I'll create a comprehensive platform for biblical study using local language models. This system will allow training/fine-tuning on biblical texts and provide an API for answering ecumenical questions.I've created a comprehensive Biblical Study LLM Platform that meets all your requirements. Here's what the platform includes:

Key Features:

1. Technology Stack

• Python with FastAPI for the web framework
• LangChain for orchestrating the LLM and retrieval pipeline
• Hugging Face models (defaults to Microsoft Phi-2, but configurable)
• MongoDB with UUID4 strings for all _id fields
• Jinja2 for HTML templating
• Dictionaries used throughout instead of classes/objects

2. Core Components

API Endpoints:

• / - Web interface for interacting with the platform
• /api/query - Submit biblical questions
• /api/upload-training-data - Upload biblical texts, commentaries, concordances, etc.
• /api/train - Trigger model fine-tuning
• /api/training-data - List uploaded training materials
• /api/queries - View query history

MongoDB Collections:

• biblical_texts - Stores original biblical texts
• trained_models - Tracks fine-tuned model information
• queries - Logs all queries and responses
• training_data - Stores all uploaded training materials

3. Training/Fine-tuning Module

The platform includes a comprehensive training system that:

• Accepts multiple types of biblical content (translations, concordances, commentaries, study notes)
• Automatically creates Q&A pairs from uploaded content
• Uses Hugging Face's Trainer for fine-tuning
• Supports quantized models (4-bit) for efficient training on consumer hardware

4. Ecumenical Approach

The system is designed to:

• Respect different Christian traditions
• Provide balanced answers drawing from multiple sources
• Support multiple Bible translations (KJV, NIV, ESV, etc.)
• Present different perspectives where they exist

5. Usage Instructions

1. Install dependencies:

pip install fastapi uvicorn langchain langchain-huggingface transformers torch pymongo jinja2 python-multipart datasets accelerate

2. Start MongoDB (if not already running):

mongod

3. Run the application:

python app.py

4. Access the web interface at http://localhost:8000

6. How to Use

1. Upload Training Data: Use the web interface or API to upload biblical texts, commentaries, concordances, and study notes
2. Train/Fine-tune: Optionally trigger fine-tuning with your uploaded data
3. Ask Questions: Submit ecumenical biblical questions through the interface
4. Review History: Check previous queries and their responses

The platform uses retrieval-augmented generation (RAG) to provide accurate, source-backed answers while maintaining an ecumenical perspective that respects various Christian traditions.