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#
#
# Agora Real Time Engagement
# Created by Wei Hu in 2024-08.
# Copyright (c) 2024 Agora IO. All rights reserved.
#
#
from collections import defaultdict
from dataclasses import dataclass
from enum import Enum
import random
import requests
from openai import AsyncOpenAI, AsyncAzureOpenAI
from openai.types.chat.chat_completion import ChatCompletion
from ten.async_ten_env import AsyncTenEnv
from ten_ai_base.config import BaseConfig
@dataclass
class OpenAIChatGPTConfig(BaseConfig):
api_key: str = ""
base_url: str = "https://api.openai.com/v1"
model: str = (
"gpt-4o" # Adjust this to match the equivalent of `openai.GPT4o` in the Python library
)
prompt: str = (
"You are a voice assistant who talks in a conversational way and can chat with me like my friends. I will speak to you in English or Chinese, and you will answer in the corrected and improved version of my text with the language I use. Don’t talk like a robot, instead I would like you to talk like a real human with emotions. I will use your answer for text-to-speech, so don’t return me any meaningless characters. I want you to be helpful, when I’m asking you for advice, give me precise, practical and useful advice instead of being vague. When giving me a list of options, express the options in a narrative way instead of bullet points."
)
frequency_penalty: float = 0.9
presence_penalty: float = 0.9
top_p: float = 1.0
temperature: float = 0.1
max_tokens: int = 512
seed: int = random.randint(0, 10000)
proxy_url: str = ""
greeting: str = "Hello, how can I help you today?"
max_memory_length: int = 10
vendor: str = "openai"
azure_endpoint: str = ""
azure_api_version: str = ""
class ReasoningMode(str, Enum):
ModeV1= "v1"
class ThinkParser:
def __init__(self):
self.state = 'NORMAL' # States: 'NORMAL', 'THINK'
self.think_content = ""
self.content = ""
def process(self, new_chars):
if new_chars == "<think>":
self.state = 'THINK'
return True
elif new_chars == "</think>":
self.state = 'NORMAL'
return True
else:
if self.state == "THINK":
self.think_content += new_chars
return False
def process_by_reasoning_content(self, reasoning_content):
state_changed = False
if reasoning_content:
if self.state == 'NORMAL':
self.state = 'THINK'
state_changed = True
self.think_content += reasoning_content
elif self.state == 'THINK':
self.state = 'NORMAL'
state_changed = True
return state_changed
class OpenAIChatGPT:
client = None
def __init__(self, ten_env: AsyncTenEnv, config: OpenAIChatGPTConfig):
self.config = config
self.ten_env = ten_env
ten_env.log_info(f"OpenAIChatGPT initialized with config: {config.api_key}")
if self.config.vendor == "azure":
self.client = AsyncAzureOpenAI(
api_key=config.api_key,
api_version=self.config.azure_api_version,
azure_endpoint=config.azure_endpoint,
)
ten_env.log_info(
f"Using Azure OpenAI with endpoint: {config.azure_endpoint}, api_version: {config.azure_api_version}"
)
else:
self.client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url, default_headers={
"api-key": config.api_key,
"Authorization": f"Bearer {config.api_key}"
})
self.session = requests.Session()
if config.proxy_url:
proxies = {
"http": config.proxy_url,
"https": config.proxy_url,
}
ten_env.log_info(f"Setting proxies: {proxies}")
self.session.proxies.update(proxies)
self.client.session = self.session
async def get_chat_completions(self, messages, tools=None) -> ChatCompletion:
req = {
"model": self.config.model,
"messages": [
{
"role": "system",
"content": self.config.prompt,
},
*messages,
],
"tools": tools,
"temperature": self.config.temperature,
"top_p": self.config.top_p,
"presence_penalty": self.config.presence_penalty,
"frequency_penalty": self.config.frequency_penalty,
"max_tokens": self.config.max_tokens,
"seed": self.config.seed,
}
try:
response = await self.client.chat.completions.create(**req)
except Exception as e:
raise RuntimeError(f"CreateChatCompletion failed, err: {e}") from e
return response
async def get_chat_completions_stream(self, messages, tools=None, listener=None):
req = {
"model": self.config.model,
"messages": [
{
"role": "system",
"content": self.config.prompt,
},
*messages,
],
"tools": tools,
"temperature": self.config.temperature,
"top_p": self.config.top_p,
"presence_penalty": self.config.presence_penalty,
"frequency_penalty": self.config.frequency_penalty,
"max_tokens": self.config.max_tokens,
"seed": self.config.seed,
"stream": True,
}
try:
response = await self.client.chat.completions.create(**req)
except Exception as e:
raise RuntimeError(f"CreateChatCompletionStream failed, err: {e}") from e
full_content = ""
# Check for tool calls
tool_calls_dict = defaultdict(
lambda: {
"id": None,
"function": {"arguments": "", "name": None},
"type": None,
}
)
# Example usage
parser = ThinkParser()
reasoning_mode = None
async for chat_completion in response:
# self.ten_env.log_info(f"Chat completion: {chat_completion}")
if len(chat_completion.choices) == 0:
continue
choice = chat_completion.choices[0]
delta = choice.delta
content = delta.content if delta and delta.content else ""
reasoning_content = delta.reasoning_content if delta and hasattr(delta, "reasoning_content") and delta.reasoning_content else ""
if reasoning_mode is None and reasoning_content is not None:
reasoning_mode = ReasoningMode.ModeV1
# Emit content update event (fire-and-forget)
if listener and (content or reasoning_mode == ReasoningMode.ModeV1):
prev_state = parser.state
if reasoning_mode == ReasoningMode.ModeV1:
self.ten_env.log_info("process_by_reasoning_content")
think_state_changed = parser.process_by_reasoning_content(reasoning_content)
else:
think_state_changed = parser.process(content)
if not think_state_changed:
# self.ten_env.log_info(f"state: {parser.state}, content: {content}, think: {parser.think_content}")
if parser.state == "THINK":
listener.emit("reasoning_update", parser.think_content)
elif parser.state == "NORMAL":
listener.emit("content_update", content)
if prev_state == "THINK" and parser.state == "NORMAL":
listener.emit("reasoning_update_finish", parser.think_content)
parser.think_content = ""
full_content += content
if delta.tool_calls:
for tool_call in delta.tool_calls:
if tool_call.id is not None:
tool_calls_dict[tool_call.index]["id"] = tool_call.id
# If the function name is not None, set it
if tool_call.function.name is not None:
tool_calls_dict[tool_call.index]["function"][
"name"
] = tool_call.function.name
# Append the arguments
tool_calls_dict[tool_call.index]["function"][
"arguments"
] += tool_call.function.arguments
# If the type is not None, set it
if tool_call.type is not None:
tool_calls_dict[tool_call.index]["type"] = tool_call.type
# Convert the dictionary to a list
tool_calls_list = list(tool_calls_dict.values())
# Emit tool calls event (fire-and-forget)
if listener and tool_calls_list:
for tool_call in tool_calls_list:
listener.emit("tool_call", tool_call)
# Emit content finished event after the loop completes
if listener:
listener.emit("content_finished", full_content)
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