import gradio as gr import os hftoken = os.environ["hftoken"] from langchain_huggingface import HuggingFaceEndpoint # repo_id = "mistralai/Mistral-7B-Instruct-v0.3" # repo_id = "google/gemma-2-9b-it" repo_id = "meta-llama/Meta-Llama-3-8B-Instruct" llm = HuggingFaceEndpoint(repo_id = repo_id, max_new_tokens = 128, temperature = 0.7, huggingfacehub_api_token = hftoken) from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate # from langchain.document_loaders.csv_loader import CSVLoader from langchain_community.document_loaders.csv_loader import CSVLoader loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt') data = loader.load() from langchain_huggingface import HuggingFaceEmbeddings from langchain_chroma import Chroma from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings # CHECK MTEB LEADERBOARD & FIND BEST EMBEDDING MODEL model = "BAAI/bge-m3" embeddings = HuggingFaceEndpointEmbeddings(model = model) vectorstore = Chroma.from_documents(documents = data, embedding = embeddings) retriever = vectorstore.as_retriever() # from langchain.prompts import PromptTemplate from langchain_core.prompts import ChatPromptTemplate # prompt = ChatPromptTemplate.from_template("""Given the following context and a question, generate an answer based on the context only. # In the answer try to provide as much text as possible from "response" section in the source document context without making much changes. # If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!" # If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at rishi@aiotsmartlabs.com" Don't try to make up an answer. # CONTEXT: {context} # QUESTION: {question}""") prompt = ChatPromptTemplate.from_template("""As an AI assistant for AIoT SMART Labs, your task is to provide accurate answers based on the given context. 1. **Use the context:** Generate an answer based only on the context provided. Try to use as much text as possible from the "response" section in the source document without making significant changes. 2. **Identify yourself:** If someone asks "Who are you?" or a similar question, reply with "I am Rishi's assistant built using a Large Language Model!" 3. **Handle unknowns:** If you cannot find the answer in the context, state "I don't know. Please ask Rishi on Discord at https://discord.gg/6ezpZGeCcM or email rishi@aiotsmartlabs.com." Do not make up an answer. 4. **Clarity and brevity:** Ensure your answers are clear and concise. CONTEXT: {context} QUESTION: {question}""") from langchain_core.runnables import RunnablePassthrough rag_chain = ( {"context": retriever, "question": RunnablePassthrough()} | prompt | llm | StrOutputParser() ) # Define the chat response function def chatresponse(message, history): output = rag_chain.invoke(message) response = output.split('ANSWER: ')[-1].strip() return response # Launch the Gradio chat interface gr.ChatInterface(chatresponse).launch() # import gradio as gr # from langchain_community.document_loaders import CSVLoader # Changed import # from langchain_community.vectorstores import FAISS # Changed import # from langchain.prompts import PromptTemplate # from langchain.chains import RetrievalQA # from langchain.llms import HuggingFaceLLM # Adjusted for correct instantiation # import warnings # from huggingface_hub import login # import os # from transformers import pipeline # # Initialize the LLM using pipeline # llm = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct") # Adjusted initialization # # Load CSV file # loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column='prompt') # data = loader.load() # # Suppress warnings # warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated") # warnings.filterwarnings("ignore", category=FutureWarning, message="`resume_download` is deprecated") # # Embedding model # model_name = "BAAI/bge-m3" # instructor_embeddings = HuggingFaceLLM(model_name=model_name) # Adjusted for correct instantiation # # Create FAISS vector store from documents # vectordb = FAISS.from_documents(documents=data, embedding=instructor_embeddings) # retriever = vectordb.as_retriever() # # Define the prompt template # prompt_template = """Given the following context and a question, generate an answer based on the context only. # In the answer try to provide as much text as possible from "response" section in the source document context without making much changes. # If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!" # If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at rishi@aiotsmartlabs.com" Don't try to make up an answer. # CONTEXT: {context} # QUESTION: {question}""" # PROMPT = PromptTemplate( # template=prompt_template, input_variables=["context", "question"] # ) # # Initialize