Update news fetching logic to use 'query' instead of 'topics' in get_news function and refine documentation

basic_llama_agent.py

@@ -9,7 +9,7 @@ SYSTEM_PROMPT = """
 You are a news assistant. 
 You can get news with 'get news'. 
 
-When getting news, fetch the latest articles for the current topics, analyze their sentiment and extract entities, then present them. 
+When getting news, fetch the latest articles for the current query, analyze their sentiment and extract entities, then present them. 
 After presenting news, ask if the user wants to know implications (e.g., 'implications for 1'), why it happened (e.g., 'why happened for 1'), or social media reactions (e.g., 'social media reaction for 1'). 
 
 For 'implications', generate possible implications. 

tools.py

@@ -1,21 +1,21 @@
-from tavily import TavilyClient
 import os
 from typing import Dict, List
 
 import requests
 from llama_index.core.llms import ChatMessage
+from tavily import TavilyClient
 
 from llms import llm_openai
 
-
 last_news = []
-    
-def get_news(topics: List[str]) -> List[Dict]:
+
+
+def get_news(query: str) -> List[Dict]:
     """
-    Fetches news articles related to the specified topics using the NewsAPI, analyzes their sentiment and named entities, and returns a list of processed news items.
+    Fetches news articles related to the specified query, analyzes their sentiment and named entities, and returns a list of processed news items.
 
     Args:
-        topics: A list of topics to search for in news articles.
+        query: A string representing the search query for news articles.
 
     Returns:
         List[Dict]: A list of dictionaries, each containing:
@@ -30,25 +30,23 @@ def get_news(topics: List[str]) -> List[Dict]:
         None. All exceptions are caught and returned as error messages in the result.
     """
     global last_news
-    print(f"Fetching news for topics: {topics}")
-    
+    print(f"Fetching news for query: {query}")
     last_news.clear()  # Clear previous news to avoid duplication
-    for topic in topics:
-        search_results = web_search(f'Find the latest news related to: {topic}')
-        if search_results:
-            for res in search_results:
-                last_news.append({
-                    "index": len(last_news) + 1,
-                    "title": res.get("title", "No title available"),
-                    "summary": res.get("content", "No summary available"),
-                    # "sentiment": analyze_sentiment(res.get("snippet", "")),
-                    # "entities": recognize_entities(res.get("snippet", ""))
-                })
+
+    search_results = web_search(f'Find the latest news related to: {query}')
+    if search_results:
+        for res in search_results:
+            last_news.append({
+                "index": len(last_news) + 1,
+                "title": res.get("title", "No title available"),
+                "summary": res.get("content", "No summary available"),
+                # "sentiment": analyze_sentiment(res.get("snippet", "")),
+                # "entities": recognize_entities(res.get("snippet", ""))
+            })
     print(f"Found {len(last_news)} articles.")
     return last_news
 
 
-
 def analyze_sentiment(text: str) -> str:
     """
     Analyzes the sentiment of the given text and returns the sentiment label.
@@ -77,7 +75,6 @@ def analyze_sentiment(text: str) -> str:
         return "NEUTRAL"
 
 
-
 def recognize_entities(text: str) -> List[str]:
     """
     Recognizes named entities in the given text.
@@ -101,7 +98,6 @@ def recognize_entities(text: str) -> List[str]:
     return entities if entities else ["No entities found."]
 
 
-
 def generate_implications(article_index: int) -> str:
     """
     Generates a string describing the possible implications of a news article based on its index.
@@ -164,7 +160,6 @@ def browse_page(url: str, query: str) -> str:
         return f"Error fetching page: {str(e)}"
 
 
-
 def get_lead_up_events(article_index: int) -> str:
     """
     Retrieves a brief timeline or background of events leading up to a news article's topic.
@@ -205,6 +200,7 @@ def get_lead_up_events(article_index: int) -> str:
     print(f"Generated background information: {result.message.content}")
     return f"Background information for article {article_index}: {result.message.content}"
 
+
 def call_llm(prompt: str) -> str:
     """
     Calls the LLM with a given prompt and returns the response.
@@ -223,6 +219,7 @@ def call_llm(prompt: str) -> str:
     except Exception as e:
         return f"Error calling LLM: {str(e)}"
 
+
 def get_social_media_opinions(article_index: int) -> str:
     """
     Analyzes social media opinions related to a news article by its index.
@@ -242,13 +239,17 @@ def get_social_media_opinions(article_index: int) -> str:
         return "Invalid article index."
     article = last_news[article_index - 1]
     title = article["title"]
-    
-    pos_posts = web_search(f'What are the positive social media reactions related to: {title}?')
-    neg_posts = web_search(f'What are the negative social media reactions related to: {title}?')
-    
+
+    pos_posts = web_search(
+        f'What are the positive social media reactions related to: {title}?')
+    neg_posts = web_search(
+        f'What are the negative social media reactions related to: {title}?')
+
     # haz un resumen con el llm de los posts positivos
-    pos_summary = call_llm('Make a summary of the following social media posts: ' + str(pos_posts))
-    neg_summary = call_llm('Make a summary of the following social media posts: ' + str(neg_posts))
+    pos_summary = call_llm(
+        'Make a summary of the following social media posts: ' + str(pos_posts))
+    neg_summary = call_llm(
+        'Make a summary of the following social media posts: ' + str(neg_posts))
 
     print(f"Positive summary: {pos_summary}")
     print(f"Negative summary: {neg_summary}")
@@ -257,4 +258,4 @@ def get_social_media_opinions(article_index: int) -> str:
     Social Media Opinions for Article {article_index}:
     Positive Summary: {pos_summary}
     Negative Summary: {neg_summary}
-    """
+    """