Initial commit

skills/pocketflow/assets/examples/03_agent.py

@@ -0,0 +1,165 @@
+"""
+PocketFlow Cookbook Example: Research Agent
+
+Difficulty: ☆☆☆ Dummy Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-agent
+
+Description:
+A research agent that can search the web and answer questions.
+Demonstrates:
+- Agent pattern with dynamic action selection
+- Branching based on decisions
+- Loop-back for iterative research
+- Tool usage (web search)
+"""
+
+from pocketflow import Node, Flow
+# from utils import call_llm, search_web  # You need to implement these
+
+
+class DecideActionNode(Node):
+    """Agent decides whether to search or answer"""
+
+    def prep(self, shared):
+        return {
+            "question": shared["question"],
+            "context": shared.get("context", "No information gathered yet")
+        }
+
+    def exec(self, inputs):
+        """Decide next action using LLM"""
+        question = inputs["question"]
+        context = inputs["context"]
+
+        prompt = f"""
+Given:
+Question: {question}
+Current Context: {context}
+
+Should I:
+1. Search web for more information
+2. Answer with current knowledge
+
+Output in format:
+Action: search/answer
+Reasoning: [why]
+Search Query: [if action is search]
+"""
+        # response = call_llm(prompt)
+        # Parse response to get action
+
+        # Placeholder logic
+        if not context or "No information" in context:
+            action = "search"
+            search_query = question
+        else:
+            action = "answer"
+            search_query = None
+
+        print(f"\n🤔 Agent decided: {action}")
+
+        return {
+            "action": action,
+            "search_query": search_query
+        }
+
+    def post(self, shared, prep_res, exec_res):
+        shared["decision"] = exec_res
+        # Branch based on action
+        return exec_res["action"]
+
+
+class SearchWebNode(Node):
+    """Search the web for information"""
+
+    def prep(self, shared):
+        return shared["decision"]["search_query"]
+
+    def exec(self, query):
+        """Perform web search"""
+        print(f"\n🔍 Searching: {query}")
+        # results = search_web(query)
+        results = f"Search results for '{query}':\n- Result 1\n- Result 2\n- Result 3"
+        return results
+
+    def post(self, shared, prep_res, exec_res):
+        # Add to context
+        current_context = shared.get("context", "")
+        shared["context"] = current_context + "\n\n" + exec_res
+        print(f"\n📚 Context updated ({len(shared['context'])} chars)")
+        # Loop back to decide again
+        return "continue"
+
+
+class AnswerNode(Node):
+    """Generate final answer"""
+
+    def prep(self, shared):
+        return {
+            "question": shared["question"],
+            "context": shared.get("context", "")
+        }
+
+    def exec(self, inputs):
+        """Generate answer from context"""
+        prompt = f"""
+Context: {inputs['context']}
+
+Question: {inputs['question']}
+
+Provide a comprehensive answer:
+"""
+        # answer = call_llm(prompt)
+        answer = f"Based on the research, here's the answer to '{inputs['question']}':\n\n[Answer based on context]"
+        return answer
+
+    def post(self, shared, prep_res, exec_res):
+        shared["final_answer"] = exec_res
+        print(f"\n✅ Answer generated")
+        return "done"
+
+
+# Build the agent flow
+def create_agent_flow():
+    """Create research agent with branching and looping"""
+    decide = DecideActionNode()
+    search = SearchWebNode()
+    answer = AnswerNode()
+
+    # Branching: decide can lead to search or answer
+    decide - "search" >> search
+    decide - "answer" >> answer
+
+    # Loop: search leads back to decide
+    search - "continue" >> decide
+
+    flow = Flow(start=decide)
+    return flow
+
+
+# Example usage
+def main():
+    """Run the research agent"""
+    # Default question
+    question = "Who won the Nobel Prize in Physics 2024?"
+
+    # Get question from command line if provided
+    import sys
+    if len(sys.argv) > 1:
+        question = " ".join(sys.argv[1:])
+
+    shared = {"question": question}
+
+    print(f"\n🤔 Processing question: {question}")
+    print("="*50)
+
+    flow = create_agent_flow()
+    flow.run(shared)
+
+    print("\n" + "="*50)
+    print("\n🎯 Final Answer:")
+    print(shared.get("final_answer", "No answer found"))
+
+
+if __name__ == "__main__":
+    main()