Job-KnowledgeGraph-QA / tools /tavily_search_v2.py
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Update ReAct Agent with Web-search Tool
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import os
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from tavily import TavilyClient
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.
"""
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}
"""
tavily = TavilyClient(
api_key = os.environ["TAVILY_API_KEY"],
)
response = tavily.search(
query = question,
include_raw_content = True,
max_results = 5
)
search_results = ""
for obj in response["results"]:
search_results += f"""
- Page content: {obj["raw_content"]}
Source: {obj["url"]}
"""
print(search_results)
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:
{search_results}
Answer:
"""
# return context
def tavily_qna_search(question: str) -> str:
tavily = TavilyClient(
api_key=os.environ["TAVILY_API_KEY"],
)
response = tavily.qna_search(query=question)
return response
if __name__ == "__main__":
question = "Software Engineer job postings in Vietnam"
result = tavily_search(question)
print(result)