Ollama

pip install valiqor

ollama pull llama3.2

import valiqor.auto  # ← Add this line

import requests

# Chat endpoint
response = requests.post("http://localhost:11434/api/chat", json={
    "model": "llama3.2",
    "messages": [{"role": "user", "content": "Explain quantum computing"}],
    "stream": False
})
print(response.json()["message"]["content"])

from valiqor.trace import ollama_autolog

ollama_autolog()

import valiqor.auto
import requests

response = requests.post("http://localhost:11434/api/chat", json={
    "model": "llama3.2",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "What is Python?"}
    ],
    "stream": False
})

result = response.json()
print(result["message"]["content"])

import valiqor.auto
import requests

response = requests.post("http://localhost:11434/api/generate", json={
    "model": "llama3.2",
    "prompt": "Explain machine learning in simple terms",
    "stream": False
})

result = response.json()
print(result["response"])

import valiqor.auto
from valiqor.trace import trace_workflow
import requests

OLLAMA_URL = "http://localhost:11434/api/chat"

with trace_workflow("local-assistant"):
    # Step 1: Research
    research = requests.post(OLLAMA_URL, json={
        "model": "llama3.2",
        "messages": [{"role": "user", "content": "What are the key facts about Mars?"}],
        "stream": False
    })

    # Step 2: Summarize
    summary = requests.post(OLLAMA_URL, json={
        "model": "llama3.2",
        "messages": [
            {"role": "user", "content": f"Summarize: {research.json()['message']['content']}"}
        ],
        "stream": False
    })

from valiqor.trace import disable_autolog

disable_autolog("ollama")    # Disable Ollama only
disable_autolog()            # Disable all providers