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Refactor/react agent (#3355)
This commit is contained in:
Yeuoly
GitHub
parent
509c640a80
commit
cea107b165
@ -238,6 +238,34 @@ class BaseAgentRunner(AppRunner):
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return prompt_tool
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def _init_prompt_tools(self) -> tuple[dict[str, Tool], list[PromptMessageTool]]:
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"""
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Init tools
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"""
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tool_instances = {}
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prompt_messages_tools = []
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for tool in self.app_config.agent.tools if self.app_config.agent else []:
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try:
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prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
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except Exception:
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# api tool may be deleted
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continue
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# save tool entity
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tool_instances[tool.tool_name] = tool_entity
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# save prompt tool
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prompt_messages_tools.append(prompt_tool)
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# convert dataset tools into ModelRuntime Tool format
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for dataset_tool in self.dataset_tools:
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prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
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# save prompt tool
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prompt_messages_tools.append(prompt_tool)
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# save tool entity
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tool_instances[dataset_tool.identity.name] = dataset_tool
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return tool_instances, prompt_messages_tools
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def update_prompt_message_tool(self, tool: Tool, prompt_tool: PromptMessageTool) -> PromptMessageTool:
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"""
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update prompt message tool
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@ -325,7 +353,7 @@ class BaseAgentRunner(AppRunner):
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tool_name: str,
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tool_input: Union[str, dict],
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thought: str,
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observation: Union[str, str],
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observation: Union[str, dict],
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tool_invoke_meta: Union[str, dict],
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answer: str,
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messages_ids: list[str],
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@ -1,30 +1,34 @@
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import json
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import re
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from abc import ABC, abstractmethod
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from collections.abc import Generator
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from typing import Literal, Union
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from typing import Union
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from core.agent.base_agent_runner import BaseAgentRunner
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from core.agent.entities import AgentPromptEntity, AgentScratchpadUnit
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from core.agent.entities import AgentScratchpadUnit
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from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
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from core.app.apps.base_app_queue_manager import PublishFrom
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from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
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from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
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from core.model_runtime.entities.message_entities import (
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AssistantPromptMessage,
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PromptMessage,
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PromptMessageTool,
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SystemPromptMessage,
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ToolPromptMessage,
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UserPromptMessage,
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)
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from core.model_runtime.utils.encoders import jsonable_encoder
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from core.tools.entities.tool_entities import ToolInvokeMeta
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from core.tools.tool.tool import Tool
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from core.tools.tool_engine import ToolEngine
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from models.model import Message
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class CotAgentRunner(BaseAgentRunner):
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class CotAgentRunner(BaseAgentRunner, ABC):
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_is_first_iteration = True
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_ignore_observation_providers = ['wenxin']
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_historic_prompt_messages: list[PromptMessage] = None
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_agent_scratchpad: list[AgentScratchpadUnit] = None
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_instruction: str = None
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_query: str = None
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_prompt_messages_tools: list[PromptMessage] = None
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def run(self, message: Message,
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query: str,
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@ -35,9 +39,7 @@ class CotAgentRunner(BaseAgentRunner):
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"""
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app_generate_entity = self.application_generate_entity
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self._repack_app_generate_entity(app_generate_entity)
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agent_scratchpad: list[AgentScratchpadUnit] = []
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self._init_agent_scratchpad(agent_scratchpad, self.history_prompt_messages)
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self._init_react_state(query)
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# check model mode
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if 'Observation' not in app_generate_entity.model_config.stop:
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@ -46,37 +48,18 @@ class CotAgentRunner(BaseAgentRunner):
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app_config = self.app_config
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# override inputs
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# init instruction
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inputs = inputs or {}
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instruction = app_config.prompt_template.simple_prompt_template
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instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
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self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
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iteration_step = 1
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max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
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prompt_messages = self.history_prompt_messages
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# convert tools into ModelRuntime Tool format
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prompt_messages_tools: list[PromptMessageTool] = []
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tool_instances = {}
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for tool in app_config.agent.tools if app_config.agent else []:
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try:
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prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
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except Exception:
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# api tool may be deleted
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continue
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# save tool entity
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tool_instances[tool.tool_name] = tool_entity
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# save prompt tool
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prompt_messages_tools.append(prompt_tool)
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tool_instances, self._prompt_messages_tools = self._init_prompt_tools()
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# convert dataset tools into ModelRuntime Tool format
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for dataset_tool in self.dataset_tools:
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prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
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# save prompt tool
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prompt_messages_tools.append(prompt_tool)
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# save tool entity
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tool_instances[dataset_tool.identity.name] = dataset_tool
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prompt_messages = self._organize_prompt_messages()
