mirror of https://github.com/zebrajr/localGPT.git synced 2025-12-06 12:20:53 +01:00

History

PromptEngineer 35697b23a4 fix: implement automatic database path detection for multi-environment compatibility - Add environment auto-detection in ChatDatabase class - Support both local development and Docker container paths - Local development: uses 'backend/chat_data.db' (relative path) - Docker containers: uses '/app/backend/chat_data.db' (absolute path) - Maintain backward compatibility with explicit path overrides - Update RAG API server to use auto-detection This resolves the SQLite database connection error that occurred when running LocalGPT in local development environments while maintaining compatibility with Docker deployments. Fixes: Database path hardcoded to Docker container path Tested: Local development and Docker environment detection Breaking: No breaking changes - maintains backward compatibility		2025-07-17 22:13:25 -07:00
..
chat_data.db	Integrate multimodal RAG codebase	2025-07-11 00:17:15 -07:00
database.py	fix: implement automatic database path detection for multi-environment compatibility	2025-07-17 22:13:25 -07:00
ollama_client.py	fix: resolve Docker networking issue for Ollama connectivity	2025-07-15 21:34:17 +00:00
README.md	Integrate multimodal RAG codebase	2025-07-11 00:17:15 -07:00
requirements.txt	Integrate multimodal RAG codebase	2025-07-11 00:17:15 -07:00
server.py	fix(db): Correct database path and chat history logic	2025-07-12 01:51:57 -07:00
simple_pdf_processor.py	refactor: Remove unused imports across codebase	2025-07-12 02:34:17 -07:00
test_backend.py	refactor: Remove unused imports across codebase	2025-07-12 02:34:17 -07:00
test_ollama_connectivity.py	fix: resolve Docker networking issue for Ollama connectivity	2025-07-15 21:34:17 +00:00

README.md

localGPT Backend

Simple Python backend that connects your frontend to Ollama for local LLM chat.

Prerequisites

Install Ollama (if not already installed):

# Visit https://ollama.ai or run:
curl -fsSL https://ollama.ai/install.sh | sh

Start Ollama:
```
ollama serve
```
Pull a model (optional, server will suggest if needed):
```
ollama pull llama3.2
```

Setup

Install Python dependencies:
```
pip install -r requirements.txt
```
Test Ollama connection:
```
python ollama_client.py
```
Start the backend server:
```
python server.py
```

Server will run on http://localhost:8000

API Endpoints

Health Check

GET /health

Returns server status and available models.

Chat

POST /chat
Content-Type: application/json

{
  "message": "Hello!",
  "model": "llama3.2:latest",
  "conversation_history": []
}

Returns:

{
  "response": "Hello! How can I help you?",
  "model": "llama3.2:latest",
  "message_count": 1
}

Testing

Test the chat endpoint:

curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello!", "model": "llama3.2:latest"}'

Frontend Integration

Your React frontend should connect to:

Backend: http://localhost:8000
Chat endpoint: http://localhost:8000/chat

What's Next

This simple backend is ready for:

✅ Real-time chat with local LLMs
🔜 Document upload for RAG
🔜 Vector database integration
🔜 Streaming responses
🔜 Chat history persistence