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Code Issues Actions 6 Packages Projects Releases Wiki Activity

removing langchain

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This commit is contained in:
João Moura
2024-09-13 02:57:50 -05:00
parent 3cd464497b
commit 7120a9860b
40 changed files with 6295 additions and 2499 deletions
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11
src/crewai/agent.py
View File

@@ -94,8 +94,9 @@ class Agent(BaseAgent):
allow_code_execution: Optional[bool] = Field(
default=False, description="Enable code execution for the agent."
)
sliding_context_window: Optional[bool] = Field(
default=False, description="Enable sliding context window for the agent."
respect_context_window: Optional[bool] = Field(
default=True,
description="Keep messages under the context window size by summarizing content.",
)
max_retry_limit: int = Field(
default=2,
@@ -171,7 +172,7 @@ class Agent(BaseAgent):
"input": task_prompt,
"tool_names": self.agent_executor.tools_names,
"tools": self.agent_executor.tools_description,
"should_ask_for_human_input": task.human_input,
"ask_for_human_input": task.human_input,
}
)["output"]
except Exception as e:
@@ -232,7 +233,7 @@ class Agent(BaseAgent):
tools_description=self._render_text_description_and_args(parsed_tools),
step_callback=self.step_callback,
function_calling_llm=self.function_calling_llm,
sliding_context_window=self.sliding_context_window,
respect_context_window=self.respect_context_window,
request_within_rpm_limit=self._rpm_controller.check_or_wait
if self._rpm_controller
else None,
@@ -257,7 +258,7 @@ class Agent(BaseAgent):
def get_output_converter(self, llm, text, model, instructions):
return Converter(llm=llm, text=text, model=model, instructions=instructions)
def _parse_tools(self, tools: List[Any]) -> List[Any]: # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
def _parse_tools(self, tools: List[Any]) -> List[Any]: # type: ignore
"""Parse tools to be used for the task."""
tools_list = []
try:

3
src/crewai/agents/agent_builder/base_agent.py
View File

@@ -102,7 +102,8 @@ class BaseAgent(ABC, BaseModel):
description="Maximum number of requests per minute for the agent execution to be respected.",
)
allow_delegation: bool = Field(
default=True, description="Allow delegation of tasks to agents"
default=False,
description="Enable agent to delegate and ask questions among each other.",
)
tools: Optional[List[Any]] = Field(
default_factory=list, description="Tools at agents' disposal"

3
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -24,9 +24,6 @@ class CrewAgentExecutorMixin:
def _should_force_answer(self) -> bool:
"""Determine if a forced answer is required based on iteration count."""
print("*** self.iterations", self.iterations)
print("*** self.max_iter", self.max_iter)
print("*** self.have_forced_answer", self.have_forced_answer)
return (self.iterations >= self.max_iter) and not self.have_forced_answer
def _create_short_term_memory(self, output) -> None:

