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feat: mypy for all type check (#10921)

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This commit is contained in:
yihong
2024-12-24 18:38:51 +08:00
committed by GitHub
parent c91e8b1737
commit 56e15d09a9
584 changed files with 3975 additions and 2826 deletions
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47
api/core/agent/base_agent_runner.py
View File

@@ -1,7 +1,6 @@
import json
import logging
import uuid
from collections.abc import Mapping, Sequence
from datetime import UTC, datetime
from typing import Optional, Union, cast
@@ -53,6 +52,7 @@ logger = logging.getLogger(__name__)
class BaseAgentRunner(AppRunner):
def __init__(
self,
*,
tenant_id: str,
application_generate_entity: AgentChatAppGenerateEntity,
conversation: Conversation,
@@ -66,7 +66,7 @@ class BaseAgentRunner(AppRunner):
prompt_messages: Optional[list[PromptMessage]] = None,
variables_pool: Optional[ToolRuntimeVariablePool] = None,
db_variables: Optional[ToolConversationVariables] = None,
model_instance: ModelInstance | None = None,
model_instance: ModelInstance,
) -> None:
self.tenant_id = tenant_id
self.application_generate_entity = application_generate_entity
@@ -117,7 +117,7 @@ class BaseAgentRunner(AppRunner):
features = model_schema.features if model_schema and model_schema.features else []
self.stream_tool_call = ModelFeature.STREAM_TOOL_CALL in features
self.files = application_generate_entity.files if ModelFeature.VISION in features else []
self.query = None
self.query: Optional[str] = ""
self._current_thoughts: list[PromptMessage] = []
def _repack_app_generate_entity(
@@ -145,7 +145,7 @@ class BaseAgentRunner(AppRunner):
message_tool = PromptMessageTool(
name=tool.tool_name,
description=tool_entity.description.llm,
description=tool_entity.description.llm if tool_entity.description else "",
parameters={
"type": "object",
"properties": {},
@@ -167,7 +167,7 @@ class BaseAgentRunner(AppRunner):
continue
enum = []
if parameter.type == ToolParameter.ToolParameterType.SELECT:
enum = [option.value for option in parameter.options]
enum = [option.value for option in parameter.options] if parameter.options else []
message_tool.parameters["properties"][parameter.name] = {
"type": parameter_type,
@@ -187,8 +187,8 @@ class BaseAgentRunner(AppRunner):
convert dataset retriever tool to prompt message tool
"""
prompt_tool = PromptMessageTool(
name=tool.identity.name,
description=tool.description.llm,
name=tool.identity.name if tool.identity else "unknown",
description=tool.description.llm if tool.description else "",
parameters={
"type": "object",
"properties": {},
@@ -210,14 +210,14 @@ class BaseAgentRunner(AppRunner):
return prompt_tool
def _init_prompt_tools(self) -> tuple[Mapping[str, Tool], Sequence[PromptMessageTool]]:
def _init_prompt_tools(self) -> tuple[dict[str, Tool], list[PromptMessageTool]]:
"""
Init tools
"""
tool_instances = {}
prompt_messages_tools = []
for tool in self.app_config.agent.tools if self.app_config.agent else []:
for tool in self.app_config.agent.tools or [] if self.app_config.agent else []:
try:
prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
except Exception:
@@ -234,7 +234,8 @@ class BaseAgentRunner(AppRunner):
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# save tool entity
tool_instances[dataset_tool.identity.name] = dataset_tool
if dataset_tool.identity is not None:
tool_instances[dataset_tool.identity.name] = dataset_tool
return tool_instances, prompt_messages_tools
@@ -258,7 +259,7 @@ class BaseAgentRunner(AppRunner):
continue
enum = []
if parameter.type == ToolParameter.ToolParameterType.SELECT:
enum = [option.value for option in parameter.options]
enum = [option.value for option in parameter.options] if parameter.options else []
prompt_tool.parameters["properties"][parameter.name] = {
"type": parameter_type,
@@ -322,16 +323,21 @@ class BaseAgentRunner(AppRunner):
tool_name: str,
tool_input: Union[str, dict],
thought: str,
observation: Union[str, dict],
tool_invoke_meta: Union[str, dict],
observation: Union[str, dict, None],
tool_invoke_meta: Union[str, dict, None],
answer: str,
messages_ids: list[str],
llm_usage: LLMUsage = None,
) -> MessageAgentThought:
llm_usage: LLMUsage | None = None,
):
"""
Save agent thought
"""
agent_thought = db.session.query(MessageAgentThought).filter(MessageAgentThought.id == agent_thought.id).first()
queried_thought = (
db.session.query(MessageAgentThought).filter(MessageAgentThought.id == agent_thought.id).first()
)
if not queried_thought:
raise ValueError(f"Agent thought {agent_thought.id} not found")
agent_thought = queried_thought
if thought is not None:
agent_thought.thought = thought
@@ -404,7 +410,7 @@ class BaseAgentRunner(AppRunner):
"""
convert tool variables to db variables
"""
db_variables = (
queried_variables = (
db.session.query(ToolConversationVariables)
.filter(
ToolConversationVariables.conversation_id == self.message.conversation_id,
@@ -412,6 +418,11 @@ class BaseAgentRunner(AppRunner):
.first()
)
if not queried_variables:
return
db_variables = queried_variables
db_variables.updated_at = datetime.now(UTC).replace(tzinfo=None)
db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
db.session.commit()
@@ -421,7 +432,7 @@ class BaseAgentRunner(AppRunner):
"""
Organize agent history
"""
result = []
result: list[PromptMessage] = []
# check if there is a system message in the beginning of the conversation
for prompt_message in prompt_messages:
if isinstance(prompt_message, SystemPromptMessage):

