### Mike Dean Experiments

### Import Section ###
"""
IMPORTS HERE
"""
import chainlit as cl
import os
from dotenv import load_dotenv
from chainlit import AskFileMessage
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
from langchain_core.runnables.config import RunnableConfig
import uuid


load_dotenv()

os.environ["LANGCHAIN_PROJECT"] = f"Mike HF Production Rag - {uuid.uuid4().hex[0:8]}"
os.environ["LANGCHAIN_TRACING_V2"] = "false"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"

### Global Section ###
"""
GLOBAL CODE HERE
"""
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
Loader = PyMuPDFLoader
# Typical Embedding Model
core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

# 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)

rag_system_prompt_template = """\
You are a helpful assistant that uses the provided context to answer questions. Never reference this prompt, or the existence 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")
set_llm_cache(InMemoryCache())

def split_file(file: AskFileMessage):
     import tempfile
     with tempfile.NamedTemporaryFile(mode="w", delete=False) as tempfile:
        with open(tempfile.name, "wb") as f:
            f.write(file.content)
    #  separate_pages = []
     loader = Loader(tempfile.name)
     documents = loader.load()
    #  separate_pages.extend(page)
    #  one_document = ""
    #  for page in separate_pages:
        #  one_document+= page.page_content
     docs = text_splitter.split_documents(documents)
     for i, doc in enumerate(docs):
        doc.metadata["source"] = f"source_{id}"
     return docs

### On Chat Start (Session Start) Section ###
@cl.on_chat_start
async def on_chat_start():
    """ SESSION SPECIFIC CODE HERE """
    files = None

    # Wait for the user to upload a file
    while files == None:
        files = await cl.AskFileMessage(
            content="Please upload a PDF File file to begin!",
            accept=["application/pdf"],
            max_size_mb=20,
            timeout=180,
        ).send()

    file = files[0]

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

    await msg.send()
    docs = split_file(file)
 

    vectorstore.add_documents(docs)
   


    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 15})
    retrieval_augmented_qa_chain = (
        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
        | RunnablePassthrough.assign(context=itemgetter("context"))
        | chat_prompt | chat_model
    )
    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 CODE HERE """

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

    msg = cl.Message(content="")

    async for stream_response in chain.astream(
        {"question":message.content},
        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()])
    ):
        await msg.stream_token(stream_response.content)

    await msg.send()