Update agent.py

agent.py

@@ -1,222 +1,279 @@
 import os
+import certifi
+os.environ['REQUESTS_CA_BUNDLE'] = certifi.where()
 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_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.vectorstores import Chroma
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
+from langchain_core.documents import Document
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_groq import ChatGroq
 
 load_dotenv()
 
+
+# ---------------- CONFIGURATION ----------------
+# Change this to any valid Hugging Face model endpoint (e.g., meta-llama/Llama-3-8b-chat-hf)
+HF_MODEL_NAME = os.getenv("LLAMA_MODEL_NAME", "meta-llama/Meta-Llama-3-8B-Instruct")
+HF_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+HF_MODEL_URL = f"https://api-inference.huggingface.co/models/{HF_MODEL_NAME}"
+# Use the OpenAI-compatible inference endpoint
+HF_OPENAI_URL = "https://api-inference.huggingface.co/openai"
+# ---------------- UTILITY TOOLS ----------------
 @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}
-
-
-
-# load the system prompt from the file
-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
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
+def multiply_numbers(x: int, y: int) -> int:
+    """Multiply two integers and return the result."""
+    return x * y
+
+@tool
+def add_numbers(x: int, y: int) -> int:
+    """Add two integers and return the sum."""
+    return x + y
+
+@tool
+def subtract_numbers(x: int, y: int) -> int:
+    """Subtract the second integer from the first and return the result."""
+    return x - y
+
+@tool
+def divide_numbers(x: int, y: int) -> float:
+    """Divide the first number by the second and return the result. Raises an error on division by zero."""
+    if y == 0:
+        raise ValueError("Division by zero is not allowed.")
+    return x / y
+
+@tool
+def modulus_numbers(x: int, y: int) -> int:
+    """Return the remainder when the first number is divided by the second."""
+    return x % y
+
+@tool
+def power_numbers(base: float, exponent: float) -> float:
+    """Raise the base to the power of exponent and return the result."""
+    return base ** exponent
+
+@tool
+def root_number(value: float, n: float) -> float:
+    """Compute the nth root of a value and return the result."""
+    return value ** (1 / n)
+
+@tool
+def wiki_lookup(query: str) -> str:
+    """Search Wikipedia for the query and return up to 2 summarized documents."""
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    return "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page", "")}"/>{d.page_content}</Document>' for d in docs
+    )
+
+@tool
+def web_lookup(query: str) -> str:
+    """Search the web using Tavily and return up to 3 summarized results."""
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    return "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page", "")}"/>{d.page_content}</Document>' for d in docs
+    )
+
+@tool
+def arxiv_lookup(query: str) -> str:
+    """Search arXiv for the query and return summaries of up to 3 papers."""
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    return "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page", "")}"/>{d.page_content[:800]}</Document>' for d in docs
+    )
+@tool
+def add_numbers(x: int, y: int) -> int:
+    """Add two integers and return the sum."""
+    return x + y
+
+@tool
+def subtract_numbers(x: int, y: int) -> int:
+    """Subtract the second integer from the first and return the result."""
+    return x - y
+
+@tool
+def divide_numbers(x: int, y: int) -> float:
+    """Divide the first number by the second and return the result. Raises an error on division by zero."""
+    if y == 0:
+        raise ValueError("Division by zero is not allowed.")
+    return x / y
+
+@tool
+def modulus_numbers(x: int, y: int) -> int:
+    """Return the remainder when the first number is divided by the second."""
+    return x % y
+
+@tool
+def power_numbers(base: float, exponent: float) -> float:
+    """Raise the base to the power of exponent and return the result."""
+    return base ** exponent
+
+@tool
+def root_number(value: float, n: float) -> float:
+    """Compute the nth root of a value and return the result."""
+    return value ** (1 / n)
+
+@tool
+def wiki_lookup(query: str) -> str:
+    """Search Wikipedia for the query and return up to 2 summarized documents."""
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    return "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page", "")}"/>{d.page_content}</Document>' for d in docs
+    )
+
+@tool
+def web_lookup(query: str) -> str:
+    """Search the web using Tavily and return up to 3 summarized results."""
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    return "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page", "")}"/>{d.page_content}</Document>' for d in docs
+    )
+
+@tool
+def arxiv_lookup(query: str) -> str:
+    """Search arXiv for the query and return summaries of up to 3 papers."""
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    return "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page", "")}"/>{d.page_content[:800]}</Document>' for d in docs
+    )
+
+# # ---------------- SETUP LOCAL VECTORSTORE ----------------
+# embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+# sample_docs = [Document(page_content="St. Thomas Aquinas was a medieval Catholic priest and philosopher.", metadata={"source": "wiki", "page": "St. Thomas Aquinas"})]
+# split_docs = text_splitter.split_documents(sample_docs)
+# vector_db = Chroma.from_documents(documents=split_docs, embedding=embedding_model)
+# retriever_tool = create_retriever_tool(
+#     retriever=vector_db.as_retriever(),
+#     name="SimilarQuestionFinder",
+#     description="Retrieve similar questions and examples from vector DB."
+# )
+
+# # ---------------- SYSTEM PROMPT ----------------
+# with open("system_prompt.txt", "r", encoding="utf-8") as f:
+#     system_content = f.read()
+# system_message = SystemMessage(content=system_content)
+
+# # ---------------- BUILD STATE GRAPH ----------------
+# def construct_agent_graph():
+#     llama_llm = ChatHuggingFace(
+#         llm=HuggingFaceEndpoint(
+#             endpoint_url=HF_OPENAI_URL,
+#             temperature=0
+#         )
+#     ).bind_tools([
+#         multiply_numbers,
+#         add_numbers,
+#         subtract_numbers,
+#         divide_numbers,
+#         modulus_numbers,
+#         power_numbers,
+#         root_number,
+#         wiki_lookup,
+#         web_lookup,
+#         arxiv_lookup,
+#         retriever_tool,
+#     ])
+
+#     def retrieve_node(state: MessagesState):
+#         similar = vector_db.similarity_search(state["messages"][0].content)
+#         hint = HumanMessage(content=f"Reference example:\n{similar[0].page_content}" if similar else "")
+#         return {"messages": [system_message] + state["messages"] + [hint]}
+
+#     def respond_node(state: MessagesState):
+#         return {"messages": [llama_llm.invoke(state["messages"]) ]}
+
+#     graph_builder = StateGraph(MessagesState)
+#     graph_builder.add_node("find_similar", retrieve_node)
+#     graph_builder.add_node("generate_answer", respond_node)
+#     graph_builder.add_node("tool_executor", ToolNode([]))
+
+#     graph_builder.add_edge(START, "find_similar")
+#     graph_builder.add_edge("find_similar", "generate_answer")
+#     graph_builder.add_conditional_edges(
+#         "generate_answer",
+#         tools_condition,
+#         {"tools": "tool_executor", "default": "generate_answer"}
+#     )
+#     graph_builder.add_edge("tool_executor", "generate_answer")
+
+#     return graph_builder.compile()
+
+# # ---------------- RUN EXAMPLE ----------------
+# if __name__ == "__main__":
+#     sample_q = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+#     agent = construct_agent_graph()
+#     msgs = [HumanMessage(content=sample_q)]
+#     out = agent.invoke({"messages": msgs})
+#     for m in out["messages"]:
+#         m.pretty_print()
+
+# ---------------- EMBEDDINGS & VECTOR DB ----------------
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+sample_docs = [Document(page_content="Sample doc.", metadata={"source":"wiki"})]
+split_docs = text_splitter.split_documents(sample_docs)
+vector_db = Chroma.from_documents(documents=split_docs, embedding=embedding_model)
+retriever_tool = create_retriever_tool(
+    retriever=vector_db.as_retriever(),
+    name="SimilarQuestionFinder",
+    description="Retrieve similar questions and examples from vector DB."
 )
 
