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Google Search
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Tavily Search
LangChain: Tavily Search API와 api-tavily-search.ipynb을 참조합니다.
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현재 날짜, 시간등의 정보 조회하기
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시스템 시간 (한국)
api-current-time.ipynb와 같이 구현합니다.
교보문고의 Search API를 이용하여 아래와 같이 도서정보를 가져오는 함수를 정의합니다.
from langchain.agents import tool
import requests
from bs4 import BeautifulSoup
@tool
def get_book_list(keyword: str) -> str:
"""
Search book list by keyword and then return book list
keyword: search keyword
return: book list
"""
keyword = keyword.replace('\'','')
answer = ""
url = f"https://search.kyobobook.co.kr/search?keyword={keyword}&gbCode=TOT&target=total"
response = requests.get(url)
if response.status_code == 200:
soup = BeautifulSoup(response.text, "html.parser")
prod_info = soup.find_all("a", attrs={"class": "prod_info"})
if len(prod_info):
answer = "추천 도서는 아래와 같습니다.\n"
for prod in prod_info[:5]:
title = prod.text.strip().replace("\n", "")
link = prod.get("href")
answer = answer + f"{title}, URL: {link}\n\n"
return answer@tool
def get_current_time(format: str)->str:
"""Returns the current date and time in the specified format"""
format = format.replace('\'','')
timestr = datetime.datetime.now(timezone('Asia/Seoul')).strftime(format)
return timestr@tool
def get_weather_info(city: str) -> str:
"""
Search weather information by city name and then return weather statement.
city: the english name of city to search
return: weather statement
"""
city = city.replace('\n','')
city = city.replace('\'','')
chat = get_chat(LLM_for_chat, selected_LLM)
if isKorean(city):
place = traslation(chat, city, "Korean", "English")
print('city (translated): ', place)
else:
place = city
city = traslation(chat, city, "English", "Korean")
print('city (translated): ', city)
print('place: ', place)
weather_str: str = f"{city}에 대한 날씨 정보가 없습니다."
if weather_api_key:
apiKey = weather_api_key
lang = 'en'
units = 'metric'
api = f"https://api.openweathermap.org/data/2.5/weather?q={place}&APPID={apiKey}&lang={lang}&units={units}"
try:
result = requests.get(api)
result = json.loads(result.text)
print('result: ', result)
if 'weather' in result:
overall = result['weather'][0]['main']
current_temp = result['main']['temp']
min_temp = result['main']['temp_min']
max_temp = result['main']['temp_max']
humidity = result['main']['humidity']
wind_speed = result['wind']['speed']
cloud = result['clouds']['all']
weather_str = f"{city}의 현재 날씨의 특징은 {overall}이며, 현재 온도는 {current_temp}도 이고, 최저온도는 {min_temp}도, 최고 온도는 {max_temp}도 입니다. 현재 습도는 {humidity}% 이고, 바람은 초당 {wind_speed} 미터 입니다. 구름은 {cloud}% 입니다."
except Exception:
err_msg = traceback.format_exc()
print('error message: ', err_msg)
print('weather_str: ', weather_str)
return weather_str아래와 같이 Travily Search를 이용해 검색합니다. 이를 위해서는 API Key를 미리 발급 받아서 아래와 같이 TAVILY_API_KEY로 등록하여야 합니다.
os.environ["TAVILY_API_KEY"] = api_key
@tool
def search_by_tavily(keyword: str) -> str:
"""
Search general information by keyword and then return the result as a string.
keyword: search keyword
return: the information of keyword
"""
answer = ""
if tavily_api_key:
keyword = keyword.replace('\'','')
search = TavilySearchResults(k=5)
output = search.invoke(keyword)
print('tavily output: ', output)
for result in output[:3]:
content = result.get("content")
url = result.get("url")
answer = answer + f"{content}, URL: {url}\n\n"
return answerOpenSearch를 이용해 RAG를 구성하고 필요한 정보를 검색하여 사용합니다.
def search_by_opensearch(keyword: str) -> str:
"""
Search technical information by keyword and then return the result as a string.
keyword: search keyword
return: the technical information of keyword
"""
print('keyword: ', keyword)
keyword = keyword.replace('\'','')
keyword = keyword.replace('|','')
keyword = keyword.replace('\n','')
print('modified keyword: ', keyword)
bedrock_embedding = get_embedding()
vectorstore_opensearch = OpenSearchVectorSearch(
index_name = "idx-*",
is_aoss = False,
ef_search = 1024,
m=48,
embedding_function = bedrock_embedding,
opensearch_url=opensearch_url,
http_auth=(opensearch_account, opensearch_passwd),
)
answer = ""
top_k = 2
relevant_documents = vectorstore_opensearch.similarity_search_with_score(
query = keyword,
k = top_k,
)
for i, document in enumerate(relevant_documents):
print(f'## Document(opensearch-vector) {i+1}: {document}')
excerpt = document[0].page_content
uri = document[0].metadata['uri']
answer = answer + f"{excerpt}, URL: {uri}\n\n"
return answer필요한 패키지는 아래와 같이 설치합니다.
pip install google-api-python-client>=2.100.0
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain.agents import Tool, AgentExecutor, create_react_agent
search = GoogleSearchAPIWrapper(
google_api_key=GOOGLE_API_KEY,
google_cse_id=GOOGLE_CSE_ID,
k=5,
siterestrict=False
)
google_tool = Tool(
name="Google Search",
func=search.run,
description="Use for when you need to perform an internet search to find information that another tool can not provide.",
)
search.run('langchain의 agent는 무엇이야?')