import textwrap import requests from bs4 import BeautifulSoup import difflib from langchain.document_loaders import GutenbergLoader import langchain from fastapi import FastAPI from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain import PromptTemplate, ConversationChain, LLMChain from langchain.vectorstores import Chroma, FAISS from langchain.llms import HuggingFacePipeline from InstructorEmbedding import INSTRUCTOR from langchain.embeddings import HuggingFaceInstructEmbeddings from langchain.chains import RetrievalQA, ConversationalRetrievalChain import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline from langchain.llms import Replicate from langchain import PromptTemplate, LLMChain llm = Replicate( model= "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", input={"temperature": 0.75, "max_length": 500, "top_p": 1}, ) class Configuration: model_name = 'llama2-13b' temperature = 0.5 top_p = 0.95 repetition_penalty = 1.15 split_chunk_size = 1000 split_overlap = 100 embeddings_model_repo = 'hkunlp/instructor-large' k = 3 Embeddings_path = '/book-vectordb-chroma' Persist_directory = './book-vectordb-chroma' # Function to search for a book by name and return the best match URL def search_book_by_name(book_name): base_url = "https://www.gutenberg.org/" search_url = base_url + "ebooks/search/?query=" + book_name.replace(" ", "+") + "&submit_search=Go%21" response = requests.get(search_url) soup = BeautifulSoup(response.content, "html.parser") # Find the best match link based on similarity ratio best_match_ratio = 0 best_match_url = "" for link in soup.find_all("li", class_="booklink"): link_title = link.find("span", class_="title").get_text() similarity_ratio = difflib.SequenceMatcher(None, book_name.lower(), link_title.lower()).ratio() if similarity_ratio > best_match_ratio: best_match_ratio = similarity_ratio best_match_url = base_url + link.find("a").get("href") return best_match_url # Function to get the "Plain Text UTF-8" download link from the book page def get_plain_text_link(book_url): response = requests.get(book_url) soup = BeautifulSoup(response.content, "html.parser") plain_text_link = "" for row in soup.find_all("tr"): format_cell = row.find("td", class_="unpadded icon_save") if format_cell and "Plain Text UTF-8" in format_cell.get_text(): plain_text_link = format_cell.find("a").get("href") break return plain_text_link # Function to get the content of the "Plain Text UTF-8" link def get_plain_text_content(plain_text_link): response = requests.get(plain_text_link) content = response.text return content def select_book(): book_name = input("Enter the name of the book: ") best_match_url = search_book_by_name(book_name) if best_match_url: book_id = best_match_url.split('/')[-1] # Extract the book ID formatted_url = f'https://www.gutenberg.org/cache/epub/{book_id}/pg{book_id}.txt' print(formatted_url) loader = GutenbergLoader(formatted_url) book_content = loader.load() return book_content else: print("No matching book found.") return None def create_book_embeddings(book_content): text_splitter = RecursiveCharacterTextSplitter(chunk_size = Configuration.split_chunk_size, chunk_overlap = Configuration.split_overlap) texts = text_splitter.split_documents(book_content) vectordb = None try: vectordb = Chroma.load(persist_directory = Configuration.Persist_directory, collection_name = 'book') except: vectordb = Chroma.from_documents(documents = texts, embedding = instructor_embeddings, persist_directory = Configuration.Persist_directory, collection_name = 'book') vectordb.add_documents(documents=texts, embedding=instructor_embeddings) vectordb.persist() return vectordb def wrap_text_preserve_newlines(text, width=200): # 110 # Split the input text into lines based on newline characters lines = text.split('\n') # Wrap each line individually wrapped_lines = [textwrap.fill(line, width=width) for line in lines] # Join the wrapped lines back together using newline characters wrapped_text = '\n'.join(wrapped_lines) return wrapped_text def process_llm_response(llm_response): print(llm_response) ans = wrap_text_preserve_newlines(llm_response['result']) sources_used = ' \n'.join([str(source.metadata['source']) for source in llm_response['source_documents']]) ans = ans + '\n\nSources: \n' + sources_used return ans prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. {context} Question: {question} Answer:""" PROMPT = PromptTemplate( template=prompt_template, input_variables=["context", "question"] ) def generate_answer_from_embeddings(query, book_embeddings): """ Retrieve documents from the vector database and then pass them to the language model to generate an answer. Args: query: The user's question. book_embeddings: The embeddings of the book. Returns: The answer to the question. """ retriever = book_embeddings.as_retriever(search_kwargs={"k": Configuration.k, "search_type": "similarity"}) docs = book_embeddings.similarity_search(query) qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever, chain_type_kwargs={"prompt": PROMPT}, return_source_documents=True, verbose=False, ) llm_response = qa_chain(query) ans = process_llm_response(llm_response) return ans app = FastAPI() llm = Replicate( model= "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", input={"temperature": 0.75, "max_length": 500, "top_p": 1}, ) instructor_embeddings = HuggingFaceInstructEmbeddings(model_name = Configuration.embeddings_model_repo, model_kwargs = {"device": "cpu"})