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function_call_state = True
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llm_usage = {
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@ -102,7 +85,7 @@ class CotAgentRunner(BaseAgentRunner):
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if iteration_step == max_iteration_steps:
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# the last iteration, remove all tools
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prompt_messages_tools = []
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self._prompt_messages_tools = []
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message_file_ids = []
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@ -119,18 +102,8 @@ class CotAgentRunner(BaseAgentRunner):
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agent_thought_id=agent_thought.id
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), PublishFrom.APPLICATION_MANAGER)
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# update prompt messages
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prompt_messages = self._organize_cot_prompt_messages(
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mode=app_generate_entity.model_config.mode,
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prompt_messages=prompt_messages,
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tools=prompt_messages_tools,
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agent_scratchpad=agent_scratchpad,
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agent_prompt_message=app_config.agent.prompt,
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instruction=instruction,
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input=query
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)
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# recalc llm max tokens
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prompt_messages = self._organize_prompt_messages()
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self.recalc_llm_max_tokens(self.model_config, prompt_messages)
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# invoke model
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chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
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@ -148,7 +121,7 @@ class CotAgentRunner(BaseAgentRunner):
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raise ValueError("failed to invoke llm")
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usage_dict = {}
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react_chunks = self._handle_stream_react(chunks, usage_dict)
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react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks)
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scratchpad = AgentScratchpadUnit(
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agent_response='',
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thought='',
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@ -164,30 +137,12 @@ class CotAgentRunner(BaseAgentRunner):
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), PublishFrom.APPLICATION_MANAGER)
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for chunk in react_chunks:
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if isinstance(chunk, dict):
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scratchpad.agent_response += json.dumps(chunk)
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try:
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if scratchpad.action:
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raise Exception("")
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scratchpad.action_str = json.dumps(chunk)
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scratchpad.action = AgentScratchpadUnit.Action(
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action_name=chunk['action'],
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action_input=chunk['action_input']
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)
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except:
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scratchpad.thought += json.dumps(chunk)
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yield LLMResultChunk(
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model=self.model_config.model,
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prompt_messages=prompt_messages,
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system_fingerprint='',
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delta=LLMResultChunkDelta(
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index=0,
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message=AssistantPromptMessage(
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content=json.dumps(chunk, ensure_ascii=False) # if ensure_ascii=True, the text in webui maybe garbled text
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),
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usage=None
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)
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)
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if isinstance(chunk, AgentScratchpadUnit.Action):
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action = chunk
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# detect action
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scratchpad.agent_response += json.dumps(chunk.dict())
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scratchpad.action_str = json.dumps(chunk.dict())
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scratchpad.action = action
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else:
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scratchpad.agent_response += chunk
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scratchpad.thought += chunk
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@ -205,7 +160,7 @@ class CotAgentRunner(BaseAgentRunner):
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)
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scratchpad.thought = scratchpad.thought.strip() or 'I am thinking about how to help you'
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agent_scratchpad.append(scratchpad)
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self._agent_scratchpad.append(scratchpad)
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# get llm usage
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if 'usage' in usage_dict:
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@ -213,19 +168,21 @@ class CotAgentRunner(BaseAgentRunner):
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else:
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usage_dict['usage'] = LLMUsage.empty_usage()
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self.save_agent_thought(agent_thought=agent_thought,
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tool_name=scratchpad.action.action_name if scratchpad.action else '',
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tool_input={
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scratchpad.action.action_name: scratchpad.action.action_input
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} if scratchpad.action else '',
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tool_invoke_meta={},
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thought=scratchpad.thought,
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observation='',
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answer=scratchpad.agent_response,
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messages_ids=[],
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llm_usage=usage_dict['usage'])
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name=scratchpad.action.action_name if scratchpad.action else '',
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tool_input={
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scratchpad.action.action_name: scratchpad.action.action_input
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} if scratchpad.action else {},
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tool_invoke_meta={},
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thought=scratchpad.thought,
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observation='',
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answer=scratchpad.agent_response,
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messages_ids=[],
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llm_usage=usage_dict['usage']
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)
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if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
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if not scratchpad.is_final():
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self.queue_manager.publish(QueueAgentThoughtEvent(
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agent_thought_id=agent_thought.id
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), PublishFrom.APPLICATION_MANAGER)
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@ -237,106 +194,43 @@ class CotAgentRunner(BaseAgentRunner):
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if scratchpad.action.action_name.lower() == "final answer":
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# action is final answer, return final answer directly
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try:
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final_answer = scratchpad.action.action_input if \
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isinstance(scratchpad.action.action_input, str) else \
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json.dumps(scratchpad.action.action_input)
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if isinstance(scratchpad.action.action_input, dict):
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final_answer = json.dumps(scratchpad.action.action_input)
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elif isinstance(scratchpad.action.action_input, str):
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final_answer = scratchpad.action.action_input
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else:
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final_answer = f'{scratchpad.action.action_input}'
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except json.JSONDecodeError:
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final_answer = f'{scratchpad.action.action_input}'
