Update agent.py

agent.py

@@ -25,7 +25,6 @@ print(f"SUPABASE_SERVICE_KEY: {SUPABASE_SERVICE_KEY[:10]}..." if SUPABASE_SERVIC
 
 
 def get_supabase_client():
-    """Create and return a Supabase client from environment variables."""
     if not SUPABASE_URL or not SUPABASE_SERVICE_KEY:
         raise ValueError("Supabase environment variables are missing.")
     return create_client(SUPABASE_URL, SUPABASE_SERVICE_KEY)
@@ -33,117 +32,46 @@ def get_supabase_client():
 
 @tool
 def multiply(a: int, b: int) -> int:
-    """Multiply two integers and return the result.
-
-    Args:
-        a (int): First multiplier.
-        b (int): Second multiplier.
-
-    Returns:
-        int: The product of a and b.
-    """
+    """Multiply two integers."""
     return a * b
 
-
 @tool
 def add(a: int, b: int) -> int:
-    """Add two integers and return the sum.
-
-    Args:
-        a (int): First addend.
-        b (int): Second addend.
-
-    Returns:
-        int: The sum of a and b.
-    """
+    """Add two integers."""
     return a + b
 
-
 @tool
 def subtract(a: int, b: int) -> int:
-    """Subtract b from a and return the difference.
-
-    Args:
-        a (int): Minuend.
-        b (int): Subtrahend.
-
-    Returns:
-        int: The difference a - b.
-    """
+    """Subtract b from a."""
     return a - b
 
-
 @tool
 def divide(a: int, b: int) -> float:
-    """Divide a by b and return the quotient.
-
-    Args:
-        a (int): Dividend.
-        b (int): Divisor.
-
-    Raises:
-        ValueError: If b is zero.
-
-    Returns:
-        float: The quotient of a divided by b.
-    """
+    """Divide a by b."""
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
 
-
 @tool
 def modulus(a: int, b: int) -> int:
-    """Return the remainder of dividing a by b.
-
-    Args:
-        a (int): Dividend.
-        b (int): Divisor.
-
-    Returns:
-        int: The remainder of a divided by b.
-    """
+    """Modulo operation."""
     return a % b
 
-
 @tool
 def wiki_search(query: str) -> str:
-    """Search Wikipedia for the query and return the combined content of top 2 documents.
-
-    Args:
-        query (str): Search query string.
-
-    Returns:
-        str: Combined content of top 2 Wikipedia documents separated by "---".
-    """
+    """Search Wikipedia for the query and return top results."""
     docs = WikipediaLoader(query=query, load_max_docs=2).load()
     return "\n\n---\n\n".join([doc.page_content for doc in docs])
 
-
 @tool
 def web_search(query: str) -> str:
-    """Search the web using TavilySearchResults and return combined content of top 3 results.
-
-    Args:
-        query (str): Search query string.
-
-    Returns:
-        str: Combined content of top 3 web search results separated by "---".
-    """
+    """Search the web and return top results."""
     docs = TavilySearchResults(max_results=3).invoke(query=query)
     return "\n\n---\n\n".join([doc.page_content for doc in docs])
 
-
 @tool
 def arvix_search(query: str) -> str:
-    """Search Arxiv for the query and return combined content (up to 1000 chars) of top 3 documents.
-
-    Args:
-        query (str): Search query string.
-
-    Returns:
-        str: Combined content snippet of top 3 Arxiv documents separated by "---".
-    """
+    """Search Arxiv for the query and return excerpts."""
     docs = ArxivLoader(query=query, load_max_docs=3).load()
     return "\n\n---\n\n".join([doc.page_content[:1000] for doc in docs])
 
@@ -151,7 +79,11 @@ def arvix_search(query: str) -> str:
 tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]
 
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
+    system_prompt = f.read().strip()
+
+if not system_prompt:
+    print("Warning: system_prompt.txt is empty. Using default system prompt.")
+    system_prompt = "You are a helpful assistant."
 
 sys_msg = SystemMessage(content=system_prompt)
 
@@ -188,7 +120,26 @@ def build_graph(provider: str = "groq"):
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+        try:
+            print("Assistant received messages:")
+            for m in state["messages"]:
+                print(f"- {m.__class__.__name__}: {m.content[:100]}")
+
+            result = llm_with_tools.invoke(state["messages"])
+            print("LLM output message:")
+            if hasattr(result, "content"):
+                print(result.content[:500])
+            else:
+                print(result)
+
+            if not result or not getattr(result, "content", None):
+                print("Warning: LLM returned empty result or no content.")
+                return {"messages": [HumanMessage(content="Sorry, I couldn't generate an answer.")]}
+            
+            return {"messages": [result]}
+        except Exception as e:
+            print(f"Error invoking LLM: {e}")
+            return {"messages": [HumanMessage(content="Sorry, I encountered an error during processing.")]}
 
     def retriever(state: MessagesState):
         similar = vector_store.similarity_search(state["messages"][0].content)
@@ -212,4 +163,4 @@ if __name__ == "__main__":
     question = "When was Aquinas added to Wikipedia page on double effect?"
     output = g.invoke({"messages": [HumanMessage(content=question)]})
     for msg in output["messages"]:
-        msg.pretty_print()
+        print(f"\n[{msg.__class__.__name__}] {msg.content}\n")