302 lines
11 KiB
Python
302 lines
11 KiB
Python
import asyncio
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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.base_retriever import BaseRetriever
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from llama_index.core.llms.types import ChatMessage, MessageRole
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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.schema import MetadataMode, NodeWithScore, QueryBundle
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from llama_index.service_context import ServiceContext
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DEFAULT_CONTEXT_TEMPLATE = (
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"Context information is below."
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"\n--------------------\n"
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"{context_str}"
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"\n--------------------\n"
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)
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class ContextChatEngine(BaseChatEngine):
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"""Context Chat Engine.
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Uses a retriever to retrieve a context, set the context in the system prompt,
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and then uses an LLM to generate a response, for a fluid chat experience.
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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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prefix_messages: List[ChatMessage],
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node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
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context_template: Optional[str] = None,
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callback_manager: Optional[CallbackManager] = None,
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) -> None:
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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._prefix_messages = prefix_messages
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self._node_postprocessors = node_postprocessors or []
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self._context_template = context_template or DEFAULT_CONTEXT_TEMPLATE
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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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@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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prefix_messages: Optional[List[ChatMessage]] = None,
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node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
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context_template: Optional[str] = None,
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**kwargs: Any,
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) -> "ContextChatEngine":
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"""Initialize a ContextChatEngine 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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if system_prompt is not None:
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if prefix_messages is not None:
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raise ValueError(
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"Cannot specify both system_prompt and prefix_messages"
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)
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prefix_messages = [
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ChatMessage(content=system_prompt, role=llm.metadata.system_role)
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]
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prefix_messages = prefix_messages or []
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node_postprocessors = node_postprocessors or []
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return cls(
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retriever,
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llm=llm,
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memory=memory,
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prefix_messages=prefix_messages,
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node_postprocessors=node_postprocessors,
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callback_manager=service_context.callback_manager,
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context_template=context_template,
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)
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def _generate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
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"""Generate context information from a message."""
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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 self._context_template.format(context_str=context_str), nodes
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async def _agenerate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
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"""Generate context information from a message."""
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nodes = await self._retriever.aretrieve(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 self._context_template.format(context_str=context_str), nodes
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def _get_prefix_messages_with_context(self, context_str: str) -> List[ChatMessage]:
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"""Get the prefix messages with context."""
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# ensure we grab the user-configured system prompt
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system_prompt = ""
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prefix_messages = self._prefix_messages
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if (
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len(self._prefix_messages) != 0
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and self._prefix_messages[0].role == MessageRole.SYSTEM
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):
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system_prompt = str(self._prefix_messages[0].content)
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prefix_messages = self._prefix_messages[1:]
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context_str_w_sys_prompt = system_prompt.strip() + "\n" + context_str
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return [
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ChatMessage(
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content=context_str_w_sys_prompt, role=self._llm.metadata.system_role
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),
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*prefix_messages,
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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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if chat_history is not None:
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self._memory.set(chat_history)
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self._memory.put(ChatMessage(content=message, role="user"))
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context_str_template, nodes = self._generate_context(message)
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prefix_messages = self._get_prefix_messages_with_context(context_str_template)
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prefix_messages_token_count = len(
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self._memory.tokenizer_fn(
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" ".join([(m.content or "") for m in prefix_messages])
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)
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)
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all_messages = prefix_messages + self._memory.get(
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initial_token_count=prefix_messages_token_count
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)
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chat_response = self._llm.chat(all_messages)
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ai_message = chat_response.message
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self._memory.put(ai_message)
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return AgentChatResponse(
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response=str(chat_response.message.content),
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sources=[
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ToolOutput(
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tool_name="retriever",
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content=str(prefix_messages[0]),
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raw_input={"message": message},
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raw_output=prefix_messages[0],
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)
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],
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source_nodes=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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if chat_history is not None:
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self._memory.set(chat_history)
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self._memory.put(ChatMessage(content=message, role="user"))
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context_str_template, nodes = self._generate_context(message)
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prefix_messages = self._get_prefix_messages_with_context(context_str_template)
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initial_token_count = len(
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self._memory.tokenizer_fn(
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" ".join([(m.content or "") for m in prefix_messages])
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)
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)
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all_messages = prefix_messages + self._memory.get(
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initial_token_count=initial_token_count
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)
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chat_response = StreamingAgentChatResponse(
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chat_stream=self._llm.stream_chat(all_messages),
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sources=[
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ToolOutput(
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tool_name="retriever",
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content=str(prefix_messages[0]),
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raw_input={"message": message},
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raw_output=prefix_messages[0],
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)
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],
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source_nodes=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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if chat_history is not None:
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self._memory.set(chat_history)
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self._memory.put(ChatMessage(content=message, role="user"))
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context_str_template, nodes = await self._agenerate_context(message)
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prefix_messages = self._get_prefix_messages_with_context(context_str_template)
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initial_token_count = len(
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self._memory.tokenizer_fn(
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" ".join([(m.content or "") for m in prefix_messages])
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)
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)
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all_messages = prefix_messages + self._memory.get(
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initial_token_count=initial_token_count
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)
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chat_response = await self._llm.achat(all_messages)
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ai_message = chat_response.message
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self._memory.put(ai_message)
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return AgentChatResponse(
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response=str(chat_response.message.content),
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sources=[
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ToolOutput(
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tool_name="retriever",
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content=str(prefix_messages[0]),
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raw_input={"message": message},
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raw_output=prefix_messages[0],
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)
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],
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source_nodes=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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if chat_history is not None:
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self._memory.set(chat_history)
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self._memory.put(ChatMessage(content=message, role="user"))
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context_str_template, nodes = await self._agenerate_context(message)
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prefix_messages = self._get_prefix_messages_with_context(context_str_template)
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initial_token_count = len(
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self._memory.tokenizer_fn(
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" ".join([(m.content or "") for m in prefix_messages])
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)
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)
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all_messages = prefix_messages + self._memory.get(
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initial_token_count=initial_token_count
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)
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chat_response = StreamingAgentChatResponse(
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achat_stream=await self._llm.astream_chat(all_messages),
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sources=[
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ToolOutput(
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tool_name="retriever",
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content=str(prefix_messages[0]),
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raw_input={"message": message},
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raw_output=prefix_messages[0],
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)
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],
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source_nodes=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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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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