import abc
import logging
from collections.abc import Sequence
from typing import Any, Literal
from llama_index.core.llms import ChatMessage, MessageRole
logger = logging.getLogger(__name__)
class AbstractPromptStyle(abc.ABC):
"""Abstract class for prompt styles.
This class is used to format a series of messages into a prompt that can be
understood by the models. A series of messages represents the interaction(s)
between a user and an assistant. This series of messages can be considered as a
session between a user X and an assistant Y.This session holds, through the
messages, the state of the conversation. This session, to be understood by the
model, needs to be formatted into a prompt (i.e. a string that the models
can understand). Prompts can be formatted in different ways,
depending on the model.
The implementations of this class represent the different ways to format a
series of messages into a prompt.
"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
logger.debug("Initializing prompt_style=%s", self.__class__.__name__)
@abc.abstractmethod
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
pass
@abc.abstractmethod
def _completion_to_prompt(self, completion: str) -> str:
pass
def messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
prompt = self._messages_to_prompt(messages)
logger.debug("Got for messages='%s' the prompt='%s'", messages, prompt)
return prompt
def completion_to_prompt(self, completion: str) -> str:
prompt = self._completion_to_prompt(completion)
logger.debug("Got for completion='%s' the prompt='%s'", completion, prompt)
return prompt
class DefaultPromptStyle(AbstractPromptStyle):
"""Default prompt style that uses the defaults from llama_utils.
It basically passes None to the LLM, indicating it should use
the default functions.
"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
# Hacky way to override the functions
# Override the functions to be None, and pass None to the LLM.
self.messages_to_prompt = None # type: ignore[method-assign, assignment]
self.completion_to_prompt = None # type: ignore[method-assign, assignment]
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
return ""
def _completion_to_prompt(self, completion: str) -> str:
return ""
class Llama2PromptStyle(AbstractPromptStyle):
"""Simple prompt style that uses llama 2 prompt style.
Inspired by llama_index/legacy/llms/llama_utils.py
It transforms the sequence of messages into a prompt that should look like:
```text
[INST] <> your system prompt here. <>
user message here [/INST] assistant (model) response here
```
"""
BOS, EOS = "", ""
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<>\n", "\n<>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. \
Always answer as helpfully as possible and follow ALL given instructions. \
Do not speculate or make up information. \
Do not reference any given instructions or context. \
"""
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
string_messages: list[str] = []
if messages[0].role == MessageRole.SYSTEM:
# pull out the system message (if it exists in messages)
system_message_str = messages[0].content or ""
messages = messages[1:]
else:
system_message_str = self.DEFAULT_SYSTEM_PROMPT
system_message_str = f"{self.B_SYS} {system_message_str.strip()} {self.E_SYS}"
for i in range(0, len(messages), 2):
# first message should always be a user
user_message = messages[i]
assert user_message.role == MessageRole.USER
if i == 0:
# make sure system prompt is included at the start
str_message = f"{self.BOS} {self.B_INST} {system_message_str} "
else:
# end previous user-assistant interaction
string_messages[-1] += f" {self.EOS}"
# no need to include system prompt
str_message = f"{self.BOS} {self.B_INST} "
# include user message content
str_message += f"{user_message.content} {self.E_INST}"
if len(messages) > (i + 1):
# if assistant message exists, add to str_message
assistant_message = messages[i + 1]
assert assistant_message.role == MessageRole.ASSISTANT
str_message += f" {assistant_message.content}"
string_messages.append(str_message)
return "".join(string_messages)
def _completion_to_prompt(self, completion: str) -> str:
system_prompt_str = self.DEFAULT_SYSTEM_PROMPT
return (
f"{self.BOS} {self.B_INST} {self.B_SYS} {system_prompt_str.strip()} {self.E_SYS} "
f"{completion.strip()} {self.E_INST}"
)
class Llama3PromptStyle(AbstractPromptStyle):
r"""Template for Meta's Llama 3.1.
The format follows this structure:
<|begin_of_text|>
<|start_header_id|>system<|end_header_id|>
[System message content]<|eot_id|>
<|start_header_id|>user<|end_header_id|>
[User message content]<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
[Assistant message content]<|eot_id|>
...
