from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import CharacterTextSplitter from langchain.chains.question_answering import load_qa_chain from langchain.llms import OpenAI import os from glob import glob import shutil files = glob("./shakespeare/**/*.html") os.mkdir('./data') destination_folder = './data/' for html_file in files: shutil.move(html_file, destination_folder + html_file.split("/")[-1]) from langchain.document_loaders import BSHTMLLoader, DirectoryLoader bshtml_dir_loader = DirectoryLoader('./data/', loader_cls=BSHTMLLoader) data = bshtml_dir_loader.load() from langchain.text_splitter import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter( chunk_size = 1000, chunk_overlap = 20, length_function = len, ) documents = text_splitter.split_documents(data) from langchain.embeddings.openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() from langchain.vectorstores import Chroma persist_directory = "vector_db" vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory) vectordb.persist() vectordb = None vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings) from langchain.chat_models import ChatOpenAI llm = ChatOpenAI(temperature=0, model="gpt-4") doc_retriever = vectordb.as_retriever() from langchain.chains import RetrievalQA shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever) #chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff") def make_inference(query): docs = shakespeare_qa.get_relevant_documents(query) return(chain.run(input_documents=docs, question=query)) if __name__ == "__main__": # make a gradio interface import gradio as gr gr.Interface( make_inference, [ gr.inputs.Textbox(lines=2, label="Query"), ], gr.outputs.Textbox(label="Response"), title="🗣️TalkToMyDoc📄", description="🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker's Guide to the Galaxy.", ).launch()