Update search_queries_generator.py

search_queries_generator.py

@@ -1,32 +1,32 @@
-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')]
+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}