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import gradio
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
from langchain.chains.conversation.memory import ConversationBufferMemory
def chat(question, vehicle, k=10, temperature=0.01):
chatgpt_chain = create_chatgpt_chain(temperature=temperature)
response = ask_question(question=question, vehicle=vehicle, k=k, embeddings=model_norm, chatgpt_chain=chatgpt_chain)
return response
def create_chatgpt_chain(temperature):
template = """
{chat_history}
Human: {question}
AI:
"""
prompt_template = PromptTemplate(input_variables=["chat_history", "question"], template=template)
return LLMChain(llm=OpenAI(temperature=temperature,model_name="gpt-3.5-turbo"),prompt=prompt_template,verbose=True,memory=ConversationBufferMemory(memory_key="chat_history"))
def ask_question(question, vehicle, k, embeddings, chatgpt_chain):
index = FAISS.load_local(folder_path=db_paths[vehicle], embeddings=embeddings)
prompt = get_prompt(question=question, vehicle=vehicle, k=k)
response = chatgpt_chain.run(question=prompt)
return response
def get_prompt(question, vehicle, k):
prompt = f"""
I need information from my {vehicle} manual.
I will provide an excerpt from the manual. Use the excerpt and nothing else to answer the question.
You must refer to the excerpt as "{vehicle} Manual" in your response. Here is the excerpt:
"""
index = FAISS.load_local(folder_path=db_paths[vehicle], embeddings=model_norm)
similar_docs = index.similarity_search(query=question, k=k)
context = []
for d in similar_docs:
content = d.page_content
context.append(content)
user_input = prompt + '\n[EXCERPT]' + '\n'.join(context[:k]) + '\nQuestion: ' + question
return user_input
db_paths = {
"2023 AMG C-Coupe-Cab": "data/amg_c_coupe_cab",
"2023 AMG C-Sedan": "data/amg_c_sedan",
"2023 AMG E-Coupe-Cab": "data/amg_e_coupe_cab",
"2023 AMG E-Sedan_wagon": "data/amg_e_sedan_wagon",
"2023 AMG_EQE-Sedan": "data/amg_eqe_sedan",
"2023 AMG_GLE-suv": "data/amg_gle_suv",
"2023 AMG_GLS SUV": "data/amg_gls_suv",
"2023 C-Cab": "data/c_cab",
"2023 C-Coupe": "data/c_coupe",
"2023 C-Sedan": "data/c_sedan",
"2023 CLA": "data/cla",
"2023 E-Cab": "data/e_cab",
"2023 E-Coupe": "data/e_coupe",
"2023 E-Sedan": "data/e_sedan",
"2023 E-wagon": "data/e_wagon",
"2023 eqb SUV": "data/eqb_suv",
"2023 EQE-Sedan": "data/eqe_sedan",
"2023 EQS_Sedan": "data/eqs_sedan",
"2023 EQS SUV": "data/eqs_suv",
"2023 GLA": "data/gla",
"2023 GLB": "data/glb",
"2023 GLC-Coupe": "data/glc_coupe",
"2023 GLE-Coupe": "data/gle_coupe",
"2023 GLE-suv": "data/gle_suv",
"2023 GLS SUV": "data/gls_suv"
}
vehicle_options = [
"2023 AMG C-Coupe-Cab",
"2023 AMG C-Sedan",
"2023 AMG E-Coupe-Cab",
"2023 AMG E-Sedan_wagon",
"2023 AMG_EQE-Sedan",
"2023 AMG_GLE-suv",
"2023 AMG_GLS SUV",
"2023 C-Cab",
"2023 C-Coupe",
"2023 C-Sedan",
"2023 CLA",
"2023 E-Cab",
"2023 E-Coupe",
"2023 E-Sedan",
"2023 E-wagon",
"2023 eqb SUV",
"2023 EQE-Sedan",
"2023 EQS SUV",
"2023 EQS_Sedan",
"2023 GLA",
"2023 GLB",
"2023 GLC-Coupe",
"2023 GLE-Coupe",
"2023 GLE-suv",
"2023 GLS SUV",
]
model_name = "BAAI/bge-large-en"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model_norm = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
def start_ui():
chatbot_interface = gradio.Interface(
fn=chat,
inputs=["text",
gradio.inputs.Dropdown(vehicle_options, label="Select Mercedes-Benz Owner's Manual")
#gradio.inputs.Slider(minimum=1, maximum=10, step=1, label="k")
],
outputs="text",
title="Mercedes-Benz Owner's Manual",
description="Ask a question and get answers from Mercedes-Benz Owner's Manual.<u>Disclaimer:</u> THIS IS NOT OFFICIAL AND MAY NOT BE AVAILABLE ALL THE TIME. ALWAYS LOOK AT THE OFFICIAL DOCUMENTATION at https://www.mbusa.com/en/owners/manuals",
examples=[["What are the different features of the dashboard console?", "2023 S-Class", 10, 0.01],
["What is flacon? Which page has that information? Show me all the exact content from that page", "2023 S-Class", 10, 0.01],
["What is hyperscreen?", "2023 EQS", 10, 0.01],
["Where can I find my vin?", "2023 EQS", 10, 0.01],
["Does it take more than 30 minutes to charge? Which page has that information? Show me all the exact content from that page", "2023 EQE", 10, 0.01]],
article = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=kaushikdatta.owner-manual" alt="visitor badge"/></center>'
)
chatbot_interface.launch() |