Ethical AI for Rigorous Academic Research
Advanced RAG and Agentic AI architecture designed specifically for historical research
Semantic Search
Vector-based retrieval system that searches through historical texts using semantic understanding rather than keyword matching. Each search query is converted into embeddings and matched against our vectorized corpus of classical texts (Hanshu, Shiji, etc.), returning the most semantically relevant passages with source citations and confidence scores.
Why Traditional Keyword Search Falls Short:
- •Requires exact keyword matching, missing conceptually related terms
- •Cannot understand semantic meaning across classical Chinese texts
- •Example: Searching '皇帝改革' misses semantically similar '天子变法'
Research Agents (RAG + Multi-Agent LLMs)
Multi-agent conversational research platform powered by advanced AI that maintains context across multiple turns. Unlike simple search, this system uses specialized AI agents that collaborate to analyze your questions, retrieve relevant historical sources, cross-reference multiple texts, and generate comprehensive answers with detailed citations. Perfect for in-depth historical inquiry and complex research questions.
Why Direct LLM Usage Falls Short for Historical Research:
- •Token Limitations: ChatGPT has limited token capacity, too small for comprehensive Han Dynasty analysis
- •Attention Deficits: Cannot focus on specific historical contexts, producing generic responses
- •Hallucination Risk: May fabricate historical facts without source verification
- •Training Data Opacity: Cannot distinguish reliable historical sources from unreliable internet content
- •Output Inconsistency: Same questions produce different answers, violating scholarly reproducibility
AI Key Fact Extraction Workflow
Agentic AI Preprocessing
Specialized agents (CrewAI) work collaboratively to extract only relevant historical data from authenticated classical Chinese texts (Shiji, Hanshu, Hou Hanshu) before LLM processing
Curated Data Feeding
Transforms overwhelming textual corpora into focused datasets that fit within token limits, maximizing LLM performance while maintaining scholarly rigor
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
Agentic Research Methodology
Multi-Turn Dialogue maintains conversation context for deeper research exploration. Multi-Agent Architecture enables specialized agents to work collaboratively for different research tasks
🛠️Advanced Digital Tools
📊Research Applications
Concrete Extraction & Analysis Examples
Extracting biographical information from classical Chinese texts
Identifying political and family connections from historical texts
Chronological ordering of key historical events
Mapping historical locations and movements