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| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain import HuggingFacePipeline | |
| from langchain.chains import RetrievalQA | |
| from transformers import AutoTokenizer | |
| import pickle | |
| import os | |
| with open('shakespeare.pkl', 'rb') as fp: | |
| data = pickle.load(fp) | |
| bloomz_tokenizer = AutoTokenizer.from_pretrained('bigscience/bloomz-1b7') | |
| text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(bloomz_tokenizer, chunk_size=100, chunk_overlap=0, separator='\n') | |
| documents = text_splitter.split_documents(data) | |
| embeddings = HuggingFaceEmbeddings() | |
| persist_directory = "vector_db" | |
| vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory) | |
| vectordb.persist() | |
| vectordb = None | |
| vectordb_persist = Chroma(persist_directory=persist_directory, embedding_function=embeddings) | |
| llm = HuggingFacePipeline.from_model_id( | |
| model_id="bigscience/bloomz-1b7", | |
| task="text-generation", | |
| model_kwargs={"temperature" : 0, "max_length" : 500}) | |
| doc_retriever = vectordb_persist.as_retriever() | |
| shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever) | |
| def make_inference(query): | |
| inference = shakespeare_qa.run(query) | |
| return inference | |
| 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="Ask_Shakespeare", | |
| description="️building_w_llms_qa_Shakespeare allows you to ask questions about the Shakespeare's plays.", | |
| ).launch() | |