OpenAIChatAtomicFlow.py

import pprint
from copy import deepcopy

import hydra

import colorama
import time

from typing import List, Dict, Optional, Any

from langchain import PromptTemplate
import langchain
from langchain.schema import HumanMessage, AIMessage, SystemMessage

from flows.history import FlowHistory
from flows.message_annotators.abstract import MessageAnnotator
from flows.base_flows.abstract import AtomicFlow
from flows.datasets import GenericDemonstrationsDataset

from flows import utils
from flows.messages.chat_message import ChatMessage
from flows.utils.caching_utils import flow_run_cache

log = utils.get_pylogger(__name__)


class OpenAIChatAtomicFlow(AtomicFlow):
    model_name: str
    generation_parameters: Dict

    system_message_prompt_template: PromptTemplate
    human_message_prompt_template: PromptTemplate

    system_name: str = "system"
    user_name: str = "user"
    assistant_name: str = "assistant"

    n_api_retries: int = 6
    wait_time_between_retries: int = 20

    query_message_prompt_template: Optional[PromptTemplate] = None
    demonstrations: GenericDemonstrationsDataset = None
    demonstrations_response_template: PromptTemplate = None
    response_annotators: Optional[Dict[str, MessageAnnotator]] = {}

    def __init__(self, **kwargs):
        self._validate_parameters(kwargs)
        super().__init__(**kwargs)

        assert self.flow_config["name"] not in [
            "system",
            "user",
            "assistant",
        ], f"Flow name '{self.flow_config['name']}' cannot be 'system', 'user' or 'assistant'"

    def set_up_flow_state(self):
        super().set_up_flow_state()
        self.flow_state["conversation_initialized"] = False

    @classmethod
    def _validate_parameters(cls, kwargs):
        # ToDo: Deal with this in a cleaner way (with less repetition)
        super()._validate_parameters(kwargs)

        # ~~~ Model generation ~~~
        if "model_name" not in kwargs["flow_config"]:
            raise KeyError("model_name not specified in the flow_config.")

        if "generation_parameters" not in kwargs["flow_config"]:
            raise KeyError("generation_parameters not specified in the flow_config.")

        # ~~~ Prompting ~~~
        if "system_message_prompt_template" not in kwargs:
            raise KeyError("system_message_prompt_template not passed to the constructor.")

        if "query_message_prompt_template" not in kwargs:
            raise KeyError("query_message_prompt_template not passed to the constructor.")

        if "human_message_prompt_template" not in kwargs:
            raise KeyError("human_message_prompt_template not passed to the constructor.")

    @classmethod
    def _set_up_prompts(cls, config):
        kwargs = {}

        kwargs["system_message_prompt_template"] = \
            hydra.utils.instantiate(config['system_message_prompt_template'], _convert_="partial")
        kwargs["query_message_prompt_template"] = \
            hydra.utils.instantiate(config['query_message_prompt_template'], _convert_="partial")
        kwargs["human_message_prompt_template"] = \
            hydra.utils.instantiate(config['human_message_prompt_template'], _convert_="partial")

        return kwargs

    @classmethod
    def _set_up_demonstration_templates(cls, config):
        kwargs = {}

        if "demonstrations_response_template" in config:
            kwargs["demonstrations_response_template"] = \
                hydra.utils.instantiate(config['demonstrations_response_template'], _convert_="partial")

        return kwargs

    @classmethod
    def _set_up_response_annotators(cls, config):
        response_annotators = config.get("response_annotators", {})
        if len(response_annotators) > 0:
            for key, config in response_annotators.items():
                response_annotators[key] = hydra.utils.instantiate(config, _convert_="partial")
        return {"response_annotators": response_annotators}

    @classmethod
    def instantiate_from_config(cls, config):
        flow_config = deepcopy(config)

        kwargs = {"flow_config": flow_config}

        # ~~~ Set up prompts ~~~
        kwargs.update(cls._set_up_prompts(flow_config))

        # ~~~ Set up demonstration templates ~~~
        kwargs.update(cls._set_up_demonstration_templates(flow_config))

        # ~~~ Set up response annotators ~~~
        kwargs.update(cls._set_up_response_annotators(flow_config))

        # ~~~ Instantiate flow ~~~
        return cls(**kwargs)

    def _is_conversation_initialized(self):
        return self.flow_state["conversation_initialized"]

    def expected_inputs_given_state(self):
        if self._is_conversation_initialized():
            return ["query"]
        else:
            return self.flow_config["expected_inputs"]

    @staticmethod
    def _get_message(prompt_template, input_data: Dict[str, Any]):
        template_kwargs = {}
        for input_variable in prompt_template.input_variables:
            template_kwargs[input_variable] = input_data[input_variable]

        msg_content = prompt_template.format(**template_kwargs)
        return msg_content

    def _get_demonstration_query_message_content(self, sample_data: Dict):
        input_variables = self.query_message_prompt_template.input_variables
        return self.query_message_prompt_template.format(**{k: sample_data[k] for k in input_variables}), []

    def _get_demonstration_response_message_content(self, sample_data: Dict):
        input_variables = self.demonstrations_response_template.input_variables
        return self.demonstrations_response_template.format(**{k: sample_data[k] for k in input_variables}), []

    def _get_annotator_with_key(self, key: str):
        for _, ra in self.response_annotators.items():
            if ra.key == key:
                return ra

    def _response_parsing(self, response: str, expected_outputs: List[str]):
        target_annotators = [ra for _, ra in self.response_annotators.items() if ra.key in expected_outputs]

