mirror of
https://github.com/Xueheng-Li/SynologyChatbotGPT.git
synced 2025-07-22 01:23:00 +00:00
880 lines
32 KiB
Python
Executable File
880 lines
32 KiB
Python
Executable File
import openai
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import io
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import sys
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import re
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from contextlib import redirect_stdout
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import subprocess
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import importlib
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import tempfile
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import os
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import ast
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import astor
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import deepl
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import logging
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from duckduckgo_search import ddg
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import json
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import time
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import requests
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import hashlib
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from datetime import datetime
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from langchain.chat_models import ChatOpenAI
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from langchain.llms import OpenAI
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from llama_index import (
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GPTSimpleVectorIndex,
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SimpleDirectoryReader,
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Document,
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LLMPredictor,
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PromptHelper,
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QuestionAnswerPrompt,
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RefinePrompt,
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download_loader
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)
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from langdetect import detect
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from settings import *
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# from webgpt import *
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from urllib.parse import quote
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if openai_api_proxy != "NULL":
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openai.api_base = openai_api_proxy
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# Capture and Execute Python Code in GPT-3 Response
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def capture_python_output(code, venv_path=venv_path):
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# The selected code modifies the input Python code to print the last expression in the code.
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code = modify_code_to_print_last_expression(code)
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output = io.StringIO()
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# Create a temporary file to store the code
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with tempfile.NamedTemporaryFile(mode="w+", suffix=".py", delete=False) as temp_file:
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temp_file.write(code)
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temp_file_path = temp_file.name
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with redirect_stdout(output):
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while True:
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try:
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# Activate virtual environment in the subprocess and execute the temporary file
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command = f"source {venv_path}/bin/activate; python {temp_file_path}"
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execution_output = subprocess.check_output(command, shell=True, text=True, stderr=subprocess.STDOUT,
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executable="/bin/bash")
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print(execution_output, file=output)
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break
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except subprocess.CalledProcessError as cpe:
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error_output = cpe.output
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print(f"error_output = cpe.output: {error_output}")
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if "ModuleNotFoundError" in error_output:
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missing_package = error_output.split()[-1]
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print(f"missing_package = error_output.split()[-1]: {missing_package}")
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try:
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command = f"source {venv_path}/bin/activate; pip install {missing_package}"
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execution_output = subprocess.check_output(command, shell=True, text=True,
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stderr=subprocess.STDOUT, executable="/bin/bash")
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break
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# subprocess.check_call([f"{venv_path}/bin/pip", "install", missing_package])
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except Exception as e:
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result = f"Error1: {e}"
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print(result, file=output)
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break
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else:
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result = f"Error2: {error_output}"
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print(result, file=output)
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break
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except Exception as e:
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result = f"Error3: {e}"
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print(result, file=output)
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break
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# Remove the temporary file
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os.remove(temp_file_path)
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execution_output = output.getvalue().strip()
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print(f"execution_output = {output.getvalue()}")
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# execution_output = output.getvalue()
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return execution_output
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# Function takes Python code string as input, modifies it by wrapping the last non-empty expression with a print statement, and returns the modified code as a string.