82
api/core/agent/cot_agent_runner.py
View File

@@ -1,7 +1,7 @@
import json
from abc import ABC, abstractmethod
from collections.abc import Generator
from typing import Optional, Union
from collections.abc import Generator, Mapping
from typing import Any, Optional
from core.agent.base_agent_runner import BaseAgentRunner
from core.agent.entities import AgentScratchpadUnit
@@ -12,6 +12,7 @@ from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk,
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
PromptMessage,
PromptMessageTool,
ToolPromptMessage,
UserPromptMessage,
)
@@ -26,18 +27,18 @@ from models.model import Message
class CotAgentRunner(BaseAgentRunner, ABC):
_is_first_iteration = True
_ignore_observation_providers = ["wenxin"]
_historic_prompt_messages: list[PromptMessage] = None
_agent_scratchpad: list[AgentScratchpadUnit] = None
_instruction: str = None
_query: str = None
_prompt_messages_tools: list[PromptMessage] = None
_historic_prompt_messages: list[PromptMessage] | None = None
_agent_scratchpad: list[AgentScratchpadUnit] | None = None
_instruction: str = "" # FIXME this must be str for now
_query: str | None = None
_prompt_messages_tools: list[PromptMessageTool] = []
def run(
self,
message: Message,
query: str,
inputs: dict[str, str],
) -> Union[Generator, LLMResult]:
inputs: Mapping[str, str],
) -> Generator:
"""
Run Cot agent application
"""
@@ -57,19 +58,19 @@ class CotAgentRunner(BaseAgentRunner, ABC):
# init instruction
inputs = inputs or {}
instruction = app_config.prompt_template.simple_prompt_template
self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
self._instruction = self._fill_in_inputs_from_external_data_tools(instruction=instruction or "", inputs=inputs)
iteration_step = 1
max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
max_iteration_steps = min(app_config.agent.max_iteration if app_config.agent else 5, 5) + 1
# convert tools into ModelRuntime Tool format
tool_instances, self._prompt_messages_tools = self._init_prompt_tools()
function_call_state = True
llm_usage = {"usage": None}
llm_usage: dict[str, Optional[LLMUsage]] = {"usage": None}
final_answer = ""
def increase_usage(final_llm_usage_dict: dict[str, LLMUsage], usage: LLMUsage):
def increase_usage(final_llm_usage_dict: dict[str, Optional[LLMUsage]], usage: LLMUsage):
if not final_llm_usage_dict["usage"]:
final_llm_usage_dict["usage"] = usage
else:
@@ -90,7 +91,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
# the last iteration, remove all tools
self._prompt_messages_tools = []
message_file_ids = []
message_file_ids: list[str] = []
agent_thought = self.create_agent_thought(
message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
@@ -105,7 +106,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
prompt_messages = self._organize_prompt_messages()
self.recalc_llm_max_tokens(self.model_config, prompt_messages)
# invoke model
chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
chunks = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_generate_entity.model_conf.parameters,
tools=[],
@@ -115,11 +116,14 @@ class CotAgentRunner(BaseAgentRunner, ABC):
callbacks=[],
)
if not isinstance(chunks, Generator):
raise ValueError("Expected streaming response from LLM")
# check llm result
if not chunks:
raise ValueError("failed to invoke llm")
usage_dict = {}
usage_dict: dict[str, Optional[LLMUsage]] = {"usage": None}
react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
scratchpad = AgentScratchpadUnit(
agent_response="",
@@ -139,25 +143,30 @@ class CotAgentRunner(BaseAgentRunner, ABC):
if isinstance(chunk, AgentScratchpadUnit.Action):
action = chunk
# detect action
scratchpad.agent_response += json.dumps(chunk.model_dump())
if scratchpad.agent_response is not None:
scratchpad.agent_response += json.dumps(chunk.model_dump())
