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from dataclasses import asdict, dataclass
from functools import partial
from math import ceil
from typing import Optional
import tiktoken
import yaml
from jinja2 import Environment, StrictUndefined, Template
from PIL import Image
from torch import Tensor, cosine_similarity
from pptagent.llms import LLM, AsyncLLM
from pptagent.utils import get_json_from_response, package_join
ENCODING = tiktoken.encoding_for_model("gpt-4o")
@dataclass
class Turn:
"""
A class to represent a turn in a conversation.
"""
id: int
prompt: str
response: str
message: list
retry: int = -1
images: list[str] = None
input_tokens: int = 0
output_tokens: int = 0
embedding: Tensor = None
def to_dict(self):
return {k: v for k, v in asdict(self).items() if k != "embedding"}
def calc_token(self):
"""
Calculate the number of tokens for the turn.
"""
if self.images is not None:
self.input_tokens += calc_image_tokens(self.images)
self.input_tokens += len(ENCODING.encode(self.prompt))
self.output_tokens = len(ENCODING.encode(self.response))
def __eq__(self, other):
return self is other
class Agent:
"""
An agent, defined by its instruction template and model.
"""
def __init__(
self,
name: str,
llm_mapping: dict[str, LLM | AsyncLLM],
text_model: Optional[LLM | AsyncLLM] = None,
record_cost: bool = False,
config: Optional[dict] = None,
env: Optional[Environment] = None,
):
"""
Initialize the Agent.
Args:
name (str): The name of the role.
env (Environment): The Jinja2 environment.
record_cost (bool): Whether to record the token cost.
llm (LLM): The language model.
config (dict): The configuration.
text_model (LLM): The text embedding model.
"""
self.name = name
self.config = config
if self.config is None:
with open(package_join("roles", f"{name}.yaml"), encoding="utf-8") as f:
self.config = yaml.safe_load(f)
assert isinstance(self.config, dict), "Agent config must be a dict"
self.llm_mapping = llm_mapping
self.llm = self.llm_mapping[self.config["use_model"]]
self.model = self.llm.model
self.record_cost = record_cost
self.text_model = text_model
self.return_json = self.config.get("return_json", False)
self.system_message = self.config["system_prompt"]
self.prompt_args = set(self.config["jinja_args"])
self.env = env
if self.env is None:
self.env = Environment(undefined=StrictUndefined)
self.template = self.env.from_string(self.config["template"])
self.retry_template = Template(
"""The previous output is invalid, please carefully analyze the traceback and feedback information, correct errors happened before.
feedback:
{{feedback}}
traceback:
{{traceback}}
Give your corrected output in the same format without including the previous output:
"""
)
self.input_tokens = 0
self.output_tokens = 0
self._history: list[Turn] = []
run_args = self.config.get("run_args", {})
self.llm.__call__ = partial(self.llm.__call__, **run_args)
self.system_tokens = len(ENCODING.encode(self.system_message))
def calc_cost(self, turns: list[Turn]):
"""
Calculate the cost of a list of turns.
"""
for turn in turns[:-1]:
self.input_tokens += turn.input_tokens
self.input_tokens += turn.output_tokens
self.input_tokens += turns[-1].input_tokens
self.output_tokens += turns[-1].output_tokens
self.input_tokens += self.system_tokens
def get_history(self, similar: int, recent: int, prompt: str):
"""
Get the conversation history.
"""
history = self._history[-recent:] if recent > 0 else []
if similar > 0:
assert isinstance(self.text_model, LLM), "text_model must be a LLM"
embedding = self.text_model.get_embedding(prompt)
history.sort(key=lambda x: cosine_similarity(embedding, x.embedding))
for turn in history:
if len(history) > similar + recent:
break
if turn not in history:
history.append(turn)
history.sort(key=lambda x: x.id)
return history
def retry(self, feedback: str, traceback: str, turn_id: int, error_idx: int):
"""
Retry a failed turn with feedback and traceback.
"""
assert error_idx > 0, "error_idx must be greater than 0"
prompt = self.retry_template.render(feedback=feedback, traceback=traceback)
history = [t for t in self._history if t.id == turn_id]
history_msg = []
for turn in history:
history_msg.extend(turn.message)
response, message = self.llm(
prompt,
history=history_msg,
return_message=True,
)
turn = Turn(
id=turn_id,
prompt=prompt,
response=response,
message=message,
retry=error_idx,
)
return self.__post_process__(response, history, turn)
def to_sync(self):
"""
Convert the agent to a synchronous agent.
"""
return Agent(
self.name,
self.llm_mapping,
self.text_model,
self.record_cost,
self.config,
self.env,
)
def to_async(self):
"""
Convert the agent to an asynchronous agent.
"""
return AsyncAgent(
self.name,
self.llm_mapping,
self.text_model,
self.record_cost,
self.config,
self.env,
)
@property
def next_turn_id(self):
if len(self._history) == 0:
return 0
return max(t.id for t in self._history) + 1
@property
def history(self):
return sorted(self._history, key=lambda x: (x.id, x.retry))
def __repr__(self) -> str:
return f"{self.__class__.__name__}(name={self.name}, model={self.model})"
def __call__(
self,
images: list[str] = None,
recent: int = 0,
similar: int = 0,
**jinja_args,
):
"""
Call the agent with prompt arguments.
