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553d849 ba9764b 766670a 553d849 f944c3f 553d849 766670a 553d849 7b404af 553d849 7b404af 553d849 7b404af 553d849 bfd13b1 e091a2c bfd13b1 556ab31 4a22da8 bfd13b1 8ecb459 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | from typing import TypedDict, Annotated
from tool import add, substract, multiply, divide, DuckDuckGoSearchTool, WikipediaSearchTool, ArxivSearchTool, PubmedSearchTool
from os import getenv
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, AIMessage
from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
HUGGINGFACEHUB_API_TOKEN = getenv("HUGGINGFACEHUB_API_TOKEN")
# Making the agent
#llm = HuggingFaceEndpoint(
# repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
# huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
#)
llm = HuggingFaceEndpoint(
repo_id="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
)
chat = ChatHuggingFace(llm=llm, verbose=True)
tools = [add,
substract,
multiply,
divide,
DuckDuckGoSearchTool,
WikipediaSearchTool,
ArxivSearchTool,
PubmedSearchTool]
chat_with_tools = chat.bind_tools(tools)
def simple_graph():
## Defining our nodes
def assistant(state: MessagesState):
"""Assistant node"""
return {"messages": state["messages"] + [chat_with_tools.invoke(state["messages"])]}
# Build graph / nodes
builder = StateGraph(MessagesState)
builder.add_node("assistant", assistant) # Assistant
builder.add_node("tools", ToolNode(tools)) # Tools
# Logic / edges
builder.add_edge(START, "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
graph = builder.compile()
return graph |