How to Add Search to an Ollama Personal Assistant

import os, requests

SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
OLLAMA_URL = 'http://localhost:11434/v1/chat/completions'

def search_web(query: str, num: int = 5) -> str:
    resp = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
        json={'query': query, 'country_code': 'us', 'num_results': num})
    results = resp.json().get('organic_results', [])
    return '\n'.join(f'[{i+1}] {r["title"]}: {r.get("snippet", "")}\nURL: {r["link"]}'
                     for i, r in enumerate(results))

def needs_search(question: str) -> bool:
    triggers = ['latest', 'current', 'today', '2026', '2025', 'now',
                'price', 'cost', 'version', 'release', 'news',
                'best', 'top', 'compare', 'vs', 'weather',
                'who won', 'what happened', 'how much']
    q = question.lower()
    return any(t in q for t in triggers)

def ask_assistant(question: str) -> dict:
    use_search = needs_search(question)
    context = ''
    if use_search:
        context = f'Web search results:\n{search_web(question)}\n\n'
    messages = [
        {'role': 'system', 'content': (
            'You are a helpful personal assistant. '
            + ('Use the web search results to answer accurately. Cite sources [1],[2]. '
               if use_search else 'Answer from your knowledge. Say if you are unsure.')
        )},
        {'role': 'user', 'content': f'{context}Question: {question}'}
    ]
    resp = requests.post(OLLAMA_URL, json={
        'model': 'llama3', 'messages': messages, 'max_tokens': 512
    })
    return {
        'answer': resp.json()['choices'][0]['message']['content'],
        'searched': use_search,
        'cost': 0.005 if use_search else 0,
    }

result = ask_assistant('What is the latest Python version in 2026?')
print(f'[{"SEARCHED" if result["searched"] else "LOCAL"}] ${result["cost"]}')
print(result['answer'])

def search_platform(query: str, platform: str = 'google') -> str:
    site_map = {'reddit': 'site:reddit.com', 'youtube': 'site:youtube.com',
                'amazon': 'site:amazon.com', 'walmart': 'site:walmart.com'}
    prefix = site_map.get(platform, '')
    full_query = f'{prefix} {query}'.strip()
    return search_web(full_query)

def detect_platform(question: str) -> tuple[str, str]:
    """Detect if a platform is mentioned and return (platform, cleaned_question)."""
    q = question.lower()
    if 'on reddit' in q or 'reddit thinks' in q:
        return 'reddit', question.replace('on reddit', '').replace('reddit thinks', '').strip()
    if 'on youtube' in q or 'youtube tutorial' in q:
        return 'youtube', question.replace('on youtube', '').replace('youtube tutorial', '').strip()
    if 'on amazon' in q or 'amazon price' in q:
        return 'amazon', question.replace('on amazon', '').replace('amazon price', '').strip()
    return 'google', question

def smart_assistant(question: str) -> dict:
    platform, clean_q = detect_platform(question)
    use_search = needs_search(clean_q) or platform != 'google'
    context = ''
    if use_search:
        results = search_platform(clean_q, platform)
        context = f'Search results from {platform.upper()}:\n{results}\n\n'
    messages = [
        {'role': 'system', 'content': f'You are a helpful assistant. '
            f'{"Answer from " + platform + " search results. Cite [1],[2]." if use_search else "Answer from knowledge."}'}
        ,
        {'role': 'user', 'content': f'{context}Question: {question}'}
    ]
    resp = requests.post(OLLAMA_URL, json={'model': 'llama3', 'messages': messages, 'max_tokens': 512})
    return {'answer': resp.json()['choices'][0]['message']['content'],
            'platform': platform, 'searched': use_search, 'cost': 0.005 if use_search else 0}

for q in ['What does reddit think about mechanical keyboards?',
          'What is a Python list comprehension?',
          'Best noise cancelling headphones on amazon']:
    r = smart_assistant(q)
    print(f'[{r["platform"]:8s}] ${r["cost"]} - {q[:50]}')
    print(f'  {r["answer"][:80]}...')
    print()

def chat_loop():
    """Interactive chat with search-augmented Ollama."""
    history = []
    total_cost = 0
    print('Ollama Assistant with Web Search')
    print('Type "quit" to exit, "cost" for session cost')
    print('-' * 40)
    while True:
        question = input('\nYou: ').strip()
        if not question:
            continue
        if question.lower() == 'quit':
            print(f'\nSession cost: ${total_cost:.3f}')
            break
        if question.lower() == 'cost':
            print(f'Session cost so far: ${total_cost:.3f}')
            continue
        result = smart_assistant(question)
        total_cost += result['cost']
        source = f'{result["platform"]} search' if result['searched'] else 'local knowledge'
        print(f'\nAssistant [{source}, ${result["cost"]}]:')
        print(result['answer'])
        history.append({'question': question, **result})

# Run the chat loop (uncomment to use interactively)
# chat_loop()

# Simulate for the tutorial
for q in ['What is the latest Node.js version?', 'Explain Python decorators']:
    r = smart_assistant(q)
    source = f'{r["platform"]} search' if r['searched'] else 'local'
    print(f'You: {q}')
    print(f'Assistant [{source}]: {r["answer"][:100]}...')
    print()

class SearchAssistant:
    def __init__(self, model: str = 'llama3'):
        self.model = model
        self.history = []
        self.total_cost = 0
        self.search_count = 0

    def ask(self, question: str) -> str:
        platform, clean_q = detect_platform(question)
        use_search = needs_search(clean_q) or platform != 'google'
        context = ''
        if use_search:
            context = f'\nWeb results ({platform}):\n{search_platform(clean_q, platform)}\n'
            self.search_count += 1
            self.total_cost += 0.005
        # Build messages with history
        messages = [{'role': 'system', 'content': (
            'You are a helpful personal assistant with web search access. '
            'Cite search results as [1],[2]. Remember previous conversation.'
        )}]
        # Add recent history (last 4 exchanges)
        for h in self.history[-4:]:
            messages.append({'role': 'user', 'content': h['question']})
            messages.append({'role': 'assistant', 'content': h['answer']})
        messages.append({'role': 'user', 'content': f'{context}\n{question}'})
        resp = requests.post(OLLAMA_URL, json={
            'model': self.model, 'messages': messages, 'max_tokens': 512
        })
        answer = resp.json()['choices'][0]['message']['content']
        self.history.append({'question': question, 'answer': answer})
        return answer

    def stats(self):
        print(f'Questions: {len(self.history)}')
        print(f'Searches: {self.search_count}')
        print(f'Total cost: ${self.total_cost:.3f}')

assistant = SearchAssistant()
print(assistant.ask('What is the latest Python version?'))
print(assistant.ask('Tell me more about its new features'))
assistant.stats()