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https://github.com/open-webui/open-webui.git
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67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import logging
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import time
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from typing import Optional
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import requests
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from open_webui.retrieval.web.main import SearchResult, get_filtered_results
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log = logging.getLogger(__name__)
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def search_brave_llm_context(
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api_key: str,
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query: str,
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count: int,
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filter_list: Optional[list[str]] = None,
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context_tokens: int = 8192,
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) -> list[SearchResult]:
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"""Search using Brave's LLM Context API and return pre-extracted, relevance-scored
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page content ready for LLM consumption.
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Uses /res/v1/llm/context instead of /res/v1/web/search. Same API key, same pricing.
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Returns full extracted passages per URL rather than short snippets, eliminating
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the need for post-search scraping.
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Args:
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api_key (str): A Brave Search API key (same key as web search)
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query (str): The query to search for
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count (int): Maximum number of results to return
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filter_list (list[str], optional): Domain filter list
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context_tokens (int): Maximum total tokens to retrieve (1024–32768, default 8192)
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"""
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url = 'https://api.search.brave.com/res/v1/llm/context'
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headers = {
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'Accept': 'application/json',
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'Accept-Encoding': 'gzip',
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'X-Subscription-Token': api_key,
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}
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params = {
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'q': query,
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'count': count,
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'maximum_number_of_tokens': context_tokens,
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}
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response = requests.get(url, headers=headers, params=params)
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# Handle 429 rate limiting - same rate limits as web search
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if response.status_code == 429:
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log.info('Brave LLM Context API rate limited (429), retrying after 1 second...')
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time.sleep(1)
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response = requests.get(url, headers=headers, params=params)
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response.raise_for_status()
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json_response = response.json()
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results = json_response.get('grounding', {}).get('generic', [])
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if filter_list:
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results = get_filtered_results(results, filter_list)
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return [
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SearchResult(
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link=result['url'],
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title=result.get('title'),
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snippet='\n\n'.join(result.get('snippets', [])),
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)
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for result in results[:count]
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]
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