        if len(target_annotators) == 0:
            return {expected_outputs[0]: response}

        parsed_outputs = {}
        for ra in target_annotators:
            parsed_out = ra(response)
            parsed_outputs.update(parsed_out)
        return parsed_outputs

    def _add_demonstrations(self):
        if self.demonstrations is not None:
            for example in self.demonstrations:
                query, parents = self._get_demonstration_query_message_content(example)
                response, parents = self._get_demonstration_response_message_content(example)

                self._log_chat_message(content=query,
                                       message_creator=self.user_name,
                                       parent_message_ids=parents)

                self._log_chat_message(content=response,
                                       message_creator=self.assistant_name,
                                       parent_message_ids=parents)

    def _log_chat_message(self, message_creator: str, content: str, parent_message_ids: List[str] = None):
        chat_message = ChatMessage(
            message_creator=message_creator,
            parent_message_ids=parent_message_ids,
            flow_runner=self.flow_config["name"],
            flow_run_id=self.flow_run_id,
            content=content
        )
        return self._log_message(chat_message)

    def _initialize_conversation(self, input_data: Dict[str, Any]):
        # ~~~ Add the system message ~~~
        system_message_content = self._get_message(self.system_message_prompt_template, input_data)

        self._log_chat_message(content=system_message_content,
                               message_creator=self.system_name)

        # ~~~ Add the demonstration query-response tuples (if any) ~~~
        self._add_demonstrations()
        self._update_state(update_data={"conversation_initialized": True})

    def get_conversation_messages(self, message_format: Optional[str] = None):
        messages = self.flow_state["history"].get_chat_messages()

        if message_format is None:
            return messages

        elif message_format == "open_ai":
            processed_messages = []

            for message in messages:
                if message.message_creator == self.system_name:
                    processed_messages.append(SystemMessage(content=message.content))
                elif message.message_creator == self.assistant_name:
                    processed_messages.append(AIMessage(content=message.content))
                elif message.message_creator == self.user_name:
                    processed_messages.append(HumanMessage(content=message.content))
                else:
                    raise ValueError(f"Unknown name: {message.message_creator}")
            return processed_messages
        else:
            raise ValueError(
                f"Currently supported conversation message formats: 'open_ai'. '{message_format}' is not supported")

    def _call(self):
        api_key = self.flow_state["api_key"]

        backend = langchain.chat_models.ChatOpenAI(
            model_name=self.flow_config["model_name"],
            openai_api_key=api_key,
            **self.flow_config["generation_parameters"],
        )

        messages = self.get_conversation_messages(
            message_format="open_ai"
        )

        _success = False
        attempts = 1
        error = None
        response = None
        while attempts <= self.n_api_retries:
            try:
                response = backend(messages).content
                _success = True
                break
            except Exception as e:
                log.error(
                    f"Error {attempts} in calling backend: {e}. Key used: `{api_key}`. "
                    f"Retrying in {self.wait_time_between_retries} seconds..."
                )
                log.error(
                    f"API call raised Exception with the following arguments arguments: "
                    f"\n{self.flow_state['history'].to_string()}"
                )
                attempts += 1
                time.sleep(self.wait_time_between_retries)
                error = e

        if not _success:
            raise error

        if self.flow_config["verbose"]:
            messages_str = self.flow_state["history"].to_string()
            log.info(
                f"\n{colorama.Fore.MAGENTA}~~~ History [{self.flow_config['name']}] ~~~\n"
                f"{colorama.Style.RESET_ALL}{messages_str}"
            )

        return response

    def _prepare_conversation(self, input_data: Dict[str, Any]):
        if self._is_conversation_initialized():
            # ~~~ Check that the message has a `query` field ~~~
            user_message_content = self.human_message_prompt_template.format(query=input_data["query"])

        else:
            self._initialize_conversation(input_data)
            user_message_content = self._get_message(self.query_message_prompt_template, input_data)

        self._log_chat_message(message_creator=self.user_name,
                               content=user_message_content)

    @flow_run_cache()
    def run(self, input_data: Dict[str, Any], expected_outputs: List[str]) -> Dict[str, Any]:
        # ~~~ Chat-specific preparation ~~~
        self._prepare_conversation(input_data)

        # ~~~ Call ~~~
        response = self._call()
        answer_message = self._log_chat_message(
            message_creator=self.flow_config["assistant_name"],
            content=response
        )

        # ~~~ Response parsing ~~~
        parsed_outputs = self._response_parsing(
            response=response,
            expected_outputs=expected_outputs
        )
        self._update_state(update_data=parsed_outputs)

        if self.flow_config["verbose"]:
            parsed_output_messages_str = pprint.pformat({k: m for k, m in parsed_outputs.items()},
                                                        indent=4)
            log.info(
                f"\n{colorama.Fore.MAGENTA}~~~ "
                f"Response [{answer_message.message_creator} -- "
                f"{answer_message.message_id} -- "
                f"{answer_message.flow_run_id}] ~~~"
                f"\n{colorama.Fore.YELLOW}Content: {answer_message}{colorama.Style.RESET_ALL}"
                f"\n{colorama.Fore.YELLOW}Parsed Outputs: {parsed_output_messages_str}{colorama.Style.RESET_ALL}"
            )

        # ~~~ The final answer should be in self.flow_state, thus allow_class_namespace=False ~~~
        return self._get_keys_from_state(keys=expected_outputs, allow_class_namespace=False)