problem facing enterprise knowledge management is the difficulty for employees to quickly find accurate answers in voluminous documents. Traditional methods rely on manual searches or simple search engines, and the results are often inaccurate and time-consuming.
Core Solutions
- Building a Knowledge Graph: Structured storage of enterprise documents to Neo4j, to establish the entity relationship network. For example , product parameters , process specifications into a "node - relationship - node" ternary .
- Upload multi-format documentsBatch upload PDF/Word documents through the system interface and automatically generate vector indexes to be stored in the Milvus database.
- Smart Q&A Optimization: Configure vector models such as BAAI/bge-m3 to improve semantic understanding accuracy, combined with DeepSeek-R1 to process complex logical queries.
Implementation steps
- Organize existing documentation and tag key entities by business modules
- Upload structured data in JSONL format via "Graph Management" (example: {"h": "Server", "t": "RAID Configuration", "r": "Procedure"}). ": "RAID Configuration", "r": "Procedure"})
- Upload unstructured documents in the "Knowledge Base Management" and the system automatically chunks and vectorizes them.
- Set up Q&A test sets to verify response accuracy with typical questions from different positions
After the implementation of a manufacturing company, the query response time for equipment troubleshooting was reduced from 15 minutes to 25 seconds, and the accuracy rate was increased by 60%
This answer comes from the articleYuxi-Know: A Knowledge Graph-based Intelligent Q&A PlatformThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of inefficient employee queries in enterprise knowledge management?
