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| import logging | |
| import os | |
| from time import asctime | |
| import gradio as gr | |
| from llama_index.core import Document, VectorStoreIndex | |
| from generate_response import generate_chat_response_with_history, set_llm, is_search_query, \ | |
| generate_chat_response_with_history_rag_return_response, google_question | |
| from read_write_index import read_write_index | |
| from web_search import search | |
| logger = logging.getLogger("agent_logger") | |
| sourced = False | |
| query = False | |
| rag_similarity = False | |
| def google_search_chat(message, history): | |
| gquestion = google_question(message, history) | |
| if is_search_query(gquestion): | |
| search_results = search(message, gquestion) | |
| print(f'Search results returned: {len(search_results)}') | |
| relevant_content = "" | |
| for result in search_results: | |
| relevant_content = relevant_content + "\n" + ''.join(result['text']) | |
| print(f'Relevant content char length: {len(relevant_content)}') | |
| if relevant_content != "": | |
| documents = [Document(text=relevant_content)] | |
| index = VectorStoreIndex.from_documents(documents) | |
| print('Search results vectorized...') | |
| response = generate_chat_response_with_history_rag_return_response(index, message, history) | |
| else: | |
| print(f'Assistant Response: Sorry, no search results found, trying with offline resources.') | |
| index = read_write_index(path='storage_search/') | |
| response = generate_chat_response_with_history_rag_return_response(index, message, history) | |
| response_text = [] | |
| string_output = "" | |
| for text in response.response_gen: | |
| response_text.append(text) | |
| string_output = ''.join(response_text) | |
| yield string_output | |
| print(f'Assistant Response: {string_output}') | |
| else: | |
| yield from generate_chat_response_with_history(message, history) | |
| if __name__ == '__main__': | |
| logging.root.setLevel(logging.INFO) | |
| filehandler = logging.FileHandler(f'agent_log_{asctime().replace(" ", "").lower().replace(":", "")}.log', | |
| 'a') | |
| formatter = logging.Formatter('%(asctime)-15s::%(levelname)s::%(filename)s::%(funcName)s::%(lineno)d::%(message)s') | |
| filehandler.setFormatter(formatter) | |
| logger = logging.getLogger("agent_logger") | |
| for hdlr in logger.handlers[:]: # remove the existing file handlers | |
| if isinstance(hdlr, logging.FileHandler): | |
| logger.removeHandler(hdlr) | |
| logger.addHandler(filehandler) # set the new handler | |
| logger.setLevel(logging.INFO) | |
| api_key = os.getenv('gpt_api_key') | |
| # GPT - 4 Turbo. The latest GPT - 4 model intended to reduce cases of “laziness” where the model doesn’t complete | |
| # a task. Returns a maximum of 4,096 output tokens. Link: | |
| # https://openai.com/blog/new-embedding-models-and-api-updates | |
| set_llm(key=api_key, model="gpt-4-0125-preview", temperature=0) | |
| print("Launching Gradio ChatInterface for searchbot...") | |
| demo = gr.ChatInterface(fn=google_search_chat, | |
| title="Search Assistant", retry_btn=None, undo_btn=None, clear_btn=None, | |
| theme="soft") | |
| demo.launch() | |
| # auth=('convo', 'session2024') | |