scratchpad.action_str = json.dumps(chunk.model_dump())
scratchpad.action = action
else:
scratchpad.agent_response += chunk
scratchpad.thought += chunk
if scratchpad.agent_response is not None:
scratchpad.agent_response += chunk
if scratchpad.thought is not None:
scratchpad.thought += chunk
yield LLMResultChunk(
model=self.model_config.model,
prompt_messages=prompt_messages,
system_fingerprint="",
delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
)
scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
self._agent_scratchpad.append(scratchpad)
if scratchpad.thought is not None:
scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
if self._agent_scratchpad is not None:
self._agent_scratchpad.append(scratchpad)
# get llm usage
if "usage" in usage_dict:
increase_usage(llm_usage, usage_dict["usage"])
if usage_dict["usage"] is not None:
increase_usage(llm_usage, usage_dict["usage"])
else:
usage_dict["usage"] = LLMUsage.empty_usage()
@@ -166,9 +175,9 @@ class CotAgentRunner(BaseAgentRunner, ABC):
tool_name=scratchpad.action.action_name if scratchpad.action else "",
tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
tool_invoke_meta={},
thought=scratchpad.thought,
thought=scratchpad.thought or "",
observation="",
answer=scratchpad.agent_response,
answer=scratchpad.agent_response or "",
messages_ids=[],
llm_usage=usage_dict["usage"],
)
@@ -209,7 +218,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
agent_thought=agent_thought,
tool_name=scratchpad.action.action_name,
tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
thought=scratchpad.thought,
thought=scratchpad.thought or "",
observation={scratchpad.action.action_name: tool_invoke_response},
tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
answer=scratchpad.agent_response,
@@ -247,8 +256,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
answer=final_answer,
messages_ids=[],
)
self.update_db_variables(self.variables_pool, self.db_variables_pool)
if self.variables_pool is not None and self.db_variables_pool is not None:
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish(
QueueMessageEndEvent(
@@ -307,8 +316,9 @@ class CotAgentRunner(BaseAgentRunner, ABC):
# publish files
for message_file_id, 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)
if save_as is not None and self.variables_pool:
# FIXME the save_as type is confusing, it should be a string or not
self.variables_pool.set_file(tool_name=tool_call_name, value=message_file_id, name=str(save_as))
# publish message file
self.queue_manager.publish(
@@ -325,7 +335,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
"""
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:
def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: Mapping[str, Any]) -> str:
"""
fill in inputs from external data tools
"""
@@ -376,11 +386,13 @@ class CotAgentRunner(BaseAgentRunner, ABC):
"""
result: list[PromptMessage] = []
scratchpads: list[AgentScratchpadUnit] = []
current_scratchpad: AgentScratchpadUnit = None
current_scratchpad: AgentScratchpadUnit | None = None
for message in self.history_prompt_messages:
if isinstance(message, AssistantPromptMessage):
if not current_scratchpad:
if not isinstance(message.content, str | None):
raise NotImplementedError("expected str type")
current_scratchpad = AgentScratchpadUnit(
agent_response=message.content,
thought=message.content or "I am thinking about how to help you",
@@ -399,8 +411,12 @@ class CotAgentRunner(BaseAgentRunner, ABC):
except:
pass
elif isinstance(message, ToolPromptMessage):
if current_scratchpad:
if not current_scratchpad:
continue
if isinstance(message.content, str):
current_scratchpad.observation = message.content
else:
raise NotImplementedError("expected str type")
elif isinstance(message, UserPromptMessage):
if scratchpads:
result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))

