Update agent.py

agent.py

@@ -1,279 +1,212 @@
+"""LangGraph Agent"""
 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, ArxivLoader
-from langchain_community.vectorstores import Chroma
+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
 from langchain.tools.retriever import create_retriever_tool
-from langchain_core.documents import Document
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_groq import ChatGroq
+from supabase.client import Client, create_client
 
 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_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 ----------------
+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_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}
+    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 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 = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+]
+
+# Build graph function
+def build_graph(provider: str = "huggingface"):
+    """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":
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
+            ),
+        )
+    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]}
+
+    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,
-        {"tools": "tool_executor", "__end__": "__end__"}
     )
-    graph.add_edge("tool_executor", "generate_answer")
+    builder.add_edge("tools", "assistant")
 
-    return graph.compile()
+    # Compile graph
+    return builder.compile()
 
-# ---------------- RUN EXAMPLE ----------------
+# test
 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"]:
+    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()
-