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"""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
from huggingface_hub import hf_hub_download
# RWKV setup flags — must come before importing RWKV
os.environ["RWKV_JIT_ON"] = "1"
os.environ["RWKV_V7_ON"] = "1" # enable RWKV-7
os.environ["RWKV_CUDA_ON"] = "0" # set to "1"
from rwkv.model import RWKV
from rwkv.utils import PIPELINE
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()
# System message
sys_msg = SystemMessage(content=system_prompt)
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-g1-0.1b-20250307-ctx4096"
pth = hf_hub_download(repo_id="BlinkDL/rwkv7-g1", filename=f"{title}.pth")
# 2) Load RWKV (drop .pth extension for RWKV loader)
rwkv_model = RWKV(model=pth.replace(".pth", ""), strategy="cpu fp32")
# 3) Build the tokenization + generation pipeline
rwkv_pipe = PIPELINE(rwkv_model, "rwkv_vocab_v20230424")
# 4) Wrap into a Chat-style interface
class ChatRWKV:
def __init__(self, pipe):
self.pipe = pipe
def invoke(self, messages):
prompt = "\n".join(m.content for m in messages)
return self.pipe(
prompt,
temperature=0.0,
top_p=0.95,
max_tokens=256,
)
def bind_tools(self, tools):
return self
llm = ChatRWKV(rwkv_pipe)
# --- 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() |