6
api/core/agent/cot_chat_agent_runner.py
View File

@@ -19,7 +19,12 @@ class CotChatAgentRunner(CotAgentRunner):
"""
Organize system prompt
"""
if not self.app_config.agent:
raise ValueError("Agent configuration is not set")
prompt_entity = self.app_config.agent.prompt
if not prompt_entity:
raise ValueError("Agent prompt configuration is not set")
first_prompt = prompt_entity.first_prompt
system_prompt = (
@@ -75,6 +80,7 @@ class CotChatAgentRunner(CotAgentRunner):
assistant_messages = []
else:
assistant_message = AssistantPromptMessage(content="")
assistant_message.content = "" # FIXME: type check tell mypy that assistant_message.content is str
for unit in agent_scratchpad:
if unit.is_final():
assistant_message.content += f"Final Answer: {unit.agent_response}"

21
api/core/agent/cot_completion_agent_runner.py
View File

@@ -2,7 +2,12 @@ import json
from typing import Optional
from core.agent.cot_agent_runner import CotAgentRunner
from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, UserPromptMessage
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
PromptMessage,
TextPromptMessageContent,
UserPromptMessage,
)
from core.model_runtime.utils.encoders import jsonable_encoder
@@ -11,7 +16,11 @@ class CotCompletionAgentRunner(CotAgentRunner):
"""
Organize instruction prompt
"""
if self.app_config.agent is None:
raise ValueError("Agent configuration is not set")
prompt_entity = self.app_config.agent.prompt
if prompt_entity is None:
raise ValueError("prompt entity is not set")
first_prompt = prompt_entity.first_prompt
system_prompt = (
@@ -33,7 +42,13 @@ class CotCompletionAgentRunner(CotAgentRunner):
if isinstance(message, UserPromptMessage):
historic_prompt += f"Question: {message.content}\n\n"
elif isinstance(message, AssistantPromptMessage):
historic_prompt += message.content + "\n\n"
if isinstance(message.content, str):
historic_prompt += message.content + "\n\n"
elif isinstance(message.content, list):
for content in message.content:
if not isinstance(content, TextPromptMessageContent):
continue
historic_prompt += content.data
return historic_prompt
@@ -50,7 +65,7 @@ class CotCompletionAgentRunner(CotAgentRunner):
# organize current assistant messages
agent_scratchpad = self._agent_scratchpad
assistant_prompt = ""
for unit in agent_scratchpad:
for unit in agent_scratchpad or []:
if unit.is_final():
assistant_prompt += f"Final Answer: {unit.agent_response}"
else:

2
api/core/agent/entities.py
View File

@@ -78,5 +78,5 @@ class AgentEntity(BaseModel):
model: str
strategy: Strategy
prompt: Optional[AgentPromptEntity] = None
tools: list[AgentToolEntity] = None
tools: list[AgentToolEntity] | None = None
max_iteration: int = 5

