What is WeKnora?
weknora — LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using the RAG paradigm.
The modular architecture supports multimodal preprocessing, chunking, semantic vector indexing, and LLM inference for retrieval-augmented generation workflows.
Integrations with vector stores such as Qdrant and configurable retrievers enable scalable semantic search, re-ranking, and parallel retrieval across heterogeneous document formats.
Agent mode and built-in tool integrations (MCP tools, web search) enable automated workflows, external tool calls, and context-aware query reasoning.
Deployment and developer tooling include Docker Compose support, database migration and retry mechanisms, API/SDK components, and configurable model settings for production RAG applications.
Target users include developers, data scientists, and enterprises building semantic search, knowledge-base, and question-answering systems; source code is available at the tencent/weknora GitHub repository.
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WeKnora's key features
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LLM-powered retrieval-augmented generation (RAG) framework for deep document understanding and context-aware answers
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Modular multimodal preprocessing and chunking pipeline with semantic vector indexing and LLM inference
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Integrations with vector stores (e.g., Qdrant) and configurable retrievers for scalable semantic search, re-ranking, and parallel retrieval across heterogeneous formats
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Agent mode with built-in tool integrations (MCP tools, web search) for automated workflows, external tool calls, and context-aware query reasoning
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Deployment and developer tooling including Docker Compose support, database migration/retry mechanisms, API/SDK components, and configurable model settings
WeKnora use cases
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Create a customer support knowledge base using Weknora that ingests multimodal files (PDFs, images, web pages), semantically indexes content in vector stores, and delivers context-aware, retrieval-augmented answers via agent mode with tool and web access for real-time resolution and scalable search
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Implement an automated contract and compliance analysis pipeline using Weknora to preprocess legal documents, perform LLM-powered clause extraction and summarization, store embeddings for fast semantic retrieval, and generate auditable reports and alerts through configurable retrieval workflows
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Develop an internal research assistant using Weknora that unifies corporate documents, chat logs, and external web sources with multimodal semantic retrieval and configurable retrievers to answer complex queries, cite sources, and trigger downstream actions or workflows via agent tooling
Who is it for?
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Semantic researchers
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Knowledge base engineers
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Data retrieval specialists
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Llm inference architects
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Rag workflow developers
