Spaces:
Sleeping
Sleeping
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| # Access the value of OPENAI_API_KEY | |
| OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") | |
| os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY | |
| from langchain_openai import ChatOpenAI | |
| llm_OpenAi = ChatOpenAI(model="gpt-3.5-turbo", temperature=0,) | |
| from langchain.chat_models import ChatAnyscale | |
| ANYSCALE_ENDPOINT_TOKEN=os.environ.get("ANYSCALE_ENDPOINT_TOKEN") | |
| anyscale_api_key =ANYSCALE_ENDPOINT_TOKEN | |
| llm=ChatAnyscale(anyscale_api_key=anyscale_api_key,temperature=0, model_name='mistralai/Mistral-7B-Instruct-v0.1', streaming=False) | |
| ## Create embeddings and splitter | |
| from langchain.embeddings import HuggingFaceBgeEmbeddings | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| # Create Embeddings | |
| model_name = "BAAI/bge-large-en" | |
| embedding = HuggingFaceBgeEmbeddings( | |
| model_name = model_name, | |
| # model_kwargs = {'device':'cuda'}, | |
| encode_kwargs = {'normalize_embeddings': True} | |
| ) | |
| # Create Splitter | |
| splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=1000, | |
| chunk_overlap=100, | |
| ) | |
| from langchain_community.vectorstores import FAISS | |
| persits_directory="./faiss_Test02_500_C_BGE_large" | |
| # persits_directory="./faiss_V03_C500_BGE_large-final" | |
| vectorstore= FAISS.load_local(persits_directory, embedding) | |
| # Define a custom prompt for Unser manual | |
| from langchain.prompts import PromptTemplate | |
| qa_template = (""" | |
| You are the AI assistant of the IronOne Technologies which provide services for companies members and novice users with learning with ATrad Aplication . | |
| You have provided context information below related to learning material. | |
| Context: {context} | |
| Given this information, please answer the question with the latest information. | |
| If you dont know the answer say you dont know, dont try to makeup answers. | |
| if context is not enough to answer the question, ask for more information. | |
| if context is not related to the question, say I dont know. | |
| give the answer with very clear structure and clear language. | |
| each answer Must start with code word ATrad Ai(QA): | |
| Question: {question} | |
| answer: let me think about it...""") | |
| qa_template2 = (""" | |
| Welcome to IronOne Technologies' AI Assistant, designed to assist you in learning with the ATrad Application. | |
| Context: {context} | |
| As your AI assistant, I'm here to help you navigate through learning materials and provide guidance. | |
| Please provide me with any questions or concerns you have regarding the ATrad Application. | |
| If you're unsure about something or need more information, feel free to ask. | |
| each answer Must start with code word ATrad Ai(QA): | |
| Question: {question} | |
| ATrad Ai(QA): Let me think about it...""") | |
| QA_PROMPT = PromptTemplate(input_variables=["context", "question"],template=qa_template,) | |
| # Chain for Web | |
| from langchain.chains import RetrievalQA | |
| Web_qa = RetrievalQA.from_chain_type( | |
| llm=llm_OpenAi, | |
| chain_type="stuff", | |
| retriever = vectorstore.as_retriever(search_kwargs={"k": 4}), | |
| return_source_documents= True, | |
| input_key="question", | |
| chain_type_kwargs={"prompt": QA_PROMPT}, | |
| ) | |