the RetrievalQA chain # chain = RetrievalQA.from_chain_type(llm=llm, # Adjusted initialization # chain_type="stuff", # retriever=retriever, # input_key="query", # return_source_documents=True, # chain_type_kwargs={"prompt": PROMPT}) # # Define the chat response function # def chatresponse(message, history): # output = chain(message) # return output['result'] # # Launch the Gradio chat interface # gr.ChatInterface(chatresponse).launch() # import gradio as gr # # from langchain.llms import GooglePalm # from langchain_google_genai import GoogleGenerativeAI # from langchain.document_loaders.csv_loader import CSVLoader # from langchain_huggingface import HuggingFaceEmbeddings # from langchain.vectorstores import FAISS # from langchain.prompts import PromptTemplate # from langchain.chains import RetrievalQA # import warnings # from huggingface_hub import login # import os # from transformers import pipeline # llm = pipeline("feature-extraction", model="mixedbread-ai/mxbai-embed-large-v1") # # from transformers import AutoModel # # llm = AutoModel.from_pretrained("Alibaba-NLP/gte-large-en-v1.5", trust_remote_code=True) # # LLAMA # # from transformers import AutoModelForCausalLM, AutoTokenizer # # from transformers import pipeline # # hf_token = os.environ['llama_token'] # # login(token=hf_token) # # llm = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct") # # llm = pipeline("text-generation", model = "meta-llama/Meta-Llama-3-70B-Instruct") # # MISTRAL # # llm = pipeline("text-generation", model="mistralai/Mixtral-8x22B-Instruct-v0.1") # # TO USE GOOGLE MODELS # # api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M" # # llm = GoogleGenerativeAI(model="models/text-bison-001", google_api_key=api_key) # # llm = GooglePalm(google_api_key = api_key, temperature=0.7) # # LOADING CSV FILE # loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt') # data = loader.load() # # SUPPRESSING WARNINGS # warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated") # warnings.filterwarnings("ignore", category=FutureWarning, message="`resume_download` is deprecated") # # EMBEDDING MODEL # model_name = "BAAI/bge-m3" # instructor_embeddings = HuggingFaceEmbeddings(model_name=model_name) # # Create FAISS vector store from documents # vectordb = FAISS.from_documents(documents=data, embedding=instructor_embeddings) # retriever = vectordb.as_retriever() # prompt_template = """Given the following context and a question, generate an answer based on the context only. # In the answer try to provide as much text as possible from "response" section in the source document context without making much changes. # If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!" # If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at rishi@aiotsmartlabs.com" Don't try to make up an answer. # CONTEXT: {context} # QUESTION: {question}""" # PROMPT = PromptTemplate( # template = prompt_template, input_variables = ["context", "question"] # ) # chain = RetrievalQA.from_chain_type(llm = llm, # chain_type="stuff", # retriever=retriever, # input_key="query", # return_source_documents=True, # chain_type_kwargs = {"prompt": PROMPT}) # def chatresponse(message, history): # output = chain(message) # return output['result'] # gr.ChatInterface(chatresponse).launch() # import gradio as gr # # from langchain.llms import GooglePalm # # from langchain.document_loaders.csv_loader import CSVLoader # # from langchain_huggingface import HuggingFaceEmbeddings # # from langchain.vectorstores import FAISS # from langchain_community.llms import GooglePalm # from langchain_community.document_loaders import CSVLoader # from langchain_community.vectorstores import FAISS # from langchain_huggingface import HuggingFaceEmbeddings # api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M" # llm = GooglePalm(google_api_key = api_key, temperature=0.7) # loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt') # data = loader.load() # instructor_embeddings = HuggingFaceEmbeddings(model_name = "BAAI/bge-m3") # vectordb = FAISS.from_documents(documents = data, embedding = instructor_embeddings) # retriever = vectordb.as_retriever() # from langchain.prompts import PromptTemplate # prompt_template = """Given the following context and a question, generate an answer based on the context only. # In the answer try to provide as much text as possible from "response" section in the source document context without making much changes. # If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!" # If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at rishi@aiotsmartlabs.com" Don't try to make up an answer. # CONTEXT: {context} # QUESTION: {question}""" # PROMPT = PromptTemplate( # template = prompt_template, input_variables = ["context", "question"] # ) # from langchain.chains import RetrievalQA # chain = RetrievalQA.from_chain_type(llm = llm, # chain_type="stuff", # retriever=retriever, # input_key="query", # return_source_documents=True, # chain_type_kwargs = {"prompt": PROMPT}) # def chatresponse(message, history): # output = chain(message) # return output['result'] # gr.ChatInterface(chatresponse).launch() # import gradio as gr # from langchain.llms import GooglePalm # api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M" # llm = GooglePalm(google_api_key = api_key, temperature=0.7) # from langchain.document_loaders.csv_loader import CSVLoader # loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt') # data = loader.load() # from langchain_huggingface import HuggingFaceEmbeddings # from langchain.vectorstores import FAISS # # instructor_embeddings = HuggingFaceEmbeddings(model_name = "Alibaba-NLP/gte-Qwen2-7B-instruct") # best model <-- but too big # instructor_embeddings = HuggingFaceEmbeddings(model_name = "BAAI/bge-m3") # # instructor_embeddings = HuggingFaceEmbeddings() # vectordb = FAISS.from_documents(documents = data, embedding = instructor_embeddings) # # e = embeddings_model.embed_query("What is your refund policy") # retriever = vectordb.as_retriever() # from langchain.prompts import PromptTemplate # prompt_template = """Given the following context and a question, generate an answer based on the context only. # In the answer try to provide as much text as possible from "response" section in the source document context without making much changes. # If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!" # If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at rishi@aiotsmartlabs.com" Don't try to make up an answer. # CONTEXT: {context} # QUESTION: {question}""" # PROMPT = PromptTemplate( # template = prompt_template, input_variables = ["context", "question"] # ) # from langchain.chains import RetrievalQA # chain = RetrievalQA.from_chain_type(llm = llm, # chain_type="stuff", # retriever=retriever, # input_key="query", # return_source_documents=True, # chain_type_kwargs = {"prompt": PROMPT}) # # Load your LLM model and necessary components # # Assume `chain` is a function defined in your notebook that takes a query and returns the output as shown # # For this example, we'll assume the model and chain function are already available # def chatbot(query): # response = chain(query) # # Extract the 'result' part of the response # result = response.get('result', 'Sorry, I could not find an answer.') # return result # # Define the Gradio interface # iface = gr.Interface( # fn=chatbot, # Function to call # inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your question here..."), # Input type # outputs="text", # Output type # title="Hugging Face LLM Chatbot", # description="Ask any question related to the documents and get an answer from the LLM model.", # ) # # Launch the interface # iface.launch() # # Save this file as app.py and push it to your Hugging Face Space repository # # import gradio as gr # # def greet(name, intensity): # # return "Hello, " + name + "!" * int(intensity) # # demo = gr.Interface( # # fn=greet, # # inputs=["text", "slider"], # # outputs=["text"], # # ) # # demo.launch() # # import gradio as gr # # from huggingface_hub import InferenceClient # # """ # # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference # # """ # # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") # # def respond( # # message, # # history: list[tuple[str, str]], # # system_message, # # max_tokens, # # temperature, # # top_p, # # ): # # messages = [{"role": "system", "content": system_message}] # # for val in history: # # if val[0]: # # messages.append({"role": "user", "content": val[0]}) # # if val[1]: # # messages.append({"role": "assistant", "content": val[1]}) # # messages.append({"role": "user", "content": message}) # # response = "" # # for message in client.chat_completion( # # messages, # # max_tokens=max_tokens, # # stream=True, # # temperature=temperature, # # top_p=top_p, # # ): # # token = message.choices[0].delta.content # # response += token # # yield response # # """ # # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface # # """ # # demo = gr.ChatInterface( # # respond, # # additional_inputs=[ # # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), # # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), # # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), # # gr.Slider( # # minimum=0.1, # # maximum=1.0, # # value=0.95, # # step=0.05, # # label="Top-p (nucleus sampling)", # # ), # # ], # # ) # # if __name__ == "__main__": # # demo.launch()