Args:
images (list[str]): A list of image file paths.
recent (int): The number of recent turns to include.
similar (int): The number of similar turns to include.
**jinja_args: Additional arguments for the Jinja2 template.
Returns:
The response from the role.
"""
if isinstance(images, str):
images = [images]
assert self.prompt_args == set(
jinja_args.keys()
), f"Invalid arguments, expected: {self.prompt_args}, got: {jinja_args.keys()}"
prompt = self.template.render(**jinja_args)
history = self.get_history(similar, recent, prompt)
history_msg = []
for turn in history:
history_msg.extend(turn.message)
response, message = self.llm(
prompt,
system_message=self.system_message,
history=history_msg,
images=images,
return_message=True,
)
turn = Turn(
id=self.next_turn_id,
prompt=prompt,
response=response,
message=message,
images=images,
)
return turn.id, self.__post_process__(response, history, turn, similar)
def __post_process__(
self, response: str, history: list[Turn], turn: Turn, similar: int = 0
) -> str | dict:
"""
Post-process the response from the agent.
"""
self._history.append(turn)
if similar > 0:
turn.embedding = self.text_model.get_embedding(turn.prompt)
if self.record_cost:
turn.calc_token()
self.calc_cost(history + [turn])
if self.return_json:
response = get_json_from_response(response)
return response
class AsyncAgent(Agent):
"""
An agent, defined by its instruction template and model.
"""
def __init__(
self,
name: str,
llm_mapping: dict[str, AsyncLLM],
text_model: Optional[AsyncLLM] = None,
record_cost: bool = False,
config: Optional[dict] = None,
env: Optional[Environment] = None,
):
super().__init__(name, llm_mapping, text_model, record_cost, config, env)
self.llm = self.llm.to_async()
async def retry(self, feedback: str, traceback: str, turn_id: int, error_idx: int):
"""
Retry a failed turn with feedback and traceback.
"""
assert error_idx > 0, "error_idx must be greater than 0"
prompt = self.retry_template.render(feedback=feedback, traceback=traceback)
history = [t for t in self._history if t.id == turn_id]
history_msg = []
for turn in history:
history_msg.extend(turn.message)
response, message = await self.llm(
prompt,
history=history_msg,
return_message=True,
)
turn = Turn(
id=turn_id,
prompt=prompt,
response=response,
message=message,
retry=error_idx,
)
return await self.__post_process__(response, history, turn)
async def __call__(
self,
images: list[str] = None,
recent: int = 0,
similar: int = 0,
**jinja_args,
):
"""
Call the agent with prompt arguments.
Args:
images (list[str]): A list of image file paths.
recent (int): The number of recent turns to include.
similar (int): The number of similar turns to include.
**jinja_args: Additional arguments for the Jinja2 template.
Returns:
The response from the role.
"""
if isinstance(images, str):
images = [images]
assert self.prompt_args == set(
jinja_args.keys()
), f"Invalid arguments, expected: {self.prompt_args}, got: {jinja_args.keys()}"
prompt = self.template.render(**jinja_args)
history = await self.get_history(similar, recent, prompt)
history_msg = []
for turn in history:
history_msg.extend(turn.message)
response, message = await self.llm(
prompt,
system_message=self.system_message,
history=history_msg,
images=images,
return_message=True,
)
turn = Turn(
id=self.next_turn_id,
prompt=prompt,
response=response,
message=message,
images=images,
)
return turn.id, await self.__post_process__(response, history, turn, similar)
async def get_history(self, similar: int, recent: int, prompt: str):
"""
Get the conversation history.
"""
history = self._history[-recent:] if recent > 0 else []
if similar > 0:
embedding = await self.text_model.get_embedding(prompt)
history.sort(key=lambda x: cosine_similarity(embedding, x.embedding))
for turn in history:
if len(history) > similar + recent:
break
if turn not in history:
history.append(turn)
history.sort(key=lambda x: x.id)
return history
async def __post_process__(
self, response: str, history: list[Turn], turn: Turn, similar: int = 0
):
"""
Post-process the response from the agent.
"""
self._history.append(turn)
if similar > 0:
turn.embedding = await self.text_model.get_embedding(turn.prompt)
if self.record_cost:
turn.calc_token()
self.calc_cost(history + [turn])
if self.return_json:
response = get_json_from_response(response)
return response
def calc_image_tokens(images: list[str]):
"""
Calculate the number of tokens for a list of images.
"""
tokens = 0
for image in images:
with open(image, "rb") as f:
width, height = Image.open(f).size
if width > 1024 or height > 1024:
if width > height:
height = int(height * 1024 / width)
width = 1024
else:
width = int(width * 1024 / height)
height = 1024
h = ceil(height / 512)
w = ceil(width / 512)
tokens += 85 + 170 * h * w
return tokens
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