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def modify_code_to_print_last_expression(code):
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# Parse the code into an AST
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tree = ast.parse(code)
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# Find the last non-empty expression in the code
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last_expr = None
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for node in reversed(tree.body):
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if isinstance(node, ast.Expr):
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last_expr = node
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break
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# If a non-empty expression is found, wrap it with a print statement
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if last_expr:
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print_node = ast.Expr(
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value=ast.Call(func=ast.Name(id='print', ctx=ast.Load()), args=[last_expr.value], keywords=[]))
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tree.body.remove(last_expr)
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tree.body.append(print_node)
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# Convert the modified AST back to code
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modified_code = astor.to_source(tree)
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return modified_code
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def capture_bash_output(code):
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output = io.StringIO()
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try:
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execution_output = subprocess.check_output(code, shell=True, text=True, stderr=subprocess.STDOUT,
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executable="/bin/bash")
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print(execution_output, file=output)
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except subprocess.CalledProcessError as cpe:
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error_output = cpe.output
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print(f"Error 1: {error_output}")
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except Exception as e:
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result = f"Error 2: {e}"
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print(result, file=output)
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return output.getvalue().strip()
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def download_image(url, save_path="static/img.png"):
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response = requests.get(url, stream=True)
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response.raise_for_status()
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with open(save_path, 'wb') as file:
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for chunk in response.iter_content(chunk_size=8192):
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file.write(chunk)
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# Sending Messages to Synology Chat
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def send_back_message(user_id, response_text, image_filename=None, image_url=None,
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your_server_ip=your_server_ip, port=PORT, INCOMING_WEBHOOK_URL=INCOMING_WEBHOOK_URL):
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message_payload = {
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"user_ids": [int(user_id)],
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"text": str(response_text)
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}
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headers = {
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"Content-Type": "application/json"
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}
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if image_filename:
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image_url = f"http://{your_server_ip}:{port}/image/{image_filename}"
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message_payload["file_url"] = image_url
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if image_url:
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message_payload["file_url"] = image_url
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payload = "payload=" + quote(json.dumps(message_payload))
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try:
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response = requests.post(INCOMING_WEBHOOK_URL, data=payload)
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response.raise_for_status()
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except requests.exceptions.RequestException as e:
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print(f"Error sending message to Synology Chat: {e}")
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return "Error sending message to Synology Chat", 500
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# Take the orginal GPT-3 response text as input, extracts and executes any Python code blocks within it,
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# and replaces the code blocks with their respective outputs in the final response text:
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def modify_response_to_include_code_output(response_text):
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python_blocks = re.findall(r"```python(.*?)```", response_text, re.DOTALL)
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print(f"python_blocks = {python_blocks}")
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if python_blocks:
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modified_response = re.sub(r"```python.*?```", "{{PYTHON_OUTPUT}}", response_text, flags=re.DOTALL)
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# modified_response = re.sub(r"```.*?```", "{{PYTHON_OUTPUT}}", modified_response, flags=re.DOTALL)
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output_list = []
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for python_code in python_blocks:
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python_code = python_code.strip()
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execution_output = capture_python_output(python_code)
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output_list.append(f"```\n{python_code}\n```\n运行结果：\n```\n{execution_output}\n```")
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response_with_output = modified_response.replace("{{PYTHON_OUTPUT}}", "{}").format(*output_list)
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return response_with_output
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else:
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return response_text
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# A function generating an image using OpenAI's API based on the given text description and size, and returns the filename of the downloaded image.
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def generate_img_from_openai(text_description, size=image_size, user_id=None):
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if size in ["small", "medium", "large"]:
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size = {"small": "256x256", "medium": "512x512", "large": "1024x1024"}[size]
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else:
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size = "256x256"
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print(f"text_description = {text_description}")
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try:
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response = openai.Image.create(
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prompt=text_description,
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n=1,
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size=size
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)
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image_url = response['data'][0]['url']
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print(f"image_url = {image_url}")
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except openai.error.OpenAIError as e:
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print(f"Error: {e.http_status}")
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print(f"Error: {e.error}")
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image_url = None
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if image_url:
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if user_id:
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send_back_message(user_id, "生成图片完成，正在下载…")
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try:
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current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"{current_time}.png"
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download_image(image_url, "static/" + filename)
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print("Image downloaded successfully!")
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return filename
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except Exception as e:
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print(f"Error: {e}")
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return None
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else:
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return None
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def generate_gpt_response(chat_history, stream=False, temperature=0.5):
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print(f"messages_to_gpt: {chat_history}")
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# get gpt response
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=chat_history,
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temperature=temperature,
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stream=stream,
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)
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if stream is False:
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return response['choices'][0]['message']['content']
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else:
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return response
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# Set your OpenAI API key
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openai.api_key = openai_api_key
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os.environ['OPENAI_API_KEY'] = openai_api_key
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def my_ddg(q, n=5):
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search_results = ddg(q, max_results=n)
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results_list = []
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for r in search_results:
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results_list.append(f'{r["title"]} ({r["href"]}). {r["body"]}')
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print(f"ddg: {len(results_list)}")
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return results_list
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# bing
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def my_bing(q, n=5, key=bing_key):
