Ollama est excellent pour exécuter des LLM localement et en privé, mais les modèles locaux ont une limite de connaissances. Votre assistant personnel ne peut pas répondre aux questions sur l'actualité, les prix en direct ou les sorties récentes. Ajouter un outil de recherche donne à Ollama accès à des données en temps réel tout en gardant le LLM lui-même local. Ce tutoriel connecte Ollama à l'API Scavio ($0,005/recherche) pour que votre assistant puisse rechercher Google, Reddit, YouTube et plus encore lorsqu'il a besoin d'informations actuelles.
Prérequis
- Ollama installé avec au moins un modèle (llama3 recommandé)
- Python 3.9+ installé
- bibliothèque requests installée
- Une clé API Scavio depuis scavio.dev
Parcours
Étape 1: Construire l'assistant avec recherche
Créez un assistant Python qui encapsule Ollama avec un outil de recherche. L'assistant décide quand rechercher en fonction de la question.
Python
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'])Étape 2: Ajouter des commandes de recherche spécifiques à la plateforme
Laissez l'assistant rechercher sur des plateformes spécifiques. Préfixez les questions avec des noms de plateformes pour cibler Reddit pour les avis, YouTube pour les tutoriels ou Amazon pour les produits.
Python
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()Étape 3: Construire une boucle de chat interactive
Créez une interface de chat en terminal qui conserve l'historique des conversations et recherche lorsque nécessaire.
Python
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()Étape 4: Ajouter la mémoire de conversation et le contexte
Suivez l'historique des conversations pour que l'assistant se souvienne des réponses précédentes et puisse référencer les recherches antérieures.
Python
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: ${self.total_cost:.3f}')
assistant = SearchAssistant()
print(assistant.ask('What is the latest Python version?'))
print(assistant.ask('Tell me more about its new features'))
assistant.stats()Exemple Python
Python
import os, requests
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
def search(query, num=5):
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'num_results': num})
return '\n'.join(f'[{i+1}] {r["title"]}: {r.get("snippet","")}'
for i, r in enumerate(resp.json().get('organic_results', [])))
def ask(question):
triggers = ['latest', 'current', '2026', 'price', 'best', 'news', 'vs']
use_search = any(t in question.lower() for t in triggers)
ctx = f'Search results:\n{search(question)}\n\n' if use_search else ''
resp = requests.post('http://localhost:11434/v1/chat/completions', json={
'model': 'llama3', 'messages': [
{'role': 'system', 'content': 'Helpful assistant. Cite search results [1],[2] when available.'},
{'role': 'user', 'content': f'{ctx}Q: {question}'}], 'max_tokens': 512})
return resp.json()['choices'][0]['message']['content']
print(ask('What is the latest Python version in 2026?'))Exemple JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function search(query, num = 5) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query, country_code: 'us', num_results: num })
});
return ((await resp.json()).organic_results || []).map((r, i) => `[${i+1}] ${r.title}: ${r.snippet || ''}`).join('\n');
}
async function ask(question) {
const needsSearch = /latest|current|2026|price|best|news|vs/i.test(question);
const ctx = needsSearch ? `Search results:\n${await search(question)}\n\n` : '';
const resp = await fetch('http://localhost:11434/v1/chat/completions', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ model: 'llama3', messages: [
{ role: 'system', content: 'Helpful assistant. Cite [1],[2] from search results.' },
{ role: 'user', content: `${ctx}Q: ${question}` }], max_tokens: 512 })
});
return (await resp.json()).choices[0].message.content;
}
ask('latest Python version 2026').then(console.log);Sortie attendue
JSON
[SEARCHED] $0.005
Based on the search results, the latest Python version as of 2026 is
Python 3.15, released in October 2025 [1]. The 3.15 release includes
performance improvements and new syntax features [2].
[reddit ] $0.005 - What does reddit think about mechanical keyboards?
According to Reddit discussions, r/MechanicalKeyboards recomm...
[google ] $0.000 - What is a Python list comprehension?
A list comprehension is a concise way to create lists in Pyth...
Questions: 2
Searches: 1
Total cost: $0.005