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else:
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function_call_state = True
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# action is tool call, invoke tool
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tool_call_name = scratchpad.action.action_name
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tool_call_args = scratchpad.action.action_input
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tool_instance = tool_instances.get(tool_call_name)
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if not tool_instance:
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answer = f"there is not a tool named {tool_call_name}"
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name='',
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tool_input='',
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tool_invoke_meta=ToolInvokeMeta.error_instance(
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f"there is not a tool named {tool_call_name}"
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).to_dict(),
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thought=None,
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observation={
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tool_call_name: answer
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},
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answer=answer,
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messages_ids=[]
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)
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self.queue_manager.publish(QueueAgentThoughtEvent(
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agent_thought_id=agent_thought.id
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), PublishFrom.APPLICATION_MANAGER)
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else:
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if isinstance(tool_call_args, str):
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try:
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tool_call_args = json.loads(tool_call_args)
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except json.JSONDecodeError:
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pass
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tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
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action=scratchpad.action,
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tool_instances=tool_instances,
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message_file_ids=message_file_ids
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)
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scratchpad.observation = tool_invoke_response
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scratchpad.agent_response = tool_invoke_response
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# invoke tool
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tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
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tool=tool_instance,
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tool_parameters=tool_call_args,
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user_id=self.user_id,
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tenant_id=self.tenant_id,
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message=self.message,
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invoke_from=self.application_generate_entity.invoke_from,
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agent_tool_callback=self.agent_callback
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)
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# publish files
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for message_file, save_as in message_files:
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if save_as:
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self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name=scratchpad.action.action_name,
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tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
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thought=scratchpad.thought,
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observation={scratchpad.action.action_name: tool_invoke_response},
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tool_invoke_meta=tool_invoke_meta.to_dict(),
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answer=scratchpad.agent_response,
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messages_ids=message_file_ids,
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llm_usage=usage_dict['usage']
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)
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# publish message file
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self.queue_manager.publish(QueueMessageFileEvent(
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message_file_id=message_file.id
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), PublishFrom.APPLICATION_MANAGER)
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# add message file ids
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message_file_ids.append(message_file.id)
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# publish files
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for message_file, save_as in message_files:
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if save_as:
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self.variables_pool.set_file(tool_name=tool_call_name,
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value=message_file.id,
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name=save_as)
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self.queue_manager.publish(QueueMessageFileEvent(
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message_file_id=message_file.id
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), PublishFrom.APPLICATION_MANAGER)
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message_file_ids = [message_file.id for message_file, _ in message_files]
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observation = tool_invoke_response
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# save scratchpad
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scratchpad.observation = observation
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# save agent thought
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name=tool_call_name,
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tool_input={
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tool_call_name: tool_call_args
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},
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tool_invoke_meta={
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tool_call_name: tool_invoke_meta.to_dict()
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},
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thought=None,
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observation={
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tool_call_name: observation
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},
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answer=scratchpad.agent_response,
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messages_ids=message_file_ids,
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)
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self.queue_manager.publish(QueueAgentThoughtEvent(
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agent_thought_id=agent_thought.id
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), PublishFrom.APPLICATION_MANAGER)
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self.queue_manager.publish(QueueAgentThoughtEvent(
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agent_thought_id=agent_thought.id
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), PublishFrom.APPLICATION_MANAGER)
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# update prompt tool message
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for prompt_tool in prompt_messages_tools:
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for prompt_tool in self._prompt_messages_tools:
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self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
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iteration_step += 1
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@ -378,96 +272,63 @@ class CotAgentRunner(BaseAgentRunner):
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system_fingerprint=''
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)), PublishFrom.APPLICATION_MANAGER)
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def _handle_stream_react(self, llm_response: Generator[LLMResultChunk, None, None], usage: dict) \
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-> Generator[Union[str, dict], None, None]:
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def parse_json(json_str):
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def _handle_invoke_action(self, action: AgentScratchpadUnit.Action,
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tool_instances: dict[str, Tool],
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message_file_ids: list[str]) -> tuple[str, ToolInvokeMeta]:
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"""
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handle invoke action
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:param action: action
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:param tool_instances: tool instances
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:return: observation, meta
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"""
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# action is tool call, invoke tool
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tool_call_name = action.action_name
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tool_call_args = action.action_input
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tool_instance = tool_instances.get(tool_call_name)
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if not tool_instance:
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answer = f"there is not a tool named {tool_call_name}"
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return answer, ToolInvokeMeta.error_instance(answer)
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if isinstance(tool_call_args, str):
