AI & LLM Integration on NexoraHost – ChatGPT, Langchain, Open-Source Models

No, GPT-4 runs only via OpenAI API (cloud). Alternative: run locally with Ollama + open-source models (LLaMA 2/3, Mistral, Llama 2-70B). With 16GB RAM: LLaMA 7B loads fast. With 32GB+: Mistral 7B or Llama 2-13B recommended.

Ollama: easier, local models, ready to use. Hugging Face: more models, larger selection, but complex setup. Recommendation: Ollama for quick start, Hugging Face for customization. Both run on Docker on NexoraHost.

LLaMA 3 8B: best for dialogue, code generation, small memory. Mistral 7B: faster, less RAM. LLaMA 2 70B: high quality, needs 32GB+. When in doubt: start with Mistral 7B (8GB RAM sufficient), upgrade later.

Langchain-Python: `pip install langchain ollama`. Then chatbot with local Ollama model: `from langchain.llms import Ollama; llm = Ollama(model='mistral')`. Integration into Flask/Django easy. RAG (Retrieval Augmented Generation) with vector DB (Weaviate, Pinecone) for custom knowledge.

OpenAI API: $0.03-0.15 per 1K tokens (expensive with high usage). Local inference: 1x investment in VPS/GPU ($200-500/month) + power. From 10M tokens/month: local inference cheaper. For prototype: OpenAI API. For production: local inference saves.

No, CPU works too (slower). With GPU (RTX 4090): 10x faster. Recommendation: book GPU server at NexoraHost or use standard VPS with CPU optimization. For non-real-time (batch processing): CPU sufficient.

RAG = Retrieval Augmented Generation. Setup: 1. load data into vector DB (Weaviate, Chroma), 2. configure Langchain + LLM, 3. user question → find similar documents → LLM answers with context. Example: company handbook + chatbot on server.

Prompting: good question formulation (system prompt, few-shot examples). Fine-tuning: train model with custom data (needs GPU). For most use-cases: prompting enough. Fine-tuning only if standard prompt doesn't help.

Yes! Local AI on own server = GDPR-compliant (no external APIs). Data doesn't leave server. Important: encrypt backups, access control, audit logs. Better than OpenAI cloud for sensitive data.

LLaMA 3, Mistral: well-trained on German. German open-source: Gertrude, dbmdz/German-Bert. Quality: German slightly worse than English (most data is English). Trick: improve German prompts for better output.