Setting up a REST API service for AI using Local LLMs with Ollama seems like a practical approach. Here’s a simple workflow.
Steps
Follow the following steps to install LLMs for Ollama and setup an API Service locally.
Install Ollama and LLMs
Begin by installing Ollama and the Local LLMs on your local machine. Ollama facilitates the local deployment of LLMs, making it easier to manage and utilize them for various tasks.
Follow this guide to install Ollama on windows locally.
Install LLMs for Ollama
ollama pull llama3
ollama run llama3Ollama Commands
Available Commands:
/set Set session variables
/show Show model information
/bye Exit
/?, /help Help for a command
Use """ to begin a multi-line messageTest Ollama
curl http://localhost:11434/api/generate -d '{
"model": "llama3",
"prompt": "Why is the sky blue?",
"stream": true
}'If stream is set to false, the response will be a single JSON object
curl http://localhost:11434/api/generate -d '{
"model": "llama3",
"prompt": "Why is the sky blue?",
"stream": false
}'Set Up FastAPI:
Set up a Python FastAPI application. FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. It’s a great choice for building robust and efficient APIs.
Develop the FastAPI routes and endpoints to interact with the Ollama server. This involves sending requests to Ollama for processing tasks such as text generation, language understanding, or any other AI-related tasks supported by your LLMs. Following codes is a simple example. (You also can use Ollama Python lib to improve following coding.)
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
import json
import requests
app = FastAPI(debug=True)
class Itemexample(BaseModel):
name: str
prompt: str
instruction: str
is_offer: Union[bool, None] = None
class Item(BaseModel):
model: str
prompt: str
urls =["http://localhost:11434/api/generate"]
headers = {
"Content-Type": "application/json"
}
@app.get("/")
def read_root():
return {"Hello": "World"}
@app.post("/chat/{llms_name}")
def update_item(llms_name: str, item: Item):
if llms_name == "llama3":
url = urls[0]
payload = {
"model": "llama3",
"prompt": "Why is the sky blue?",
"stream": False
}
response = requests.post(url, headers=headers, data=json.dumps(payload))
if response.status_code == 200:
return {"data": response.text, "llms_name": llms_name}
else:
print("error:", response.status_code, response.text)
return {"item_name": item.model, "error": response.status_code, "data": response.text}
return {"item_name": item.model, "llms_name": llms_name}Test REST-API service
curl --location 'http://127.0.0.1:8000/chat/llama3' \
--header 'Content-Type: application/json' \
--data '{
"model": "llama3",
"prompt": "Why is the sky blue?"
}'Deploy:
Once you’re satisfied with the functionality and performance of REST API, this service can be deployed to a production environment if needed. This might involve deploying it to a cloud platform, containerizing it with Docker, or deploying it on a server.
In this simple example, by leveraging Ollama for local LLM deployment and integrating it with FastAPI for building the REST API server, you’re creating a free solution for AI services. This model can be fine-tuning by your own training data for customized purpose (we will discuss in future).
Happy reading, happy coding.