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try:
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return json.loads(json_str.strip())
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except:
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return json_str
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tool_call_args = json.loads(tool_call_args)
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except json.JSONDecodeError:
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pass
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def extra_json_from_code_block(code_block) -> Generator[Union[dict, str], None, None]:
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code_blocks = re.findall(r'```(.*?)```', code_block, re.DOTALL)
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if not code_blocks:
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return
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for block in code_blocks:
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json_text = re.sub(r'^[a-zA-Z]+\n', '', block.strip(), flags=re.MULTILINE)
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yield parse_json(json_text)
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# invoke tool
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tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
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tool=tool_instance,
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tool_parameters=tool_call_args,
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user_id=self.user_id,
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tenant_id=self.tenant_id,
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message=self.message,
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invoke_from=self.application_generate_entity.invoke_from,
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agent_tool_callback=self.agent_callback
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)
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code_block_cache = ''
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code_block_delimiter_count = 0
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in_code_block = False
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json_cache = ''
|
||||
json_quote_count = 0
|
||||
in_json = False
|
||||
got_json = False
|
||||
# publish files
|
||||
for message_file, save_as in message_files:
|
||||
if save_as:
|
||||
self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
|
||||
|
||||
for response in llm_response:
|
||||
response = response.delta.message.content
|
||||
if not isinstance(response, str):
|
||||
continue
|
||||
# publish message file
|
||||
self.queue_manager.publish(QueueMessageFileEvent(
|
||||
message_file_id=message_file.id
|
||||
), PublishFrom.APPLICATION_MANAGER)
|
||||
# add message file ids
|
||||
message_file_ids.append(message_file.id)
|
||||
|
||||
# stream
|
||||
index = 0
|
||||
while index < len(response):
|
||||
steps = 1
|
||||
delta = response[index:index+steps]
|
||||
if delta == '`':
|
||||
code_block_cache += delta
|
||||
code_block_delimiter_count += 1
|
||||
else:
|
||||
if not in_code_block:
|
||||
if code_block_delimiter_count > 0:
|
||||
yield code_block_cache
|
||||
code_block_cache = ''
|
||||
else:
|
||||
code_block_cache += delta
|
||||
code_block_delimiter_count = 0
|
||||
return tool_invoke_response, tool_invoke_meta
|
||||
|
||||
if code_block_delimiter_count == 3:
|
||||
if in_code_block:
|
||||
yield from extra_json_from_code_block(code_block_cache)
|
||||
code_block_cache = ''
|
||||
|
||||
in_code_block = not in_code_block
|
||||
code_block_delimiter_count = 0
|
||||
|
||||
if not in_code_block:
|
||||
# handle single json
|
||||
if delta == '{':
|
||||
json_quote_count += 1
|
||||
in_json = True
|
||||
json_cache += delta
|
||||
elif delta == '}':
|
||||
json_cache += delta
|
||||
if json_quote_count > 0:
|
||||
json_quote_count -= 1
|
||||
if json_quote_count == 0:
|
||||
in_json = False
|
||||
got_json = True
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if in_json:
|
||||
json_cache += delta
|
||||
|
||||
if got_json:
|
||||
got_json = False
|
||||
yield parse_json(json_cache)
|
||||
json_cache = ''
|
||||
json_quote_count = 0
|
||||
in_json = False
|
||||
|
||||
if not in_code_block and not in_json:
|
||||
yield delta.replace('`', '')
|
||||
|
||||
index += steps
|
||||
|
||||
if code_block_cache:
|
||||
yield code_block_cache
|
||||
|
||||
if json_cache:
|
||||
yield parse_json(json_cache)
|
||||
def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
|
||||
"""
|
||||
convert dict to action
|
||||
"""
|
||||
return AgentScratchpadUnit.Action(
|
||||
action_name=action['action'],
|
||||
action_input=action['action_input']
|
||||
)
|
||||
|
||||
def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: dict) -> str:
|
||||
"""
|
||||
@ -481,15 +342,46 @@ class CotAgentRunner(BaseAgentRunner):
|
||||
|
||||
return instruction
|
||||
|
||||
def _init_agent_scratchpad(self,
|
||||
agent_scratchpad: list[AgentScratchpadUnit],
|
||||
messages: list[PromptMessage]
|
||||
) -> list[AgentScratchpadUnit]:
|
||||
def _init_react_state(self, query) -> None:
|
||||
"""
|
||||
init agent scratchpad
|
||||
"""
|
||||
self._query = query
|
||||
self._agent_scratchpad = []
|
||||
self._historic_prompt_messages = self._organize_historic_prompt_messages()
|
||||
|
||||
@abstractmethod
|
||||
def _organize_prompt_messages(self) -> list[PromptMessage]:
|
||||
"""
|
||||
organize prompt messages
|
||||
"""
|
||||
|
||||
def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
|
||||
"""
|
||||
format assistant message
|
||||
"""
|
||||
message = ''
|
||||
for scratchpad in agent_scratchpad:
|
||||
if scratchpad.is_final():
|
||||
message += f"Final Answer: {scratchpad.agent_response}"
|
||||
else:
|
||||
message += f"Thought: {scratchpad.thought}\n\n"
|
||||
if scratchpad.action_str:
|
||||
message += f"Action: {scratchpad.action_str}\n\n"
|
||||
if scratchpad.observation:
|
||||
message += f"Observation: {scratchpad.observation}\n\n"
|
||||
|
||||
return message
|
||||
|
||||
def _organize_historic_prompt_messages(self) -> list[PromptMessage]:
|
||||
"""
|
||||
organize historic prompt messages
|
||||
"""
|
||||
result: list[PromptMessage] = []
|
||||
scratchpad: list[AgentScratchpadUnit] = []
|
||||
current_scratchpad: AgentScratchpadUnit = None
|
||||
for message in messages:
|
||||
|
||||
for message in self.history_prompt_messages:
|
||||
if isinstance(message, AssistantPromptMessage):
|
||||
current_scratchpad = AgentScratchpadUnit(
|
||||
agent_response=message.content,
|
||||
@ -504,186 +396,29 @@ class CotAgentRunner(BaseAgentRunner):
|
||||
action_name=message.tool_calls[0].function.name,
|
||||
action_input=json.loads(message.tool_calls[0].function.arguments)
|
||||
)
|
||||
current_scratchpad.action_str = json.dumps(
|
||||
current_scratchpad.action.to_dict()
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
agent_scratchpad.append(current_scratchpad)
|
||||
scratchpad.append(current_scratchpad)
|
||||
elif isinstance(message, ToolPromptMessage):
|
||||
if current_scratchpad:
|
||||
current_scratchpad.observation = message.content
|
||||
elif isinstance(message, UserPromptMessage):
|
||||
result.append(message)
|
||||
|
||||
return agent_scratchpad
|
||||
if scratchpad:
|
||||
result.append(AssistantPromptMessage(
|
||||
content=self._format_assistant_message(scratchpad)
|
||||
))
|
||||
|
||||
def _check_cot_prompt_messages(self, mode: Literal["completion", "chat"],
|
||||
agent_prompt_message: AgentPromptEntity,
|
||||
):
|
||||
"""
|
||||
check chain of thought prompt messages, a standard prompt message is like:
|
||||
Respond to the human as helpfully and accurately as possible.
|
||||
scratchpad = []
|
||||
|
||||
{{instruction}}
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{{tools}}
|
||||
|
||||
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||
Valid action values: "Final Answer" or {{tool_names}}
|
||||
|
||||
Provide only ONE action per $JSON_BLOB, as shown:
|
||||
|
||||
```
|
||||
{
|
||||
"action": $TOOL_NAME,
|
||||
"action_input": $ACTION_INPUT
|
||||
}
|
||||
```
|
||||
"""
|
||||
|
||||
# parse agent prompt message
|
||||
first_prompt = agent_prompt_message.first_prompt
|
||||
next_iteration = agent_prompt_message.next_iteration
|
||||
|
||||
if not isinstance(first_prompt, str) or not isinstance(next_iteration, str):
|
||||
raise ValueError("first_prompt or next_iteration is required in CoT agent mode")
|
||||
|
||||
# check instruction, tools, and tool_names slots
|
||||
if not first_prompt.find("{{instruction}}") >= 0:
|
||||
raise ValueError("{{instruction}} is required in first_prompt")
|
||||
if not first_prompt.find("{{tools}}") >= 0:
|
||||
raise ValueError("{{tools}} is required in first_prompt")
|
||||
if not first_prompt.find("{{tool_names}}") >= 0:
|
||||
raise ValueError("{{tool_names}} is required in first_prompt")
|
||||
|
||||
if mode == "completion":
|
||||
if not first_prompt.find("{{query}}") >= 0:
|
||||
raise ValueError("{{query}} is required in first_prompt")
|
||||
if not first_prompt.find("{{agent_scratchpad}}") >= 0:
|
||||
raise ValueError("{{agent_scratchpad}} is required in first_prompt")
|
||||
|
||||
if mode == "completion":
|
||||
if not next_iteration.find("{{observation}}") >= 0:
|
||||
raise ValueError("{{observation}} is required in next_iteration")
|
||||
|
||||
def _convert_scratchpad_list_to_str(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
|
||||
"""
|
||||
convert agent scratchpad list to str
|
||||
"""
|
||||
next_iteration = self.app_config.agent.prompt.next_iteration
|
||||
|
||||
result = ''
|
||||
for scratchpad in agent_scratchpad:
|
||||
result += (scratchpad.thought or '') + (scratchpad.action_str or '') + \
|
||||
next_iteration.replace("{{observation}}", scratchpad.observation or 'It seems that no response is available')
|
||||
if scratchpad:
|
||||
result.append(AssistantPromptMessage(
|
||||
content=self._format_assistant_message(scratchpad)
|
||||
))
|
||||
|
||||
return result
|
||||
|
||||
def _organize_cot_prompt_messages(self, mode: Literal["completion", "chat"],
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: list[PromptMessageTool],
|
||||
agent_scratchpad: list[AgentScratchpadUnit],
|
||||
agent_prompt_message: AgentPromptEntity,
|
||||
instruction: str,
|
||||
input: str,
|
||||
) -> list[PromptMessage]:
|
||||
"""
|
||||
organize chain of thought prompt messages, a standard prompt message is like:
|
||||
Respond to the human as helpfully and accurately as possible.
