363 lines
14 KiB
Python
363 lines
14 KiB
Python
import asyncio
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import logging
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from threading import Thread
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from typing import Any, List, Optional, Tuple
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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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ToolOutput,
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)
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from llama_index.core.llms.types import ChatMessage, MessageRole
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from llama_index.indices.base_retriever import BaseRetriever
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from llama_index.indices.query.schema import QueryBundle
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from llama_index.indices.service_context import ServiceContext
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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.postprocessor.types import BaseNodePostprocessor
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from llama_index.prompts.base import PromptTemplate
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from llama_index.schema import MetadataMode, NodeWithScore
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from llama_index.utilities.token_counting import TokenCounter
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logger = logging.getLogger(__name__)
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DEFAULT_CONTEXT_PROMPT_TEMPLATE = """
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The following is a friendly conversation between a user and an AI assistant.
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The assistant is talkative and provides lots of specific details from its context.
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If the assistant does not know the answer to a question, it truthfully says it
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does not know.
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Here are the relevant documents for the context:
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{context_str}
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Instruction: Based on the above documents, provide a detailed answer for the user question below.
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Answer "don't know" if not present in the document.
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"""
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DEFAULT_CONDENSE_PROMPT_TEMPLATE = """
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Given the following conversation between a user and an AI assistant and a follow up question from user,
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rephrase the follow up question to be a standalone question.
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Chat History:
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{chat_history}
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Follow Up Input: {question}
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Standalone question:"""
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class CondensePlusContextChatEngine(BaseChatEngine):
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"""Condensed Conversation & Context Chat Engine.
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First condense a conversation and latest user message to a standalone question
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Then build a context for the standalone question from a retriever,
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Then pass the context along with prompt and user message to LLM to generate a response.
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"""
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def __init__(
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self,
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retriever: BaseRetriever,
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llm: LLM,
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memory: BaseMemory,
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context_prompt: Optional[str] = None,
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condense_prompt: Optional[str] = None,
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system_prompt: Optional[str] = None,
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skip_condense: bool = False,
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node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
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callback_manager: Optional[CallbackManager] = None,
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verbose: bool = False,
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):
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self._retriever = retriever
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self._llm = llm
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self._memory = memory
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self._context_prompt_template = (
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context_prompt or DEFAULT_CONTEXT_PROMPT_TEMPLATE
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)
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condense_prompt_str = condense_prompt or DEFAULT_CONDENSE_PROMPT_TEMPLATE
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self._condense_prompt_template = PromptTemplate(condense_prompt_str)
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self._system_prompt = system_prompt
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self._skip_condense = skip_condense
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self._node_postprocessors = node_postprocessors or []
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self.callback_manager = callback_manager or CallbackManager([])
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for node_postprocessor in self._node_postprocessors:
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node_postprocessor.callback_manager = self.callback_manager
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self._token_counter = TokenCounter()
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self._verbose = verbose
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@classmethod
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def from_defaults(
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cls,
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retriever: BaseRetriever,
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service_context: Optional[ServiceContext] = None,
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chat_history: Optional[List[ChatMessage]] = None,
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memory: Optional[BaseMemory] = None,
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system_prompt: Optional[str] = None,
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context_prompt: Optional[str] = None,
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condense_prompt: Optional[str] = None,
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skip_condense: bool = False,
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node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
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verbose: bool = False,
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**kwargs: Any,
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) -> "CondensePlusContextChatEngine":
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"""Initialize a CondensePlusContextChatEngine from default parameters."""
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service_context = service_context or ServiceContext.from_defaults()
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llm = service_context.llm
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chat_history = chat_history or []
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memory = memory or ChatMemoryBuffer.from_defaults(
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chat_history=chat_history, token_limit=llm.metadata.context_window - 256
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)
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return cls(
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retriever=retriever,
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llm=llm,
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memory=memory,
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context_prompt=context_prompt,
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condense_prompt=condense_prompt,
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skip_condense=skip_condense,
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callback_manager=service_context.callback_manager,
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node_postprocessors=node_postprocessors,
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system_prompt=system_prompt,
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verbose=verbose,
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)
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def _condense_question(
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self, chat_history: List[ChatMessage], latest_message: str
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) -> str:
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"""Condense a conversation history and latest user message to a standalone question."""
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if self._skip_condense or len(chat_history) == 0:
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return latest_message
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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_prompt_template,
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question=latest_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], latest_message: str
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) -> str:
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"""Condense a conversation history and latest user message to a standalone question."""
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if self._skip_condense or len(chat_history) == 0:
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return latest_message
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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_prompt_template,
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question=latest_message,
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chat_history=chat_history_str,
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)
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def _retrieve_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
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"""Build context for a message from retriever."""
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nodes = self._retriever.retrieve(message)
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for postprocessor in self._node_postprocessors:
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nodes = postprocessor.postprocess_nodes(
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nodes, query_bundle=QueryBundle(message)
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)
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context_str = "\n\n".join(
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[n.node.get_content(metadata_mode=MetadataMode.LLM).strip() for n in nodes]
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)
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return context_str, nodes
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async def _aretrieve_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
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"""Build context for a message from retriever."""