111
src/crewai/agents/crew_agent_executor.py
View File

@@ -34,7 +34,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
step_callback: Any = None,
original_tools: List[Any] = [],
function_calling_llm: Any = None,
sliding_context_window: bool = False,
respect_context_window: bool = False,
request_within_rpm_limit: Any = None,
callbacks: List[Any] = [],
):
@@ -54,65 +54,75 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.step_callback = step_callback
self.tools_description = tools_description
self.function_calling_llm = function_calling_llm
self.sliding_context_window = sliding_context_window
self.respect_context_window = respect_context_window
self.request_within_rpm_limit = request_within_rpm_limit
self.should_ask_for_human_input = False
self.ask_for_human_input = False
self.messages = []
self.iterations = 0
self.have_forced_answer = False
self.name_to_tool_map = {tool.name: tool for tool in self.tools}
def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
formatted_answer = None
formatted_prompt = self._format_prompt(self.prompt, inputs)
self.should_ask_for_human_input = inputs.get(
"should_ask_for_human_input", False
)
self.messages = self._messages(formatted_prompt)
print("starting invoke")
if "system" in self.prompt:
system_prompt = self._format_prompt(self.prompt["system"], inputs)
user_prompt = self._format_prompt(self.prompt["user"], inputs)
formatted_answer = self._invoke_loop(formatted_answer)
self.messages.append(self._format_msg(system_prompt, role="system"))
self.messages.append(self._format_msg(user_prompt))
else:
user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
self.messages.append(self._format_msg(user_prompt))
print("after messages")
print(self.messages)
self.ask_for_human_input = inputs.get("ask_for_human_input", False)
if self.should_ask_for_human_input:
formatted_answer = self._invoke_loop()
print("111after formatted_answer")
print(formatted_answer)
if self.ask_for_human_input:
human_feedback = self._ask_human_input(formatted_answer.output)
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer, human_feedback)
# Making sure we only ask for it once, so disabling for the next thought loop
self.should_ask_for_human_input = False
self.messages.append(
{"role": "user", "content": f"Feedback: {human_feedback}"}
)
self.ask_for_human_input = False
self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
formatted_answer = self._invoke_loop(None)
return {"output": formatted_answer.output}
def _invoke_loop(self, formatted_answer):
def _invoke_loop(self, formatted_answer=None):
print("starting _invoke_loop")
try:
while not isinstance(formatted_answer, AgentFinish):
# print('2222222')
if not self.request_within_rpm_limit or self.request_within_rpm_limit():
# print('3333333')
print("******* messages")
print(self.messages)
answer = LLM(
self.llm, stop=self.stop, callbacks=self.callbacks
).call(self.messages)
print("after answer")
print(answer)
self.iterations += 1
print("*** self.iterations", self.iterations)
# if self.iterations > 11:
# sadasd
formatted_answer = self._format_answer(answer)
print("222after formatted_answer")
print(formatted_answer)
if isinstance(formatted_answer, AgentAction):
# print('4444444')
action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}"
# print(formatted_answer)
print("after formatted_answer.text")
print(formatted_answer.text)
if self.step_callback:
formatted_answer.result = action_result
self.step_callback(formatted_answer)
if self._should_force_answer():
print("starting _should_force_answer")
if self.have_forced_answer:
print("forcing answer")
return {
"output": self._i18n.errors(
"force_final_answer_error"
@@ -123,25 +133,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
f'\n{self._i18n.errors("force_final_answer")}'
)
self.have_forced_answer = True
self.messages.append(
{"role": "assistant", "content": formatted_answer.text}
)
self.messages.append(
self._format_msg(formatted_answer.text, role="assistant")
)
except OutputParserException as e:
# print('5555555')
print("********* ERROR1")
self.messages.append({"role": "assistant", "content": e.error})
self._invoke_loop(formatted_answer)
except Exception as e:
# print('6666666')
print("*** e", e)
print("********* ERRORw")
print(e)
if LLMContextLengthExceededException(str(e))._is_context_limit_error(
str(e)
):
self._handle_context_length()
self._invoke_loop(formatted_answer)
# print('7777777')
return formatted_answer
def _use_tool(self, agent_action: AgentAction) -> None:
@@ -176,25 +186,23 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
def _summarize_messages(self) -> None:
llm = LLM(self.llm)
grouped_messages = []
messages_groups = []
for message in self.messages:
content = message["content"]
for i in range(0, len(content), 5000):
grouped_messages.append(content[i : i + 5000])
messages_groups.append(content[i : i + 5000])
summarized_contents = []
for group in grouped_messages:
for group in messages_groups:
summary = llm.call(
[
{
"role": "system",
"content": "You are a helpful assistant that summarizes text.",
},
{
"role": "user",
"content": f"Summarize the following text, make sure to include all the important information: {group}",
},
self._format_msg(
self._i18n.slices("summarizer_system_message"), role="system"
),
self._format_msg(
self._i18n.errors("sumamrize_instruction").format(group=group),
),
]
)
summarized_contents.append(summary)
@@ -202,14 +210,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
merged_summary = " ".join(summarized_contents)
self.messages = [
{
"role": "user",
"content": f"This is a summary of our conversation so far:\n{merged_summary}",
}
self._format_msg(
self._i18n.errors("summary").format(merged_summary=merged_summary)
)
]
def _handle_context_length(self) -> None:
if self.sliding_context_window:
if self.respect_context_window:
self._logger.log(
"debug",
"Context length exceeded. Summarizing content to fit the model context window.",
@@ -234,7 +241,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if (
CrewTrainingHandler(TRAINING_DATA_FILE).load()
and not self.should_ask_for_human_input
and not self.ask_for_human_input
):
training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
if training_data.get(agent_id):
@@ -243,7 +250,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
] = result.output
CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
if self.should_ask_for_human_input and human_feedback is not None:
if self.ask_for_human_input and human_feedback is not None:
training_data = {
"initial_output": result.output,
"human_feedback": human_feedback,
@@ -261,7 +268,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
return prompt
def _format_answer(self, answer: str) -> str:
return CrewAgentParser(agent=self).parse(answer)
return CrewAgentParser(agent=self.agent).parse(answer)
def _messages(self, prompt: str) -> List[Dict[str, str]]:
return [{"role": "user", "content": prompt}]
def _format_msg(self, prompt: str, role: str = "user") -> List[Dict[str, str]]:
return {"role": role, "content": prompt}

