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You're working for a well-known car manufacturer who is looking at implementing LLMs into vehicles to provide guidance to drivers. You've been asked to experiment with integrating car manuals with an LLM to create a context-aware chatbot. They hope that this context-aware LLM can be hooked up to a text-to-speech software to read the model's response aloud.

As a proof of concept, you'll integrate several pages from a car manual that contains car warning messages and their meanings and recommended actions. This particular manual, stored as an HTML file, mg-zs-warning-messages.html, is from an MG ZS, a compact SUV. Armed with your newfound knowledge of LLMs and LangChain, you'll implement Retrieval Augmented Generation (RAG) to create the context-aware chatbot.

# Update your environment to Python 3.10 as described above before running this cell
import subprocess
import pkg_resources

def install_if_needed(package, version):
    '''Function to ensure that the libraries used are consistent to avoid errors.'''
    try:
        pkg = pkg_resources.get_distribution(package)
    except pkg_resources.DistributionNotFound:
        subprocess.check_call(["pip", "install", f"{package}=={version}"])

install_if_needed("langchain", "0.2.2")
install_if_needed("langchain-openai", "0.1.8")
install_if_needed("langchain-community", "0.2.3")
install_if_needed("unstructured", "0.14.4")
install_if_needed("chromadb", "0.5.0")
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# Set your API key to a variable
import os
openai_api_key = os.environ["OPENAI_API_KEY"]

# Import the required packages
import langchain
from langchain import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain_openai import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain_community.document_loaders import UnstructuredHTMLLoader
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# Load the HTML as a LangChain document loader
loader = UnstructuredHTMLLoader(file_path="data/mg-zs-warning-messages.html")
raw_car_docs = loader.load()
str_car_docs = str(raw_car_docs)
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text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=100,
    chunk_overlap=20,
    length_function=len,
    is_separator_regex=False,
)

texts = text_splitter.create_documents([str_car_docs])
db = Chroma.from_documents(texts, OpenAIEmbeddings())
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prompt = PromptTemplate(input_variables=['question', 'context'], template="You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question} \nContext: {context} \nAnswer:")

query = "The Gasoline Particular Filter Full warning has appeared. What does this mean and what should I do about it?"
retriever = db.as_retriever(k=4)

docs = retriever.invoke(query)

llm = ChatOpenAI(
    model_name='gpt-4o-mini',
    temperature=0.0
)

rag_chain = (
    {"context": retriever , "question": RunnablePassthrough()}
    | prompt
    | llm
)

answer = llm.invoke(query).content

print(answer)