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WeKnora is an open source framework developed for deep document understanding and semantic information retrieval using large language models. It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. This approach enables the system to provide more reliable answers by grounding model reasoning in the content of uploaded documents. WeKnora is designed with a modular architecture that separates components for document processing, search strategies, and model inference, allowing developers to customize or extend different parts of the pipeline. It supports knowledge base management and conversational question answering built on top of structured and unstructured documents.
Features
- Multimodal document parsing for formats such as PDFs, Word files, and images
- Retrieval-Augmented Generation pipeline for context-aware responses
- Modular architecture separating document processing, retrieval, and inference
- Hybrid search strategies combining vector, keyword, and other retrieval methods
- Knowledge base management for organizing and querying document collections
- Web interface and APIs for integration and system interaction
Project Samples
