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import json
import logging
from collections.abc import Generator
from copy import deepcopy
from typing import Any, Optional, Union
from core.agent.base_agent_runner import BaseAgentRunner
from core.app.apps.base_app_queue_manager import PublishFrom
from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
from core.file import file_manager
from core.model_runtime.entities import (
AssistantPromptMessage,
LLMResult,
LLMResultChunk,
LLMResultChunkDelta,
LLMUsage,
PromptMessage,
PromptMessageContent,
PromptMessageContentType,
SystemPromptMessage,
TextPromptMessageContent,
ToolPromptMessage,
UserPromptMessage,
)
from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
from core.tools.entities.tool_entities import ToolInvokeMeta
from core.tools.tool_engine import ToolEngine
from models.model import Message
logger = logging.getLogger(__name__)
class FunctionCallAgentRunner(BaseAgentRunner):
def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
"""
Run FunctionCall agent application
"""
self.query = query
app_generate_entity = self.application_generate_entity
app_config = self.app_config
# convert tools into ModelRuntime Tool format
tool_instances, prompt_messages_tools = self._init_prompt_tools()
iteration_step = 1
max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
# continue to run until there is not any tool call
function_call_state = True
llm_usage = {"usage": None}
final_answer = ""
# get tracing instance
trace_manager = app_generate_entity.trace_manager
def increase_usage(final_llm_usage_dict: dict[str, LLMUsage], usage: LLMUsage):
if not final_llm_usage_dict["usage"]:
final_llm_usage_dict["usage"] = usage
else:
llm_usage = final_llm_usage_dict["usage"]
llm_usage.prompt_tokens += usage.prompt_tokens
llm_usage.completion_tokens += usage.completion_tokens
llm_usage.prompt_price += usage.prompt_price
llm_usage.completion_price += usage.completion_price
llm_usage.total_price += usage.total_price
model_instance = self.model_instance
while function_call_state and iteration_step <= max_iteration_steps:
function_call_state = False
if iteration_step == max_iteration_steps:
# the last iteration, remove all tools
prompt_messages_tools = []
message_file_ids = []
agent_thought = self.create_agent_thought(
message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
)
# recalc llm max tokens
prompt_messages = self._organize_prompt_messages()
self.recalc_llm_max_tokens(self.model_config, prompt_messages)
# invoke model
chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_generate_entity.model_conf.parameters,
tools=prompt_messages_tools,
stop=app_generate_entity.model_conf.stop,
stream=self.stream_tool_call,
user=self.user_id,
callbacks=[],
)
tool_calls: list[tuple[str, str, dict[str, Any]]] = []
# save full response
response = ""
# save tool call names and inputs
tool_call_names = ""
tool_call_inputs = ""
current_llm_usage = None
if self.stream_tool_call:
is_first_chunk = True
for chunk in chunks:
if is_first_chunk:
self.queue_manager.publish(
QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
)
is_first_chunk = False
# 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_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
try:
tool_call_inputs = json.dumps(
{tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
)
except json.JSONDecodeError as e:
# ensure ascii to avoid encoding error
tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
if chunk.delta.message and chunk.delta.message.content:
if isinstance(chunk.delta.message.content, list):
for content in chunk.delta.message.content:
response += content.data
else:
response += 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
# 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_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
try:
tool_call_inputs = json.dumps(
{tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
)
except json.JSONDecodeError as e:
# ensure ascii to avoid encoding error
tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
if result.usage:
increase_usage(llm_usage, result.usage)
current_llm_usage = result.usage
if result.message and result.message.content:
if isinstance(result.message.content, list):
for content in result.message.content:
response += content.data
else:
response += result.message.content
if not result.message.content:
result.message.content = ""
self.queue_manager.publish(
QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
)
yield LLMResultChunk(
model=model_instance.model,
prompt_messages=result.prompt_messages,
system_fingerprint=result.system_fingerprint,
delta=LLMResultChunkDelta(
index=0,
message=result.message,
usage=result.usage,
),
)
assistant_message = AssistantPromptMessage(content="", tool_calls=[])
if tool_calls:
assistant_message.tool_calls = [
AssistantPromptMessage.ToolCall(
id=tool_call[0],
type="function",
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
),
)
for tool_call in tool_calls
]
else:
assistant_message.content = response
self._current_thoughts.append(assistant_message)
# save thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=tool_call_names,
tool_input=tool_call_inputs,
thought=response,
tool_invoke_meta=None,
observation=None,
answer=response,
messages_ids=[],
llm_usage=current_llm_usage,
)
self.queue_manager.publish(
QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
)
final_answer += response + "\n"
