Fabelous-Ai-Chat/scripts/Rag.py

from langchain_community.document_loaders import UnstructuredHTMLLoader, WebBaseLoader, UnstructuredMarkdownLoader, PyPDFLoader
from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain_community.embeddings import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.chat_models import ChatOllama
from langchain.chains import RetrievalQA



class Rag:
    def __init__(self, embeddings, model,
                 base_url_llm,base_url_embed, base_header,
                 embeddings_header):
        self.retriever = None
        self.data = None
        self.model = model
        
        self.base_url_llm = base_url_llm
        self.base_url_embed = base_url_embed

        if self.is_empty(base_header):
            self.base_header = ""
        else: 
            self.base_header = base_header
        if self.is_empty(embeddings_header):
            self.embeddings_header = ""
        else:
            self.embeddings_header = embeddings_header
        self.embeddings = OllamaEmbeddings(model=embeddings, headers=self.embeddings_header, base_url=self.base_url_embed)

    def init_ollama(self):
        self.chat_ollama =  ChatOllama(
            base_url=self.base_url_llm,
            model=self.model,
            headers = self.base_header
        )

    def get_file(self, file_path):
        # Check if the file path starts with 'https://'
        if file_path.startswith('https://'):
            loader = WebBaseLoader(file_path)
            data = loader.load()
            if data is None:
                return False
        else:
            file_type = file_path.split(".")[-1]
            try:
                match file_type:
                    case "csv":
                        loader = CSVLoader(file_path=file_path)
                        data = loader.load()
                    case "html": # Corrected the typo in the variable name
                        loader = UnstructuredHTMLLoader(file_path=file_path)
                        data = loader.load()
                    case "md":
                        loader = UnstructuredMarkdownLoader(file_path=file_path)
                        data = loader.load()
                    case "pdf":
                        loader = PyPDFLoader(file_path=file_path)
                        data = loader.load_and_split()
                    case _:
                        loader = WebBaseLoader(file_path)
                        data = loader.load()
            except OSError:
                return False
            
        self.data = data    
        return True
            

    def is_empty(self, dictionary):
        return len(dictionary) == 1 and list(dictionary.keys())[0] == '' and list(dictionary.values())[0] == ''

        

    def receive_data(self, file_path):
        try:
            if self.get_file(file_path):
                text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=0)
                splitted =  text_splitter.split_documents(self.data)
                self.retriever = Chroma.from_documents(documents=splitted, embedding=self.embeddings).as_retriever()
                return (False, "Success")
            else:
                return (True, f"'{file_path}' unsupported, read documentation for more information")    
        except (ValueError, AttributeError):
            return (True, "An unexpected Error occuried")    
    def get_request(self, prompt):
        qachain=RetrievalQA.from_chain_type(self.chat_ollama, retriever=self.retriever)
        try:
            return qachain.invoke({"query": prompt})["result"]
        except ValueError:
            return (True, "An unexpected Error occuried")