Here is a sample of the code you will need to implement a RAG-FUSION. You would have to structure your requirements with this code, this serves as a guide for json files, you can implement others such as pdf, images following the same procedure.

def determine_extension(file):
    if file.endswith(".jpg", ".png"):
       send_image_to_rag_classifier(file)
    elif ...
    else ...


""" Implement the RAG fusion using the langchain library"""

import asyncio
import json
import logging
import os
import pathlib as path
from operator import itemgetter
from typing import Any

from dotenv import find_dotenv, load_dotenv
from langchain.prompts import ChatPromptTemplate
from langchain_community.vectorstores import chroma
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.vectorstores import VectorStoreRetriever
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveJsonSplitter

logger = logging.getLogger(__name__)


# read OPENAI_API_KEY from the environment
load_dotenv(find_dotenv())

# Define a prompt for the RAG model
SYSTEM_PROMPT = """ 
                  Your prompt
                """


# recursive pass data in the retriever_data
def collect_data_files(filepath: path) -> list:
    """Walk through the file path and collect the files

    Args: filepath: The file path to be walked through

    Returns:
    list: List of files
    """
    
    return store_file


# Create a recursive json splitter to split the data into chunks
def retrieve_data(data) -> list[chroma.Document]:
    """
    Retrieve the data from the file

    Args: data: The data to be retrieved

    Returns: list: List of documents
    """

    docs = collect_data_files(data)

    for file in docs:
        with open(file, "r") as f:
            data = json.loads(f.read())
            # Split the data into chunks
            splitter = RecursiveJsonSplitter(max_chunk_size=300)

            # create documents from the vector database
            documents = splitter.create_documents(texts=data, convert_lists=True)

    return documents


# vectorstore database from chroma
def vectorstore_db(data) -> VectorStoreRetriever:
    """
    Create a vectorstore database from the data
    Args: data: The data to be indexed

    Returns: VectorStoreRetriever: The vectorstore retriever
    """


    return vector_retriever


# create a function to generate queries from the RAG model
def get_unique_union_of_documents(docs: list[list]) -> list[Any]:
    """
    Get the unique union of the documents
    Args:
    docs: The documents to be processed

    Returns:
    list: The unique union of the documents"""

    return [json.loads(doc) for doc in unique_union]


# RAG FUSION
class RAGFusion:
    """
    Implement the RAG fusion
    Args:
    data: The data to be used for the RAG fusion
    """

    def __init__(self, data) -> None:

        self.data = data

    def __call__(self, question: str) -> str:
        """
        Implement the RAG fusion
        Args:
        question: The question to be answered

        Returns:
        str: The answer to the question
        """

        try:
            # create a retrieval chain
            prompt_for_rag_fusion = ChatPromptTemplate.from_template(SYSTEM_PROMPT)

            generate_query = (
                prompt_for_rag_fusion
                | ChatOpenAI(temperature=0.5, max_tokens=4096)
                | StrOutputParser()
                | (lambda x: x.split("n"))
            )

            vb = vectorstore_db(self.data)

            # create a retrieval chain
            retrieval_chain = generate_query | vb.map() | get_unique_union_of_documents

            chat_template = """
                        Answer the following questions{question} n
                        Based on the data and context provided {context} n
                        Question: {question} n
                    """

            # get the chat prompt template
            prompt = ChatPromptTemplate.from_template(chat_template)

            # use this llm
            llm = ChatOpenAI(temperature=0.5, max_tokens=4096)

            # implement the final rag fusion
            final_rag_fusion = (
                {"context": retrieval_chain, "question": itemgetter("question")}
                | prompt
                | llm
                | StrOutputParser()
            )

            return final_rag_fusion.invoke({"question": question})
        except Exception as e:
            logger.error(f"An error occurred: {e}")