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| import langchain | |
| import gradio as gr | |
| import random | |
| import time | |
| import transformers | |
| from langchain import HuggingFacePipeline | |
| from langchain import PromptTemplate, LLMChain | |
| import os | |
| import torch | |
| import torch | |
| from transformers import LlamaForCausalLM, LlamaTokenizer | |
| # Hugging Face model_path | |
| model_id = 'SachinKaushik/docGPT' | |
| tokenizer = LlamaTokenizer.from_pretrained(model_id) | |
| model = LlamaForCausalLM.from_pretrained( | |
| model_id, torch_dtype=torch.float16, device_map='auto', | |
| ) | |
| # set model to eval mode | |
| model.eval() | |
| # Build HF Transformers pipeline | |
| pipeline=transformers.pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| device_map="auto", | |
| max_length=768, | |
| do_sample=True, | |
| top_k=10, | |
| num_return_sequences=1, | |
| eos_token_id=tokenizer.eos_token_id | |
| ) | |
| # Setup prompt template | |
| template = PromptTemplate(input_variables=['input'], template='{input}') | |
| # Pass hugging face pipeline to langchain class | |
| llm = HuggingFacePipeline(pipeline=pipeline) | |
| # Build stacked LLM chain i.e. prompt-formatting + LLM | |
| chain = LLMChain(llm=llm, prompt=template) | |
| # setup prompt template for an instruction with no input | |
| prompt = PromptTemplate( | |
| input_variables=["query"], | |
| template="""You are a helpful AI assistant, you will answer the users query | |
| with a short but precise answer. If you are not sure about the answer you state | |
| "I don't know". This is a conversation, not a webpage, there should be ZERO HTML | |
| in the response. | |
| Remember, Assistant responses are concise. Here is the conversation: | |
| User: {query} | |
| Assistant: """ | |
| ) | |
| # Pass hugging face pipeline to langchain class | |
| llm = HuggingFacePipeline(pipeline=pipeline) | |
| # Build stacked LLM chain i.e. prompt-formatting + LLM | |
| llm_chain = LLMChain(llm=llm, prompt=prompt) | |
| # import PDF document loaders and splitter | |
| from langchain.document_loaders import PyPDFLoader, TextLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| # Import chroma as the vector store | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import RetrievalQA | |
| # Import vector store tools | |
| from langchain.agents.agent_toolkits import ( | |
| create_vectorstore_agent, | |
| VectorStoreToolkit, | |
| VectorStoreInfo | |
| ) | |
| # embedding Class | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| # function to generate embeddings | |
| from langchain.document_loaders import WebBaseLoader | |
| def load_data_in_VectorDB(data_source,doc_type='text', model_id='intfloat/e5-base-v2', chunk_size=300, chunk_overlap=100): | |
| if doc_type=="text": | |
| loader = TextLoader(data_source,encoding="utf-8" ) | |
| documents = loader.load() | |
| else: | |
| loader = WebBaseLoader(data_source) | |
| documents = loader.load() | |
| text_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0,add_start_index=True ) | |
| texts = text_splitter.split_documents(documents) | |
| embeddings = HuggingFaceEmbeddings(model_name=model_id) | |
| return texts, embeddings | |
| texts, embeddings = load_data_in_VectorDB(data_source='https://en.wikipedia.org/wiki/2022%E2%80%9323_NBA_season',doc_type='web') | |
| db = Chroma.from_documents(texts, embeddings) | |
| retriever = db.as_retriever() | |
| # Pass hugging face pipeline to langchain class | |
| llm = HuggingFacePipeline(pipeline=pipeline) | |
| # qa agent using LLM and Retriever | |
| qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True) | |
| import gradio as gr | |
| def generate_answer(query): | |
| ans = qa({"query": query}) | |
| ans = ans['result'] | |
| meta= "\n".join([i for i in {i.metadata['source'] for i in result['source_documents']}]) | |
| return f"DocGPT Response: {ans} \nSource: {meta}" | |
| theme = gr.themes.Default(#color contructors | |
| primary_hue="red", | |
| secondary_hue="blue", | |
| neutral_hue="green") | |
| with gr.Blocks(css="""#col_container {margin-left: auto; margin-right: auto;} | |
| # DocumentGPT {height: 520px; overflow: auto;} """, theme=theme ) as demo: | |
| chatbot = gr.Chatbot(label="DocumentGPTBot") | |
| msg = gr.Textbox(label = "DocGPT") | |
| clear = gr.ClearButton([msg, chatbot]) | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot(history): | |
| bot_message = generate_answer(history[-1][0]) | |
| history[-1][1] = "" | |
| for character in bot_message: | |
| history[-1][1] += character | |
| time.sleep(0.05) | |
| yield history | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, chatbot, chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| with gr.Row(visible=True) as button_row: | |
| upvote_btn = gr.Button(value="👍 Upvote", interactive=True) | |
| downvote_btn = gr.Button(value="👎 Downvote", interactive=True) | |
| demo.queue() | |
| demo.launch(debug=True) |