|
||||
|
||||
{{instruction}}
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{{tools}}
|
||||
|
||||
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||
Valid action values: "Final Answer" or {{tool_names}}
|
||||
|
||||
Provide only ONE action per $JSON_BLOB, as shown:
|
||||
|
||||
```
|
||||
{{{{
|
||||
"action": $TOOL_NAME,
|
||||
"action_input": $ACTION_INPUT
|
||||
}}}}
|
||||
```
|
||||
"""
|
||||
|
||||
self._check_cot_prompt_messages(mode, agent_prompt_message)
|
||||
|
||||
# parse agent prompt message
|
||||
first_prompt = agent_prompt_message.first_prompt
|
||||
|
||||
# parse tools
|
||||
tools_str = self._jsonify_tool_prompt_messages(tools)
|
||||
|
||||
# parse tools name
|
||||
tool_names = '"' + '","'.join([tool.name for tool in tools]) + '"'
|
||||
|
||||
# get system message
|
||||
system_message = first_prompt.replace("{{instruction}}", instruction) \
|
||||
.replace("{{tools}}", tools_str) \
|
||||
.replace("{{tool_names}}", tool_names)
|
||||
|
||||
# organize prompt messages
|
||||
if mode == "chat":
|
||||
# override system message
|
||||
overridden = False
|
||||
prompt_messages = prompt_messages.copy()
|
||||
for prompt_message in prompt_messages:
|
||||
if isinstance(prompt_message, SystemPromptMessage):
|
||||
prompt_message.content = system_message
|
||||
overridden = True
|
||||
break
|
||||
|
||||
# convert tool prompt messages to user prompt messages
|
||||
for idx, prompt_message in enumerate(prompt_messages):
|
||||
if isinstance(prompt_message, ToolPromptMessage):
|
||||
prompt_messages[idx] = UserPromptMessage(
|
||||
content=prompt_message.content
|
||||
)
|
||||
|
||||
if not overridden:
|
||||
prompt_messages.insert(0, SystemPromptMessage(
|
||||
content=system_message,
|
||||
))
|
||||
|
||||
# add assistant message
|
||||
if len(agent_scratchpad) > 0 and not self._is_first_iteration:
|
||||
prompt_messages.append(AssistantPromptMessage(
|
||||
content=(agent_scratchpad[-1].thought or '') + (agent_scratchpad[-1].action_str or ''),
|
||||
))
|
||||
|
||||
# add user message
|
||||
if len(agent_scratchpad) > 0 and not self._is_first_iteration:
|
||||
prompt_messages.append(UserPromptMessage(
|
||||
content=(agent_scratchpad[-1].observation or 'It seems that no response is available'),
|
||||
))
|
||||
|
||||
self._is_first_iteration = False
|
||||
|
||||
return prompt_messages
|
||||
elif mode == "completion":
|
||||
# parse agent scratchpad
|
||||
agent_scratchpad_str = self._convert_scratchpad_list_to_str(agent_scratchpad)
|
||||
self._is_first_iteration = False
|
||||
# parse prompt messages
|
||||
return [UserPromptMessage(
|
||||
content=first_prompt.replace("{{instruction}}", instruction)
|
||||
.replace("{{tools}}", tools_str)
|
||||
.replace("{{tool_names}}", tool_names)
|
||||
.replace("{{query}}", input)
|
||||
.replace("{{agent_scratchpad}}", agent_scratchpad_str),
|
||||
)]
|
||||
else:
|
||||
raise ValueError(f"mode {mode} is not supported")
|
||||
|
||||
def _jsonify_tool_prompt_messages(self, tools: list[PromptMessageTool]) -> str:
|
||||
"""
|
||||
jsonify tool prompt messages
|
||||
"""
|
||||
tools = jsonable_encoder(tools)
|
||||
try:
|
||||
return json.dumps(tools, ensure_ascii=False)
|
||||
except json.JSONDecodeError:
|
||||
return json.dumps(tools)
|
||||
71
api/core/agent/cot_chat_agent_runner.py
Normal file
71
api/core/agent/cot_chat_agent_runner.py
Normal file
@ -0,0 +1,71 @@
|
||||
import json
|
||||
|
||||
from core.agent.cot_agent_runner import CotAgentRunner
|
||||
from core.model_runtime.entities.message_entities import (
|
||||
AssistantPromptMessage,
|
||||
PromptMessage,
|
||||
SystemPromptMessage,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from core.model_runtime.utils.encoders import jsonable_encoder
|
||||
|
||||
|
||||
class CotChatAgentRunner(CotAgentRunner):
|
||||
def _organize_system_prompt(self) -> SystemPromptMessage:
|
||||
"""
|
||||
Organize system prompt
|
||||
"""
|
||||
prompt_entity = self.app_config.agent.prompt
|
||||
first_prompt = prompt_entity.first_prompt
|
||||
|
||||
system_prompt = first_prompt \
|
||||
.replace("{{instruction}}", self._instruction) \
|
||||
.replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools))) \
|
||||
.replace("{{tool_names}}", ', '.join([tool.name for tool in self._prompt_messages_tools]))
|
||||
|
||||
return SystemPromptMessage(content=system_prompt)
|
||||
|
||||
def _organize_prompt_messages(self) -> list[PromptMessage]:
|
||||
"""
|
||||
Organize
|
||||
"""
|
||||
# organize system prompt
|
||||
system_message = self._organize_system_prompt()
|
