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import datetime
import os
from prompts.llm import qa_prompt_template
from prompts.condense_llm import condense_template
from typing import Dict, Any
from config import HISTORY_DIR
def get_messages_last_content(data: Dict[str, Any], **_: Any) -> str:
""" get the last content of the llm request messages array
:param data: the user llm request data
:type data: Dict[str, Any]
Example:
.. code-block:: python
from gptcache.processor.pre import get_messages_last_content
content = get_messages_last_content({"messages": [{"content": "hello"}, {"content": "world"}]})
# "world"
"""
result = data.get("messages")[-1].content.split("Human:")[-1].split("Assistant:")[0].strip()
print(result)
return result
def transcribe(current_model, audio):
return current_model.audio_response(audio)
def history_file_path(username):
dirname = os.path.join(HISTORY_DIR, username)
os.makedirs(dirname, exist_ok=True)
now = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
history_path = os.path.join(dirname, f"{now}.json")
return history_path
def load_lasted_file_username(username):
if username not in os.listdir(HISTORY_DIR):
return None
date_time_list = []
for filename in os.listdir(os.path.join(HISTORY_DIR, username)):
date_time_list.append(datetime.datetime.strptime(filename[:19], '%Y-%m-%d_%H-%M-%S'))
lasted_time = max(date_time_list)
lasted_file = lasted_time.strftime('%Y-%m-%d_%H-%M-%S')
return os.path.join(HISTORY_DIR, username, lasted_file)
def load_chat_history(current_model, *args):
return current_model.load_history(*args)
def predict(chatbot, model, inputs, use_websearch, custom_websearch):
iter = model.inference(inputs=inputs, chatbot=chatbot, streaming=True, use_websearch=use_websearch,
custom_websearch=custom_websearch, qa_prompt_template=qa_prompt_template,
condense_prompt_template=condense_template)
for response in iter:
yield response
def set_user_indentifier(current_model, *args):
return current_model.set_user_indentifier(*args)
def retry(chatbot, model, use_websearch, custom_websearch):
model.delete_last_conversation()
if len(chatbot) > 0:
inputs = chatbot[-1][0]
chatbot = predict(chatbot, model, inputs, use_websearch, custom_websearch)
yield chatbot
def reset(current_model):
return current_model.reset_conversation()
def delete_chat_history(current_model, *args):
return current_model.delete_history(*args)
def delete_first_conversation(current_model):
return current_model.delete_first_conversation()
def delete_last_conversation(current_model, chatbot):
if len(chatbot) > 0:
chatbot.pop()
current_model.delete_last_conversation()
return chatbot
def add_source_numbers(lst, source_name = "Source", use_source = True):
if use_source:
return [f'[{idx+1}]\t "{item[0]}"\n{source_name}: {item[1]}' for idx, item in enumerate(lst)]
else:
return [f'[{idx+1}]\t "{item}"' for idx, item in enumerate(lst)]
def add_details(lst):
nodes = []
for txt in lst:
brief = txt[:25].replace("\n", "")
nodes.append(
f"<details><summary>{brief}...</summary><p>{txt}</p></details>"
)
return nodes |