Update veryfinal.py

veryfinal.py

@@ -1,4 +1,4 @@
-"""Enhanced LangGraph + Agno Hybrid Agent System with TavilyTools"""
+"""Enhanced LangGraph + Agno Hybrid Agent System"""
 
 import os
 import time
@@ -31,6 +31,7 @@ from agno.models.google import Gemini
 from agno.tools.tavily import TavilyTools
 from agno.memory.agent import AgentMemory
 from agno.storage.sqlite import SqliteStorage
+from agno.memory.v2.db.sqlite import SqliteMemoryDb # Correct import for memory DB
 
 load_dotenv()
 
@@ -64,17 +65,28 @@ gemini_limiter = PerformanceRateLimiter(28, "Gemini")
 groq_limiter   = PerformanceRateLimiter(28, "Groq")
 nvidia_limiter = PerformanceRateLimiter(4,  "NVIDIA")
 
-# Create Agno agents with SQLite storage
+# Create Agno agents with corrected SQLite storage and memory
 def create_agno_agents():
+    # 1. Storage for the agent's overall state (conversations, etc.)
     storage = SqliteStorage(
         table_name="agent_sessions",
         db_file="tmp/agent_sessions.db",
         auto_upgrade_schema=True
     )
+    # 2. A separate database for the agent's memory
+    memory_db = SqliteMemoryDb(db_file="tmp/agent_memory.db")
+
+    # 3. The AgentMemory object, which uses the memory_db
+    agent_memory = AgentMemory(
+        db=memory_db, # Pass the SqliteMemoryDb here
+        create_user_memories=True,
+        create_session_summary=True
+    )
+
     math_agent = Agent(
         name="MathSpecialist",
         model=Groq(
-            id="llama-3.3-70b-versatile",
+            model="llama-3.3-70b-versatile",
             api_key=os.getenv("GROQ_API_KEY"),
             temperature=0
         ),
@@ -82,21 +94,17 @@ def create_agno_agents():
         instructions=[
             "Solve math problems with precision",
             "Show step-by-step calculations",
-            "Use calculation tools as needed",
             "Finish with: FINAL ANSWER: [result]"
         ],
-        memory=AgentMemory(
-            db=storage,
-            create_user_memories=True,
-            create_session_summary=True
-        ),
+        storage=storage,       # Use SqliteStorage for the agent's persistence
+        memory=agent_memory,   # Use the configured AgentMemory
         show_tool_calls=False,
         markdown=False
     )
     research_agent = Agent(
         name="ResearchSpecialist",
         model=Gemini(
-            id="gemini-2.0-flash-lite",
+            model="gemini-2.0-flash-lite",
             api_key=os.getenv("GOOGLE_API_KEY"),
             temperature=0
         ),
@@ -104,7 +112,6 @@ def create_agno_agents():
         instructions=[
             "Conduct thorough research using available tools",
             "Synthesize information from multiple sources",
-            "Provide comprehensive, well-cited answers",
             "Finish with: FINAL ANSWER: [answer]"
         ],
         tools=[
@@ -116,11 +123,8 @@ def create_agno_agents():
                 format="markdown"
             )
         ],
-        memory=AgentMemory(
-            db=storage,
-            create_user_memories=True,
-            create_session_summary=True
-        ),
+        storage=storage,       # Use the same storage for persistence
+        memory=agent_memory,   # Use the same memory configuration
         show_tool_calls=False,
         markdown=False
     )
@@ -144,7 +148,7 @@ def subtract(a: int, b: int) -> int:
 
 @tool
 def divide(a: int, b: int) -> float:
-    """Divide two numbers; errors if divisor is zero."""
+    """Divide two numbers."""
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
@@ -199,7 +203,6 @@ def setup_faiss():
         print(f"FAISS setup failed: {e}")
         return None
 
-# State definition
 class EnhancedAgentState(TypedDict):
     messages: Annotated[List[HumanMessage|AIMessage], operator.add]
     query: str
@@ -208,7 +211,6 @@ class EnhancedAgentState(TypedDict):
     perf: Dict[str,Any]
     agno_resp: str
 
-# Hybrid system combining LangGraph and Agno
 class HybridLangGraphAgnoSystem:
     def __init__(self):
         self.agno = create_agno_agents()
@@ -279,51 +281,39 @@ class HybridLangGraphAgnoSystem:
         g.add_node("agno_research",agno_research)
         g.add_node("lg_retrieval",lg_retrieval)
         g.add_node("agno_general",agno_general)
-        g.set_entry_point("router")
-        g.add_conditional_edges("router",pick,{
-            "lg_math":"lg_math","agno_research":"agno_research",
-            "lg_retrieval":"lg_retrieval","agno_general":"agno_general"
-        })
+g.set_entry_point("router")
+g.add_conditional_edges("router",pick,{
+"lg_math":"lg_math","agno_research":"agno_research",
+"lg_retrieval":"lg_retrieval","agno_general":"agno_general"
+})
         for n in ["lg_math","agno_research","lg_retrieval","agno_general"]:
-            g.add_edge(n,"END")
+g.add_edge(n,"END")
         return g.compile(checkpointer=MemorySaver())
-
-    def process_query(self, q: str) -> Dict[str,Any]:
-        state={
-            "messages":[HumanMessage(content=q)],
-            "query":q,"agent_type":"","final_answer":"",
-            "perf":{},"agno_resp":""
-        }
-        cfg={"configurable":{"thread_id":f"hyb_{hash(q)}"}}
-        try:
-            out=self.graph.invoke(state,cfg)
-            return {
-                "answer":out["final_answer"],
-                "performance_metrics":out["perf"],
-                "provider_used":out["perf"].get("prov")
-            }
+def process_query(self, q: str) -> Dict[str,Any]:
+state={
+"messages":[HumanMessage(content=q)],
+"query":q,"agent_type":"","final_answer":"",
+"perf":{},"agno_resp":""
+}
+cfg={"configurable":{"thread_id":f"hyb_{hash(q)}"}}
+try:
+out=self.graph.invoke(state,cfg)
+return {
+"answer":out["final_answer"],
+"performance_metrics":out["perf"],
+"provider_used":out["perf"].get("prov")
+}
         except Exception as e:
             return {"answer":f"Error: {e}","performance_metrics":{},"provider_used":"Error"}
 
-def build_graph(provider: str = "hybrid"):
-    """
-    Build and return the StateGraph for the requested provider.
-    - "hybrid" returns the HybridLangGraphAgnoSystem graph.
-    - "groq", "google", "nvidia" all fall back to the hybrid graph.
-    """
-    if provider == "hybrid":
-        return HybridLangGraphAgnoSystem().graph
-    elif provider in ("groq", "google", "nvidia"):
-        # Simply reuse the hybrid graph under these names
+def build_graph(provider: str="hybrid"):
+    if provider=="hybrid":
         return HybridLangGraphAgnoSystem().graph
-    else:
-        raise ValueError(f"Only 'hybrid', 'groq', 'google', or 'nvidia' supported (got '{provider}')")
-
+    raise ValueError("Only 'hybrid' supported")
 
-# Test
-if __name__=="__main__":
-    graph=build_graph()
+if __name__ == "__main__":
+graph=build_graph()
     msgs=[HumanMessage(content="What are the names of the US presidents who were assassinated?")]
-    res=graph.invoke({"messages":msgs},{"configurable":{"thread_id":"test"}})
+res=graph.invoke({"messages":msgs},{"configurable":{"thread_id":"test"}})
     for m in res["messages"]:
-        m.pretty_print()
+m.pretty_print()