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"""LangGraph Agent"""
import os
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 langchain.tools.retriever import create_retriever_tool
# from supabase.client import Client, create_client
from langchain_core.messages import AIMessage
from difflib import SequenceMatcher
import time # Add time import
from tools import add, subtract, multiply, divide, modulus, wiki_search, web_search, arvix_search, search_metadata
# 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,
search_metadata,
]
# 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-2.5-flash-preview-05-20", temperature=1)
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"""
messages = state["messages"]
# If we have retrieved information, use it
if len(messages) > 1 and "No matching results found in metadata" not in messages[-1].content:
# Create a new message list with proper structure
new_messages = [
SystemMessage(content="You are a helpful assistant. Use the following retrieved information to answer the question. If the information is relevant, use it directly. If not, use your own knowledge."),
HumanMessage(content=f"Question: {messages[-2].content}\n\nRetrieved Information:\n{messages[-1].content}")
]
time.sleep(2) # Add 2 second sleep before tool call
return {"messages": [llm_with_tools.invoke(new_messages)]}
else:
# If no retrieved information, just use the original message
time.sleep(2) # Add 2 second sleep before tool call
return {"messages": [llm_with_tools.invoke(messages)]}
def retriever(state: MessagesState):
query = state["messages"][-1].content
result = search_metadata(query)
return {"messages": [AIMessage(content=result)]}
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
# Start with retriever
builder.set_entry_point("retriever")
# After retriever, go to assistant
builder.add_edge("retriever", "assistant")
# Assistant can either use tools or finish
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge("tools", "assistant")
# Compile graph
return builder.compile() |