### Import Section ###
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyMuPDFLoader
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain.storage import LocalFileStore
from langchain_qdrant import QdrantVectorStore
from langchain.embeddings import CacheBackedEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.globals import set_llm_cache
from langchain_openai import ChatOpenAI
from langchain_core.caches import InMemoryCache
from operator import itemgetter
from langchain_core.runnables.passthrough import RunnablePassthrough
import uuid
import chainlit as cl




### Global Section ###
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
Loader = PyMuPDFLoader

# Typical Embedding Model
core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

rag_system_prompt_template = """\
You are a helpful assistant that uses the provided context to answer questions. Never reference this prompt, or the existance of context.
"""

rag_message_list = [
    {"role" : "system", "content" : rag_system_prompt_template},
]

rag_user_prompt_template = """\
Question:
{question}
Context:
{context}
"""

chat_prompt = ChatPromptTemplate.from_messages([
    ("system", rag_system_prompt_template),
    ("human", rag_user_prompt_template)
])

chat_model = ChatOpenAI(model="gpt-4o-mini")
set_llm_cache(InMemoryCache())
chat_openai = ChatOpenAI()


### On Chat Start (Session Start) Section ###
@cl.on_chat_start
async def on_chat_start():
    files = None

    # Wait for the user to upload a file
    while files == None:
        files = await cl.AskFileMessage(
            content="Please upload a Text or PDF File file to begin!",
            accept=["text/plain", "application/pdf"],
            max_size_mb=2,
            timeout=180,
        ).send()

    file = files[0]
    

    msg = cl.Message(
        content=f"Processing `{file.name}`...", disable_human_feedback=True
    )
    await msg.send()

    # load the file
    loader = Loader(file.name)
    documents = loader.load()
    docs = text_splitter.split_documents(documents)
    for i, doc in enumerate(docs):
        doc.metadata["source"] = f"source_{i}"
        
    print(f"Processing {len(docs)} text chunks")

    # Typical QDrant Client Set-up
    collection_name = f"pdf_to_parse_{uuid.uuid4()}"
    client = QdrantClient(":memory:")
    client.create_collection(
        collection_name=collection_name,
        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
    )
    
    # Adding cache!
    store = LocalFileStore("./cache/")
    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
        core_embeddings, store, namespace=core_embeddings.model
    )

    # Typical QDrant Vector Store Set-up
    vectorstore = QdrantVectorStore(
        client=client,
        collection_name=collection_name,
        embedding=cached_embedder)
    vectorstore.add_documents(docs)
    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
    
    # Create a chain
    retrieval_augmented_qa_chain = (
        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
        | RunnablePassthrough.assign(context=itemgetter("context"))
        | chat_prompt | chat_model
    )
    
    # Let the user know that the system is ready
    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
    await msg.update()
    cl.user_session.set("chain", retrieval_augmented_qa_chain)


### Rename Chains ###
@cl.author_rename
def rename(orig_author: str):
    rename_dict = {"LLMMathChain": "Albert Einstein", "Chatbot": "Assistant"}
    return rename_dict.get(orig_author, orig_author)


### On Message Section ###
@cl.on_message
async def main(message: cl.Message):
    chain = cl.user_session.get("chain")

    msg = cl.Message(content="")
    result = chain.invoke({"question": message.content})
    msg = cl.Message(content=result)

    await msg.send()