363 lines
14 KiB
Python
363 lines
14 KiB
Python
import logging
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from threading import Thread
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from typing import Any, List, Optional, Type
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from llama_index.callbacks import CallbackManager, trace_method
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from llama_index.chat_engine.types import (
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AgentChatResponse,
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BaseChatEngine,
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StreamingAgentChatResponse,
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)
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from llama_index.chat_engine.utils import response_gen_from_query_engine
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from llama_index.core.base_query_engine import BaseQueryEngine
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from llama_index.core.llms.types import ChatMessage, MessageRole
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from llama_index.core.response.schema import RESPONSE_TYPE, StreamingResponse
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from llama_index.llm_predictor.base import LLMPredictorType
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from llama_index.llms.generic_utils import messages_to_history_str
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from llama_index.llms.llm import LLM
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from llama_index.memory import BaseMemory, ChatMemoryBuffer
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from llama_index.prompts.base import BasePromptTemplate, PromptTemplate
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from llama_index.service_context import ServiceContext
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from llama_index.token_counter.mock_embed_model import MockEmbedding
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from llama_index.tools import ToolOutput
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logger = logging.getLogger(__name__)
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DEFAULT_TEMPLATE = """\
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Given a conversation (between Human and Assistant) and a follow up message from Human, \
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rewrite the message to be a standalone question that captures all relevant context \
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from the conversation.
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<Chat History>
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{chat_history}
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<Follow Up Message>
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{question}
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<Standalone question>
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"""
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DEFAULT_PROMPT = PromptTemplate(DEFAULT_TEMPLATE)
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class CondenseQuestionChatEngine(BaseChatEngine):
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"""Condense Question Chat Engine.
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First generate a standalone question from conversation context and last message,
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then query the query engine for a response.
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"""
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def __init__(
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self,
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query_engine: BaseQueryEngine,
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condense_question_prompt: BasePromptTemplate,
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memory: BaseMemory,
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llm: LLMPredictorType,
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verbose: bool = False,
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callback_manager: Optional[CallbackManager] = None,
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) -> None:
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self._query_engine = query_engine
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self._condense_question_prompt = condense_question_prompt
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self._memory = memory
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self._llm = llm
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self._verbose = verbose
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self.callback_manager = callback_manager or CallbackManager([])
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@classmethod
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def from_defaults(
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cls,
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query_engine: BaseQueryEngine,
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condense_question_prompt: Optional[BasePromptTemplate] = None,
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chat_history: Optional[List[ChatMessage]] = None,
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memory: Optional[BaseMemory] = None,
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memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
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service_context: Optional[ServiceContext] = None,
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verbose: bool = False,
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system_prompt: Optional[str] = None,
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prefix_messages: Optional[List[ChatMessage]] = None,
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llm: Optional[LLM] = None,
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**kwargs: Any,
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) -> "CondenseQuestionChatEngine":
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"""Initialize a CondenseQuestionChatEngine from default parameters."""
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condense_question_prompt = condense_question_prompt or DEFAULT_PROMPT
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if llm is None:
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service_context = service_context or ServiceContext.from_defaults(
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embed_model=MockEmbedding(embed_dim=2)
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)
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llm = service_context.llm
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else:
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service_context = service_context or ServiceContext.from_defaults(
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llm=llm, embed_model=MockEmbedding(embed_dim=2)
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)
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chat_history = chat_history or []
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memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm)
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if system_prompt is not None:
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raise NotImplementedError(
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"system_prompt is not supported for CondenseQuestionChatEngine."
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)
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if prefix_messages is not None:
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raise NotImplementedError(
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"prefix_messages is not supported for CondenseQuestionChatEngine."
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)
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return cls(
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query_engine,
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condense_question_prompt,
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memory,
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llm,
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verbose=verbose,
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callback_manager=service_context.callback_manager,
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)
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def _condense_question(
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self, chat_history: List[ChatMessage], last_message: str
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) -> str:
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"""
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Generate standalone question from conversation context and last message.
