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| import os | |
| from dotenv import load_dotenv | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain_community.tools.tavily_search import TavilySearchResults | |
| from langchain.tools import BaseTool, StructuredTool, tool | |
| load_dotenv() | |
| os.environ["TAVILY_API_KEY"] = os.getenv("TAVILY_API_KEY") | |
| os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY") | |
| def tavily_search(question: str) -> str: | |
| """ | |
| useful for when you need to search relevant informations such as: jobs, companies from Web sites. | |
| """ | |
| # setup prompt | |
| # prompt = [{ | |
| # "role": "system", | |
| # "content": f'You are an AI critical thinker research assistant. ' \ | |
| # f'Your sole purpose is to write well written, critically acclaimed,' \ | |
| # f'objective and structured reports on given text.' | |
| # }, { | |
| # "role": "user", | |
| # "content": f'Information: """{content}"""\n\n' \ | |
| # f'Using the above information, answer the following' \ | |
| # f'query: "{query}" in a detailed report --' \ | |
| # f'Please use MLA format and markdown syntax.' | |
| # }] | |
| tool_search = TavilySearchResults( | |
| max_results = 3, | |
| include_raw_content = True | |
| ) | |
| # prompt_search = f"""You are an expert at finding information about the job, | |
| # the company, and the skills required for that job. | |
| # Try to find out what is relevant to the company, the job, and the skills required for that job. | |
| # If the questions are not relevant, answer them in your own words. | |
| # | |
| # Query: {question} | |
| # """ | |
| # Search | |
| # for information on Web sites: Indeed, LinkedIn, TopCV | |
| # by | |
| # using | |
| # entity in user | |
| # question(Job | |
| # Titles, Company, Location, etc). | |
| # Using | |
| # search | |
| # pattern: site:indeed | |
| search_prompt = f""" | |
| Response to user question by search job descriptions include: job titles, company, required skill, education, etc related to job recruitment posts in Vietnam. | |
| Query: {question} | |
| """ | |
| result = tool_search.invoke({"query": search_prompt}) | |
| # llm_chat = ChatGoogleGenerativeAI( | |
| # model = "gemini-1.5-flash-latest", | |
| # temperature = 0 | |
| # ) | |
| # content = [] | |
| # for i in result: | |
| # content.append(i['content']) | |
| # prompt = f""" | |
| # | |
| # You are a career consultant, based on the information you have contents: {content}, | |
| # consider yourself an expert to summarize summary details not too short the content and | |
| # highlight the content related to the company's job and the necessary skills and return must 1 URL | |
| # | |
| # You can add information you know about the question {question} | |
| # """ | |
| # response_prompt = f""" | |
| # Generate a concise and informative summary of the results in a polite and easy-to-understand manner based on question and Tavily search results. | |
| # Returns URLs at the end of the summary for proof. | |
| # | |
| # Question: {question} | |
| # Search Results: {str(result)} | |
| # | |
| # Answer: | |
| # """ | |
| # response = llm_chat.invoke(response_prompt) | |
| return result | |
| if __name__ == "__main__": | |
| question = "Recruitment information for the position of Software Engineer?" | |
| result = tavily_search(question) | |
| print(result) |