An intelligent Q&A system powered by Retrieval-Augmented Generation (RAG).
DeepKnowledge.net is an advanced chatbot that integrates large language models with your private data sources using Retrieval-Augmented Generation (RAG). This approach provides precise, source-grounded answers while ensuring data privacy.
- Multi-source Integration: Seamlessly process content from websites and documents (PDF/DOCX).
- Source Citation: Offers transparent references to original data sources for every response.
- Relevance Scoring: Efficiently ranks information based on query relevance.
- Conversational Memory: Supports context-aware follow-up questions to maintain dialogue continuity.
- Language Models: Utilizes DeepSeek-V3 for chat interactions and OpenAI's text-embedding-ada-002 for embeddings.
- RAG Framework: Powered by LlamaIndex.
- Vector Store: Employs LlamaIndex In-Memory Vector Store for efficient data retrieval.
- User Interface: Built with Streamlit for a seamless web experience.
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Clone the repository:
git clone https://github.com/ErnestAroozoo/DeepKnowledge.net.git cd DeepKnowledge.net
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Install necessary dependencies:
pip install -r requirements.txt
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Set up environment variables:
cp .env.example .env
Update the .env
file with your API credentials:
# OpenAI Embeddings Configuration
OPENAI_API_KEY=your-openai-key
OPENAI_API_HOST=https://api.openai.com/v1
# DeepSeek Chat Configuration
DEEPSEEK_API_KEY=your-deepseek-key
DEEPSEEK_API_HOST=https://api.deepseek.com/v1
Note: API keys can be obtained from:
- OpenAI: OpenAI Platform
- DeepSeek: DeepSeek Platform
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Launch the application:
streamlit run app.py
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Add data sources:
- Websites: Input valid URLs for content parsing.
- Documents: Upload PDF/DOCX files for text extraction.
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Engage with the chatbot by:
- Asking natural language queries.
- Following up with questions using chat history.
- Requesting source verification for responses.
Type | Formats | Processing Method |
---|---|---|
Web Content | URLs | Web page parsing |
Documents | PDF, DOCX | Text extraction |