[**WeKnora**](https://weknora.weixin.qq.com) is an LLM-powered framework designed for deep document understanding and semantic retrieval, especially for handling complex, heterogeneous documents.
It adopts a modular architecture that combines multimodal preprocessing, semantic vector indexing, intelligent retrieval, and large language model inference. At its core, WeKnora follows the **RAG (Retrieval-Augmented Generation)** paradigm, enabling high-quality, context-aware answers by combining relevant document chunks with model reasoning.
**Important:** Starting from v0.1.3, WeKnora includes login authentication functionality to enhance system security. For production deployments, we strongly recommend:
- Deploy WeKnora services in internal/private network environments rather than public internet
- Avoid exposing the service directly to public networks to prevent potential information leakage
- Configure proper firewall rules and access controls for your deployment environment
- Regularly update to the latest version for security patches and improvements
WeKnora employs a modern modular design to build a complete document understanding and retrieval pipeline. The system primarily includes document parsing, vector processing, retrieval engine, and large model inference as core modules, with each component being flexibly configurable and extendable.
| **Academic Research Analysis** | Paper retrieval, research report analysis, scholarly material organization | Accelerate literature review, assist research decisions |
| Document Formats | ✅ PDF / Word / Txt / Markdown / Images (with OCR / Caption) | Support for structured and unstructured documents with text extraction from images |
| Embedding Models | ✅ Local models, BGE / GTE APIs, etc. | Customizable embedding models, compatible with local deployment and cloud vector generation APIs |
| Vector DB Integration | ✅ PostgreSQL (pgvector), Elasticsearch | Support for mainstream vector index backends, flexible switching for different retrieval scenarios |
| Retrieval Strategies | ✅ BM25 / Dense Retrieval / GraphRAG | Support for sparse/dense recall and knowledge graph-enhanced retrieval with customizable retrieve-rerank-generate pipelines |
| LLM Integration | ✅ Support for Qwen, DeepSeek, etc., with thinking/non-thinking mode switching | Compatible with local models (e.g., via Ollama) or external API services with flexible inference configuration |
| QA Capabilities | ✅ Context-aware, multi-turn dialogue, prompt templates | Support for complex semantic modeling, instruction control and chain-of-thought Q&A with configurable prompts and context windows |
| E2E Testing | ✅ Retrieval+generation process visualization and metric evaluation | End-to-end testing tools for evaluating recall hit rates, answer coverage, BLEU/ROUGE and other metrics |
| Deployment Modes | ✅ Support for local deployment / Docker images | Meets private, offline deployment and flexible operation requirements |
| User Interfaces | ✅ Web UI + RESTful API | Interactive interface and standard API endpoints, suitable for both developers and business users |
WeKnora serves as the core technology framework for the [WeChat Dialog Open Platform](https://chatbot.weixin.qq.com), providing a more convenient usage approach:
- **Zero-code Deployment**: Simply upload knowledge to quickly deploy intelligent Q&A services within the WeChat ecosystem, achieving an "ask and answer" experience
- **Efficient Question Management**: Support for categorized management of high-frequency questions, with rich data tools to ensure accurate, reliable, and easily maintainable answers
- **WeChat Ecosystem Integration**: Through the WeChat Dialog Open Platform, WeKnora's intelligent Q&A capabilities can be seamlessly integrated into WeChat Official Accounts, Mini Programs, and other WeChat scenarios, enhancing user interaction experiences
To help users quickly configure various models and reduce trial-and-error costs, we've improved the original configuration file initialization method by adding a Web UI interface for model configuration. Before using, please ensure the code is updated to the latest version. The specific steps are as follows:
If this is your first time using this project, you can skip steps ①② and go directly to steps ③④.
On your first visit, you will be automatically redirected to the registration/login page. After completing registration, please create a new knowledge base and finish the relevant settings on its configuration page.
**Knowledge Base Management:** Support for dragging and dropping various documents, automatically identifying document structures and extracting core knowledge to establish indexes. The system clearly displays processing progress and document status, achieving efficient knowledge base management.
WeKnora supports transforming documents into knowledge graphs, displaying the relationships between different sections of the documents. Once the knowledge graph feature is enabled, the system analyzes and constructs an internal semantic association network that not only helps users understand document content but also provides structured support for indexing and retrieval, enhancing the relevance and breadth of search results.
We welcome community contributions! For suggestions, bugs, or feature requests, please submit an [Issue](https://github.com/Tencent/WeKnora/issues) or directly create a Pull Request.
