# AI assistant with a RAG system to query information from the CAMELS cosmological simulations using Langchain
# Author: Pablo Villanueva Domingo

import gradio as gr
from langchain import hub
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_mistralai import ChatMistralAI
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.rate_limiters import InMemoryRateLimiter

# Define a limiter to avoid rate limit issues with MistralAI
rate_limiter = InMemoryRateLimiter(
    requests_per_second=0.1,  # <-- MistralAI free. We can only make a request once every second
    check_every_n_seconds=0.01,  # Wake up every 100 ms to check whether allowed to make a request,
    max_bucket_size=10,  # Controls the maximum burst size.
)

# Get urls
urlsfile = open("urls.txt")
urls = urlsfile.readlines()
urls = [url.replace("\n","") for url in urls]
urlsfile.close()

# Load, chunk and index the contents of the blog.
loader = WebBaseLoader(urls)
docs = loader.load()

print("Pages loaded:",len(docs))

# Join content pages for processing
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

# Create a RAG chain
def RAG(llm, docs, embeddings):

    # Split text
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    splits = text_splitter.split_documents(docs)

    # Create vector store
    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)

    # Retrieve and generate using the relevant snippets of the documents
    retriever = vectorstore.as_retriever()

    # Prompt basis example for RAG systems
    prompt = hub.pull("rlm/rag-prompt")

    # Create the chain
    rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )

    return rag_chain

# LLM model
llm = ChatMistralAI(model="mistral-large-latest", rate_limiter=rate_limiter)

# Embeddings
embed_model = "sentence-transformers/multi-qa-distilbert-cos-v1"
# embed_model = "nvidia/NV-Embed-v2"
embeddings = HuggingFaceInstructEmbeddings(model_name=embed_model)

# RAG chain
rag_chain = RAG(llm, docs, embeddings)

# Function to handle prompt and query the RAG chain
def handle_prompt(message, history):
    try:
        # Stream output
        out=""
        for chunk in rag_chain.stream(message):
            out += chunk
            yield out
    except:
        raise gr.Error("Requests rate limit exceeded")

# Predefined messages and examples
description = "AI powered assistant which answers any question related to the [CAMELS simulations](https://www.camel-simulations.org/)."
greetingsmessage = "Hi, I'm the CAMELS DocBot, I'm here to assist you with any question related to the CAMELS simulations."
example_questions = [
                    "How can I read a halo file?",
                    "Which simulation suites are included in CAMELS?",
                    "Which are the largest volumes in CAMELS simulations?",
                    "Write a complete snippet of code getting the power spectrum of a simulation"
                     ]

# Define customized Gradio chatbot
chatbot = gr.Chatbot([{"role":"assistant", "content":greetingsmessage}],
                     type="messages",
                     avatar_images=["ims/userpic.png","ims/camelslogo.jpg"],
                     height="60vh")

# Define Gradio interface
demo = gr.ChatInterface(handle_prompt,
                        type="messages",
                        title="CAMELS DocBot",
                        fill_height=True,
                        examples=example_questions,
                        theme=gr.themes.Soft(),
                        description=description,
                        cache_examples=False,
                        chatbot=chatbot)

demo.launch()