RAG-Based AI Document Processing & Integration System

AI Model Integration – Local and Cloud Support

The system should support both cloud-based AI models and locally hosted models. The goal is to give us the flexibility to use a powerful cloud model when needed or run the entire system privately on a local machine or server.

Local and Self-Hosted Models

The system should support local AI model platforms such as:

Ollama
LM Studio
vLLM
Hugging Face models
Other local LLM servers or OpenAI-compatible APIs

The developer should ensure compatibility with popular open-source models, including:

Llama
Mistral
Qwen
Gemma
DeepSeek
Other suitable open-source models
Cloud AI Models

The system should also support cloud-based AI providers, including:

OpenAI
Anthropic Claude
Google Gemini

The AI architecture should be flexible enough to switch between local and cloud models without requiring major changes to the application.

Configuration

The system should allow easy configuration of:

AI provider
Model name
Local API endpoint
API keys for cloud providers
Embedding model
Context window
Temperature
Maximum response length
Privacy and Offline Processing

A key requirement is the ability to run the system privately using a local model. When using a local setup, documents and user data should remain on the local machine or private server and should not be sent to external AI providers.

The RAG system should be capable of working fully offline or in a private environment, including document processing, embeddings, vector search, retrieval, and AI-generated responses.

System Architecture

The overall workflow should follow a structure similar to:

User → Document Upload → Document Processing → Embeddings → Vector Database → RAG Retrieval → AI Model → Response

The AI model and embedding components should be modular, allowing us to choose between local or cloud-based options depending on the deployment and privacy requirements.

Deployment

The developer should provide support for:

Local machine deployment
Private server deployment
GPU acceleration, where available
CPU-only operation as a fallback
Docker-based deployment

The preferred setup should allow the entire application to run on a local machine or private server, including the AI model, document processing pipeline, vector database, and RAG system, without requiring a cloud-based AI service.