| from langchain.chains import RetrievalQA, ConversationalRetrievalChain | |
| from langchain.vectorstores import Chroma | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.document_loaders import DirectoryLoader, TextLoader | |
| from transformers import pipeline | |
| from langchain.llms import HuggingFacePipeline | |
| from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| import gradio as gr | |
| from InstructorEmbedding import INSTRUCTOR | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") | |
| pipe = pipeline( | |
| "text2text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| max_length=512, | |
| temperature=0.5, | |
| top_p=0.95, | |
| repetition_penalty=1.15 | |
| ) | |
| local_llm = HuggingFacePipeline(pipeline=pipe) | |
| print(local_llm('What is the capital of Syria?')) | |
| loader = TextLoader('info.txt') | |
| document = loader.load() | |
| text_spliter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
| texts = text_spliter.split_documents(document) | |
| embedding = HuggingFaceInstructEmbeddings() | |
| docsearch = Chroma.from_documents(texts, embedding, persist_directory='db') | |
| retriever = docsearch.as_retriever(search_kwargs={"k": 3}) | |
| qa_chain = RetrievalQA.from_chain_type(llm=local_llm, | |
| chain_type="map_reduce", | |
| retriever=retriever, | |
| return_source_documents=True) | |
| question = input('prompt: ') | |
| result = qa_chain({'query': question}) | |
| print('result: ', result['result']) | |
| def gradinterface(query): | |
| result = qa_chain({'query': query}) | |
| return result['result'] | |
| demo = gr.ChatInterface(fn=gradinterface, title='OUR_OWN_BOT') | |
| if __name__ == "__main__": | |
| demo.launch(show_api=False, share=True) | |