SecureGPT is a secure, local Large Language Model (LLM) solution designed to process and analyze data with high security and privacy standards. The system facilitates the ingestion of documents, extracts relevant context, and applies semantic search to provide highly accurate and secure responses to user queries. This project integrates a robust security architecture to ensure data confidentiality and access control across its components.
- Document Processing: Ingests various formats such as PDF, TXT, and other files, then extracts context and relevant data.
- Chunking & Embedding: Splits the extracted data into manageable chunks and generates embeddings for each chunk to build a semantic index.
- Semantic Search: Leverages the semantic index for precise and contextually relevant search results.
- User Authentication: Secure access control for authorized users.
- LLM Integration: Combines semantic search results with the capabilities of a large language model to generate accurate responses.
- Load Balancing: Distributes workloads across servers for optimal performance.
- End-to-End Security: Includes encryption and secure communication channels to safeguard data throughout the system.
- Input: Documents (PDF, TXT, etc.) are ingested by the system.
- Processing: The system extracts relevant data and context, which is then split into smaller text chunks.
- Embedding Generation: Each text chunk is converted into embeddings, forming the basis for a semantic index.
- Indexing: Embeddings are used to build a semantic index in a knowledge base.
- Query Processing: Authorized users submit queries that are converted into embeddings and matched against the semantic index for relevant information.
- LLM Integration: The LLM refines the search results to provide the final ranked output.
- User Authentication: Only authorized users can interact with the system.
- Secure Communication: Data is encrypted during transmission and stored securely.
- Load Balancing: A front-end engine and load balancer ensure optimal distribution of tasks across the system.
- Containerized Architecture: The LLM is encapsulated within a container engine to ensure isolated and secure processing.
- Clone the repository:
git clone https://github.com/ddcrpf/SecureGPT-Your-Localized-AI-Solution.git cd SecureGPT-Your-Localized-AI-Solution - Install dependencies:
pip install -r requirements.txt
- Run the application:
docker-compose up
- Upload your documents through the web interface.
- Process the documents to generate the semantic index.
- Authorized users can query the system and retrieve contextually accurate responses.
- The system can be queried through a secure client-side application.
We welcome contributions! Please fork the repository and create a pull request with your updates.
This project is licensed under the MIT License - see the LICENSE file for details.