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| from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ServiceContext, set_global_service_context, load_index_from_storage, StorageContext, PromptHelper | |
| from llama_index.llms import OpenAI | |
| import gradio as gr | |
| import sys | |
| import os | |
| try: | |
| from Config import openai_key | |
| os.environ["OPENAI_API_KEY"] = openai_key | |
| except: | |
| pass | |
| """ | |
| Code adopted from Beebom article: "How to Train an AI Chatbot With Custom Knowledge Base Using ChatGPT API" by Arjun Sha | |
| https://beebom.com/how-train-ai-chatbot-custom-knowledge-base-chatgpt-api/ | |
| """ | |
| max_input_size = 4096 | |
| num_outputs = 512 | |
| chunk_size_limit = 600 | |
| prompt_helper = PromptHelper(context_window=max_input_size, num_output=num_outputs, chunk_overlap_ratio=0.1, chunk_size_limit=chunk_size_limit) | |
| llm = OpenAI(model="gpt-3.5-turbo", temperature=0.5, max_tokens=num_outputs) | |
| service_context = ServiceContext.from_defaults(llm=llm, prompt_helper=prompt_helper) | |
| set_global_service_context(service_context) | |
| def retrieve_index(index_path): | |
| storage_context = StorageContext.from_defaults(persist_dir=index_path) | |
| index = load_index_from_storage(storage_context) | |
| return index | |
| def chatbot(input_text): | |
| response = QE.query(input_text) | |
| response_stream = "" | |
| for r in response.response_gen: | |
| response_stream += r | |
| yield response_stream | |
| if __name__ == "__main__": | |
| iface = gr.Interface(fn=chatbot, | |
| inputs=gr.components.Textbox(lines=7, label="Enter your text"), | |
| outputs="text", | |
| title="AI Chatbot for the Doing What Works Library") | |
| index = retrieve_index("dww_vectors") | |
| QE = index.as_query_engine(streaming=True) | |
| iface.launch(share=False) |