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"""
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chat_history_str = messages_to_history_str(chat_history)
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logger.debug(chat_history_str)
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return self._llm.predict(
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self._condense_question_prompt,
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question=last_message,
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chat_history=chat_history_str,
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)
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async def _acondense_question(
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self, chat_history: List[ChatMessage], last_message: str
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) -> str:
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"""
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Generate standalone question from conversation context and last message.
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"""
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chat_history_str = messages_to_history_str(chat_history)
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logger.debug(chat_history_str)
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return await self._llm.apredict(
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self._condense_question_prompt,
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question=last_message,
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chat_history=chat_history_str,
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)
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def _get_tool_output_from_response(
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self, query: str, response: RESPONSE_TYPE
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) -> ToolOutput:
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if isinstance(response, StreamingResponse):
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return ToolOutput(
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content="",
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tool_name="query_engine",
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raw_input={"query": query},
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raw_output=response,
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)
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else:
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return ToolOutput(
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content=str(response),
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tool_name="query_engine",
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raw_input={"query": query},
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raw_output=response,
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)
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@trace_method("chat")
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def chat(
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self, message: str, chat_history: Optional[List[ChatMessage]] = None
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) -> AgentChatResponse:
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chat_history = chat_history or self._memory.get()
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# Generate standalone question from conversation context and last message
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condensed_question = self._condense_question(chat_history, message)
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log_str = f"Querying with: {condensed_question}"
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logger.info(log_str)
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if self._verbose:
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print(log_str)
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# TODO: right now, query engine uses class attribute to configure streaming,
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# we are moving towards separate streaming and non-streaming methods.
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# In the meanwhile, use this hack to toggle streaming.
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from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
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if isinstance(self._query_engine, RetrieverQueryEngine):
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is_streaming = self._query_engine._response_synthesizer._streaming
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self._query_engine._response_synthesizer._streaming = False
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# Query with standalone question
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query_response = self._query_engine.query(condensed_question)
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# NOTE: reset streaming flag
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if isinstance(self._query_engine, RetrieverQueryEngine):
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self._query_engine._response_synthesizer._streaming = is_streaming
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tool_output = self._get_tool_output_from_response(
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condensed_question, query_response
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)
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# Record response
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self._memory.put(ChatMessage(role=MessageRole.USER, content=message))
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self._memory.put(
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ChatMessage(role=MessageRole.ASSISTANT, content=str(query_response))
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)
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return AgentChatResponse(response=str(query_response), sources=[tool_output])
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@trace_method("chat")
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def stream_chat(
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self, message: str, chat_history: Optional[List[ChatMessage]] = None
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) -> StreamingAgentChatResponse:
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chat_history = chat_history or self._memory.get()
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# Generate standalone question from conversation context and last message
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condensed_question = self._condense_question(chat_history, message)
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log_str = f"Querying with: {condensed_question}"
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logger.info(log_str)
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if self._verbose:
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print(log_str)
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# TODO: right now, query engine uses class attribute to configure streaming,
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# we are moving towards separate streaming and non-streaming methods.
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# In the meanwhile, use this hack to toggle streaming.
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from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
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if isinstance(self._query_engine, RetrieverQueryEngine):
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is_streaming = self._query_engine._response_synthesizer._streaming
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self._query_engine._response_synthesizer._streaming = True
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# Query with standalone question
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query_response = self._query_engine.query(condensed_question)
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# NOTE: reset streaming flag
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if isinstance(self._query_engine, RetrieverQueryEngine):
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self._query_engine._response_synthesizer._streaming = is_streaming
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tool_output = self._get_tool_output_from_response(
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condensed_question, query_response
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)
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# Record response
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if (
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isinstance(query_response, StreamingResponse)
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and query_response.response_gen is not None
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):
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# override the generator to include writing to chat history
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self._memory.put(ChatMessage(role=MessageRole.USER, content=message))
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response = StreamingAgentChatResponse(
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chat_stream=response_gen_from_query_engine(query_response.response_gen),
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sources=[tool_output],
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)
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thread = Thread(
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target=response.write_response_to_history, args=(self._memory, True)
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)
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thread.start()
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else:
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raise ValueError("Streaming is not enabled. Please use chat() instead.")
