| import gradio as gr | |
| import os | |
| os.environ["hftoken"] = hftoken | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| repo_id = "mistralai/Mistral-7B-Instruct-v0.3" | |
| 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 | |
| prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}") | |
| chain = prompt | llm | StrOutputParser() | |
| 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_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 [email protected]" Don't try to make up an answer. | |
| 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() | |
| print(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 [email protected]" 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 [email protected]" 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 [email protected]" 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 [email protected]" 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() | |