||||
|
||||
# organize historic prompt messages
|
||||
historic_messages = self._historic_prompt_messages
|
||||
|
||||
# organize current assistant messages
|
||||
agent_scratchpad = self._agent_scratchpad
|
||||
if not agent_scratchpad:
|
||||
assistant_messages = []
|
||||
else:
|
||||
assistant_message = AssistantPromptMessage(content='')
|
||||
for unit in agent_scratchpad:
|
||||
if unit.is_final():
|
||||
assistant_message.content += f"Final Answer: {unit.agent_response}"
|
||||
else:
|
||||
assistant_message.content += f"Thought: {unit.thought}\n\n"
|
||||
if unit.action_str:
|
||||
assistant_message.content += f"Action: {unit.action_str}\n\n"
|
||||
if unit.observation:
|
||||
assistant_message.content += f"Observation: {unit.observation}\n\n"
|
||||
|
||||
assistant_messages = [assistant_message]
|
||||
|
||||
# query messages
|
||||
query_messages = UserPromptMessage(content=self._query)
|
||||
|
||||
if assistant_messages:
|
||||
messages = [
|
||||
system_message,
|
||||
*historic_messages,
|
||||
query_messages,
|
||||
*assistant_messages,
|
||||
UserPromptMessage(content='continue')
|
||||
]
|
||||
else:
|
||||
messages = [system_message, *historic_messages, query_messages]
|
||||
|
||||
# join all messages
|
||||
return messages
|
||||
69
api/core/agent/cot_completion_agent_runner.py
Normal file
69
api/core/agent/cot_completion_agent_runner.py
Normal file
@ -0,0 +1,69 @@
|
||||
import json
|
||||
|
||||
from core.agent.cot_agent_runner import CotAgentRunner
|
||||
from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, UserPromptMessage
|
||||
from core.model_runtime.utils.encoders import jsonable_encoder
|
||||
|
||||
|
||||
class CotCompletionAgentRunner(CotAgentRunner):
|
||||
def _organize_instruction_prompt(self) -> str:
|
||||
"""
|
||||
Organize instruction prompt
|
||||
"""
|
||||
prompt_entity = self.app_config.agent.prompt
|
||||
first_prompt = prompt_entity.first_prompt
|
||||
|
||||
system_prompt = first_prompt.replace("{{instruction}}", self._instruction) \
|
||||
.replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools))) \
|
||||
.replace("{{tool_names}}", ', '.join([tool.name for tool in self._prompt_messages_tools]))
|
||||
|
||||
return system_prompt
|
||||
|
||||
def _organize_historic_prompt(self) -> str:
|
||||
"""
|
||||
Organize historic prompt
|
||||
"""
|
||||
historic_prompt_messages = self._historic_prompt_messages
|
||||
historic_prompt = ""
|
||||
|
||||
for message in historic_prompt_messages:
|
||||
if isinstance(message, UserPromptMessage):
|
||||
historic_prompt += f"Question: {message.content}\n\n"
|
||||
elif isinstance(message, AssistantPromptMessage):
|
||||
historic_prompt += message.content + "\n\n"
|
||||
|
||||
return historic_prompt
|
||||
|
||||
def _organize_prompt_messages(self) -> list[PromptMessage]:
|
||||
"""
|
||||
Organize prompt messages
|
||||
"""
|
||||
# organize system prompt
|
||||
system_prompt = self._organize_instruction_prompt()
|
||||
|
||||
# organize historic prompt messages
|
||||
historic_prompt = self._organize_historic_prompt()
|
||||
|
||||
# organize current assistant messages
|
||||
agent_scratchpad = self._agent_scratchpad
|
||||
assistant_prompt = ''
|
||||
for unit in agent_scratchpad:
|
||||
if unit.is_final():
|
||||
assistant_prompt += f"Final Answer: {unit.agent_response}"
|
||||
else:
|
||||
assistant_prompt += f"Thought: {unit.thought}\n\n"
|
||||
if unit.action_str:
|
||||
assistant_prompt += f"Action: {unit.action_str}\n\n"
|
||||
if unit.observation:
|
||||
assistant_prompt += f"Observation: {unit.observation}\n\n"
|
||||
|
||||
# query messages
|
||||
query_prompt = f"Question: {self._query}"
|
||||
|
||||
# join all messages
|
||||
prompt = system_prompt \
|
||||
.replace("{{historic_messages}}", historic_prompt) \
|
||||
.replace("{{agent_scratchpad}}", assistant_prompt) \
|
||||
.replace("{{query}}", query_prompt)
|
||||
|
||||
return [UserPromptMessage(content=prompt)]
|
||||
@ -34,12 +34,29 @@ class AgentScratchpadUnit(BaseModel):
|
||||
action_name: str
|
||||
action_input: Union[dict, str]
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""
|
||||
Convert to dictionary.
|
||||
"""
|
||||
return {
|
||||
'action': self.action_name,
|
||||
'action_input': self.action_input,
|
||||
}
|
||||
|
||||
agent_response: Optional[str] = None
|
||||
thought: Optional[str] = None
|
||||
action_str: Optional[str] = None
|
||||
observation: Optional[str] = None
|
||||
action: Optional[Action] = None
|
||||
|
||||
def is_final(self) -> bool:
|
||||
"""
|
||||
Check if the scratchpad unit is final.
