Update llm_agent.py

llm_agent.py

@@ -1,174 +1,168 @@
-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,GoogleGenerativeAIEmbeddings
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.vectorstores import FAISS
-from langchain_core.messages import SystemMessage,HumanMessage
-from langchain_core.tools import tool
-
-
-load_dotenv()
-
-os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
-
-@tool
-def add(a:int,b:int)->int:
-    """Add two Numbers
-     Args:
-        a:int
-        b:int
-
-    """
-    return a+b
-
-@tool
-def subtract(a:int,b:int)->int:
-    """Subtract two numbers
-    Args:
-        a:int
-        b:int 
-    """
-    return a-b
-
-@tool
-def multiply(a:int,b:int)->int:
-    """Multiply Two Numbers
-     Args:
-        a:int
-        b:int
-    """
-    return a*b
-
-@tool 
-def divide(a:int,b:int)->int:
-    """Divide two numbers
-     Args:
-        a:int
-        b:int
-    """
-    if b==0:
-        raise ValueError("Cannot Divide by Zero")
-    return a//b
-
-@tool 
-def modulus(a:int,b:int)->int:
-    """Modulus of the two numbers
-    Args:
-        a:int
-        b: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 : str
-    """
-    print(query)
-
-    search_docs = WikipediaLoader(query=query,load_max_docs=2).load()
-
-    return {"wiki_results": 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(input=query)
-  
-    return {"web_results": search_docs}
-
-@tool
-def arxiv_search(query:str)->str:
-    """"search Arxiv for a query and return maximum 3 results
-    Args:
-        query: search query
-    """
-
-    search_docs = ArxivLoader(query=query,load_max_docs = 3).load()
-   
-    return {"arxiv_resutls": search_docs}
-
-
-system_prompt = """
-You are a helpful assistant tasked with answering questions using a set of tools. 
-Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
-FINAL ANSWER: [YOUR FINAL ANSWER]. 
-YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
-Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
-"""
-
-sys_msg = SystemMessage(content=system_prompt)
-
-
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-
-
-tools = [
-    add,
-    subtract,
-    multiply,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arxiv_search,
-]
-
-
-
-def build_graph():
-    llm = ChatGoogleGenerativeAI(model = "gemini-2.0-flash")
-    print(tools)
-    # 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}",
-    #     )
-    #     return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-
-    builder = StateGraph(MessagesState)
-    # builder.add_node("retriever", retriever)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "assistant")
-    # builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
-
-    # Compile graph
-    return builder.compile()
-
-
-if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    graph = build_graph()
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
+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_groq import ChatGroq
+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
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+
+
+load_dotenv()
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+@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
+    """
+    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(
+        [
+            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)
+    formatted_search_docs = "\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}
+
+@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(
+        [
+            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}
+
+
+
+system_prompt = """
+You are a helpful assistant tasked with answering questions using a set of tools. 
+Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+FINAL ANSWER: [YOUR FINAL ANSWER]. 
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
+"""
+sys_msg = SystemMessage(content=system_prompt)
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
+
+
+
+tools = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+]
+
+# Build graph function
+def build_graph():
+    
+    llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+   
+    llm_with_tools = llm.bind_tools(tools)
+
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+   
+    builder = StateGraph(MessagesState)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_edge(START, "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+
+    return builder.compile()
+
+if __name__ == "__main__":
+    question = "What is the first name of the only Malko Competition recipient from the 20th Century (after 1977) whose nationality on record is a country that no longer exists?"
+    graph = build_graph()
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()