WeKnora
WeKnora is an LLM-powered framework for deep document understanding and retrieval-augmented generation (RAG), enabling scalable semantic search, multimodal preprocessing, and context-aware answers with agent mode and tool integrations.
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Key Features
RAG Framework
Combines retrieval and generation for context-aware document understanding.
Multimodal Preprocessing
Processes PDFs, images, web pages, and more for unified semantic indexing.
Vector Store Integration
Supports Qdrant and others for scalable semantic vector indexing.
Developer Tooling
Includes Docker Compose, API/SDK, and database migration support.
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Why Choose WeKnora
Modular Design:
Flexible components enable easy customization and extension of workflows.Agent Mode:
Supports automated workflows with built-in tool and web search integrations.Scalable Retrieval:
Integrates with vector stores for efficient semantic search and re-ranking.
Pricing
WeKnora is an open-source project available on GitHub. There is no direct pricing; users can deploy and use it freely under the MIT License. For enterprise or commercial use, consult the repository or contact the developers for support options.
About WeKnora
WeKnora is an LLM-powered framework for deep document understanding and retrieval-augmented generation (RAG), enabling scalable semantic search, multimodal preprocessing, and context-aware answers with agent mode and tool integrations.
What WeKnora Does
WeKnora enables deep understanding and semantic retrieval of complex, heterogeneous documents by leveraging large language models and the RAG paradigm. It provides context-aware answers by combining document chunks with model reasoning to enhance information discovery.
The framework includes modular pipelines for multimodal preprocessing, chunking, semantic vector indexing, and LLM inference. It integrates with vector stores like Qdrant and supports configurable retrievers for scalable search, re-ranking, and parallel retrieval across diverse document formats. Agent mode allows automated workflows and external tool calls, including web search and MCP tools.
Use cases include enterprise knowledge management, legal and compliance review, academic research assistance, and product technical support, serving industries requiring efficient document retrieval and question answering.
Pros & Cons
Open Source
Free to use and modify with a permissive MIT License.
Comprehensive Features
Supports multimodal data, agent workflows, and scalable retrieval.
Technical Setup
Requires developer expertise to deploy and configure effectively.
No Direct Pricing
No commercial pricing or support details provided in the repository.
Frequently Asked Questions
It is used for deep document understanding, semantic retrieval, and context-aware Q&A using LLMs.
Yes, it is open-source under the MIT License and available on GitHub.
Requires Docker, Docker Compose, and basic knowledge of LLM and vector stores for deployment.
Yes, it supports PDFs, images, web pages, and other heterogeneous document formats.
Yes, it supports agent mode with MCP tools and web search integrations.
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