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nodes = await self._retriever.aretrieve(message)
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context_str = "\n\n".join(
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[n.node.get_content(metadata_mode=MetadataMode.LLM).strip() for n in nodes]
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)
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return context_str, nodes
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def _run_c3(
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self, message: str, chat_history: Optional[List[ChatMessage]] = None
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) -> Tuple[List[ChatMessage], ToolOutput, List[NodeWithScore]]:
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if chat_history is not None:
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self._memory.set(chat_history)
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chat_history = self._memory.get()
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# Condense conversation history and latest message to a standalone question
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condensed_question = self._condense_question(chat_history, message)
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logger.info(f"Condensed question: {condensed_question}")
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if self._verbose:
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print(f"Condensed question: {condensed_question}")
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# Build context for the standalone question from a retriever
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context_str, context_nodes = self._retrieve_context(condensed_question)
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context_source = ToolOutput(
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tool_name="retriever",
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content=context_str,
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raw_input={"message": condensed_question},
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raw_output=context_str,
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)
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logger.debug(f"Context: {context_str}")
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if self._verbose:
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print(f"Context: {context_str}")
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system_message_content = self._context_prompt_template.format(
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context_str=context_str
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)
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if self._system_prompt:
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system_message_content = self._system_prompt + "\n" + system_message_content
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system_message = ChatMessage(
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content=system_message_content, role=self._llm.metadata.system_role
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)
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initial_token_count = self._token_counter.estimate_tokens_in_messages(
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[system_message]
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)
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self._memory.put(ChatMessage(content=message, role=MessageRole.USER))
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chat_messages = [
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system_message,
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*self._memory.get(initial_token_count=initial_token_count),
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]
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return chat_messages, context_source, context_nodes
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async def _arun_c3(
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self, message: str, chat_history: Optional[List[ChatMessage]] = None
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) -> Tuple[List[ChatMessage], ToolOutput, List[NodeWithScore]]:
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if chat_history is not None:
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self._memory.set(chat_history)
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chat_history = self._memory.get()
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# Condense conversation history and latest message to a standalone question
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condensed_question = await self._acondense_question(chat_history, message)
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logger.info(f"Condensed question: {condensed_question}")
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if self._verbose:
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print(f"Condensed question: {condensed_question}")
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# Build context for the standalone question from a retriever
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context_str, context_nodes = await self._aretrieve_context(condensed_question)
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context_source = ToolOutput(
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tool_name="retriever",
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content=context_str,
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raw_input={"message": condensed_question},
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raw_output=context_str,
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)
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logger.debug(f"Context: {context_str}")
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if self._verbose:
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print(f"Context: {context_str}")
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system_message_content = self._context_prompt_template.format(
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context_str=context_str
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)
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if self._system_prompt:
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system_message_content = self._system_prompt + "\n" + system_message_content
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system_message = ChatMessage(
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content=system_message_content, role=self._llm.metadata.system_role
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)
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initial_token_count = self._token_counter.estimate_tokens_in_messages(
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[system_message]
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)
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self._memory.put(ChatMessage(content=message, role=MessageRole.USER))
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chat_messages = [
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system_message,
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*self._memory.get(initial_token_count=initial_token_count),
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]
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return chat_messages, context_source, context_nodes
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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_messages, context_source, context_nodes = self._run_c3(
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message, chat_history
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)
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# pass the context, system prompt and user message as chat to LLM to generate a response
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chat_response = self._llm.chat(chat_messages)
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assistant_message = chat_response.message
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self._memory.put(assistant_message)
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return AgentChatResponse(
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response=str(assistant_message.content),
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sources=[context_source],
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source_nodes=context_nodes,
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)
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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_messages, context_source, context_nodes = self._run_c3(
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message, chat_history
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)
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# pass the context, system prompt and user message as chat to LLM to generate a response
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chat_response = StreamingAgentChatResponse(
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chat_stream=self._llm.stream_chat(chat_messages),
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sources=[context_source],
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source_nodes=context_nodes,
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)
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thread = Thread(
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target=chat_response.write_response_to_history, args=(self._memory,)
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)
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thread.start()
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return chat_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_messages, context_source, context_nodes = await self._arun_c3(
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message, chat_history
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)
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# pass the context, system prompt and user message as chat to LLM to generate a response
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chat_response = await self._llm.achat(chat_messages)
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assistant_message = chat_response.message
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self._memory.put(assistant_message)
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return AgentChatResponse(
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response=str(assistant_message.content),
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sources=[context_source],
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source_nodes=context_nodes,
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)
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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_messages, context_source, context_nodes = await self._arun_c3(
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message, chat_history
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)
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# pass the context, system prompt and user message as chat to LLM to generate a response
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chat_response = StreamingAgentChatResponse(
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achat_stream=await self._llm.astream_chat(chat_messages),
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sources=[context_source],
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source_nodes=context_nodes,
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)
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thread = Thread(
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target=lambda x: asyncio.run(chat_response.awrite_response_to_history(x)),
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args=(self._memory,),
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)
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thread.start()
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return chat_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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