Update agent.py

agent.py

@@ -16,134 +16,75 @@ from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase.client import Client, create_client
 
-from supabase import create_client
-
-supabase = None
-
-def init_supabase_client(url, key):
-    global supabase
-    supabase = create_client(url, key)
-
-# Now you can use `supabase` in your agent code safely.
-
-
-
-#end
-
-
 load_dotenv()
 
 @tool
 def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-
-    Args:
-        a: first int
-        b: second int
-    """
     return a * b
 
 @tool
 def add(a: int, b: int) -> int:
-    """Add two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
     return a + b
 
 @tool
 def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
     return a - b
 
 @tool
-def divide(a: int, b: int) -> int:
-    """Divide two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
+def divide(a: int, b: int) -> float:
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
 
 @tool
 def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
     return a % b
 
 @tool
 def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
-    
-    Args:
-        query: The search query."""
     search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
+    formatted = "\n\n---\n\n".join(
         [
             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
             for doc in search_docs
         ])
-    return {"wiki_results": formatted_search_docs}
+    return {"wiki_results": formatted}
 
 @tool
 def web_search(query: str) -> str:
-    """Search Tavily for a query and return maximum 3 results.
-    
-    Args:
-        query: The search query."""
     search_docs = TavilySearchResults(max_results=3).invoke(query=query)
-    formatted_search_docs = "\n\n---\n\n".join(
+    formatted = "\n\n---\n\n".join(
         [
             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
             for doc in search_docs
         ])
-    return {"web_results": formatted_search_docs}
+    return {"web_results": formatted}
 
 @tool
 def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
-    
-    Args:
-        query: The search query."""
     search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
+    formatted = "\n\n---\n\n".join(
         [
             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
             for doc in search_docs
         ])
-    return {"arvix_results": formatted_search_docs}
-
+    return {"arvix_results": formatted}
 
 
-# load the system prompt from the file
+# Load system prompt
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 
-# System message
 sys_msg = SystemMessage(content=system_prompt)
 
-# build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
+# Setup vector store retriever
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 supabase: Client = create_client(
     os.environ.get("SUPABASE_URL"), 
     os.environ.get("SUPABASE_SERVICE_KEY"))
 vector_store = SupabaseVectorStore(
     client=supabase,
-    embedding= embeddings,
+    embedding=embeddings,
     table_name="documents",
     query_name="match_documents_langchain",
 )
@@ -153,8 +94,6 @@ create_retriever_tool = create_retriever_tool(
     description="A tool to retrieve similar questions from a vector store.",
 )
 
-
-
 tools = [
     multiply,
     add,
@@ -166,18 +105,12 @@ tools = [
     arvix_search,
 ]
 
-# Build graph function
 def build_graph(provider: str = "groq"):
-    """Build the graph"""
-    # Load environment variables from .env file
     if provider == "google":
-        # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
     elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
@@ -186,16 +119,13 @@ def build_graph(provider: str = "groq"):
         )
     else:
         raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    # Bind tools to LLM
+
     llm_with_tools = llm.bind_tools(tools)
 
-    # Node
     def assistant(state: MessagesState):
-        """Assistant node"""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
+
     def retriever(state: MessagesState):
-        """Retriever node"""
         similar_question = vector_store.similarity_search(state["messages"][0].content)
         example_msg = HumanMessage(
             content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
@@ -208,22 +138,16 @@ def build_graph(provider: str = "groq"):
     builder.add_node("tools", ToolNode(tools))
     builder.add_edge(START, "retriever")
     builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
+    builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
 
-    # Compile graph
     return builder.compile()
 
-# test
+
 if __name__ == "__main__":
     question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    # Build the graph
     graph = build_graph(provider="groq")
-    # Run the graph
     messages = [HumanMessage(content=question)]
     messages = graph.invoke({"messages": messages})
     for m in messages["messages"]:
-        m.pretty_print()
+        m.pretty_print()