+all_tools = [multiply_numbers, add_numbers, subtract_numbers, divide_numbers,
+             modulus_numbers, power_numbers, root_number,
+             wiki_lookup, web_lookup, arxiv_lookup, retriever_tool]
+
+# ---------------- SYSTEM PROMPT ----------------
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_content = f.read()
+system_message = SystemMessage(content=system_content)
+# ---------------- BUILD GRAPH ----------------
+def construct_agent_graph():
+    llama_llm = ChatGroq(
+        model="qwen-qwq-32b",
+        api_key=os.environ["GROQ_API_KEY"],
+        temperature=0,
+    )
+
+    def retrieve_node(state: MessagesState):
+        msgs = [system_message] + state["messages"]
+        similar = vector_db.similarity_search(state["messages"][0].content)
+        if similar:
+            msgs.append(HumanMessage(content=f"Reference example:\n{similar[0].page_content}"))
+        return {"messages": msgs}
+
+    def respond_node(state: MessagesState):
+        return {"messages": [llama_llm.invoke(state["messages"])]}
+
+    graph = StateGraph(MessagesState)
+    graph.add_node("find_similar", retrieve_node)
+    graph.add_node("generate_answer", respond_node)
+    graph.add_node("tool_executor", ToolNode(tools=all_tools))
+
+    graph.add_edge(START, "find_similar")
+    graph.add_edge("find_similar", "generate_answer")
+    graph.add_conditional_edges(
+        "generate_answer",
+        tools_condition,
+        {"tools": "tool_executor", "__end__": "__end__"}
+    )
+    graph.add_edge("tool_executor", "generate_answer")
+
+    return graph.compile()
 
+# ---------------- RUN EXAMPLE ----------------
+if __name__ == "__main__":
+    agent = construct_agent_graph()
+    sample_q = "When was St. Thomas Aquinas added to that page?"
+    out = agent.invoke({"messages": [HumanMessage(content=sample_q)]})
+    for m in out["messages"]:
+        m.pretty_print()
 
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-]
-
-# Build graph function
-def build_graph(provider: str = "google"):
-    """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
-    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",
-                temperature=0,
-            ),
-        )
-    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}",
-       # )
-       # return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-
-    from langchain_core.messages import AIMessage
-
-    def retriever(state: MessagesState):
-        query = state["messages"][-1].content
-        similar_doc = vector_store.similarity_search(query, k=1)[0]
-
-        content = similar_doc.page_content
-        if "Final answer :" in content:
-            answer = content.split("Final answer :")[-1].strip()
-        else:
-            answer = content.strip()
-
-        return {"messages": [AIMessage(content=answer)]}
-
-   # builder = StateGraph(MessagesState)
-    #builder.add_node("retriever", retriever)
-    #builder.add_node("assistant", assistant)
-    #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_edge("tools", "assistant")
-
-    builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
-
-    # Retriever ist Start und Endpunkt
-    builder.set_entry_point("retriever")
-    builder.set_finish_point("retriever")
-
-    # Compile graph
-    return builder.compile()