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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, condense_question, \
generate_chat_response_with_history_rag_return_response
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):
condensed_question = condense_question(message, history)
if is_search_query(condensed_question):
search_results = search(message, condensed_question)
print(f'Search results returned: {len(search_results)}')
relevant_content = ""
for index, result in enumerate(search_results):
relevant_content = relevant_content + "\n" + ''.join(result['text'])
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')
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