# call tools
tool_responses = []
for tool_call_id, tool_call_name, tool_call_args in tool_calls:
tool_instance = tool_instances.get(tool_call_name)
if not tool_instance:
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": f"there is not a tool named {tool_call_name}",
"meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
}
else:
# invoke tool
tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
tool=tool_instance,
tool_parameters=tool_call_args,
user_id=self.user_id,
tenant_id=self.tenant_id,
message=self.message,
invoke_from=self.application_generate_entity.invoke_from,
agent_tool_callback=self.agent_callback,
trace_manager=trace_manager,
)
# 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)
# 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)
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": tool_invoke_response,
"meta": tool_invoke_meta.to_dict(),
}
tool_responses.append(tool_response)
if tool_response["tool_response"] is not None:
self._current_thoughts.append(
ToolPromptMessage(
content=tool_response["tool_response"],
tool_call_id=tool_call_id,
name=tool_call_name,
)
)
if len(tool_responses) > 0:
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=None,
tool_input=None,
thought=None,
tool_invoke_meta={
tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
},
observation={
tool_response["tool_call_name"]: tool_response["tool_response"]
for tool_response in tool_responses
},
answer=None,
messages_ids=message_file_ids,
)
self.queue_manager.publish(
QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
)
# update prompt tool
for prompt_tool in prompt_messages_tools:
self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
iteration_step += 1
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish(
QueueMessageEndEvent(
llm_result=LLMResult(
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(content=final_answer),
usage=llm_usage["usage"] or LLMUsage.empty_usage(),
system_fingerprint="",
)
),
PublishFrom.APPLICATION_MANAGER,
)
def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
"""
Check if there is any tool call in llm result chunk
"""
if llm_result_chunk.delta.message.tool_calls:
return True
return False
def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
"""
Check if there is any blocking tool call in llm result
"""
if llm_result.message.tool_calls:
return True
return False
def extract_tool_calls(
self, llm_result_chunk: LLMResultChunk
) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
"""
Extract tool calls from llm result chunk
Returns:
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
"""
tool_calls = []
for prompt_message in llm_result_chunk.delta.message.tool_calls:
args = {}
if prompt_message.function.arguments != "":
args = json.loads(prompt_message.function.arguments)
tool_calls.append(
(
prompt_message.id,
prompt_message.function.name,
args,
)
)
return tool_calls
def extract_blocking_tool_calls(self, llm_result: LLMResult) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
"""
Extract blocking tool calls from llm result
Returns:
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
"""
tool_calls = []
for prompt_message in llm_result.message.tool_calls:
args = {}
if prompt_message.function.arguments != "":
args = json.loads(prompt_message.function.arguments)
tool_calls.append(
(
prompt_message.id,
prompt_message.function.name,
args,
)
)
return tool_calls
def _init_system_message(
self, prompt_template: str, prompt_messages: Optional[list[PromptMessage]] = None
) -> list[PromptMessage]:
"""
Initialize system message
"""
if not prompt_messages and prompt_template:
return [
SystemPromptMessage(content=prompt_template),
]
if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
return prompt_messages
def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
"""
Organize user query
"""
if self.files:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents.append(TextPromptMessageContent(data=query))
for file_obj in self.files:
prompt_message_contents.append(file_manager.to_prompt_message_content(file_obj))
prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
else:
prompt_messages.append(UserPromptMessage(content=query))
return prompt_messages
def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
"""
As for now, gpt supports both fc and vision at the first iteration.
We need to remove the image messages from the prompt messages at the first iteration.
"""
prompt_messages = deepcopy(prompt_messages)
for prompt_message in prompt_messages:
if isinstance(prompt_message, UserPromptMessage):
if isinstance(prompt_message.content, list):
prompt_message.content = "\n".join(
[
content.data
if content.type == PromptMessageContentType.TEXT
else "[image]"
if content.type == PromptMessageContentType.IMAGE
else "[file]"
for content in prompt_message.content
]
)
return prompt_messages
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, [])
self.history_prompt_messages = AgentHistoryPromptTransform(
model_config=self.model_config,
prompt_messages=[*query_prompt_messages, *self._current_thoughts],
history_messages=self.history_prompt_messages,
memory=self.memory,
).get_prompt()
prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
if len(self._current_thoughts) != 0:
# clear messages after the first iteration
prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
return prompt_messages
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