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Refactor to use AI provider classes

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This commit is contained in:
bucolucas
2024-08-18 12:54:11 -05:00
parent 731e655ce1
commit a7abc5ebd0
1 changed files with 35 additions and 117 deletions
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telegram_inference_bot.py
+33 -115
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@@ -3,34 +3,15 @@ import os
import importlib
import inspect
import logging
import anthropic
from telegram import Update
from telegram.ext import Application, CommandHandler, MessageHandler, filters, ContextTypes
from openai import OpenAI
from dotenv import load_dotenv
from tools.base_tool import BaseTool
from ai_providers import create_ai_provider
# Load environment variables
load_dotenv()
openai_client = OpenAI()
anthropic_client = anthropic.Anthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY"),
default_headers={"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15"}
)
GPT_4O = "gpt-4o"
GPT_4O_MINI = "gpt-4o-mini"
model_max_tokens = {
GPT_4O: 4096,
GPT_4O_MINI: 16384
}
use_smart_model = True
use_anthropic = True
# Set up logging to console and file
logging.basicConfig(level=logging.WARNING, handlers=[
logging.StreamHandler(),
@@ -63,6 +44,9 @@ functions = []
for tool in tools:
functions.extend(tool.get_functions())
# Initialize AI provider
ai_provider = create_ai_provider("anthropic")
async def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
logging.info("Bot started")
await update.message.reply_text("Hello! I'm your AI assistant. How can I help you today? You can send me images and then ask questions about them.")
@@ -91,138 +75,72 @@ async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE) ->
messages = conversation_history[user_id]
response = get_chat_response(messages)
tool_calls = []
if use_anthropic:
for message in response.content:
if message.type == "tool_use":
tool_calls.append(message)
else:
messages.append({"role": "assistant", "content": response.content})
else:
assistant_message = response.choices[0].message
if hasattr(assistant_message, 'function_call') and assistant_message.function_call is not None:
tool_calls.append(assistant_message.function_call)
response = ai_provider.get_chat_response([{"role": "system", "content": system_prompt}] + messages)
tool_calls = ai_provider.format_tool_calls(response)
toolUseCount = 0
while len(tool_calls) > 0 and toolUseCount < 50:
tool_call = tool_calls.pop(0)
function_name = tool_call.name
tool_response = call_tool(tool_call)
formatted_result = {}
if use_anthropic:
formatted_result = {"role": "user", "content":[{"type": "tool_result", "tool_use_id": tool_call.id, "content": json.dumps(tool_response)}]}
else:
formatted_result = {"role": "function", "name": function_name, "content": json.dumps(tool_response)}
formatted_result = ai_provider.format_tool_result(tool_call, tool_response)
messages.append(formatted_result)
response = get_chat_response(messages)
assistant_message = ""
if use_anthropic:
for message in response.content:
if message.type == "tool_use":
tool_calls.append(message)
else:
messages.append({"role": "assistant", "content": response.content})
else:
assistant_message = response.choices[0].message
conversation_history[user_id].append({"role": "assistant", "content": assistant_message})
if hasattr(assistant_message, 'function_call') and assistant_message.function_call is not None:
tool_calls.append(assistant_message.function_call)
assistant_reply = assistant_message
response = ai_provider.get_chat_response([{"role": "system", "content": system_prompt}] + messages)
tool_calls = ai_provider.format_tool_calls(response)
toolUseCount += 1
if (toolUseCount == 0):
if use_anthropic:
assistant_reply = response.content
else:
assistant_reply = assistant_message
if toolUseCount == 0:
assistant_reply = ai_provider.format_assistant_reply(response)
conversation_history[user_id].append({"role": "assistant", "content": assistant_reply})
if len(conversation_history[user_id]) > 20:
conversation_history[user_id] = conversation_history[user_id][-20:]
if use_anthropic:
await update.message.reply_text(messages[-1]["content"][0].text)
else:
await update.message.reply_text(assistant_reply.content)
await update.message.reply_text(ai_provider.get_reply_text(response))
except Exception as e:
logging.error(f"An error occurred: {str(e)}")
await update.message.reply_text("Sorry, an error occurred while processing your request.")
def call_tool(function_call):
function_name = function_call.name if use_anthropic else function_call.name
function_args = json.dumps(function_call.input) if use_anthropic else function_call.arguments
function_name = function_call.name
function_args = json.loads(function_call.arguments if hasattr(function_call, 'arguments') else json.dumps(function_call.input))
for tool in tools:
if function_name in [f["name"] for f in tool.get_functions()]:
return tool.execute(function_name, **json.loads(function_args))
def get_chat_response(messages):
return get_claude_response(messages) if use_anthropic else get_openai_response(messages)
def get_openai_response(messages):
model = GPT_4O if use_smart_model else GPT_4O_MINI
response = openai_client.chat.completions.create(
model=model,
messages = [{"role": "system", "content": system_prompt}] + messages,
functions=functions,
function_call="auto",
max_tokens=model_max_tokens[model]
)
return response
def get_claude_response(messages):
anthropic_tools = [
{
"name": function['name'],
"description": function['description'],
"input_schema": function['parameters'] if function['parameters'] not in [None, {}] else {"type": "object", "properties": {"param1": {"type": "string", "description": "Unnecessary"}}, "required": []}
}
for function in functions
]
try:
response = anthropic_client.messages.create(
model="claude-3-5-sonnet-20240620",
system=system_prompt,
messages=messages,
max_tokens=8192,
tools=anthropic_tools
)
except Exception as e:
logging.error(f"An error occurred: {str(e)}")
return None
return response
return tool.execute(function_name, **function_args)
async def switch(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
global use_smart_model
use_smart_model = not use_smart_model
model = GPT_4O if use_smart_model else GPT_4O_MINI
global ai_provider
if isinstance(ai_provider, OpenAIProvider):
ai_provider.use_smart_model = not ai_provider.use_smart_model
model = ai_provider.get_model()
logging.info(f"Switched to model: {model}")
await update.message.reply_text(f"Switched to model: {model}")
else:
await update.message.reply_text("Switching models is only available for OpenAI provider.")
async def switch_providers(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
await clear(update, context)
global use_anthropic
use_anthropic = not use_anthropic
logging.info("Using Anthropic" if use_anthropic else "Using OpenAI")
await update.message.reply_text("Using Anthropic" if use_anthropic else "Using OpenAI")
global ai_provider
if isinstance(ai_provider, AnthropicProvider):
ai_provider = create_ai_provider("openai")
logging.info("Switched to OpenAI provider")
await update.message.reply_text("Switched to OpenAI provider")
else:
ai_provider = create_ai_provider("anthropic")
logging.info("Switched to Anthropic provider")
await update.message.reply_text("Switched to Anthropic provider")
async def status(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
if use_anthropic:
await update.message.reply_text("Currently using claude-3-5-sonnet-20240620")
if isinstance(ai_provider, AnthropicProvider):
await update.message.reply_text(f"Currently using Anthropic: {ai_provider.model}")
else:
model = GPT_4O if use_smart_model else GPT_4O_MINI
await update.message.reply_text(f"Currently using: {model}")
await update.message.reply_text(f"Currently using OpenAI: {ai_provider.get_model()}")
def main() -> None:
# Create the Application and pass it your bot's token