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from langchain.chains import ConversationalRetrievalChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.memory import ConversationTokenBufferMemory
from langchain.llms import HuggingFacePipeline
# from langchain import PromptTemplate
from langchain.prompts import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.document_loaders import (
CSVLoader,
DirectoryLoader,
GitLoader,
NotebookLoader,
OnlinePDFLoader,
PythonLoader,
TextLoader,
UnstructuredFileLoader,
UnstructuredHTMLLoader,
UnstructuredPDFLoader,
UnstructuredWordDocumentLoader,
WebBaseLoader,
PyPDFLoader,
UnstructuredMarkdownLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredPowerPointLoader,
UnstructuredODTLoader,
NotebookLoader,
UnstructuredFileLoader
)
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
StoppingCriteria,
StoppingCriteriaList,
pipeline,
GenerationConfig,
TextStreamer,
pipeline
)
from langchain.llms import HuggingFaceHub
import torch
from transformers import BitsAndBytesConfig
import os
from langchain.llms import CTransformers
import streamlit as st
from langchain.document_loaders.base import BaseLoader
from langchain.schema import Document
import gradio as gr
import tempfile
import timeit
FILE_LOADER_MAPPING = {
"csv": (CSVLoader, {"encoding": "utf-8"}),
"doc": (UnstructuredWordDocumentLoader, {}),
"docx": (UnstructuredWordDocumentLoader, {}),
"epub": (UnstructuredEPubLoader, {}),
"html": (UnstructuredHTMLLoader, {}),
"md": (UnstructuredMarkdownLoader, {}),
"odt": (UnstructuredODTLoader, {}),
"pdf": (PyPDFLoader, {}),
"ppt": (UnstructuredPowerPointLoader, {}),
"pptx": (UnstructuredPowerPointLoader, {}),
"txt": (TextLoader, {"encoding": "utf8"}),
"ipynb": (NotebookLoader, {}),
"py": (PythonLoader, {}),
# Add more mappings for other file extensions and loaders as needed
}
def load_model():
config = {'max_new_tokens': 1024,
'repetition_penalty': 1.1,
'temperature': 0.1,
'top_k': 50,
'top_p': 0.9,
'stream': True,
'threads': int(os.cpu_count() / 2)
}
llm = CTransformers(
model = "TheBloke/zephyr-7B-beta-GGUF",
model_file = "zephyr-7b-beta.Q4_0.gguf",
callbacks=[StreamingStdOutCallbackHandler()],
lib="avx2", #for CPU use
**config
# model_type=model_type,
# max_new_tokens=max_new_tokens, # type: ignore
# temperature=temperature, # type: ignore
)
return llm |