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# assert key
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search_url = "https://api.bing.microsoft.com/v7.0/search"
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headers = {"Ocp-Apim-Subscription-Key": key}
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params = {'q': q,
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# 'mkt': 'zh-CN',
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"answerCount": n,
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"count": n}
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response = requests.get(search_url, headers=headers, params=params)
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response.raise_for_status()
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search_results = response.json()
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search_results = search_results["webPages"]["value"]
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results_list = []
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for r in search_results:
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results_list.append(f'{r["name"]} ({r["url"]}). {r["snippet"]}. 获取时间（Parsed time）: {r["dateLastCrawled"]}')
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print(f"bing: {len(results_list)}")
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return results_list
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def my_google(q, n=5, engine="google", key=serpapi_key, serpapi_endpoint=serpapi_endpoint):
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params = {
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"api_key": key,
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"engine": engine,
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"q": q,
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"num": n,
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"rn": n,
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# "hl": "zh-CN",
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}
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response = requests.get(serpapi_endpoint, params=params)
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response_json = response.json()
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search_results = response_json.get("organic_results", [])
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results_list = []
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for r in search_results:
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# print(r)
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try:
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results_list.append(f"{r['title']} ({r['link']}). {r['snippet']}")
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except Exception as e:
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print(e)
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continue
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print(f"{engine}: {len(results_list)}")
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return results_list
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def my_baidu(q, n=5, engine="baidu", key=serpapi_key, serpapi_endpoint=serpapi_endpoint):
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params = {
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"api_key": key,
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"engine": engine,
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"q": q,
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"num": n,
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"rn": n,
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# "ct":2,
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}
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response = requests.get(serpapi_endpoint, params=params)
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response_json = response.json()
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search_results = response_json.get("organic_results", [])
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results_list = []
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for r in search_results:
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# print(r)
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try:
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results_list.append(f"{r['title']} ({r['link']}). {r['snippet']}. 获取时间（Parsed time）: {r['date']}")
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except Exception as e:
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print(e)
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continue
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print(f"{engine}: {len(results_list)}")
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return results_list
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def replace_today(prompt):
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today = datetime.today().strftime("%Y-%m-%d")
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return prompt.replace("{current_date}", today)
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def get_documents(file_src):
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documents = []
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index_name = ""
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logging.debug("Loading documents...")
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logging.debug(f"file_src: {file_src}")
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for file in file_src:
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logging.debug(f"file: {file}")
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index_name += file
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if os.path.splitext(file)[1] == ".pdf":
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logging.debug("Loading PDF...")
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CJKPDFReader = download_loader("CJKPDFReader")
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loader = CJKPDFReader()
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documents += loader.load_data(file=file)
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elif os.path.splitext(file)[1] == ".docx":
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logging.debug("Loading DOCX...")
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DocxReader = download_loader("DocxReader")
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loader = DocxReader()
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documents += loader.load_data(file=file)
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elif os.path.splitext(file)[1] == ".epub":
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logging.debug("Loading EPUB...")
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EpubReader = download_loader("EpubReader")
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loader = EpubReader()
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documents += loader.load_data(file=file)
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else:
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logging.debug("Loading text file...")
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with open(file, "r", encoding="utf-8") as f:
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text = add_space(f.read())
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documents += [Document(text)]
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index_name = sha1sum(index_name)
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return documents, index_name
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def add_space(text):
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punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
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for cn_punc, en_punc in punctuations.items():
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text = text.replace(cn_punc, en_punc)
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return text
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def sha1sum(filename):
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sha1 = hashlib.sha1()
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sha1.update(filename.encode("utf-8"))
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return sha1.hexdigest()
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def llama_process(keywords, index=None, reindex=False, file_source=[], user_id=None):
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if os.path.exists("./index") is False:
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os.mkdir("./index")
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llm_predictor = LLMPredictor(
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llm=ChatOpenAI(
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temperature=0.5,
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# messages=[{"role": "system", "content": chatbot_character}],
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# max_tokens=4096,
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# max_input_size=4096,
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# model_name="gpt-3.5-turbo-0301",
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model_name="gpt-3.5-turbo"
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)
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)
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# prompt_helper = PromptHelper(
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# max_input_size=4096,
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# max_chunk_overlap=20,
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# num_output=1,
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# embedding_limit=None,
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# chunk_size_limit=600,
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# separator=" ",
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# )
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context_dir = "searchResults"
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if len(file_source) == 0:
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files = [f"{context_dir}/{f}" for f in os.listdir(context_dir) if keywords in f]
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else:
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files = file_source
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if len(files) == 0:
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print(f"No files found in {context_dir} with keywords {keywords}")
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return None
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else:
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print(f"Found files in {context_dir}: {files}")
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documents, index_name = get_documents(file_src=files)
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print(f"Loaded documents: {len(documents)}")
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if os.path.exists(f"./index/{index_name}.json") and reindex is False:
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logging.info("找到了缓存的索引文件，加载中……")
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index = GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
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else:
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current_time = time.time()
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print("Indexing...")
