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#####################################
## BitsAndBytes
#####################################
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "bn22/Mistral-7B-Instruct-v0.1-sharded"
###### other models:
# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
# "bn22/Mistral-7B-Instruct-v0.1-sharded"
# "HuggingFaceH4/zephyr-7b-beta"
# function for loading 4-bit quantized model
def load_quantized_model(model_name: str):
model = HuggingFaceHub(
repo_id="google/flan-ul2",
model_kwargs={"temperature":0.1,
"max_new_tokens":256})
"""
:param model_name: Name or path of the model to be loaded.
:return: Loaded quantized model.
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
quantization_config=bnb_config
)"""
return model
##################################################
## vs chat
##################################################
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
#from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores.faiss import FAISS
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
load_dotenv()
def get_vectorstore_from_url(url):
# get the text in document form
loader = WebBaseLoader(url)
document = loader.load()
# split the document into chunks
text_splitter = RecursiveCharacterTextSplitter()
document_chunks = text_splitter.split_documents(document)
#######
'''
FAISS
A FAISS vector store containing the embeddings of the text chunks.
'''
model = "BAAI/bge-base-en-v1.5"
encode_kwargs = {
"normalize_embeddings": True
} # set True to compute cosine similarity
embeddings = HuggingFaceBgeEmbeddings(
model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
)
# load from disk
vector_store = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
#vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
vector_store = Chroma.from_documents(document_chunks, embeddings, persist_directory="./chroma_db")
print("-----")
print(vector_store.similarity_search("What is ALiBi?"))
print("-----")
#######
# create a vectorstore from the chunks
return vector_store
def get_context_retriever_chain(vector_store):
# specify model huggingface mode name
model_name = "anakin87/zephyr-7b-alpha-sharded"
# model_name = "bn22/Mistral-7B-Instruct-v0.1-sharded"
###### other models:
# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
# "bn22/Mistral-7B-Instruct-v0.1-sharded"
# "HuggingFaceH4/zephyr-7b-beta"
# function for loading 4-bit quantized model
llm = load_quantized_model(model_name)
retriever = vector_store.as_retriever()
prompt = ChatPromptTemplate.from_messages([
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
])
retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
return retriever_chain
def get_conversational_rag_chain(retriever_chain):
llm = load_quantized_model(model_name)
prompt = ChatPromptTemplate.from_messages([
("system", "Answer the user's questions based on the below context:\n\n{context}"),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
])
stuff_documents_chain = create_stuff_documents_chain(llm,prompt)
return create_retrieval_chain(retriever_chain, stuff_documents_chain)
def get_response(user_input):
retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
response = conversation_rag_chain.invoke({
"chat_history": st.session_state.chat_history,
"input": user_query
})
return response['answer']
###################
###################
import gradio as gr
##from langchain_core.runnables.base import ChatPromptValue
#from torch import tensor
# Create Gradio interface
#vector_store = None # Set your vector store here
chat_history = [] # Set your chat history here
# Define your function here
def get_response(user_input):
# Define the prompt as a ChatPromptValue object
#user_input = ChatPromptValue(user_input)
# Convert the prompt to a tensor
#input_ids = user_input.tensor
#vs = get_vectorstore_from_url(user_url, all_domain)
vs = get_vectorstore_from_url("https://de.wikipedia.org/wiki/K%C3%BCnstliche_Intelligenz")
print("------ here 22 " )
chat_history =[]
retriever_chain = get_context_retriever_chain(vs)
conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
response = conversation_rag_chain.invoke({
"chat_history": chat_history,
"input": user_input
})
return response['answer']
def simple(text:str):
return text +" hhhmmm "
app = gr.Interface(
fn=get_response,
#fn=simple,
inputs=["text"],
outputs="text",
title="Chat with Websites",
description="Type your message and chat with websites.",
#allow_flagging=False
)
app.launch(debug=True, share=True)#wie registriere ich mich bei bofrost? Was kosten Linguine |