File size: 4,645 Bytes
9142765 8a52178 b20f69e 9142765 a63f8bd 9142765 a63f8bd 9142765 a63f8bd 9142765 7290c34 fe07d7a 2b6a599 7290c34 9142765 08557a7 277f4a7 2434d18 9142765 2434d18 9142765 |
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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
from langchain_community.chat_models import ChatOllama
from langgraph.graph import MessagesState, StateGraph, START, END
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode
from langchain_community.document_loaders import WikipediaLoader
from langgraph.prebuilt import tools_condition
from langchain_huggingface import HuggingFaceEndpoint
from langchain_huggingface import ChatHuggingFace
from langchain.llms import HuggingFaceHub
import os
from huggingface_hub import login
from dotenv import load_dotenv
load_dotenv()
os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HF_TOKEN")
@tool
def use_search_tool(query: str) -> str:
"""Use the search tool to find information.
Args: query (str): The search query.
Returns: str: The search result.
"""
search_result = DuckDuckGoSearchRun(verbose=0).run(query)
return {"messages": search_result}
@tool
def use_wikipedia_tool(query: str) -> str:
"""Fetch a summary from Wikipedia.
Args:
query (str): The topic to search on Wikipedia.
Returns:
str: A summary of the topic from Wikipedia.
"""
result = WikipediaLoader(query=query, load_max_docs=2).load()
if result:
return {"messages": result}
else:
return {"messages" : f"Sorry, I couldn't find any information on '{query}' in Wikipedia."}
def build_agent():
# llm = ChatOllama(model="llama3.1")
# llm = HuggingFaceEndpoint(
# endpoint_url="https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-Prover-V2-671B",
# huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN")
# )
llm = HuggingFaceHub(repo_id="openai-community/gpt2-medium", task="text-generation",
model_kwargs={
"temperature": 0.7, # Controls randomness (0.0 = deterministic)
"max_new_tokens": 100 # Optional: max output length
},
verbose=True)
tools = [use_wikipedia_tool, use_search_tool]
# system_template = (
# "You are a helpful, friendly, and respectful AI assistant. "
# "Always address the user politely and answer their questions in a positive manner.\n"
# "When reasoning, always use the following format:\n"
# "Thought: [your reasoning here]\n"
# "Action: [the action to take, should be one of [{tool_names}]]\n"
# "Action Input: [the input to the action]\n"
# "If you know the answer without using a tool, respond with:\n"
# "Thought: [your reasoning here]\n"
# "Final Answer: [your answer here]\n"
# "Always ensure your responses are polite, accurate, and helpful."
# )
# system_prompt = SystemMessage(content=system_template.format(
# tool_names=", ".join([tool.name for tool in tools])
# ))
system_template = (
"You are a helpful assistant tasked with answering questions using a set of tools. "
"""Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:
FINAL ANSWER: [YOUR FINAL ANSWER].
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
Your answer should only start with "FINAL ANSWER: ", then follows with the answer. """
)
def call_model(state: MessagesState):
"""Call the LLM with the given state."""
messages = [SystemMessage(content=system_template)] + state["messages"]
response = llm.invoke(messages)
return {"messages" : response}
workflow = StateGraph(MessagesState)
workflow.add_node("Assistent", call_model)
workflow.add_node("tools", ToolNode(tools))
workflow.add_edge(START, "Assistent")
workflow.add_conditional_edges("Assistent", tools_condition)
workflow.add_edge("tools", "Assistent")
workflow.add_edge("Assistent", END)
return workflow.compile()
if __name__ == "__main__":
graph = build_agent()
input = HumanMessage(content="Hello, how are you?")
response = graph.invoke(input)
print(response)
|