|
||||
"""
|
||||
return self.action is None or (
|
||||
'final' in self.action.action_name.lower() and
|
||||
'answer' in self.action.action_name.lower()
|
||||
)
|
||||
|
||||
class AgentEntity(BaseModel):
|
||||
"""
|
||||
|
||||
@ -12,7 +12,6 @@ from core.model_runtime.entities.message_entities import (
|
||||
AssistantPromptMessage,
|
||||
PromptMessage,
|
||||
PromptMessageContentType,
|
||||
PromptMessageTool,
|
||||
SystemPromptMessage,
|
||||
TextPromptMessageContent,
|
||||
ToolPromptMessage,
|
||||
@ -25,8 +24,8 @@ from models.model import Message
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class FunctionCallAgentRunner(BaseAgentRunner):
|
||||
def run(self, message: Message,
|
||||
query: str,
|
||||
def run(self,
|
||||
message: Message, query: str, **kwargs: Any
|
||||
) -> Generator[LLMResultChunk, None, None]:
|
||||
"""
|
||||
Run FunctionCall agent application
|
||||
@ -41,26 +40,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
|
||||
prompt_messages = self._organize_user_query(query, prompt_messages)
|
||||
|
||||
# convert tools into ModelRuntime Tool format
|
||||
prompt_messages_tools: list[PromptMessageTool] = []
|
||||
tool_instances = {}
|
||||
for tool in app_config.agent.tools if app_config.agent else []:
|
||||
try:
|
||||
prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
|
||||
except Exception:
|
||||
# api tool may be deleted
|
||||
continue
|
||||
# save tool entity
|
||||
tool_instances[tool.tool_name] = tool_entity
|
||||
# save prompt tool
|
||||
prompt_messages_tools.append(prompt_tool)
|
||||
|
||||
# convert dataset tools into ModelRuntime Tool format
|
||||
for dataset_tool in self.dataset_tools:
|
||||
prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
|
||||
# save prompt tool
|
||||
prompt_messages_tools.append(prompt_tool)
|
||||
# save tool entity
|
||||
tool_instances[dataset_tool.identity.name] = dataset_tool
|
||||
tool_instances, prompt_messages_tools = self._init_prompt_tools()
|
||||
|
||||
iteration_step = 1
|
||||
max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
|
||||
|
||||
183
api/core/agent/output_parser/cot_output_parser.py
Normal file
183
api/core/agent/output_parser/cot_output_parser.py
Normal file
@ -0,0 +1,183 @@
|
||||
import json
|
||||
import re
|
||||
from collections.abc import Generator
|
||||
from typing import Union
|
||||
|
||||
from core.agent.entities import AgentScratchpadUnit
|
||||
from core.model_runtime.entities.llm_entities import LLMResultChunk
|
||||
|
||||
|
||||
class CotAgentOutputParser:
|
||||
@classmethod
|
||||
def handle_react_stream_output(cls, llm_response: Generator[LLMResultChunk, None, None]) -> \
|
||||
Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
|
||||
def parse_action(json_str):
|
||||
try:
|
||||
action = json.loads(json_str)
|
||||
action_name = None
|
||||
action_input = None
|
||||
|
||||
for key, value in action.items():
|
||||
if 'input' in key.lower():
|
||||
action_input = value
|
||||
else:
|
||||
action_name = value
|
||||
|
||||
if action_name is not None and action_input is not None:
|
||||
return AgentScratchpadUnit.Action(
|
||||
action_name=action_name,
|
||||
action_input=action_input,
|
||||
)
|
||||
else:
|
||||
return json_str or ''
|
||||
except:
|
||||
return json_str or ''
|
||||
|
||||
def extra_json_from_code_block(code_block) -> Generator[Union[dict, str], None, None]:
|
||||
code_blocks = re.findall(r'```(.*?)```', code_block, re.DOTALL)
|
||||
if not code_blocks:
|
||||
return
|
||||
for block in code_blocks:
|
||||
json_text = re.sub(r'^[a-zA-Z]+\n', '', block.strip(), flags=re.MULTILINE)
|
||||
yield parse_action(json_text)
|
||||
|
||||
code_block_cache = ''
|
||||
code_block_delimiter_count = 0
|
||||
in_code_block = False
|
||||
json_cache = ''
|
||||
json_quote_count = 0
|
||||
in_json = False
|
||||
got_json = False
|
||||
|
||||
action_cache = ''
|
||||
action_str = 'action:'
|
||||
action_idx = 0
|
||||
|
||||
thought_cache = ''
|
||||
thought_str = 'thought:'
|
||||
thought_idx = 0
|
||||
|
||||
for response in llm_response:
|
||||
response = response.delta.message.content
|
||||
if not isinstance(response, str):
|
||||
continue
|
||||
|
||||
# stream
|
||||
index = 0
|
||||
while index < len(response):
|
||||
steps = 1
|
||||
delta = response[index:index+steps]
|
||||
last_character = response[index-1] if index > 0 else ''
|
||||
|
||||
if delta == '`':
|
||||
code_block_cache += delta
|
||||
code_block_delimiter_count += 1
|
||||
else:
|
||||
if not in_code_block:
|
||||
if code_block_delimiter_count > 0:
|
||||
yield code_block_cache
|
||||
code_block_cache = ''
|
||||
else:
|
||||
code_block_cache += delta
|
||||
code_block_delimiter_count = 0
|
||||
|
||||
if not in_code_block and not in_json:
|
||||
if delta.lower() == action_str[action_idx] and action_idx == 0:
|
||||
if last_character not in ['\n', ' ', '']:
|
||||
index += steps
|
||||
yield delta
|
||||
continue
|
||||
|
||||
action_cache += delta
|
||||
action_idx += 1
|
||||
if action_idx == len(action_str):
|
||||
action_cache = ''
|
||||
action_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
elif delta.lower() == action_str[action_idx] and action_idx > 0:
|
||||
action_cache += delta
|
||||
action_idx += 1
|
||||
if action_idx == len(action_str):
|
||||
action_cache = ''
|
||||
action_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if action_cache:
|
||||
yield action_cache
|
||||
action_cache = ''
|
||||
action_idx = 0
|
||||
|
||||
if delta.lower() == thought_str[thought_idx] and thought_idx == 0:
|
||||
if last_character not in ['\n', ' ', '']:
|
||||
index += steps
|
||||
yield delta
|
||||
continue
|
||||
|
||||
thought_cache += delta
|
||||
thought_idx += 1
|
||||
if thought_idx == len(thought_str):
|
||||
thought_cache = ''
|
||||
thought_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
elif delta.lower() == thought_str[thought_idx] and thought_idx > 0:
|
||||
thought_cache += delta
|
||||
