No Vectors · No Chunking · No Hallucinations
Leave behind the fragmented chunking and vector drift of traditional RAG. Archive Manager preserves complete document structure via a hierarchical tree index — letting LLMs read and retrieve long documents like an expert, returning precise, traceable paragraph text rather than stitched fragments.
Traditional RAG trades fragmentation for scalability — at the cost of systematic precision loss. Archive Manager trades structure for genuine accuracy.
Full coverage from knowledge base construction to retrieval to integration
After upload, the document is auto-parsed for heading hierarchy to build a complete directory tree. Each node corresponds to a real document section, preserving parent-child and sibling relationships. LLMs reason over this tree rather than doing similarity matching over fragments.
The retrieval process mimics how a human expert reads: first scan the table of contents to narrow scope, then deep-read relevant sections, then synthesize across chapters. Every reasoning step is recorded and fully explainable.
Optional module: automatically extracts entities (people, organizations, concepts, events) and relationships from documents to build a cross-document knowledge graph. D3.js force-directed graph for real-time visualization with click-to-explore navigation.
Comprehensive support for common enterprise document formats, automatically handling complex layouts (tables, captions, footnotes, appendices) to ensure content extraction completeness above 99%.
Provides MCP-protocol-compliant Server endpoints. Any MCP-compatible AI Agent (including Claude, Lyna Agent, etc.) can mount the knowledge base directly via API Key authentication — no additional integration development required.
Built-in retrieval strategy configurations deeply optimized for different document types — achieving optimal retrieval performance in each scenario without manual tuning.
From document upload to Agent-ready — as fast as 5 minutes
Drag and drop PDFs, DOCX, or Markdown — supports bulk upload of entire folders.
The system parses document structure, builds a hierarchical directory tree, and generates node summaries and keyword indexes.
Ask questions directly in plain language. The system reasons over the knowledge tree and returns precise original paragraphs.
Mount to any AI Agent via MCP interface to enable knowledge-driven automated workflows.
From architectural design to engineering implementation — every detail serves precise retrieval
Completely free from vector database ops burden. Knowledge indexes are stored as structured tree data — no Pinecone, Weaviate, or Chroma infrastructure needed. Simple deployment, low cost.
When documents are added or modified, the system only rebuilds changed nodes rather than the full index. Large knowledge base (thousands of documents) update time drops from minutes to seconds.
Retrieval results are accurate to paragraph level with complete citation paths (document → section → sub-node number). Citation accuracy approaches 100%, meeting strict compliance requirements for legal and financial use cases.
Each retrieval returns a confidence score and relevance explanation. The system records retrieval history and user feedback to continuously optimize retrieval strategies for similar documents.
Native support for Chinese-English mixed documents. Auto-detects the primary document language, intelligently translates search terms for cross-language queries, and manages Chinese and English knowledge bases together.
Documents can be queried immediately after upload — no need to wait for full indexing to complete. Indexing a large document (500-page PDF) typically completes within 30 seconds.
Archive Manager provides a standard MCP Server interface. Any MCP-compatible AI Agent can mount the knowledge base through configuration — no integration code required.
{
"mcpServers": {
"archive-manager": {
"url": "https://archive.runemind.com.cn/mcp",
"apiKey": "am_sk_xxxxxxxxxxxxxxxx",
"knowledgeBases": [
"legal-docs",
"product-manual",
"research-reports"
]
}
}
}Any scenario requiring precise information retrieval from long documents
Consolidate product manuals, internal standards, technical documentation, and meeting minutes into one KB. New employees ask onboarding questions, veterans look up policies — all through natural language, no manual FAQ maintenance required.
Upload contracts, regulations, and court rulings. AI precisely locates relevant clauses with full contextual understanding — no out-of-context misinterpretation from chunking. Ideal for law firms and compliance departments.
Cross-document retrieval and comparative analysis across large volumes of research reports, annual reports, and prospectuses. Organize knowledge trees by company, industry, and time dimension to quickly locate specific financial data or analytical views.
Consolidate API docs, architecture design documents, and operations manuals. Developers and ops teams ask questions in natural language and get precise answers — no more Ctrl+F through piles of documents.
Upload documents and get precise, traceable knowledge retrieval within 5 minutes. No vector database, no complex setup — ready out of the box.