如何通过搜索 API 打好本地 LLM 基础

import requests

# Common local LLM endpoints:
# Ollama:     http://localhost:11434/v1/chat/completions
# llama.cpp:  http://localhost:8080/v1/chat/completions
# vLLM:       http://localhost:8000/v1/chat/completions

LLM_URL = 'http://localhost:11434/v1/chat/completions'  # Ollama default
LLM_MODEL = 'llama3'  # or 'mistral', 'codellama', etc.

def ask_llm(messages: list, max_tokens: int = 512) -> str:
    resp = requests.post(LLM_URL, json={
        'model': LLM_MODEL,
        'messages': messages,
        'max_tokens': max_tokens,
        'temperature': 0.3
    }, timeout=120)
    return resp.json()['choices'][0]['message']['content']

# Test connection
try:
    answer = ask_llm([{'role': 'user', 'content': 'Say hello in one word.'}], max_tokens=10)
    print(f'LLM connected: {answer}')
except Exception as e:
    print(f'LLM connection error: {e}')
    print('Make sure Ollama/llama.cpp is running.')

import os

SCAVIO_KEY = os.environ['SCAVIO_API_KEY']

def search_context(query: str, count: int = 5) -> str:
    """Search the web and return formatted context for the LLM."""
    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': count})
    results = resp.json().get('organic_results', [])
    if not results:
        return 'No search results found.'
    context = 'Search results (use these to answer accurately):\n\n'
    for i, r in enumerate(results, 1):
        context += f'[{i}] {r["title"]}\n'
        context += f'    {r.get("snippet", "")}\n'
        context += f'    Source: {r["link"]}\n\n'
    return context

# Test
ctx = search_context('Python 3.14 release date')
print(ctx[:300])

def grounded_answer(question: str) -> dict:
    """Answer a question using search-grounded local LLM."""
    # Step 1: Search for context
    context = search_context(question, count=5)
    # Step 2: Ask LLM with context
    messages = [
        {'role': 'system', 'content': (
            'You are a helpful assistant. Answer ONLY based on the search results provided. '
            'Cite sources as [1], [2], etc. If the search results do not contain the answer, '
            'say "I could not find this information in the search results."'
        )},
        {'role': 'user', 'content': f'{context}\nQuestion: {question}'}
    ]
    answer = ask_llm(messages, max_tokens=512)
    return {
        'question': question,
        'answer': answer,
        'grounded': True,
        'search_cost': 0.005
    }

# Test with a question that requires current data
result = grounded_answer('What is the latest version of Python?')
print(f'Q: {result["question"]}')
print(f'A: {result["answer"]}')
print(f'Grounded: {result["grounded"]}, Cost: ${result["search_cost"]}')

def needs_grounding(question: str) -> bool:
    """Heuristic: does this question need real-time data?"""
    grounding_triggers = [
        'latest', 'current', 'today', '2026', '2025', 'now',
        'price', 'cost', 'version', 'release', 'new', 'update',
        'best', 'top', 'compare', 'vs', 'alternative',
        'how much', 'where to', 'who is',
    ]
    q_lower = question.lower()
    return any(trigger in q_lower for trigger in grounding_triggers)

def smart_answer(question: str) -> dict:
    """Answer with search grounding only when needed."""
    if needs_grounding(question):
        return grounded_answer(question)
    # Direct LLM answer (no search cost)
    messages = [{'role': 'user', 'content': question}]
    answer = ask_llm(messages, max_tokens=512)
    return {
        'question': question,
        'answer': answer,
        'grounded': False,
        'search_cost': 0
    }

# Test both paths
for q in ['What is a Python list comprehension?',
          'What is the latest Python version in 2026?']:
    result = smart_answer(q)
    print(f'[{"GROUNDED" if result["grounded"] else "DIRECT"}] '
          f'${result["search_cost"]} - {q}')
    print(f'  {result["answer"][:100]}...')
    print()