thought_idx += 1
|
||||
if thought_idx == len(thought_str):
|
||||
thought_cache = ''
|
||||
thought_idx = 0
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if thought_cache:
|
||||
yield thought_cache
|
||||
thought_cache = ''
|
||||
thought_idx = 0
|
||||
|
||||
if code_block_delimiter_count == 3:
|
||||
if in_code_block:
|
||||
yield from extra_json_from_code_block(code_block_cache)
|
||||
code_block_cache = ''
|
||||
|
||||
in_code_block = not in_code_block
|
||||
code_block_delimiter_count = 0
|
||||
|
||||
if not in_code_block:
|
||||
# handle single json
|
||||
if delta == '{':
|
||||
json_quote_count += 1
|
||||
in_json = True
|
||||
json_cache += delta
|
||||
elif delta == '}':
|
||||
json_cache += delta
|
||||
if json_quote_count > 0:
|
||||
json_quote_count -= 1
|
||||
if json_quote_count == 0:
|
||||
in_json = False
|
||||
got_json = True
|
||||
index += steps
|
||||
continue
|
||||
else:
|
||||
if in_json:
|
||||
json_cache += delta
|
||||
|
||||
if got_json:
|
||||
got_json = False
|
||||
yield parse_action(json_cache)
|
||||
json_cache = ''
|
||||
json_quote_count = 0
|
||||
in_json = False
|
||||
|
||||
if not in_code_block and not in_json:
|
||||
yield delta.replace('`', '')
|
||||
|
||||
index += steps
|
||||
|
||||
if code_block_cache:
|
||||
yield code_block_cache
|
||||
|
||||
if json_cache:
|
||||
yield parse_action(json_cache)
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
import logging
|
||||
from typing import cast
|
||||
|
||||
from core.agent.cot_agent_runner import CotAgentRunner
|
||||
from core.agent.cot_chat_agent_runner import CotChatAgentRunner
|
||||
from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
|
||||
from core.agent.entities import AgentEntity
|
||||
from core.agent.fc_agent_runner import FunctionCallAgentRunner
|
||||
from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
|
||||
@ -11,8 +12,8 @@ from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity, Mo
|
||||
from core.app.entities.queue_entities import QueueAnnotationReplyEvent
|
||||
from core.memory.token_buffer_memory import TokenBufferMemory
|
||||
from core.model_manager import ModelInstance
|
||||
from core.model_runtime.entities.llm_entities import LLMUsage
|
||||
from core.model_runtime.entities.model_entities import ModelFeature
|
||||
from core.model_runtime.entities.llm_entities import LLMMode, LLMUsage
|
||||
from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
|
||||
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
|
||||
from core.moderation.base import ModerationException
|
||||
from core.tools.entities.tool_entities import ToolRuntimeVariablePool
|
||||
@ -207,48 +208,40 @@ class AgentChatAppRunner(AppRunner):
|
||||
|
||||
# start agent runner
|
||||
if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
|
||||
assistant_cot_runner = CotAgentRunner(
|
||||
tenant_id=app_config.tenant_id,
|
||||
application_generate_entity=application_generate_entity,
|
||||
conversation=conversation,
|
||||
app_config=app_config,
|
||||
model_config=application_generate_entity.model_config,
|
||||
config=agent_entity,
|
||||
queue_manager=queue_manager,
|
||||
message=message,
|
||||
user_id=application_generate_entity.user_id,
|
||||
memory=memory,
|
||||
prompt_messages=prompt_message,
|
||||
variables_pool=tool_variables,
|
||||
db_variables=tool_conversation_variables,
|
||||
model_instance=model_instance
|
||||
)
|
||||
invoke_result = assistant_cot_runner.run(
|
||||
message=message,
|
||||
query=query,
|
||||
inputs=inputs,
|
||||
)
|
||||
# check LLM mode
|
||||
if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
|
||||
runner_cls = CotChatAgentRunner
|
||||
elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION.value:
|
||||
runner_cls = CotCompletionAgentRunner
|
||||
else:
|
||||
raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")
|
||||
elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
|
||||
assistant_fc_runner = FunctionCallAgentRunner(
|
||||
tenant_id=app_config.tenant_id,
|
||||
application_generate_entity=application_generate_entity,
|
||||
conversation=conversation,
|
||||
app_config=app_config,
|
||||
model_config=application_generate_entity.model_config,
|
||||
config=agent_entity,
|
||||
queue_manager=queue_manager,
|
||||
message=message,
|
||||
user_id=application_generate_entity.user_id,
|
||||
memory=memory,
|
||||
prompt_messages=prompt_message,
|
||||
variables_pool=tool_variables,
|
||||
db_variables=tool_conversation_variables,
|
||||
model_instance=model_instance
|
||||
)
|
||||
invoke_result = assistant_fc_runner.run(
|
||||
message=message,
|
||||
query=query,
|
||||
)
|
||||
runner_cls = FunctionCallAgentRunner
|
||||
else:
|
||||
raise ValueError(f"Invalid agent strategy: {agent_entity.strategy}")
|
||||
|
||||
runner = runner_cls(
|
||||
tenant_id=app_config.tenant_id,
|
||||
application_generate_entity=application_generate_entity,
|
||||
conversation=conversation,
|
||||
app_config=app_config,
|
||||
model_config=application_generate_entity.model_config,
|
||||
config=agent_entity,
|
||||
queue_manager=queue_manager,
|
||||
message=message,
|
||||
user_id=application_generate_entity.user_id,
|
||||
memory=memory,
|
||||
prompt_messages=prompt_message,
|
||||
variables_pool=tool_variables,
|
||||
db_variables=tool_conversation_variables,
|
||||
model_instance=model_instance
|
||||
)
|
||||
|
||||
invoke_result = runner.run(
|
||||
message=message,
|
||||
query=query,
|
||||
inputs=inputs,
|
||||
)
|
||||
|
||||
# handle invoke result
|
||||
self._handle_invoke_result(
|
||||
|
||||
@ -38,8 +38,10 @@ Action:
|
||||
```
|
||||
|
||||
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
|
||||
{{historic_messages}}
|
||||
Question: {{query}}
|
||||
Thought: {{agent_scratchpad}}"""
|
||||
{{agent_scratchpad}}
|
||||
Thought:"""
|
||||
|
||||
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
|
||||
Thought:"""
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user