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if user_id:
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send_back_message(user_id, "indexing...")
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if index is None:
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index = GPTSimpleVectorIndex(
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documents,
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# prompt_helper=prompt_helper
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)
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else:
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for doc in documents:
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index.insert(doc)
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print(f"Indexing finished. Time used: {time.time() - current_time} seconds.")
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index.save_to_disk(f"./index/{index_name}.json")
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print(f"Index saved to 'index/{index_name}.json'.")
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# Querying the index
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print("Querying...")
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if user_id:
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send_back_message(user_id, "querying...")
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qa_prompt = QuestionAnswerPrompt(replace_today(PROMPT_TEMPLATE))
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response = index.query(
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keywords,
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llm_predictor=llm_predictor,
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similarity_top_k=1,
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text_qa_template=qa_prompt,
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# response_mode="compact" # or "tree_summarize"
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mode="embedding" # or "default"
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)
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print(f"type of index.query response: {type(response)}")
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response_text = response.response
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print(f"type of response.response: {type(response_text)}")
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print("Query finished.")
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print(f"Response from llama: {response_text}")
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return response_text, index
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# detect main language of a text
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def is_chinese(text):
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try:
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lang = detect(text)
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if lang.startswith('zh'):
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print("The text is primarily in Chinese.")
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return True
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else:
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return False
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except Exception as e:
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print(f"Error: {e}")
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return None
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# prepare deepL translator
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if dl_key is not None:
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translator = deepl.Translator(dl_key)
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else:
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translator = None
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def translate_to_CN(text):
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if dl_key is not None:
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return translator.translate_text(text, target_lang="ZH").text
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else:
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return text
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def translate_to_EN(text):
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if dl_key is not None:
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return translator.translate_text(text, target_lang="EN-US").text
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else:
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return text
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def detect_and_translate(text):
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if is_chinese(text) is False:
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try:
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return translate_to_CN(text)
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except Exception as e:
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print(f"Error: {e}")
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return text
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else:
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return text
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|
||
|
||
def send_stream(user_id, text, cut="\n"):
|
||
sentences = text.split(cut)
|
||
for s in sentences:
|
||
if len(s) > 0:
|
||
send_back_message(user_id, s)
|
||
|
||
|
||
|
||
def send(user_id, text, stream=False, cut="\n"):
