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441 | class CondensePlusContextChatEngine(BaseChatEngine):
"""
Condensed Conversation & Context Chat Engine.
First condense a conversation and latest user message to a standalone question
Then build a context for the standalone question from a retriever,
Then pass the context along with prompt and user message to LLM to generate a response.
"""
def __init__(
self,
retriever: BaseRetriever,
llm: LLM,
memory: BaseMemory,
context_prompt: Optional[Union[str, PromptTemplate]] = None,
context_refine_prompt: Optional[Union[str, PromptTemplate]] = None,
condense_prompt: Optional[Union[str, PromptTemplate]] = 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
context_prompt = context_prompt or DEFAULT_CONTEXT_PROMPT_TEMPLATE
if isinstance(context_prompt, str):
context_prompt = PromptTemplate(context_prompt)
self._context_prompt_template = context_prompt
context_refine_prompt = (
context_refine_prompt or DEFAULT_CONTEXT_REFINE_PROMPT_TEMPLATE
)
if isinstance(context_refine_prompt, str):
context_refine_prompt = PromptTemplate(context_refine_prompt)
self._context_refine_prompt_template = context_refine_prompt
condense_prompt = condense_prompt or DEFAULT_CONDENSE_PROMPT_TEMPLATE
if isinstance(condense_prompt, str):
condense_prompt = PromptTemplate(condense_prompt)
self._condense_prompt_template = condense_prompt
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,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
system_prompt: Optional[str] = None,
context_prompt: Optional[Union[str, PromptTemplate]] = None,
context_refine_prompt: Optional[Union[str, PromptTemplate]] = None,
condense_prompt: Optional[Union[str, PromptTemplate]] = None,
skip_condense: bool = False,
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
verbose: bool = False,
**kwargs: Any,
) -> "CondensePlusContextChatEngine":
"""Initialize a CondensePlusContextChatEngine from default parameters."""
llm = llm or Settings.llm
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,
context_refine_prompt=context_refine_prompt,
condense_prompt=condense_prompt,
skip_condense=skip_condense,
callback_manager=Settings.callback_manager,
node_postprocessors=node_postprocessors,
system_prompt=system_prompt,
verbose=verbose,
)
def _condense_question(
self, chat_history: List[ChatMessage], latest_message: str
) -> str:
"""Condense a conversation history and latest user message to a standalone question."""
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)
llm_input = self._condense_prompt_template.format(
chat_history=chat_history_str, question=latest_message
)
return str(self._llm.complete(llm_input))
async def _acondense_question(
self, chat_history: List[ChatMessage], latest_message: str
) -> str:
"""Condense a conversation history and latest user message to a standalone question."""
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)
llm_input = self._condense_prompt_template.format(
chat_history=chat_history_str, question=latest_message
)
return str(await self._llm.acomplete(llm_input))
def _get_nodes(self, message: str) -> List[NodeWithScore]:
"""Generate context information from a message."""
nodes = self._retriever.retrieve(message)
for postprocessor in self._node_postprocessors:
nodes = postprocessor.postprocess_nodes(
nodes, query_bundle=QueryBundle(message)
)
return nodes
async def _aget_nodes(self, message: str) -> List[NodeWithScore]:
"""Generate context information from a message."""
