import streamlit as st import langchain_core 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_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 from langchain_community.embeddings import HuggingFaceBgeEmbeddings from langchain_community.llms import CTransformers from ctransformers import AutoModelForCausalLM from langchain.llms import HuggingFaceHub from transformers import AutoModelForCausalLM, AutoTokenizer from langchain.embeddings import HuggingFaceEmbeddings from langchain.llms import HuggingFacePipeline from transformers import pipeline import os import transformers import torch # from langchain_community.llms import LlamaCpp # from langchain_retrieval import BaseRetrieverChain # from dotenv import load_dotenv # load_dotenv() def get_vector_store_from_url(url): # model_name = "BAAI/bge-large-en" # model_kwargs = {'device': 'cpu'} # encode_kwargs = {'normalize_embeddings': False} # embeddings = HuggingFaceBgeEmbeddings( # model_name=model_name, # model_kwargs=model_kwargs, # encode_kwargs=encode_kwargs # ) embeddings = HuggingFaceEmbeddings(model_name='thenlper/gte-large', model_kwargs={'device': 'cpu'}) loader = WebBaseLoader(url) document = loader.load() # split the document into chunks text_splitter = RecursiveCharacterTextSplitter() document_chunks = text_splitter.split_documents(document) # create a vectorstore from the chunks # vector_store = Chroma.from_documents(document_chunks, OpenAIEmbeddings()) vector_store = Chroma.from_documents(document_chunks, embeddings) return vector_store def get_context_retriever_chain(vector_store,llm): # llm = ChatOpenAI() llm = llm 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): # llm=llm # template = "Answer the user's questions based on the below context:\n\n{context}" # human_template = "{input}" # prompt = ChatPromptTemplate.from_messages([ # ("system", template), # MessagesPlaceholder(variable_name="chat_history"), # ("user", human_template), # ]) # stuff_documents_chain = create_stuff_documents_chain(llm,prompt) # return create_retrieval_chain(retriever_chain, stuff_documents_chain) def get_conversational_rag_chain(retriever_chain,llm): if not retriever_chain: raise ValueError("`retriever_chain` cannot be None or an empty object.") template = "Answer the user's questions based on the below context:\n\n{context}" human_template = "{input}" prompt = ChatPromptTemplate.from_messages([ ("system", template), MessagesPlaceholder(variable_name="chat_history"), ("user", human_template), ]) def safe_llm(input_str: str) -> str: if isinstance(input_str, langchain_core.prompts.chat.ChatPromptValue): input_str = str(input_str) # input_str = input_str.to_messages() # Call the original llm, which should now work correctly return llm(input_str) stuff_documents_chain = create_stuff_documents_chain(safe_llm, prompt) return create_retrieval_chain(retriever_chain, stuff_documents_chain) def get_response(user_input): # llm = CTransformers( # # model = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF", # model= "TheBloke/Llama-2-7B-Chat-GGUF", # model_file = "llama-2-7b-chat.Q3_K_S.gguf", # model_type="llama", # max_new_tokens = 300, # temperature = 0.3, # lib="avx2", # for CPU # ) # model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" # # llm = HuggingFaceHub( # # repo_id=llm_model, # # model_kwargs={"temperature": 0.3, "max_new_tokens": 250, "top_k": 3} # # ) # llm = transformers.AutoModelForCausalLM.from_pretrained( # model_name, # trust_remote_code=True, # torch_dtype=torch.bfloat16, # device_map='auto' # ) # llm = HuggingFacePipeline.from_model_id( # model_id="google/flan-t5-base", # task="text2text-generation", # # model_kwargs={"temperature": 0.2}, # ) # llm = HuggingFacePipeline.from_model_id( # model_id="google-t5/t5-small", # task="text2text-generation", # # model_kwargs={"temperature": 0.2}, # ) # llm = pipeline(task="conversational", model="facebook/blenderbot-400M-distill") llm = LlamaCpp( model_path="TheBloke/OpenOrca-Platypus2-13B-GGUF", temperature=0.75, max_tokens=2000, top_p=1, # callback_manager=callback_manager, # verbose=True, # Verbose is required to pass to the callback manager ) # llm = HuggingFacePipeline.from_model_id( # model_id="lmsys/fastchat-t5-3b-v1.0", # task="text2text-generation", # # model_kwargs={"temperature": 0.2}, # ) retriever_chain = get_context_retriever_chain(st.session_state.vector_store,llm) conversation_rag_chain = get_conversational_rag_chain(retriever_chain,llm) response = conversation_rag_chain.invoke({ "chat_history": st.session_state.chat_history, "input": user_query }) return response['answer'] # app config st.set_page_config(page_title= "Chat with Websites", page_icon="🤖") st.title("Chat with Websites") #sidebar with st.sidebar: st.header("Settings") website_url = st.text_input("Website URL") # openai_apikey = st.text_input("Enter your OpenAI API key") if (website_url is None or website_url == ""): st.info("Please ensure if website URL is entered") else: if "chat_history" not in st.session_state: st.session_state.chat_history = [ AIMessage(content = "Hello, I am a bot. How can I help you"), ] if "vector_store" not in st.session_state: st.session_state.vector_store = get_vector_store_from_url(website_url) #user_input user_query = st.chat_input("Type your message here...") if user_query is not None and user_query !="": response = get_response(user_query) st.session_state.chat_history.append(HumanMessage(content=user_query)) st.session_state.chat_history.append(AIMessage(content=response)) #conversation for message in st.session_state.chat_history: if isinstance(message, AIMessage): # checking if the messsage is the instance of an AI message with st.chat_message("AI"): st.write(message.content) elif isinstance(message, HumanMessage): # checking if the messsage is the instance of a Human with st.chat_message("Human"): st.write(message.content)