How to Build a Production-Ready Local AI Agent with Zero API Costs: FastAPI, ChromaDB e LlamaIndex

Description and use

The recipe builds a local RAG agent with zero token cost, saving over 86 % on infrastructure and ensuring data sovereignty.

Problem

Software teams face high recurring costs and vendor lock‑in when using cloud AI APIs for RAG agents.

Outcome

The recipe yields a production‑ready local RAG agent with zero token cost, 86% infrastructure savings, and full data sovereignty.

Workflow

• 1. Install Ollama and pull Llama 3 and nomic‑embed‑text models
• 2. Install Python dependencies: fastapi, uvicorn, chromadb, llama‑index, llama‑index‑llms‑ollama, llama‑index‑embeddings‑ollama
• 3. Configure LocalRAGService class: set Ollama LLM and embedding, initialize ChromaDB persistent client, create collection, build VectorStoreIndex
• 4. Load local documents into Chroma if collection empty
• 5. Expose FastAPI endpoint that forwards queries to LlamaIndex query engine
• 6. Run uvicorn to serve the API

Evidence

Un risparmio di oltre l’86% sui costi infrastrutturali al primo anno

Source

Inference Engine (Ollama): Esecuzione locale quantizzata (4-bit / 8-bit) di Llama 3 8B

Source