WeKnora is an intelligent retrieval and understanding framework designed for complex document scenarios. It integrates multimodal preprocessing, semantic vector indexing, and large model reasoning. At its core, it adopts the RAG (Retrieval-Augmented Generation) mechanism and supports parsing multiple formats such as PDF, Word, and images.
Main Features
- Supports structured parsing and knowledge extraction from multiple document formats
- Built-in automatic knowledge graph construction and visualization
- Flexible integration with local/cloud large models, supporting Qwen, DeepSeek, etc.
- Provides Web UI and standard RESTful API for easy secondary development
- Supports enterprise on-premises deployment and data security management
Use Cases
Applicable to enterprise knowledge management, scientific literature analysis, technical support, legal compliance review, medical knowledge assistance, and more. Significantly improves information retrieval efficiency and intelligent Q&A quality.
Technical Features
Adopts a modular architecture and supports multiple retrieval strategies (BM25, Dense Retrieve, GraphRAG). Allows flexible combination of recall, rerank, and generation processes. Compatible with mainstream vector databases (PostgreSQL, Elasticsearch) and supports knowledge graph-enhanced retrieval.