Enterprise RAG Knowledge Base
Vector Retrieval + Knowledge Graph + LLM Reasoning
Leveraging the latest RAG (Retrieval-Augmented Generation) technology with Milvus/Pinecone vector databases, Neo4j knowledge graphs, and mainstream LLMs, so every answer in enterprise knowledge retrieval and Q&A traces back to its source passage
Core Technical Capabilities
Multimodal Document Parsing
Support PDF/Word/Excel/PPT/Images/Scans with OCR+Table Structure Recovery+Layout Analysis, so scans and complex tables stay retrievable
Hybrid Knowledge Retrieval
Vector + Keyword + Knowledge Graph retrieval with BM25 + Dense Retrieval fusion algorithms
Enterprise Vector Database
Support Milvus/Pinecone/Weaviate/Qdrant with millisecond retrieval for 10M+ vectors
Knowledge Graph Reasoning
Build entity-relationship networks with Neo4j/ArangoDB, support multi-hop reasoning and path queries
Technical Advantages
High-Precision RAG Architecture
Latest RAG 2.0 with Query Rewriting + Multi-path Recall + Reranking; hybrid vector and knowledge-graph recall returns answers with citable source passages
- •HyDE Hypothetical Document Enhancement
- •Multi-vector Indexing (Dense + Sparse)
- •LLM-assisted Reranking
- •Context Compression & Filtering
Enterprise Knowledge Graph
Automatic entity-relationship extraction, domain knowledge graph construction with graph reasoning and path queries
- •NER Named Entity Recognition
- •Relation Extraction & Alignment
- •Ontology Construction & Fusion
- •Multi-hop Reasoning Queries
Private & Secure Deployment
Support on-premise/private cloud deployment, data stays within internal network, compliant with MLPS 2.0/GDPR
- •End-to-end Encryption (AES-256)
- •Fine-grained Access Control (RBAC)
- •Complete Audit Logs
- •Support for Localized Adaptation
Core Application Scenarios
Enterprise Intelligent Customer Service
7x24 intelligent Q&A with real-time knowledge base updates; unanswered questions escalate to a human and flow back into the knowledge base
R&D Knowledge Assistant
Unified search across tech docs, API docs and code repositories — one question covers cross-document retrieval and summary instead of opening each file
Legal Compliance Review
Clause-by-clause contract comparison, regulatory knowledge base, risk spotting with the governing clause cited
Training & Learning System
Intelligent training assistant, exam questions generated from internal materials, role-based learning path recommendations
Applications YGG Built Itself
Knowledge base retrieval at ten-million-chunk scale, covering multi-turn Q&A and source citation
DataWeaver - Cross-database knowledge and data Q&A
The Knowledge Agent wires RAG document QA and a knowledge graph onto PostgreSQL / MySQL / Oracle / ClickHouse / MongoDB, so one natural-language question runs a federated search across all of them. See /cases/dataweaver
Siemens SCL Agent - Three-layer industrial knowledge base
A language layer (SCL syntax + IEC 61131-3), a template layer (motor / valve / PID / sequential / safety FBs) and a convention layer (variable prefixes + FB numbering + comments) feed code generation; every output is reviewed by a qualified electrical engineer. See /cases/scl-agent
Township Health Agent - Policy and common-illness Q&A
Policy documents and common-illness handbooks are bulk-imported, then chunked and vectorized automatically; pgvector similarity search plus keyword recall answers only from documents the clinic uploaded, each passage cited. See /cases/clinic-agent
FAQ
知识库常见问题
RAG 知识库的准确率如何？
我们不报单一准确率数字——脱离评测集的准确率既不可比也不可验证。机制上采用 RAG 2.0 混合检索（向量 + 知识图谱 + 语义摘要），每条回答都带可回溯到原文的出处引用，检索不到时拒答而不是凭模型记忆编答案。验收按检索召回率、回答正确率、幻觉率、拒答表现四项分开测，评测集由客户业务方编写（建议 100-300 条真实问题 + 参考答案）。
知识库支持哪些文档格式？
100+ 种：PDF、Word、Excel、PPT、TXT、Markdown、HTML、JSON、XML 等，支持图片 / 表格 / 公式 OCR 解析。单文档最大 500 MB，批量导入最大 10 GB。
可以私有化部署吗？
是。默认交付形态就是私有化部署——本地机房、客户 VPC、专有云均可。数据不出企业边界，AES-256 静态加密，符合等保 2.0 / ISO 27001 对齐要求。