agents.py

"""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_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

os.environ["RWKV_V7_ON"] = '1' # ==> enable RWKV-7 mode
os.environ['RWKV_JIT_ON'] = '1' # '1' for better speed
os.environ["RWKV_CUDA_ON"] = '0' # '1' to compile CUDA kernel (10x faster), requires c++ compiler & cuda libraries

from huggingface_hub import hf_hub_download
from rwkv.model import RWKV
from rwkv.utils import PIPELINE, PIPELINE_ARGS

load_dotenv()

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()
        
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        messages = [HumanMessage(content=question)]
        messages = self.graph.invoke({"messages": messages})
        answer = messages['messages'][-1].content
        return answer[14:]

@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()


tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
]

# Build graph function
def build_graph(provider: str = "rwkv"):
    """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,
            ),
        )
    elif provider == "rwkv":
        # --- BEGIN RWKV SETUP ---
        title = "rwkv7-g1-1.5b-20250429-ctx4096"
        pth = hf_hub_download(repo_id="BlinkDL/rwkv7-g1", filename=f"{title}.pth")
        model_path = pth.replace(".pth", "")

        raw_llm = RWKV(model=model_path, strategy='cpu fp16')
        pipeline = PIPELINE(raw_llm, "rwkv_vocab_v20230424")

        class RWKVWithTools:
            def __init__(self, pipeline, system_prompt: str):
                self.pipeline = pipeline
                self.system_prompt = system_prompt
                self.tools = []

            def bind_tools(self, tools):
                self.tools = tools
                return self

            def invoke(self, messages):
                # Build a tools spec block
                specs = []
                for t in self.tools:
                    specs.append(f"- {t.name}({getattr(t, 'args_schema', {})}): {t.description}")

                header = (
                    f"{self.system_prompt}\n\n"
                    "TOOLS AVAILABLE:\n"
                    + "\n".join(specs)
                    + "\n\n"
                    "To call a tool, respond exactly with:\n"
                    "`<tool_name>(arg1=…,arg2=…)` and nothing else.\n\n"
                )

                # Reconstruct conversation
                convo = "\n".join(
                    f"{'User:' if isinstance(m, HumanMessage) else 'Assistant:'} {m.content}"
                    for m in messages
                )

                prompt = header + convo

                print(f'Prompt: {prompt}')
                
                # delegate to RWKV invoke()
                out_str = self.pipeline.generate(prompt, token_count=300)

                print(f'Response: {out_str}')

                return out_str

        llm = RWKVWithTools(pipeline, system_prompt=system_prompt)
        # --- END RWKV SETUP ---
    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"])]}

    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")

    # Compile graph
    return builder.compile()