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369 | class CondensePlusContextChatEngine(BaseChatEngine):
"""对话内容压缩和上下文聊天引擎。
首先将对话内容和最新用户消息压缩为一个独立的问题，
然后从检索器中构建独立问题的上下文，
然后将上下文与提示和用户消息一起传递给LLM以生成响应。"""
def __init__(
self,
retriever: BaseRetriever,
llm: LLM,
memory: BaseMemory,
context_prompt: Optional[str] = None,
condense_prompt: Optional[str] = None,
system_prompt: Optional[str] = None,
skip_condense: bool = False,
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
callback_manager: Optional[CallbackManager] = None,
verbose: bool = False,
):
self._retriever = retriever
self._llm = llm
self._memory = memory
self._context_prompt_template = (
context_prompt or DEFAULT_CONTEXT_PROMPT_TEMPLATE
)
condense_prompt_str = condense_prompt or DEFAULT_CONDENSE_PROMPT_TEMPLATE
self._condense_prompt_template = PromptTemplate(condense_prompt_str)
self._system_prompt = system_prompt
self._skip_condense = skip_condense
self._node_postprocessors = node_postprocessors or []
self.callback_manager = callback_manager or CallbackManager([])
for node_postprocessor in self._node_postprocessors:
node_postprocessor.callback_manager = self.callback_manager
self._token_counter = TokenCounter()
self._verbose = verbose
@classmethod
def from_defaults(
cls,
retriever: BaseRetriever,
llm: Optional[LLM] = None,
service_context: Optional[ServiceContext] = None,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
system_prompt: Optional[str] = None,
context_prompt: Optional[str] = None,
condense_prompt: Optional[str] = None,
skip_condense: bool = False,
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
verbose: bool = False,
**kwargs: Any,
) -> "CondensePlusContextChatEngine":
"""使用默认参数初始化一个CondensePlusContextChatEngine。"""
llm = llm or llm_from_settings_or_context(Settings, service_context)
chat_history = chat_history or []
memory = memory or ChatMemoryBuffer.from_defaults(
chat_history=chat_history, token_limit=llm.metadata.context_window - 256
)
return cls(
retriever=retriever,
llm=llm,
memory=memory,
context_prompt=context_prompt,
condense_prompt=condense_prompt,
skip_condense=skip_condense,
callback_manager=callback_manager_from_settings_or_context(
Settings, service_context
),
node_postprocessors=node_postprocessors,
system_prompt=system_prompt,
verbose=verbose,
)
def _condense_question(
self, chat_history: List[ChatMessage], latest_message: str
) -> str:
"""将对话历史和最新用户消息压缩为一个独立的问题。"""
if self._skip_condense or len(chat_history) == 0:
return latest_message
chat_history_str = messages_to_history_str(chat_history)
logger.debug(chat_history_str)
return self._llm.predict(
self._condense_prompt_template,
question=latest_message,
chat_history=chat_history_str,
)
async def _acondense_question(
self, chat_history: List[ChatMessage], latest_message: str
) -> str:
"""将对话历史和最新用户消息压缩为一个独立的问题。"""
if self._skip_condense or len(chat_history) == 0:
return latest_message
chat_history_str = messages_to_history_str(chat_history)
logger.debug(chat_history_str)
return await self._llm.apredict(
self._condense_prompt_template,
question=latest_message,
chat_history=chat_history_str,
)
def _retrieve_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
"""为从检索器中获取的消息构建上下文。"""
nodes = self._retriever.retrieve(message)
for postprocessor in self._node_postprocessors:
nodes = postprocessor.postprocess_nodes(
nodes, query_bundle=QueryBundle(message)
)
context_str = "\n\n".join(
[n.node.get_content(metadata_mode=MetadataMode.LLM).strip() for n in nodes]
)
return context_str, nodes
async def _aretrieve_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
"""为从检索器中获取的消息构建上下文。"""
nodes = await self._retriever.aretrieve(message)
for postprocessor in self._node_postprocessors:
nodes = postprocessor.postprocess_nodes(
nodes, query_bundle=QueryBundle(message)
)
context_str = "\n\n".join(
[n.node.get_content(metadata_mode=MetadataMode.LLM).strip() for n in nodes]
)
return context_str, nodes
def _run_c3(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> Tuple[List[ChatMessage], ToolOutput, List[NodeWithScore]]:
if chat_history is not None:
self._memory.set(chat_history)
chat_history = self._memory.get(input=message)
# Condense conversation history and latest message to a standalone question
condensed_question = self._condense_question(chat_history, message) # type: ignore
logger.info(f"Condensed question: {condensed_question}")
if self._verbose:
print(f"Condensed question: {condensed_question}")
