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Create app.py

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  1. app.py +278 -0
app.py ADDED
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+ import gradio as gr
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+ import os
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+ from getpass import getpass
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+ from langchain_community.document_loaders import PyPDFLoader
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+ from langchain.text_splitter import RecursiveCharacterTextSplitter
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+ from langchain_community.vectorstores import Chroma
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+ from langchain.chains import ConversationalRetrievalChain
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+ from langchain_community.embeddings import HuggingFaceEmbeddings
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+ from langchain_community.llms import HuggingFacePipeline
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+ from langchain.chains import ConversationChain
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+ from langchain.memory import ConversationBufferMemory
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+ from langchain_anthropic import ChatAnthropic
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+
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+ from pathlib import Path
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+ import chromadb
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+ from unidecode import unidecode
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+
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+ from transformers import AutoTokenizer
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+ import transformers
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+ import torch
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+ import tqdm
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+ import accelerate
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+ import re
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+
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+ # Load PDF document and create doc splits
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+ def load_doc(list_file_path, chunk_size, chunk_overlap):
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+ # Processing for one document only
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+ loaders = [PyPDFLoader(x) for x in list_file_path]
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+ pages = []
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+ for loader in loaders:
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+ pages.extend(loader.load())
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+ # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
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+ text_splitter = RecursiveCharacterTextSplitter(
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+ chunk_size = chunk_size,
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+ chunk_overlap = chunk_overlap)
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+ doc_splits = text_splitter.split_documents(pages)
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+ return doc_splits
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+
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+ # Create vector database
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+ def create_db(splits, collection_name):
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+ embedding = HuggingFaceEmbeddings()
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+ new_client = chromadb.EphemeralClient()
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+ vectordb = Chroma.from_documents(
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+ documents=splits,
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+ embedding=embedding,
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+ client=new_client,
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+ collection_name=collection_name,
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+ )
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+ return vectordb
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+
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+
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+ # Load vector database
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+ def load_db():
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+ embedding = HuggingFaceEmbeddings()
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+ vectordb = Chroma(
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+ embedding_function=embedding)
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+ return vectordb
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+
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+
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+ # Initialize langchain LLM chain
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+ def initialize_llmchain(key, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
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+ progress(0.1, desc="Initializing...")
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+
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+ llm = ChatAnthropic(model_name="claude-3-opus-20240229",
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+ temperature=temperature,
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+ anthropic_api_key=key
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+ # max_new_tokens = max_tokens,
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+ # top_k = top_k,
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+ )
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+
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+ progress(0.75, desc="Defining buffer memory...")
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+ memory = ConversationBufferMemory(
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+ memory_key="chat_history",
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+ output_key='answer',
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+ return_messages=True
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+ )
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+ # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
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+ retriever=vector_db.as_retriever()
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+ progress(0.8, desc="Defining retrieval chain...")
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+ qa_chain = ConversationalRetrievalChain.from_llm(
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+ llm,
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+ retriever=retriever,
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+ chain_type="stuff",
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+ memory=memory,
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+ # combine_docs_chain_kwargs={"prompt": your_prompt})
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+ return_source_documents=True,
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+ #return_generated_question=False,
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+ verbose=False,
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+ )
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+ progress(0.9, desc="Done!")
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+ return qa_chain
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+
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+
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+ # Generate collection name for vector database
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+ # - Use filepath as input, ensuring unicode text
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+ def create_collection_name(filepath):
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+ # Extract filename without extension
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+ collection_name = Path(filepath).stem
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+ # Fix potential issues from naming convention
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+ ## Remove space
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+ collection_name = collection_name.replace(" ","-")
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+ ## ASCII transliterations of Unicode text
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+ collection_name = unidecode(collection_name)
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+ ## Remove special characters
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+ #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
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+ collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
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+ ## Limit length to 50 characters
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+ collection_name = collection_name[:50]
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+ ## Minimum length of 3 characters
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+ if len(collection_name) < 3:
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+ collection_name = collection_name + 'xyz'
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+ ## Enforce start and end as alphanumeric character
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+ if not collection_name[0].isalnum():
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+ collection_name = 'A' + collection_name[1:]
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+ if not collection_name[-1].isalnum():
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+ collection_name = collection_name[:-1] + 'Z'
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+ print('Filepath: ', filepath)
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+ print('Collection name: ', collection_name)
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+ return collection_name
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+
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+
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+ # Initialize database
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+ def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
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+ # Create list of documents (when valid)
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+ list_file_path = [x.name for x in list_file_obj if x is not None]
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+ # Create collection_name for vector database
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+ progress(0.1, desc="Creating collection name...")
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+ collection_name = create_collection_name(list_file_path[0])
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+ progress(0.25, desc="Loading document...")
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+ # Load document and create splits
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+ doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
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+ # Create or load vector database
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+ progress(0.5, desc="Generating vector database...")
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+ # global vector_db
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+ vector_db = create_db(doc_splits, collection_name)
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+ progress(0.9, desc="Done!")
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+ return vector_db, collection_name, "Complete!"
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+
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+
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+ def initialize_LLM( key, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
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+ qa_chain = initialize_llmchain( key, llm_temperature, max_tokens, top_k, vector_db, progress)
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+ return qa_chain, "Complete!"
