Early China Research Assistant
Maximizing AI for Early China Studies
Intelligent Person Profiles
AI analyzes historical records to build multi-dimensional biographical profiles, including political status, family relations, and social networks—visualizing ancient figures as intuitively as social media profiles
Event Timeline Reconstruction
AI semantic analysis extracts key political events from historical texts, constructing precise timelines that reveal the power dynamics
Relationship Network Visualization
Analyzing personal connections, visualizing the complex political, blood, and marital relationships of the Han Dynasty with multi-dimensional interactive exploration
Geospatial Analysis
Mapping historical figures and events across geographic locations, revealing patterns of political influence, military campaigns, and administrative mobility throughout the Han Dynasty
Why ECRA? The Problem with Current Approaches
Traditional databases require exact keyword matching, missing conceptually related terms. Direct LLM usage faces practical constraints:
- •Token Limitations: ChatGPT can only process 8K-32K tokens, far too small for comprehensive Han Dynasty analysis
- •Attention Deficits: Cannot focus on specific historical contexts, producing generic responses
- •Output Inconsistency: Same questions produce different answers, violating scholarly reproducibility
ECRA's Solution: Advanced RAG + Agentic AI Architecture
ECRA employs a sophisticated multi-layered AI architecture that combines several advanced technologies:
- •Agentic AI Preprocessing (CrewAI)Specialized AI agents collaborate to extract and validate relevant historical data from authenticated classical Chinese texts (Shiji, Hanshu, Hou Hanshu) before LLM processing
- •Retrieval-Augmented Generation (RAG)Combines extracted historical data with generative AI, ensuring responses draw from verified sources and enabling deeper insights than direct LLM queries
- •Multi-Turn Conversational ContextMaintains conversation history for deeper, contextual research exploration across multiple dialogue turns
- •Multi-Agent Collaborative ArchitectureDifferent specialized agents work together—one for document retrieval, another for historical context analysis, and others for cross-referencing sources—producing comprehensive, research-grade answers
We believe this integrated approach, when carefully tuned, will achieve research-grade accuracy that far exceeds what general-purpose LLMs can provide, while maintaining scholarly reproducibility and source traceability.