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from utils import _set_env
_set_env("OPENAI_API_KEY")
from utils import *
def create_graph():
from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import ToolNode, tools_condition
## ADD TRACKING
response_model = init_chat_model("gpt-4o", temperature=0)
grader_model = init_chat_model("gpt-4o", temperature=0)
workflow = StateGraph(MessagesState)
# Define the nodes we will cycle between
workflow.add_node(generate_query_or_respond)
workflow.add_node("retrieve", ToolNode([retriever_tool]))
workflow.add_node(rewrite_question)
workflow.add_node(generate_answer)
workflow.add_edge(START, "generate_query_or_respond")
# Decide whether to retrieve
workflow.add_conditional_edges(
"generate_query_or_respond",
# Assess LLM decision (call `retriever_tool` tool or respond to the user)
tools_condition,
{
# Translate the condition outputs to nodes in our graph
"tools": "retrieve",
END: END,
},
)
# Edges taken after the `action` node is called.
workflow.add_conditional_edges(
"retrieve",
# Assess agent decision
grade_documents,
)
workflow.add_edge("generate_answer", END)
workflow.add_edge("rewrite_question", "generate_query_or_respond")
# Compile
graph = workflow.compile()
return graph
graph = create_graph()
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
# This is a simple general-purpose chatbot built on top of LangChain and Gradio.
# Before running this, make sure you have exported your OpenAI API key as an environment variable:
# export OPENAI_API_KEY="your-openai-api-key"
from langchain_openai import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
import gradio as gr
model = ChatOpenAI(model="gpt-4o-mini")
def predict(message, history):
history_langchain_format = []
for msg in history:
if msg['role'] == "user":
history_langchain_format.append(HumanMessage(content=msg['content']))
elif msg['role'] == "assistant":
history_langchain_format.append(AIMessage(content=msg['content']))
history_langchain_format.append(HumanMessage(content=message))
gpt_response = model.invoke(history_langchain_format)
return gpt_response.content
demo = gr.ChatInterface(
predict,
api_name="chat",
)
demo.launch() |