(Repeat for each message, including possible 'ipython' role)
"""
BOS, EOS = "<|begin_of_text|>", "<|end_of_text|>"
B_INST, E_INST = "<|start_header_id|>", "<|end_header_id|>"
EOT = "<|eot_id|>"
B_SYS, E_SYS = "<|start_header_id|>system<|end_header_id|>", "<|eot_id|>"
ASSISTANT_INST = "<|start_header_id|>assistant<|end_header_id|>"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. \
Always answer as helpfully as possible and follow ALL given instructions. \
Do not speculate or make up information. \
Do not reference any given instructions or context. \
"""
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
prompt = ""
has_system_message = False
for i, message in enumerate(messages):
if not message or message.content is None:
continue
if message.role == MessageRole.SYSTEM:
prompt += f"{self.B_SYS}\n\n{message.content.strip()}{self.E_SYS}"
has_system_message = True
else:
role_header = f"{self.B_INST}{message.role.value}{self.E_INST}"
prompt += f"{role_header}\n\n{message.content.strip()}{self.EOT}"
# Add assistant header if the last message is not from the assistant
if i == len(messages) - 1 and message.role != MessageRole.ASSISTANT:
prompt += f"{self.ASSISTANT_INST}\n\n"
# Add default system prompt if no system message was provided
if not has_system_message:
prompt = (
f"{self.B_SYS}\n\n{self.DEFAULT_SYSTEM_PROMPT}{self.E_SYS}" + prompt
)
# TODO: Implement tool handling logic
return prompt
def _completion_to_prompt(self, completion: str) -> str:
return (
f"{self.B_SYS}\n\n{self.DEFAULT_SYSTEM_PROMPT}{self.E_SYS}"
f"{self.B_INST}user{self.E_INST}\n\n{completion.strip()}{self.EOT}"
f"{self.ASSISTANT_INST}\n\n"
)
class TagPromptStyle(AbstractPromptStyle):
"""Tag prompt style (used by Vigogne) that uses the prompt style `<|ROLE|>`.
It transforms the sequence of messages into a prompt that should look like:
```text
<|system|>: your system prompt here.
<|user|>: user message here
(possibly with context and question)
<|assistant|>: assistant (model) response here.
```
FIXME: should we add surrounding `` and `` tags, like in llama2?
"""
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
"""Format message to prompt with `<|ROLE|>: MSG` style."""
prompt = ""
for message in messages:
role = message.role
content = message.content or ""
message_from_user = f"<|{role.lower()}|>: {content.strip()}"
message_from_user += "\n"
prompt += message_from_user
# we are missing the last <|assistant|> tag that will trigger a completion
prompt += "<|assistant|>: "
return prompt
def _completion_to_prompt(self, completion: str) -> str:
return self._messages_to_prompt(
[ChatMessage(content=completion, role=MessageRole.USER)]
)
class MistralPromptStyle(AbstractPromptStyle):
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
inst_buffer = []
text = ""
for message in messages:
if message.role == MessageRole.SYSTEM or message.role == MessageRole.USER:
inst_buffer.append(str(message.content).strip())
elif message.role == MessageRole.ASSISTANT:
text += "[INST] " + "\n".join(inst_buffer) + " [/INST]"
text += " " + str(message.content).strip() + ""
inst_buffer.clear()
else:
raise ValueError(f"Unknown message role {message.role}")
if len(inst_buffer) > 0:
text += "[INST] " + "\n".join(inst_buffer) + " [/INST]"
return text
def _completion_to_prompt(self, completion: str) -> str:
return self._messages_to_prompt(
[ChatMessage(content=completion, role=MessageRole.USER)]
)
class ChatMLPromptStyle(AbstractPromptStyle):
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
prompt = "<|im_start|>system\n"
for message in messages:
role = message.role
content = message.content or ""
if role.lower() == "system":
message_from_user = f"{content.strip()}"
prompt += message_from_user
elif role.lower() == "user":
prompt += "<|im_end|>\n<|im_start|>user\n"
message_from_user = f"{content.strip()}<|im_end|>\n"
prompt += message_from_user
prompt += "<|im_start|>assistant\n"
return prompt
def _completion_to_prompt(self, completion: str) -> str:
return self._messages_to_prompt(
[ChatMessage(content=completion, role=MessageRole.USER)]
)
def get_prompt_style(
prompt_style: Literal["default", "llama2", "llama3", "tag", "mistral", "chatml"]
| None
) -> AbstractPromptStyle:
"""Get the prompt style to use from the given string.
:param prompt_style: The prompt style to use.
:return: The prompt style to use.
"""
if prompt_style is None or prompt_style == "default":
return DefaultPromptStyle()
elif prompt_style == "llama2":
return Llama2PromptStyle()
elif prompt_style == "llama3":
return Llama3PromptStyle()
elif prompt_style == "tag":
return TagPromptStyle()
elif prompt_style == "mistral":
return MistralPromptStyle()
elif prompt_style == "chatml":
return ChatMLPromptStyle()
raise ValueError(f"Unknown prompt_style='{prompt_style}'")