|
||
if stream:
|
||
send_stream(user_id, text, cut=cut)
|
||
else:
|
||
send_back_message(user_id, text)
|
||
|
||
|
||
# This detect_channel function takes a message text as input and checks if it starts with specific keywords to determine which channel (python, bash, image, or gpt) the message belongs to, returning a dictionary containing the detected channel and the corresponding message content.
|
||
def separate_channel(message_text):
|
||
keywords = {}
|
||
keywords["python"] = ["python:", "py:", "python ", "py ", "Python:", "Python "]
|
||
keywords["bash"] = ["bash:", "b:", "bash ", "Bash:"]
|
||
keywords["image"] = ["图片:", "图片：", "图片 ", "img:", "Img:", "生成图片：", "生成图片:"]
|
||
keywords["gpt"] = ["生成程序：", "程序生成：", "generator:", "Generator:", "ai:", "AI:", "gpt:", "Gpt:", "Ai:"]
|
||
keywords["google"] = ["google:", "Google:", "谷歌:", "谷歌：", "搜索:", "搜索：", "search:", "Search:", "Search：",
|
||
"search：", "bb", "ss",
|
||
"gl", "gg"]
|
||
|
||
results = {}
|
||
for channel in ["python", "bash", "image", "gpt", "google"]:
|
||
results[channel] = None
|
||
for keyword in keywords[channel]:
|
||
if message_text.startswith(keyword):
|
||
results[channel] = message_text[len(keyword):].strip()
|
||
break
|
||
print(f"results = {results}")
|
||
|
||
return results
|
||
|
||
|
||
|
||
|
||
class ChatBot:
|
||
|
||
def __init__(self, user_id,
|
||
refresh_keywords=None,
|
||
max_conversation_length=10,
|
||
max_time_gap=15,
|
||
index=None,
|
||
system_prompt=system_prompt,
|
||
stream=False,
|
||
temperature=0.5,
|
||
translate=translate_to_chinese,
|
||
model="gpt4",
|
||
):
|
||
self.user_id = user_id
|
||
self.chat_history = [{"role": "system", "content": system_prompt}]
|
||
self.last_timestamp = int(time.time())
|
||
if index == None:
|
||
self.index = GPTSimpleVectorIndex([])
|
||
else:
|
||
self.index = index
|
||
if refresh_keywords is None:
|
||
self.refresh_keywords = ["new", "refresh", "00", "restart", "刷新", "新话题", "退下", "结束", "over"]
|
||
else:
|
||
self.refresh_keywords = refresh_keywords
|
||
self.max_conversation_length = max_conversation_length
|
||
self.max_time_gap = max_time_gap
|
||
self.stream = stream
|
||
self.message = ""
|
||
self.temperature = temperature
|
||
self.translate = translate
|
||
self.system_prompt = system_prompt
|
||
self.model = model
|
||
self.gpt4 = None
|
||
if self.model == "gpt4" or self.model == "gpt-4":
|
||
self.gpt4 = WebGPT(model="gpt-4")
|
||
try:
|
||
self.gpt4.start_session(system_prompt=self.system_prompt)
|
||
self.stream = False
|
||
except Exception as e:
|
||
print(f"Error: {e}")
|
||
self.gpt4 = None
|
||
self.model = "gpt3"
|
||
self.stream = True
|
||
|
||
|
||
def process(self, message,
|
||
num_search_results=10,
|
||
):
|
||
|
||
# update the latest user message
|
||
self.message = message
|
||
|
||
# Check for refresh_prompt input to start a new conversation
|
||
if self.message.strip().lower() in self.refresh_keywords:
|
||
send_back_message(self.user_id, "好的，开启一下新话题。")
|
||
self.chat_history = self.chat_history[0:1]
|
||
self.index = GPTSimpleVectorIndex([])
|
||
self.last_timestamp = int(time.time())
|
||
if self.model == "gpt4":
|
||
self.gpt4.start_session(system_prompt=self.system_prompt)
|
||
return "----------------------------"
|
||
|
||
# Check if the conversation has been idle for 30 minutes (1800 seconds)
|
||
if int(time.time()) - self.last_timestamp >= max_time_gap * 60:
|
||
self.chat_history = self.chat_history[0:1]
|
||
self.index = GPTSimpleVectorIndex([])
|
||
|
||
# Truncate conversation history if it exceeds the maximum length
|
||
if len(self.chat_history) > max_conversation_length:
|
||
self.chat_history = self.chat_history[-max_conversation_length:]
|
||
|
||
self.chat_history.append({"role": "user", "content": self.message})
|
||
|
||
# check and execute python code
|
||
code_results = separate_channel(self.message)
|
||
if code_results["python"]:
|
||
print("python code found")
|
||
send_back_message(self.user_id, f"Python input: \n```{code_results['python']}``` ")
|
||
code_output = capture_python_output(code_results["python"])
|
||
send_back_message(self.user_id, f"Output: \n```{code_output}```")
|
||
self.chat_history.append({
|
||
"role": "assistant",
|
||
"content": f"Python input: \n```{code_results['python']}``` " + f"Output: \n```{code_output}```"
|
||
})
|
||
|
||
elif code_results["bash"]:
|
||
print("bash code found")
|
||
send_back_message(self.user_id, f"Bash input: \n```{code_results['bash']}``` ")
|
||
code_output = capture_bash_output(code_results["bash"])
|
||
send_back_message(self.user_id, f"Output: \n```{code_output}```")
|
||
self.chat_history.append({
|
||
"role": "assistant",
|
||
"content": f"Bash input: \n```{code_results['bash']}``` " + f"Output: \n```{code_output}```"
|
||
})
|
||
|
||
elif code_results["image"]:
|
||
print("image description found")
|
||
text_description = code_results["image"]
|
||
send_back_message(self.user_id, f"收到👌🏻我会按你要求生成图片：{text_description}")
|
||
img_filename = generate_img_from_openai(text_description, user_id=self.user_id)
|
||
print(f"img_filename = {img_filename}")
|
||
send_back_message(self.user_id, text_description, image_filename=img_filename)
|
||
self.chat_history.append({
|
||
"role": "assistant",
|
||
"content": f"收到👌🏻我会按你要求生成图片：{text_description}. [An image link here]"
|
||
})
|
||
|
||
elif code_results["google"]:
|
||
question = code_results["google"]
|
||
print(f"Search request found: {question}")
|
||
self.search(
|
||
keywords=question,
|
||
num_results=num_search_results,
|
||
engines=["ddg", "bing", "google"])
|
||
|
||
else:
|
||
send_back_message(self.user_id, "...")
