!pip install -qU langchain_experimental langchain_community langchain fastembed pypdf langchain-groq chromadb
%pip install --upgrade --quiet  langchain langchain-community
%pip install langchain_openai

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
loader = PyPDFLoader("hesc103.pdf")
documents = loader.load()

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=100,
    length_function=len,
    is_separator_regex=False
)
naive_chunks = text_splitter.split_documents(documents)


from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
embed_model = FastEmbedEmbeddings(model_name="BAAI/bge-base-en-v1.5")

from google.colab import userdata
from langchain_groq import ChatGroq
groq_api_key = "key"

from langchain_community.vectorstores import Chroma
naive_chunk_vectorstore = Chroma.from_documents(naive_chunks, embedding=embed_model)

from langchain_core.prompts import ChatPromptTemplate

rag_template = """\
Answer the question based only on the following Context:
{context}

Question:
{question}
"""

rag_prompt = ChatPromptTemplate.from_template(rag_template)

from langchain.chains import ConversationChain
# from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory

chat_model = ChatGroq(temperature=0,
                      model_name="mixtral-8x7b-32768",
                      api_key="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",)

from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

naive_rag_chain = (
    {"context" : naive_chunk_retriever, "question" : RunnablePassthrough()}
    | rag_prompt
    | chat_model
    | StrOutputParser()
)
memory = ConversationBufferMemory()
conversation = ConversationChain(llm=naive_rag_chain, memory=memory, verbose=True)

while True:
    user_input = input("You: ")

    if user_input.lower() == "exit":
        break

    response = conversation.predict(input=user_input)
    print("AI: ", response)