Update agent.py

agent.py

@@ -1,12 +1,19 @@
 """LangGraph Agent"""
+
 import os
 from dotenv import load_dotenv
 from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.prebuilt import tools_condition
 from langgraph.prebuilt import ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_openai import ChatOpenAI
+from langchain.agents import initialize_agent, Tool
 from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_huggingface import (
+    ChatHuggingFace,
+    HuggingFaceEndpoint,
+    HuggingFaceEmbeddings,
+)
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_community.document_loaders import WikipediaLoader
 from langchain_community.document_loaders import ArxivLoader
@@ -16,43 +23,42 @@ from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase.client import Client, create_client
 
-
 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
@@ -61,20 +67,20 @@ def divide(a: int, b: int) -> int:
         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()
@@ -82,13 +88,14 @@ def wiki_search(query: str) -> str:
         [
             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}
 
+
 @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)
@@ -96,13 +103,14 @@ def web_search(query: str) -> str:
         [
             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}
 
+
 @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()
@@ -110,11 +118,11 @@ def arvix_search(query: str) -> str:
         [
             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}
 
 
-
 # load the system prompt from the file
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
@@ -123,13 +131,15 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
 sys_msg = SystemMessage(content=system_prompt)
 
 # build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
+embeddings = HuggingFaceEmbeddings(
+    model_name="sentence-transformers/all-mpnet-base-v2"
+)  #  dim=768
 supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
+    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",
 )
@@ -140,7 +150,6 @@ create_retriever_tool = create_retriever_tool(
 )
 
 
-
 tools = [
     multiply,
     add,
@@ -152,6 +161,7 @@ tools = [
     arvix_search,
 ]
 
+
 # Build graph function
 def build_graph(provider: str = "groq"):
     """Build the graph"""
@@ -161,9 +171,13 @@ def build_graph(provider: str = "groq"):
         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
+        )  # optional : qwen-qwq-32b gemma2-9b-it
+    elif provider == "openai":
+        # OpenAI
+        llm = ChatOpenAI(model="gpt-4", 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",
@@ -179,7 +193,7 @@ def build_graph(provider: str = "groq"):
     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)
@@ -201,15 +215,4 @@ def build_graph(provider: str = "groq"):
     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()
+    return builder.compile()