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| import openai, os | |
| openai.api_key = "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" | |
| # openai.api_key = os.environ["OPENAI_API_KEY"] | |
| from llama_index.core import SimpleDirectoryReader | |
| documents = SimpleDirectoryReader( | |
| input_files = ["./HFNWAY.pdf"] | |
| ).load_data() | |
| from llama_index.core.schema import Document | |
| document = Document(text = "\n\n".join([doc.text for doc in documents])) | |
| # above step merges it the 218 different documents into 1 document | |
| from llama_index.core import VectorStoreIndex | |
| from llama_index.core import ServiceContext | |
| from llama_index.llms.openai import OpenAI | |
| # resp = OpenAI().complete("Lord Ram is ") | |
| # print(resp) | |
| # does not need api key | |
| # the embedding model we use is hugging face bge small | |
| # the service context class already hass this | |
| llm = OpenAI(model= "gpt-3.5-turbo", temperature=0.2) | |
| service_context = ServiceContext.from_defaults( | |
| llm = llm, embed_model = "local:BAAI/bge-small-en-v1.5" | |
| ) | |
| # https://huggingface.co/BAAI/bge-small-en | |
| index = VectorStoreIndex.from_documents([document], service_context=service_context) | |
| # pip install llama-index-embeddings-huggingface | |
| # this cell takes care of chunking embedding, indexing | |
| query_engine = index.as_query_engine() | |
| import gradio as gr | |
| def chat_interface(message, history): | |
| # Update history with the current message | |
| # Call the query function with the user's message | |
| response = str(query_engine.query(message)) | |
| # Update history with the bot's response | |
| history.append([message, response]) | |
| return response | |
| # Initialize an empty history | |
| history = [] | |
| # Create the chat interface using gr.ChatInterface | |
| interface = gr.ChatInterface(chat_interface, examples = ["Who is DAAJI?", "What is Heartfulness way of meditation all about?"] , title = "HFN BOT") | |
| # Launch the interface | |
| interface.launch() | |