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302 | class ContextChatEngine(BaseChatEngine):
"""上下文聊天引擎。
使用检索器检索上下文，在系统提示中设置上下文，然后使用LLM生成响应，实现流畅的聊天体验。"""
def __init__(
self,
retriever: BaseRetriever,
llm: LLM,
memory: BaseMemory,
prefix_messages: List[ChatMessage],
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
context_template: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
) -> None:
self._retriever = retriever
self._llm = llm
self._memory = memory
self._prefix_messages = prefix_messages
self._node_postprocessors = node_postprocessors or []
self._context_template = context_template or DEFAULT_CONTEXT_TEMPLATE
self.callback_manager = callback_manager or CallbackManager([])
for node_postprocessor in self._node_postprocessors:
node_postprocessor.callback_manager = self.callback_manager
@classmethod
def from_defaults(
cls,
retriever: BaseRetriever,
service_context: Optional[ServiceContext] = None,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
system_prompt: Optional[str] = None,
prefix_messages: Optional[List[ChatMessage]] = None,
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
context_template: Optional[str] = None,
llm: Optional[LLM] = None,
**kwargs: Any,
) -> "ContextChatEngine":
"""使用默认参数初始化一个ContextChatEngine。"""
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
)
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 []
node_postprocessors = node_postprocessors or []
return cls(
retriever,
llm=llm,
memory=memory,
prefix_messages=prefix_messages,
node_postprocessors=node_postprocessors,
callback_manager=callback_manager_from_settings_or_context(
Settings, service_context
),
context_template=context_template,
)
def _generate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
"""从消息中生成上下文信息。"""
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 self._context_template.format(context_str=context_str), nodes
async def _agenerate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
"""从消息中生成上下文信息。"""
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 self._context_template.format(context_str=context_str), nodes
def _get_prefix_messages_with_context(self, context_str: str) -> List[ChatMessage]:
"""获取带有上下文的前缀消息。"""
# ensure we grab the user-configured system prompt
system_prompt = ""
prefix_messages = self._prefix_messages
if (
len(self._prefix_messages) != 0
and self._prefix_messages[0].role == MessageRole.SYSTEM
):
system_prompt = str(self._prefix_messages[0].content)
prefix_messages = self._prefix_messages[1:]
context_str_w_sys_prompt = system_prompt.strip() + "\n" + context_str
return [
ChatMessage(
content=context_str_w_sys_prompt, role=self._llm.metadata.system_role
),
*prefix_messages,
]
@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"))
context_str_template, nodes = self._generate_context(message)
prefix_messages = self._get_prefix_messages_with_context(context_str_template)
prefix_messages_token_count = len(
self._memory.tokenizer_fn(
" ".join([(m.content or "") for m in prefix_messages])
)
)
all_messages = prefix_messages + self._memory.get(
initial_token_count=prefix_messages_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),
sources=[
ToolOutput(
tool_name="retriever",
content=str(prefix_messages[0]),
raw_input={"message": message},
raw_output=prefix_messages[0],
)
],
source_nodes=nodes,
)
@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"))
context_str_template, nodes = self._generate_context(message)
prefix_messages = self._get_prefix_messages_with_context(context_str_template)
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join([(m.content or "") for m in prefix_messages])
)
)
all_messages = prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = StreamingAgentChatResponse(
chat_stream=self._llm.stream_chat(all_messages),
sources=[
ToolOutput(
tool_name="retriever",
content=str(prefix_messages[0]),
raw_input={"message": message},
raw_output=prefix_messages[0],
)
],
source_nodes=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:
if chat_history is not None:
self._memory.set(chat_history)
self._memory.put(ChatMessage(content=message, role="user"))
context_str_template, nodes = await self._agenerate_context(message)
prefix_messages = self._get_prefix_messages_with_context(context_str_template)
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join([(m.content or "") for m in prefix_messages])
)
)
all_messages = prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = await self._llm.achat(all_messages)
ai_message = chat_response.message
self._memory.put(ai_message)
return AgentChatResponse(
response=str(chat_response.message.content),
sources=[
ToolOutput(
tool_name="retriever",
content=str(prefix_messages[0]),
raw_input={"message": message},
raw_output=prefix_messages[0],
)
],
source_nodes=nodes,
)
@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)
self._memory.put(ChatMessage(content=message, role="user"))
context_str_template, nodes = await self._agenerate_context(message)
prefix_messages = self._get_prefix_messages_with_context(context_str_template)
initial_token_count = len(
self._memory.tokenizer_fn(
" ".join([(m.content or "") for m in prefix_messages])
)
)
all_messages = prefix_messages + self._memory.get(
initial_token_count=initial_token_count
)
chat_response = StreamingAgentChatResponse(
achat_stream=await self._llm.astream_chat(all_messages),
sources=[
ToolOutput(
tool_name="retriever",
content=str(prefix_messages[0]),
raw_input={"message": message},
raw_output=prefix_messages[0],
)
],
source_nodes=nodes,
)
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]:
"""获取聊天记录。"""
return self._memory.get_all()
|