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| from langchain_core.prompts import PromptTemplate | |
| queries_prompt_template = PromptTemplate.from_template(""" | |
| You are an expert in generating effective search queries to discover the latest trends, news, laws, regulations, and recent developments from {year} onward related to a given Topic in United Kingdom. | |
| Based on the Topic provided, create a comprehensive and highly relevant search queries that can be used in search engines to find up-to-date information. | |
| # The queries should cover: | |
| - Latest trends and emerging topics | |
| - Recent news and noteworthy events | |
| - New or updated laws and regulations | |
| - Market insights and innovations | |
| - Expert opinions and industry reports | |
| # Generate at least 5 specific, diverse, and concise search queries that capture different aspects of the Topic. | |
| # Use variations in phrasing to ensure comprehensive search results. | |
| # Make sure the queries are unique So that we get diverse search results. | |
| # Don't include any irrelevant information in the queries like Markdown, Newlines etc. Only give the search queries. | |
| Topic = {topic} | |
| """) | |
| # Generate Queries | |
| from datetime import date | |
| current_year = date.today().strftime("%Y") | |
| def generate_queries(llm, state): | |
| topic = state["topic"][0].content | |
| prompt = queries_prompt_template.invoke({"topic": topic, "year": current_year}) | |
| response = llm.invoke(prompt) | |
| content = response.content | |
| response_list = [item.strip('"') for item in content.split('\n')] | |
| return {"search_queries": response_list} |