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215 | class SimpleChatEngine(BaseChatEngine):
"""
Simple Chat Engine.
Have a conversation with the LLM.
This does not make use of a knowledge base.
"""
def __init__(
self,
llm: LLM,
memory: BaseMemory,
prefix_messages: List[ChatMessage],
callback_manager: Optional[CallbackManager] = None,
) -> None:
self._llm = llm
self._memory = memory
self._prefix_messages = prefix_messages
self.callback_manager = callback_manager or CallbackManager([])
@classmethod
def from_defaults(
cls,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
system_prompt: Optional[str] = None,
prefix_messages: Optional[List[ChatMessage]] = None,
llm: Optional[LLM] = None,
**kwargs: Any,
) -> "SimpleChatEngine":
"""Initialize a SimpleChatEngine from default parameters."""
llm = llm or Settings.llm
chat_history = chat_history or []
memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm)
if system_prompt is not None:
if prefix_messages is not None:
raise ValueError(
"Cannot specify both system_prompt and prefix_messages"
)
prefix_messages = [
ChatMessage(content=system_prompt, role=llm.metadata.system_role)
]
prefix_messages = prefix_messages or []
return cls(
llm=llm,
memory=memory,
prefix_messages=prefix_messages,
callback_manager=Settings.callback_manager,
)
@trace_method("chat")
def chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
if chat_history is not None:
self._memory.set(chat_history)
self._memory.put(ChatMessage(content=message, role="user"))
if hasattr(self._memory, "tokenizer_fn"):
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join(
[
(m.content or "")
for m in self._prefix_messages
if isinstance(m.content, str)
]
)
)
)
else:
initial_token_count = 0
all_messages = self._prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = self._llm.chat(all_messages)
ai_message = chat_response.message
self._memory.put(ai_message)
return AgentChatResponse(response=str(chat_response.message.content))
@trace_method("chat")
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
if chat_history is not None:
self._memory.set(chat_history)
self._memory.put(ChatMessage(content=message, role="user"))
if hasattr(self._memory, "tokenizer_fn"):
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join(
[
(m.content or "")
for m in self._prefix_messages
if isinstance(m.content, str)
]
)
)
)
else:
initial_token_count = 0
all_messages = self._prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = StreamingAgentChatResponse(
chat_stream=self._llm.stream_chat(all_messages)
)
thread = Thread(
target=chat_response.write_response_to_history, args=(self._memory,)
)
thread.start()
return chat_response
@trace_method("chat")
async def achat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
if chat_history is not None:
self._memory.set(chat_history)
await self._memory.aput(ChatMessage(content=message, role="user"))
if hasattr(self._memory, "tokenizer_fn"):
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join(
[
(m.content or "")
for m in self._prefix_messages
if isinstance(m.content, str)
]
)
)
)
else:
initial_token_count = 0
all_messages = self._prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = await self._llm.achat(all_messages)
ai_message = chat_response.message
await self._memory.aput(ai_message)
return AgentChatResponse(response=str(chat_response.message.content))
@trace_method("chat")
async def astream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
if chat_history is not None:
self._memory.set(chat_history)
await self._memory.aput(ChatMessage(content=message, role="user"))
if hasattr(self._memory, "tokenizer_fn"):
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join(
[
(m.content or "")
for m in self._prefix_messages
if isinstance(m.content, str)
]
)
)
)
else:
initial_token_count = 0
all_messages = self._prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = StreamingAgentChatResponse(
achat_stream=await self._llm.astream_chat(all_messages)
)
asyncio.create_task(chat_response.awrite_response_to_history(self._memory))
return chat_response
def reset(self) -> None:
self._memory.reset()
@property
def chat_history(self) -> List[ChatMessage]:
"""Get chat history."""
return self._memory.get_all()
|
