https://github.com/ernestaroozoo/deepknowledge.net
DeepKnowledge.net is an advanced Q&A chatbot leveraging Retrieval-Augmented Generation (RAG) to deliver precise, source-grounded responses. It integrates DeepSeek-V3 for chat interactions and OpenAI's text-embedding-ada-002 for embeddings, utilizing Streamlit for a seamless web interface.
https://github.com/ernestaroozoo/deepknowledge.net
chatbot deepseek deepseek-v3 llamaindex openai python rag retrieval-augmented-generation streamlit text-embedding-ada-002
Last synced: 3 months ago
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DeepKnowledge.net is an advanced Q&A chatbot leveraging Retrieval-Augmented Generation (RAG) to deliver precise, source-grounded responses. It integrates DeepSeek-V3 for chat interactions and OpenAI's text-embedding-ada-002 for embeddings, utilizing Streamlit for a seamless web interface.
- Host: GitHub
- URL: https://github.com/ernestaroozoo/deepknowledge.net
- Owner: ErnestAroozoo
- License: mit
- Created: 2025-01-21T15:48:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-27T15:52:43.000Z (over 1 year ago)
- Last Synced: 2025-03-22T11:47:57.200Z (over 1 year ago)
- Topics: chatbot, deepseek, deepseek-v3, llamaindex, openai, python, rag, retrieval-augmented-generation, streamlit, text-embedding-ada-002
- Language: Python
- Homepage: https://DeepKnowledge.net
- Size: 1.04 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepKnowledge.net
[](https://python.org)
[](https://opensource.org/licenses/MIT)
An intelligent Q&A system powered by Retrieval-Augmented Generation (RAG).
## Project Overview
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.
## Key Features
- **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.
## Technical Specifications
- **Language Models**: Uses OpenRouter as the single API provider while keeping DeepSeek 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.
## Installation Instructions
1. Clone the repository:
```bash
git clone https://github.com/ErnestAroozoo/DeepKnowledge.net.git
cd DeepKnowledge.net
```
2. Install necessary dependencies:
```bash
pip install -r requirements.txt
```
3. Set up environment variables:
```bash
cp .env.example .env
# then edit .env and paste your OpenRouter API key
```
## Configuration
Update the `.env` file with your OpenRouter credentials:
```ini
# OpenRouter Configuration
OPENROUTER_API_KEY=your-openrouter-key
OPENROUTER_API_HOST=https://openrouter.ai/api/v1
OPENROUTER_CHAT_MODEL=deepseek/deepseek-chat
OPENROUTER_EMBED_MODEL=text-embedding-ada-002
# OpenRouter Headers
OPENROUTER_SITE_URL=https://DeepKnowledge.net
OPENROUTER_APP_NAME=DeepKnowledge.net
```
> **Note**: You only need an OpenRouter API key now.
## Usage Guide
1. Launch the application:
```bash
streamlit run app.py
```
2. Add data sources:
- **Websites**: Input valid URLs for content parsing.
- **Documents**: Upload PDF/DOCX files for text extraction.
3. Engage with the chatbot by:
- Asking natural language queries.
- Following up with questions using chat history.
- Requesting source verification for responses.
## Supported Data Sources
| Type | Formats | Processing Method |
|-------------|-----------------------|-------------------------|
| Web Content | URLs | Web page parsing |
| Documents | PDF, DOCX | Text extraction |