Create websearch_agent.py

websearch_agent.py

@@ -0,0 +1,58 @@
+from langchain_groq import ChatGroq
+from langchain_tavily import TavilySearch
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.prebuilt import create_react_agent
+from loguru import logger
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# =======================
+# 1. LLM MODEL (Groq) - Optimized for Low Latency
+# =======================
+model = ChatGroq(
+    model="openai/gpt-oss-20b",  # Faster than gpt-oss-20b
+    max_tokens=256,  # Reduced from 512 for faster responses
+    api_key=os.getenv("GROQ_API_KEY"),
+    temperature=0.7,
+)
+
+# =======================
+# 2. TAVILY SEARCH TOOL - Optimized for Speed
+# =======================
+tavily_tool = TavilySearch(
+    max_results=2,  # Reduced from 5 for faster responses
+    topic="general",
+    api_key=os.getenv("TAVILY_API_KEY")
+)
+
+tools = [tavily_tool]   # 🔥 Replace math tools with Tavily
+
+# =======================
+# 3. SYSTEM PROMPT - Optimized for Speed
+# =======================
+system_prompt = """
+You are Samantha, a helpful assistant. Use Tavily for factual or current information.
+Keep responses brief and conversational for audio playback.
+"""
+
+# =======================
+# 4. MEMORY
+# =======================
+memory = InMemorySaver()
+
+# =======================
+# 5. BUILD THE AGENT
+# =======================
+agent = create_react_agent(
+    model=model,
+    tools=tools,
+    prompt=system_prompt,
+    checkpointer=memory,
+)
+
+agent_config = {
+    "configurable": {
+        "thread_id": "default_user"
+    }
+}