# Build context for the standalone question from a retriever
context_str, context_nodes = self._retrieve_context(condensed_question)
context_source = ToolOutput(
tool_name="retriever",
content=context_str,
raw_input={"message": condensed_question},
raw_output=context_str,
)
logger.debug(f"Context: {context_str}")
if self._verbose:
print(f"Context: {context_str}")
system_message_content = self._context_prompt_template.format(
context_str=context_str
)
if self._system_prompt:
system_message_content = self._system_prompt + "\n" + system_message_content
system_message = ChatMessage(
content=system_message_content, role=self._llm.metadata.system_role
)
initial_token_count = self._token_counter.estimate_tokens_in_messages(
[system_message]
)
self._memory.put(ChatMessage(content=message, role=MessageRole.USER))
chat_messages = [
system_message,
*self._memory.get(initial_token_count=initial_token_count),
]
return chat_messages, context_source, context_nodes
async def _arun_c3(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> Tuple[List[ChatMessage], ToolOutput, List[NodeWithScore]]:
if chat_history is not None:
self._memory.set(chat_history)
chat_history = self._memory.get(input=message)
# Condense conversation history and latest message to a standalone question
condensed_question = await self._acondense_question(chat_history, message) # type: ignore
logger.info(f"Condensed question: {condensed_question}")
if self._verbose:
print(f"Condensed question: {condensed_question}")
# Build context for the standalone question from a retriever
context_str, context_nodes = await self._aretrieve_context(condensed_question)
context_source = ToolOutput(
tool_name="retriever",
content=context_str,
raw_input={"message": condensed_question},
raw_output=context_str,
)
logger.debug(f"Context: {context_str}")
if self._verbose:
print(f"Context: {context_str}")
system_message_content = self._context_prompt_template.format(
context_str=context_str
)
if self._system_prompt:
system_message_content = self._system_prompt + "\n" + system_message_content
system_message = ChatMessage(
content=system_message_content, role=self._llm.metadata.system_role
)
initial_token_count = self._token_counter.estimate_tokens_in_messages(
[system_message]
)
self._memory.put(ChatMessage(content=message, role=MessageRole.USER))
chat_messages = [
system_message,
*self._memory.get(initial_token_count=initial_token_count),
]
return chat_messages, context_source, context_nodes
@trace_method("chat")
def chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
chat_messages, context_source, context_nodes = self._run_c3(
message, chat_history
)
# pass the context, system prompt and user message as chat to LLM to generate a response
chat_response = self._llm.chat(chat_messages)
assistant_message = chat_response.message
self._memory.put(assistant_message)
return AgentChatResponse(
response=str(assistant_message.content),
sources=[context_source],
source_nodes=context_nodes,
)
@trace_method("chat")
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
chat_messages, context_source, context_nodes = self._run_c3(
message, chat_history
)
# pass the context, system prompt and user message as chat to LLM to generate a response
chat_response = StreamingAgentChatResponse(
chat_stream=self._llm.stream_chat(chat_messages),
sources=[context_source],
source_nodes=context_nodes,
)
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:
chat_messages, context_source, context_nodes = await self._arun_c3(
message, chat_history
)
# pass the context, system prompt and user message as chat to LLM to generate a response
chat_response = await self._llm.achat(chat_messages)
assistant_message = chat_response.message
self._memory.put(assistant_message)
return AgentChatResponse(
response=str(assistant_message.content),
sources=[context_source],
source_nodes=context_nodes,
)
@trace_method("chat")
async def astream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
chat_messages, context_source, context_nodes = await self._arun_c3(
message, chat_history
)
# pass the context, system prompt and user message as chat to LLM to generate a response
chat_response = StreamingAgentChatResponse(
achat_stream=await self._llm.astream_chat(chat_messages),
sources=[context_source],
source_nodes=context_nodes,
)
asyncio.create_task(chat_response.awrite_response_to_history(self._memory))
return chat_response
def reset(self) -> None:
# Clear chat history
self._memory.reset()
@property
def chat_history(self) -> List[ChatMessage]:
"""获取聊天记录。"""
return self._memory.get_all()
|