nodes = await self._retriever.aretrieve(message)
for postprocessor in self._node_postprocessors:
nodes = postprocessor.postprocess_nodes(
nodes, query_bundle=QueryBundle(message)
)
return nodes
def _get_response_synthesizer(
self, chat_history: List[ChatMessage], streaming: bool = False
) -> CompactAndRefine:
system_prompt = self._system_prompt or ""
qa_messages = get_prefix_messages_with_context(
self._context_prompt_template,
system_prompt,
[],
chat_history,
self._llm.metadata.system_role,
)
refine_messages = get_prefix_messages_with_context(
self._context_refine_prompt_template,
system_prompt,
[],
chat_history,
self._llm.metadata.system_role,
)
return get_response_synthesizer(
self._llm,
self.callback_manager,
qa_messages,
refine_messages,
streaming,
qa_function_mappings=self._context_prompt_template.function_mappings,
refine_function_mappings=self._context_refine_prompt_template.function_mappings,
)
def _run_c3(
self,
message: str,
chat_history: Optional[List[ChatMessage]] = None,
streaming: bool = False,
) -> Tuple[CompactAndRefine, 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}")
# get the context nodes using the condensed question
context_nodes = self._get_nodes(condensed_question)
context_source = ToolOutput(
tool_name="retriever",
content=str(context_nodes),
raw_input={"message": condensed_question},
raw_output=context_nodes,
)
# build the response synthesizer
response_synthesizer = self._get_response_synthesizer(
chat_history, streaming=streaming
)
return response_synthesizer, context_source, context_nodes
async def _arun_c3(
self,
message: str,
chat_history: Optional[List[ChatMessage]] = None,
streaming: bool = False,
) -> Tuple[CompactAndRefine, 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}")
# get the context nodes using the condensed question
context_nodes = await self._aget_nodes(condensed_question)
context_source = ToolOutput(
tool_name="retriever",
content=str(context_nodes),
raw_input={"message": condensed_question},
raw_output=context_nodes,
)
# build the response synthesizer
response_synthesizer = self._get_response_synthesizer(
chat_history, streaming=streaming
)
return response_synthesizer, context_source, context_nodes
@trace_method("chat")
def chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
synthesizer, context_source, context_nodes = self._run_c3(message, chat_history)
response = synthesizer.synthesize(message, context_nodes)
user_message = ChatMessage(content=message, role=MessageRole.USER)
assistant_message = ChatMessage(
content=str(response), role=MessageRole.ASSISTANT
)
self._memory.put(user_message)
self._memory.put(assistant_message)
return AgentChatResponse(
response=str(response),
sources=[context_source],
source_nodes=context_nodes,
)
@trace_method("chat")
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
synthesizer, context_source, context_nodes = self._run_c3(
message, chat_history, streaming=True
)
response = synthesizer.synthesize(message, context_nodes)
def wrapped_gen(response: StreamingResponse) -> ChatResponseGen:
full_response = ""
for token in response.response_gen:
full_response += token
yield ChatResponse(
message=ChatMessage(
content=full_response, role=MessageRole.ASSISTANT
),
delta=token,
)
user_message = ChatMessage(content=message, role=MessageRole.USER)
assistant_message = ChatMessage(
content=full_response, role=MessageRole.ASSISTANT
)
self._memory.put(user_message)
self._memory.put(assistant_message)
return StreamingAgentChatResponse(
chat_stream=wrapped_gen(response),
sources=[context_source],
source_nodes=context_nodes,
is_writing_to_memory=False,
)
@trace_method("chat")
async def achat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
synthesizer, context_source, context_nodes = await self._arun_c3(
message, chat_history
)
response = await synthesizer.asynthesize(message, context_nodes)
user_message = ChatMessage(content=message, role=MessageRole.USER)
assistant_message = ChatMessage(
content=str(response), role=MessageRole.ASSISTANT
)
await self._memory.aput(user_message)
await self._memory.aput(assistant_message)
return AgentChatResponse(
response=str(response),
sources=[context_source],
source_nodes=context_nodes,
)
@trace_method("chat")
async def astream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
synthesizer, context_source, context_nodes = await self._arun_c3(
message, chat_history, streaming=True
)
response = await synthesizer.asynthesize(message, context_nodes)
async def wrapped_gen(response: AsyncStreamingResponse) -> ChatResponseAsyncGen:
full_response = ""
async for token in response.async_response_gen():
full_response += token
yield ChatResponse(
message=ChatMessage(
content=full_response, role=MessageRole.ASSISTANT
),
delta=token,
)
user_message = ChatMessage(content=message, role=MessageRole.USER)
assistant_message = ChatMessage(
content=full_response, role=MessageRole.ASSISTANT
)
await self._memory.aput(user_message)
await self._memory.aput(assistant_message)
return StreamingAgentChatResponse(
achat_stream=wrapped_gen(response),
sources=[context_source],
source_nodes=context_nodes,
is_writing_to_memory=False,
)
def reset(self) -> None:
# Clear chat history
self._memory.reset()
@property
def chat_history(self) -> List[ChatMessage]:
"""Get chat history."""
return self._memory.get_all()
|