46
api/core/agent/fc_agent_runner.py
View File

@@ -40,6 +40,8 @@ class FunctionCallAgentRunner(BaseAgentRunner):
app_generate_entity = self.application_generate_entity
app_config = self.app_config
assert app_config is not None, "app_config is required"
assert app_config.agent is not None, "app_config.agent is required"
# convert tools into ModelRuntime Tool format
tool_instances, prompt_messages_tools = self._init_prompt_tools()
@@ -49,7 +51,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
# continue to run until there is not any tool call
function_call_state = True
llm_usage = {"usage": None}
llm_usage: dict[str, LLMUsage] = {"usage": LLMUsage.empty_usage()}
final_answer = ""
# get tracing instance
@@ -75,7 +77,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
# the last iteration, remove all tools
prompt_messages_tools = []
message_file_ids = []
message_file_ids: list[str] = []
agent_thought = self.create_agent_thought(
message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
)
@@ -105,7 +107,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
current_llm_usage = None
if self.stream_tool_call:
if self.stream_tool_call and isinstance(chunks, Generator):
is_first_chunk = True
for chunk in chunks:
if is_first_chunk:
@@ -116,7 +118,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
# check if there is any tool call
if self.check_tool_calls(chunk):
function_call_state = True
tool_calls.extend(self.extract_tool_calls(chunk))
tool_calls.extend(self.extract_tool_calls(chunk) or [])
tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
try:
tool_call_inputs = json.dumps(
@@ -131,19 +133,19 @@ class FunctionCallAgentRunner(BaseAgentRunner):
for content in chunk.delta.message.content:
response += content.data
else:
response += chunk.delta.message.content
response += str(chunk.delta.message.content)
if chunk.delta.usage:
increase_usage(llm_usage, chunk.delta.usage)
current_llm_usage = chunk.delta.usage
yield chunk
else:
result: LLMResult = chunks
elif not self.stream_tool_call and isinstance(chunks, LLMResult):
result = chunks
# check if there is any tool call
if self.check_blocking_tool_calls(result):
function_call_state = True
tool_calls.extend(self.extract_blocking_tool_calls(result))
tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
try:
tool_call_inputs = json.dumps(
@@ -162,7 +164,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
for content in result.message.content:
response += content.data
else:
response += result.message.content
response += str(result.message.content)
if not result.message.content:
result.message.content = ""
@@ -181,6 +183,8 @@ class FunctionCallAgentRunner(BaseAgentRunner):
usage=result.usage,
),
)
else:
raise RuntimeError(f"invalid chunks type: {type(chunks)}")
assistant_message = AssistantPromptMessage(content="", tool_calls=[])
if tool_calls:
@@ -243,7 +247,10 @@ class FunctionCallAgentRunner(BaseAgentRunner):
# publish files
for message_file_id, 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)
if self.variables_pool:
self.variables_pool.set_file(
tool_name=tool_call_name, value=message_file_id, name=save_as
)
# publish message file
self.queue_manager.publish(
@@ -263,7 +270,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
if tool_response["tool_response"] is not None:
self._current_thoughts.append(
ToolPromptMessage(
content=tool_response["tool_response"],
content=str(tool_response["tool_response"]),
tool_call_id=tool_call_id,
name=tool_call_name,
)
@@ -273,9 +280,9 @@ class FunctionCallAgentRunner(BaseAgentRunner):
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=None,
tool_input=None,
thought=None,
tool_name="",
tool_input="",
thought="",
tool_invoke_meta={
tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
},
@@ -283,7 +290,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
tool_response["tool_call_name"]: tool_response["tool_response"]
for tool_response in tool_responses
},
answer=None,
answer="",
messages_ids=message_file_ids,
)
self.queue_manager.publish(
@@ -296,7 +303,8 @@ class FunctionCallAgentRunner(BaseAgentRunner):
iteration_step += 1
self.update_db_variables(self.variables_pool, self.db_variables_pool)
if self.variables_pool and self.db_variables_pool:
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish(
QueueMessageEndEvent(
@@ -389,9 +397,9 @@ class FunctionCallAgentRunner(BaseAgentRunner):
if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
return prompt_messages
return prompt_messages or []
def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
"""
Organize user query
"""
@@ -449,7 +457,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
def _organize_prompt_messages(self):
prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
query_prompt_messages = self._organize_user_query(self.query, [])
query_prompt_messages = self._organize_user_query(self.query or "", [])
self.history_prompt_messages = AgentHistoryPromptTransform(
model_config=self.model_config,

10
api/core/agent/output_parser/cot_output_parser.py
View File

@@ -38,7 +38,7 @@ class CotAgentOutputParser:
except:
return json_str or ""
def extra_json_from_code_block(code_block) -> Generator[Union[dict, str], None, None]:
def extra_json_from_code_block(code_block) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
code_blocks = re.findall(r"```(.*?)```", code_block, re.DOTALL)
if not code_blocks:
return
@@ -67,15 +67,15 @@ class CotAgentOutputParser:
for response in llm_response:
if response.delta.usage:
usage_dict["usage"] = response.delta.usage
response = response.delta.message.content
if not isinstance(response, str):
response_content = response.delta.message.content
if not isinstance(response_content, str):
continue
# stream
index = 0
while index < len(response):
while index < len(response_content):
steps = 1
delta = response[index : index + steps]
delta = response_content[index : index + steps]
yield_delta = False
if delta == "`":