Update src/langgraphagenticai/nodes/ai_news_node.py

src/langgraphagenticai/nodes/ai_news_node.py

@@ -1,131 +1,133 @@
-from tavily import TavilyClient
-from langchain_core.prompts import ChatPromptTemplate
-
-
-class AINewsNode:
-    def __init__(self, llm):
-        """
-        Initializes the AI News Node.
-
-        This class handles fetching and summarizing recent AI-related news
-        using Tavily (a web search API) and an LLM (language model such as Groq).
-
-        Args:
-            llm: The language model used for generating summaries.
-        """
-        # Create an instance of Tavily client to perform news searches
-        self.tavily = TavilyClient()
-        # Store the LLM (used later for summarization)
-        self.llm = llm
-        # A dictionary to store intermediate data such as frequency, fetched news, and summaries
-        self.state = {}
-
-    # ----------------------------------------------------------------------
-    def fetch_news(self, state: dict) -> dict:
-        """
-        Fetch AI-related news articles using the Tavily API.
-
-        Args:
-            state (dict): The current graph state, expected to include
-                          'messages' containing the news frequency (e.g., daily, weekly, monthly).
-
-        Returns:
-            dict: Updated state with 'news_data' containing the fetched news articles.
-        """
-
-        # Extract frequency from user input (e.g., "daily", "weekly", etc.)
-        frequency = state['messages'][0].content.lower()
-        self.state['frequency'] = frequency
-
-        # Mapping for Tavily's time range codes and days
-        time_range_map = {'daily': 'd', 'weekly': 'w', 'monthly': 'm', 'year': 'y'}
-        days_map = {'daily': 1, 'weekly': 7, 'monthly': 30, 'year': 366}
-
-        # Perform a Tavily API search for the latest AI news
-        response = self.tavily.search(
-            query="Top Artificial Intelligence (AI) technology news globally",
-            topic="news",
-            time_range=time_range_map[frequency],  # How far back to look
-            include_answer="advanced",             # Request detailed information
-            max_results=20,                        # Limit number of news items
-            days=days_map[frequency],              # Number of days to consider
-            # include_domains=["techcrunch.com", "venturebeat.com/ai", ...]  # (Optional) restrict sources
-        )
-
-        # Store the fetched results in the state dictionary
-        state['news_data'] = response.get('results', [])
-        self.state['news_data'] = state['news_data']
-
-        return state
-
-    # ----------------------------------------------------------------------
-    def summarize_news(self, state: dict) -> dict:
-        """
-        Summarize the fetched AI news articles using the provided LLM.
-
-        Args:
-            state (dict): The current graph state containing 'news_data'.
-
-        Returns:
-            dict: Updated state with 'summary' containing the summarized news in markdown format.
-        """
-
-        # Get the list of fetched news articles
-        news_items = self.state['news_data']
-
-        # Define how the summary should be formatted (markdown structure)
-        prompt_template = ChatPromptTemplate.from_messages([
-            ("system", """Summarize AI news articles into markdown format. For each item include:
-            - Date in **YYYY-MM-DD** format (IST timezone)
-            - A concise summary of the news
-            - Sorted by latest date first
-            - Include the source URL as a hyperlink
-            Use this format:
-            ### [Date]
-            - [Summary](URL)"""),
-            ("user", "Articles:\n{articles}")
-        ])
-
-        # Convert each article into a formatted string with content, URL, and publication date
-        articles_str = "\n\n".join([
-            f"Content: {item.get('content', '')}\nURL: {item.get('url', '')}\nDate: {item.get('published_date', '')}"
-            for item in news_items
-        ])
-
-        # Ask the LLM to generate a markdown summary from the provided articles
-        response = self.llm.invoke(prompt_template.format(articles=articles_str))
-
-        # Save the summary to the state
-        state['summary'] = response.content
-        self.state['summary'] = state['summary']
-
-        return self.state
-
-    # ----------------------------------------------------------------------
-    def save_result(self, state):
-        """
-        Save the summarized AI news to a markdown (.md) file.
-
-        Args:
-            state (dict): The current graph state (contains 'frequency' and 'summary').
-
-        Returns:
-            dict: Updated state with 'filename' indicating where the summary file was saved.
-        """
-
-        # Retrieve frequency and summary content from the state
-        frequency = self.state['frequency']
-        summary = self.state['summary']
-
-        # Define output file path (e.g., ./AINews/daily_summary.md)
-        filename = f"./AINews/{frequency}_summary.md"
-
-        # Write the summary content to a markdown file
-        with open(filename, 'w') as f:
-            f.write(f"# {frequency.capitalize()} AI News Summary\n\n")
-            f.write(summary)
-
-        # Save filename in the state for reference
-        self.state['filename'] = filename
-
-        return self.state
+from tavily import TavilyClient
+from langchain_core.prompts import ChatPromptTemplate
+import os
+
+
+class AINewsNode:
+    def __init__(self, llm):
+        """
+        Initializes the AI News Node.