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+
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+
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+ def format_chat_history(message, chat_history):
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+ formatted_chat_history = []
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+ for user_message, bot_message in chat_history:
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+ formatted_chat_history.append(f"User: {user_message}")
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+ formatted_chat_history.append(f"Assistant: {bot_message}")
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+ return formatted_chat_history
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+
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+
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+ def conversation(qa_chain, message, history):
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+ formatted_chat_history = format_chat_history(message, history)
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+
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+ # Generate response using QA chain
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+ response = qa_chain({"question": message, "chat_history": formatted_chat_history})
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+ response_answer = response["answer"]
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+ if response_answer.find("Helpful Answer:") != -1:
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+ response_answer = response_answer.split("Helpful Answer:")[-1]
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+ response_sources = response["source_documents"]
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+ response_source1 = response_sources[0].page_content.strip()
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+ response_source2 = response_sources[1].page_content.strip()
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+ response_source3 = response_sources[2].page_content.strip()
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+
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+ # Langchain sources are zero-based
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+ response_source1_page = response_sources[0].metadata["page"] + 1
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+ response_source2_page = response_sources[1].metadata["page"] + 1
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+ response_source3_page = response_sources[2].metadata["page"] + 1
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+
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+ # Append user message and response to chat history
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+ new_history = history + [(message, response_answer)]
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+ return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
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+
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+
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+ def upload_file(file_obj):
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+ list_file_path = []
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+ for idx, file in enumerate(file_obj):
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+ file_path = file_obj.name
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+ list_file_path.append(file_path)
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+ return list_file_path
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+
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+
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+ def demo():
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+ with gr.Blocks(theme="base") as demo:
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+ vector_db = gr.State()
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+ qa_chain = gr.State()
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+ collection_name = gr.State()
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+
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+ gr.Markdown(
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+ """<center><h2>PDF-based chatbot (powered by LangChain and Anthropic Claude-3)</center></h2>
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+ <h3>Ask any questions about your PDF documents, along with follow-ups</h3>
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+ <b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents. \
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+ When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i>
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+ <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate an output.<br>
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+ """)
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+ with gr.Tab("Step 1 - Document pre-processing"):
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+ with gr.Row():
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+ document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
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+ # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
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+ with gr.Row():
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+ db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
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+ with gr.Accordion("Advanced options - Document text splitter", open=False):
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+ with gr.Row():
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+ slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
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+ with gr.Row():
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+ slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
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+ with gr.Row():
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+ db_progress = gr.Textbox(label="Vector database initialization", value="None")
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+ with gr.Row():
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+ db_btn = gr.Button("Generate vector database...")
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+
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+ with gr.Tab("Step 2 - Claude QA chain initialization"):
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+ with gr.Row():
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+ gr.Markdown(
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+ """<h3>To use Anthropic models, you will need to set the ANTHROPIC_API_KEY environment variable. You can get an Anthropic API key <a href="https://console.anthropic.com/settings/keys">here</a></h3>""")
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+ with gr.Row():
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+ claude_key = gr.Textbox(placeholder="Enter your Anthropic API Key...", container=True,label="Anthropic API Key")
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+ with gr.Accordion("Advanced options - LLM model", open=False):
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+ with gr.Row():
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+ slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
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+ with gr.Row():
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+ slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
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+ with gr.Row():
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+ slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
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+ with gr.Row():
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+ llm_progress = gr.Textbox(value="None",label="QA chain initialization")
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+ with gr.Row():
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+ qachain_btn = gr.Button("Initialize question-answering chain...")
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+
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+ with gr.Tab("Step 3 - Conversation with chatbot"):
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+ chatbot = gr.Chatbot(height=300)
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+ with gr.Accordion("Advanced - Document references", open=False):
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+ with gr.Row():
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+ doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
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+ source1_page = gr.Number(label="Page", scale=1)
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+ with gr.Row():
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+ doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
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+ source2_page = gr.Number(label="Page", scale=1)
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+ with gr.Row():
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+ doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
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+ source3_page = gr.Number(label="Page", scale=1)
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+ with gr.Row():
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+ msg = gr.Textbox(placeholder="Type message", container=True)
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+ with gr.Row():
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+ submit_btn = gr.Button("Submit")
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+ clear_btn = gr.ClearButton([msg, chatbot])
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+
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+ # Preprocessing events
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+ #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
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+ db_btn.click(initialize_database, \
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+ inputs=[document, slider_chunk_size, slider_chunk_overlap], \
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+ outputs=[vector_db, collection_name, db_progress])
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+ qachain_btn.click(initialize_LLM, \
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+ inputs=[ claude_key, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
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+ outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
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+ inputs=None, \
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+ outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
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+ queue=False)
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+
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+ # Chatbot events
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+ msg.submit(conversation, \
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+ inputs=[qa_chain, msg, chatbot], \
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+ outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
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+ queue=False)
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+ submit_btn.click(conversation, \
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+ inputs=[qa_chain, msg, chatbot], \
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+ outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
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+ queue=False)
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+ clear_btn.click(lambda:[None,"",0,"",0,"",0], \
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+ inputs=None, \
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+ outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
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+ queue=False)
274
+ demo.queue().launch(debug=True)
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+
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+
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+ if __name__ == "__main__":
278
+ demo()