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return response
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@trace_method("chat")
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async def achat(
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self, message: str, chat_history: Optional[List[ChatMessage]] = None
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) -> AgentChatResponse:
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chat_history = chat_history or self._memory.get()
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# Generate standalone question from conversation context and last message
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condensed_question = await self._acondense_question(chat_history, message)
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log_str = f"Querying with: {condensed_question}"
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logger.info(log_str)
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if self._verbose:
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print(log_str)
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# TODO: right now, query engine uses class attribute to configure streaming,
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# we are moving towards separate streaming and non-streaming methods.
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# In the meanwhile, use this hack to toggle streaming.
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from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
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if isinstance(self._query_engine, RetrieverQueryEngine):
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is_streaming = self._query_engine._response_synthesizer._streaming
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self._query_engine._response_synthesizer._streaming = False
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# Query with standalone question
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query_response = await self._query_engine.aquery(condensed_question)
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# NOTE: reset streaming flag
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if isinstance(self._query_engine, RetrieverQueryEngine):
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self._query_engine._response_synthesizer._streaming = is_streaming
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tool_output = self._get_tool_output_from_response(
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condensed_question, query_response
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)
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# Record response
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self._memory.put(ChatMessage(role=MessageRole.USER, content=message))
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self._memory.put(
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ChatMessage(role=MessageRole.ASSISTANT, content=str(query_response))
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)
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return AgentChatResponse(response=str(query_response), sources=[tool_output])
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@trace_method("chat")
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async def astream_chat(
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self, message: str, chat_history: Optional[List[ChatMessage]] = None
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) -> StreamingAgentChatResponse:
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chat_history = chat_history or self._memory.get()
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# Generate standalone question from conversation context and last message
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condensed_question = await self._acondense_question(chat_history, message)
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log_str = f"Querying with: {condensed_question}"
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logger.info(log_str)
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if self._verbose:
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print(log_str)
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# TODO: right now, query engine uses class attribute to configure streaming,
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# we are moving towards separate streaming and non-streaming methods.
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# In the meanwhile, use this hack to toggle streaming.
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from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
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if isinstance(self._query_engine, RetrieverQueryEngine):
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is_streaming = self._query_engine._response_synthesizer._streaming
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self._query_engine._response_synthesizer._streaming = True
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# Query with standalone question
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query_response = await self._query_engine.aquery(condensed_question)
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# NOTE: reset streaming flag
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if isinstance(self._query_engine, RetrieverQueryEngine):
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self._query_engine._response_synthesizer._streaming = is_streaming
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tool_output = self._get_tool_output_from_response(
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condensed_question, query_response
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)
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# Record response
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if (
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isinstance(query_response, StreamingResponse)
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and query_response.response_gen is not None
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):
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# override the generator to include writing to chat history
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# TODO: query engine does not support async generator yet
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self._memory.put(ChatMessage(role=MessageRole.USER, content=message))
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response = StreamingAgentChatResponse(
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chat_stream=response_gen_from_query_engine(query_response.response_gen),
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sources=[tool_output],
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)
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thread = Thread(
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target=response.write_response_to_history, args=(self._memory,)
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)
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thread.start()
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else:
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raise ValueError("Streaming is not enabled. Please use achat() instead.")
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return response
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def reset(self) -> None:
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# Clear chat history
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self._memory.reset()
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@property
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def chat_history(self) -> List[ChatMessage]:
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"""Get chat history."""
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return self._memory.get_all()
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