|
||
if self.gpt4 is not None:
|
||
print(f"Sending message to gpt4: {self.message}")
|
||
gpt_response = self.gpt4.send_message(self.message)
|
||
print(f"Got response from gpt4: {gpt_response}")
|
||
else:
|
||
gpt_response = generate_gpt_response(
|
||
chat_history=self.chat_history,
|
||
stream=self.stream,
|
||
temperature=self.temperature,
|
||
)
|
||
|
||
print("Got gpt response")
|
||
if self.stream:
|
||
text = []
|
||
whole_text = []
|
||
code_block = False
|
||
for r in gpt_response:
|
||
# print(r.choices[0]["delta"])
|
||
if "content" in r.choices[0]["delta"]:
|
||
word = r.choices[0]["delta"]["content"]
|
||
if word is not None:
|
||
text.append(word)
|
||
whole_text.append(word)
|
||
|
||
# Check if the current output ends with \n
|
||
if re.search(r'[\n]', word[-1]):
|
||
sentence = ''.join(text).strip()
|
||
|
||
if re.search(r'`{3}$', sentence) and code_block:
|
||
# print("code: " , code_block , ", sentence: " + sentence)
|
||
send_back_message(self.user_id, sentence)
|
||
text = []
|
||
code_block = False
|
||
else:
|
||
if re.search(r'`{3}', sentence) and not code_block:
|
||
code_block = True
|
||
if re.search(r'\n', sentence):
|
||
sentences = text.split("\n")
|
||
send_back_message(self.user_id, sentences[0])
|
||
sentence = sentences[1]
|
||
|
||
if not code_block:
|
||
sentence = sentence.replace("\n", "")
|
||
send_stream(self.user_id, sentence)
|
||
text = []
|
||
text = ''.join(text)
|
||
if len(text) > 0:
|
||
send(self.user_id, text, stream=self.stream)
|
||
response_text = ''.join(whole_text)
|
||
else:
|
||
response_text = gpt_response
|
||
if re.findall(r"```python(.*?)```", response_text, re.DOTALL):
|
||
print(f"Original response: {response_text}\n")
|
||
response_text = modify_response_to_include_code_output(response_text)
|
||
print(f"With code output: {response_text}")
|
||
send(self.user_id, response_text, stream=False)
|
||
|
||
self.chat_history.append({"role": "assistant", "content": response_text})
|
||
|
||
if is_chinese(response_text) is False and self.translate:
|
||
print("Translating...")
|
||
try:
|
||
response_text = translate_to_CN(response_text)
|
||
print(f"Translated gpt_response = {response_text}")
|
||
send(self.user_id, f"翻译:\n\n{response_text}", stream=self.stream)
|
||
except Exception as e:
|
||
print(f"Error in translation: {e}")
|
||
|
||
# update timestamp
|
||
self.last_timestamp = int(time.time())
|
||
|
||
return "Message processed", 200
|
||
|
||
|
||
def search(self,
|
||
keywords=None,
|
||
num_results=10,
|
||
engines=["ddg", "bing", "google", "baidu"]):
|
||
|
||
if keywords is None:
|
||
keywords = self.message
|
||
print(f"Search keywords: {keywords}")
|
||
print("Searching...")
|
||
send_back_message(self.user_id, f"...")
