adding agent

gemini_agent.py

@@ -0,0 +1,69 @@
+from typing import List
+from langchain_community.tools.ddg_search import DuckDuckGoSearchRun
+from langchain.agents import AgentType, initialize_agent
+from langchain.tools.base import BaseTool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import SystemMessage
+from langchain.memory import ConversationBufferMemory
+
+class GeminiAgent:
+    def __init__(self, api_key: str, model_name: str = "gemini-2.0-flash"):
+        # Suppress warnings
+        import warnings
+        warnings.filterwarnings("ignore", category=UserWarning)
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        warnings.filterwarnings("ignore", message=".*will be deprecated.*")
+        warnings.filterwarnings("ignore", "LangChain.*")
+        
+        self.api_key = api_key
+        self.model_name = model_name
+        self.agent = self._setup_agent()
+
+    def _setup_agent(self):
+        # Initialize model with system message
+        model = ChatGoogleGenerativeAI(
+            model=self.model_name,
+            google_api_key=self.api_key,
+            temperature=0,  # Lower temperature for faster, more focused responses
+            max_output_tokens=200,  # Limit response length
+            convert_system_message_to_human=True,  # Faster processing of system message
+            stream=True,  # Enable streaming for faster initial response
+            system_message=SystemMessage(content="You are a concise AI assistant. Provide a short and accurate answer. Preferable answer should be in one word or line. Unless if query asked expects an elaborate answer.")
+        )
+
+        # Setup tools
+        tools: List[BaseTool] = [DuckDuckGoSearchRun()]
+
+        # Setup memory
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            return_messages=True
+        )
+
+        # Create and return agent
+        return initialize_agent(
+            tools,
+            model,
+            agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
+            memory=memory,
+            verbose=False,
+            handle_parsing_errors=True
+        )
+
+    def run(self, query: str) -> str:
+        try:
+            result = self.agent.invoke({"input": query})
+            return result["output"]
+        except Exception as e:
+            return f"Error: {e}"
+
+    def run_interactive(self):
+        print("AI Assistant Ready! (Type 'exit' to quit)")
+        
+        while True:
+            query = input("You: ").strip()
+            if query.lower() == 'exit':
+                print("Goodbye!")
+                break
+            
+            print("Assistant:", self.run(query))