+
+        This class handles fetching and summarizing recent AI-related news
+        using Tavily (a web search API) and an LLM (language model such as Groq).
+
+        Args:
+            llm: The language model used for generating summaries.
+        """
+        # Create an instance of Tavily client to perform news searches
+        self.tavily = TavilyClient()
+        # Store the LLM (used later for summarization)
+        self.llm = llm
+        # A dictionary to store intermediate data such as frequency, fetched news, and summaries
+        self.state = {}
+
+    # ----------------------------------------------------------------------
+    def fetch_news(self, state: dict) -> dict:
+        """
+        Fetch AI-related news articles using the Tavily API.
+
+        Args:
+            state (dict): The current graph state, expected to include
+                          'messages' containing the news frequency (e.g., daily, weekly, monthly).
+
+        Returns:
+            dict: Updated state with 'news_data' containing the fetched news articles.
+        """
+
+        # Extract frequency from user input (e.g., "daily", "weekly", etc.)
+        frequency = state['messages'][0].content.lower()
+        self.state['frequency'] = frequency
+
+        # Mapping for Tavily's time range codes and days
+        time_range_map = {'daily': 'd', 'weekly': 'w', 'monthly': 'm', 'year': 'y'}
+        days_map = {'daily': 1, 'weekly': 7, 'monthly': 30, 'year': 366}
+
+        # Perform a Tavily API search for the latest AI news
+        response = self.tavily.search(
+            query="Top Artificial Intelligence (AI) technology news globally",
+            topic="news",
+            time_range=time_range_map[frequency],  # How far back to look
+            include_answer="advanced",             # Request detailed information
+            max_results=20,                        # Limit number of news items
+            days=days_map[frequency],              # Number of days to consider
+            # include_domains=["techcrunch.com", "venturebeat.com/ai", ...]  # (Optional) restrict sources
+        )
+
+        # Store the fetched results in the state dictionary
+        state['news_data'] = response.get('results', [])
+        self.state['news_data'] = state['news_data']
+
+        return state
+
+    # ----------------------------------------------------------------------
+    def summarize_news(self, state: dict) -> dict:
+        """
+        Summarize the fetched AI news articles using the provided LLM.
+
+        Args:
+            state (dict): The current graph state containing 'news_data'.
+
+        Returns:
+            dict: Updated state with 'summary' containing the summarized news in markdown format.
+        """
+
+        # Get the list of fetched news articles
+        news_items = self.state['news_data']
+
+        # Define how the summary should be formatted (markdown structure)
+        prompt_template = ChatPromptTemplate.from_messages([
+            ("system", """Summarize AI news articles into markdown format. For each item include:
+            - Date in **YYYY-MM-DD** format (IST timezone)
+            - A concise summary of the news
+            - Sorted by latest date first
+            - Include the source URL as a hyperlink
+            Use this format:
+            ### [Date]
+            - [Summary](URL)"""),
+            ("user", "Articles:\n{articles}")
+        ])
+
+        # Convert each article into a formatted string with content, URL, and publication date
+        articles_str = "\n\n".join([
+            f"Content: {item.get('content', '')}\nURL: {item.get('url', '')}\nDate: {item.get('published_date', '')}"
+            for item in news_items
+        ])
+
+        # Ask the LLM to generate a markdown summary from the provided articles
+        response = self.llm.invoke(prompt_template.format(articles=articles_str))
+
+        # Save the summary to the state
+        state['summary'] = response.content
+        self.state['summary'] = state['summary']
+
+        return self.state
+
+    # ----------------------------------------------------------------------
+    def save_result(self, state):
+        """
+        Save the summarized AI news to a markdown (.md) file.
+
+        Args:
+            state (dict): The current graph state (contains 'frequency' and 'summary').
+
+        Returns:
+            dict: Updated state with 'filename' indicating where the summary file was saved.
+        """
+
+        # Retrieve frequency and summary content from the state
+        frequency = self.state['frequency']
+        summary = self.state['summary']
+
+        # Define output file path (e.g., ./AINews/daily_summary.md)
+        filename = f"./AINews/{frequency}_summary.md"
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        
+        # Write the summary content to a markdown file
+        with open(filename, 'w') as f:
+            f.write(f"# {frequency.capitalize()} AI News Summary\n\n")
+            f.write(summary)
+
+        # Save filename in the state for reference
+        self.state['filename'] = filename
+
+        return self.state