Utilizes environment variables through .env files for configuration settings like API keys and connection parameters.
Provides compatibility with Linux systems, supporting installation of dependencies like Tesseract for OCR functionality.
Supports Node.js for frontend functionality, enabling web-based interface to interact with the multi-model platform.
Uses npm for frontend package management and running the web interface components.
Integrates with OpenAI's API for LLM capabilities, using API keys to access models for text generation and processing.
Offers optional support for Poetry dependency management for Python packages, simplifying installation and environment management.
Built on Python with support for creating virtual environments and managing dependencies for the backend system.
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Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Serverextract text from this scanned document and summarize it"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server (Multi-Model + RAG + LLM Platform)
Kurulum Rehberi
Gereksinimler
Python 3.9+
Node.js 16+ (frontend için)
Tesseract (OCR desteği için)
(Linux/Mac:
sudo apt install tesseract-ocrveyabrew install tesseract)pip veya poetry (isteğe bağlı)
Related MCP server: Agentset
1. Backend Kurulumu
a) Sanal Ortam Oluştur
python -m venv venv
source venv/bin/activateb) Bağımlılıkları Yükle
pip install -r requirements.txt
# veya
poetry installc) Ortam Değişkenleri
.env dosyasını oluştur:
cp .env.example .envGerekirse OPENAI_API_KEY ve diğer alanları doldur.
d) Veritabanını Başlat
python -c "from src.models.database import init_db; init_db()"e) Sunucuyu Çalıştır
uvicorn src.main:app --reloadUygulama arayüzü: http://localhost:8000/docs
2. Frontend (Web) Kurulumu
cd web
npm install
npm startArayüz: http://localhost:3000
3. Notlar
PDF/OCR için Tesseract kurulmalı.
LLM entegrasyonu için
OPENAI_API_KEYveya HuggingFace modeli indirecek internet bağlantısı gereklidir.Vektör veritabanı ve LLM eklemek için ilgili Python dosyalarından kolayca genişletebilirsiniz.
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