|
||
|
||
results_list = []
|
||
if "ddg" in engines:
|
||
try:
|
||
ddg_results = my_ddg(keywords, n=num_results)
|
||
results_list.append(ddg_results)
|
||
send_back_message(self.user_id, f"ddg: {len(ddg_results)}")
|
||
except Exception as e:
|
||
print(f"Error: {e}; ddg_results = {ddg_results}")
|
||
if "bing" in engines:
|
||
try:
|
||
bing_results = my_bing(self.message, n=num_results)
|
||
results_list.append(bing_results)
|
||
send_back_message(self.user_id, f"bing: {len(bing_results)}")
|
||
except Exception as e:
|
||
print(f"Error: {e}; bing_results = {bing_results}")
|
||
if "google" in engines:
|
||
try:
|
||
google_results = my_google(keywords, n=num_results)
|
||
results_list.append(google_results)
|
||
send_back_message(self.user_id, f"google: {len(google_results)}")
|
||
except Exception as e:
|
||
print(f"Error: {e}; google_results = {google_results}")
|
||
if "baidu" in engines:
|
||
try:
|
||
baidu_results = my_baidu(keywords, n=num_results)
|
||
results_list.append(baidu_results)
|
||
send_back_message(self.user_id, f"baidu: {baidu_results}")
|
||
except Exception as e:
|
||
print(f"Error: {e}")
|
||
results_text = "# Web search results:\n\n"
|
||
i = 1
|
||
for results in results_list:
|
||
try:
|
||
for r in results:
|
||
results_text += f"{i}. {r}\n\n"
|
||
i += 1
|
||
except Exception as e:
|
||
print(f"Error: {e}")
|
||
continue
|
||
|
||
prompt_input = replace_today(PROMPT_TEMPLATE).replace("{context_str}", results_text).replace("{query_str}", keywords)
|
||
|
||
if len(prompt_input) <= 4000:
|
||
|
||
gpt_response = generate_gpt_response(
|
||
chat_history=[
|
||
{"role": "system", "content": self.system_prompt},
|
||
{"role": "user", "content": prompt_input}
|
||
],
|
||
stream=self.stream,
|
||
temperature=self.temperature,
|
||
)
|
||
print("Got gpt response")
|
||
if self.stream:
|
||
text = []
|
||
whole_text = []
|
||
for r in gpt_response:
|
||
# print(r.choices[0]["delta"])
|
||
if "content" in r.choices[0]["delta"]:
|
||
word = r.choices[0]["delta"]["content"]
|
||
text.append(word)
|
||
whole_text.append(word)
|
||
# Check if the current output ends with \n
|
||
if re.search(r'[\n]', word[-1]):
|
||
sentence = ''.join(text).strip().replace("\n", "")
|
||
send_stream(self.user_id, sentence)
|
||
text = []
|
||
text = ''.join(text)
|
||
if len(text) > 0:
|
||
send(self.user_id, text, stream=self.stream)
|
||
answer = ''.join(whole_text)
|
||
else:
|
||
answer = gpt_response
|
||
print(f"Original response: {answer}\n")
|
||
if re.findall(r"```python(.*?)```", answer, re.DOTALL):
|
||
answer = modify_response_to_include_code_output(answer)
|
||
print(f"With code output: {answer}")
|
||
send(self.user_id, answer, stream=self.stream)
|
||
|
||
else:
|
||
|
||
results_text += "# Conversation history:\n\n"
|
||
for item in self.chat_history:
|
||
results_text += f"{item['role']}: {item['content']}\n"
|
||
|
||
print(f"Search finished.")
|
||
|
||
context_dir = "searchResults"
|
||
if os.path.exists(context_dir) is False:
|
||
os.mkdir(context_dir)
|
||
print(f"Saving search results...")
|
||
search_results_name = sha1sum(results_text)
|
||
with open(f'{context_dir}/{search_results_name}.txt', 'w') as f:
|
||
f.write(results_text)
|
||
print(f"Saved to search results to {context_dir}/{search_results_name}.txt")
|
||
context_files = [f"{context_dir}/{search_results_name}.txt"]
|
||
|
||
try:
|
||
answer, self.index = llama_process(keywords,
|
||
file_source=context_files,
|
||
index=self.index,
|
||
user_id=self.user_id)
|
||
with open('output.txt', 'w') as f:
|
||
f.write(str(answer).strip())
|
||
# return answer
|
||
except Exception as e:
|
||
print(f"Error: Could not summarize content. - {e}")
|
||
# return "Error: No answer provided."
|
||
|
||
if answer is None:
|
||
answer = "Error: No answer provided."
|
||
print(f"original answer = {answer}")
|
||
send(self.user_id, answer, stream=self.stream)
|
||
|
||
|
||
self.chat_history.append({
|
||
"role": "assistant",
|
||
"content": answer
|
||
})
|
||
|
||
print(self.chat_history)
|
||
|
||
if is_chinese(answer) is False and self.translate:
|
||
print("Translating...")
|
||
try:
|
||
answer_cn = translate_to_CN(answer)
|
||
print(f"Translated gpt_response = {answer_cn}")
|
||
send(self.user_id, f"翻译:\n{answer_cn}", stream=self.stream)
|
||
except Exception as e:
|
||
print(f"Error in translation: {e}")
|
||
|
||
|
||
|
||
|
||
|