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Excited to share groundbreaking research from @Baidu_Inc on enterprise information search! The team has developed EICopilot, a revolutionary agent-based solution that transforms how we explore enterprise data in large-scale knowledge graphs.
>> Technical Innovation
EICopilot leverages Large Language Models to interpret natural language queries and automatically generates Gremlin scripts for enterprise data exploration. The system processes hundreds of millions of nodes and billions of edges in real-time, handling complex enterprise relationships with remarkable precision.
Key Technical Components:
- Advanced data pre-processing pipeline that builds vector databases of representative queries
- Novel query masking strategy that significantly improves intent recognition
- Comprehensive reasoning pipeline combining Chain-of-Thought with In-context learning
- Named Entity Recognition and Natural Language Processing Customization for precise entity matching
- Schema Linking Module for efficient graph database query generation
>> Performance Metrics
The results are impressive - EICopilot achieves a syntax error rate as low as 10% and execution correctness up to 82.14%. The system handles 5000+ daily active users, demonstrating its robustness in real-world applications.
>> Implementation Details
The system uses Apache TinkerPop for graph database construction and employs sophisticated disambiguation processes, including anaphora resolution and entity retrieval. The architecture includes both offline and online phases, with continuous learning from user interactions to improve query accuracy.
Kudos to the research team from Baidu Inc., South China University of Technology, and other collaborating institutions for this significant advancement in enterprise information retrieval technology.
>> Technical Innovation
EICopilot leverages Large Language Models to interpret natural language queries and automatically generates Gremlin scripts for enterprise data exploration. The system processes hundreds of millions of nodes and billions of edges in real-time, handling complex enterprise relationships with remarkable precision.
Key Technical Components:
- Advanced data pre-processing pipeline that builds vector databases of representative queries
- Novel query masking strategy that significantly improves intent recognition
- Comprehensive reasoning pipeline combining Chain-of-Thought with In-context learning
- Named Entity Recognition and Natural Language Processing Customization for precise entity matching
- Schema Linking Module for efficient graph database query generation
>> Performance Metrics
The results are impressive - EICopilot achieves a syntax error rate as low as 10% and execution correctness up to 82.14%. The system handles 5000+ daily active users, demonstrating its robustness in real-world applications.
>> Implementation Details
The system uses Apache TinkerPop for graph database construction and employs sophisticated disambiguation processes, including anaphora resolution and entity retrieval. The architecture includes both offline and online phases, with continuous learning from user interactions to improve query accuracy.
Kudos to the research team from Baidu Inc., South China University of Technology, and other collaborating institutions for this significant advancement in enterprise information retrieval technology.
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