2
src/crewai/cli/templates/crew/crew.py
View File

@@ -10,8 +10,6 @@ from crewai.project import CrewBase, agent, crew, task
@CrewBase
class {{crew_name}}Crew():
"""{{crew_name}} crew"""
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'
@agent
def researcher(self) -> Agent:

6
src/crewai/crew.py
View File

@@ -6,7 +6,6 @@ from concurrent.futures import Future
from hashlib import md5
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
from langchain_core.callbacks import BaseCallbackHandler
from pydantic import (
UUID4,
BaseModel,
@@ -68,7 +67,6 @@ class Crew(BaseModel):
manager_llm: The language model that will run manager agent.
manager_agent: Custom agent that will be used as manager.
memory: Whether the crew should use memory to store memories of it's execution.
manager_callbacks: The callback handlers to be executed by the manager agent when hierarchical process is used
cache: Whether the crew should use a cache to store the results of the tools execution.
function_calling_llm: The language model that will run the tool calling for all the agents.
process: The process flow that the crew will follow (e.g., sequential, hierarchical).
@@ -126,10 +124,6 @@ class Crew(BaseModel):
manager_agent: Optional[BaseAgent] = Field(
description="Custom agent that will be used as manager.", default=None
)
manager_callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
default=None,
description="A list of callback handlers to be executed by the manager agent when hierarchical process is used",
)
function_calling_llm: Optional[Any] = Field(
description="Language model that will run the agent.", default=None
)

95
src/crewai/lol.py
View File

@@ -1,95 +0,0 @@
from typing import Callable, List, Dict, Any
from functools import wraps
class Flow:
def __init__(self):
self._start_method = None
self._listeners: Dict[str, List[str]] = {}
self._methods: Dict[str, Callable] = {}
def run(self):
if not self._start_method:
raise ValueError("No start method defined")
result = self._methods[self._start_method](self)
self._execute_listeners(self._start_method, result)
def _execute_listeners(self, trigger_method: str, result: Any):
if trigger_method in self._listeners:
for listener in self._listeners[trigger_method]:
listener_result = self._methods[listener](self, result)
self._execute_listeners(listener, listener_result)
def start():
def decorator(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
if not self._start_method:
self._start_method = func.__name__
return func(self, *args, **kwargs)
return wrapper
return decorator
def listen(*trigger_methods):
def decorator(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
for trigger in trigger_methods:
trigger_name = trigger.__name__ if callable(trigger) else trigger
if trigger_name not in self._listeners:
self._listeners[trigger_name] = []
self._listeners[trigger_name].append(func.__name__)
return func(self, *args, **kwargs)
return wrapper
return decorator
class FlowMeta(type):
def __new__(mcs, name, bases, attrs):
new_cls = super().__new__(mcs, name, bases, attrs)
for name, method in attrs.items():
if hasattr(method, "_is_start"):
new_cls._start_method = name
if hasattr(method, "_listeners"):
for trigger in method._listeners:
if trigger not in new_cls._listeners:
new_cls._listeners[trigger] = []
new_cls._listeners[trigger].append(name)
new_cls._methods[name] = method
return new_cls
class BaseFlow(Flow, metaclass=FlowMeta):
_start_method = None
_listeners = {}
_methods = {}
# Example usage:
class ExampleFlow(BaseFlow):
@start()
def start_method(self):
print("Starting the flow")
return "Start result"
@listen(start_method)
def second_method(self, result):
print(f"Second method, received: {result}")
return "Second result"
@listen(second_method)
def third_method(self, result):
print(f"Third method, received: {result}")
return "Third result"
# Run the flow
flow = ExampleFlow()
flow.run()

11
src/crewai/task.py
View File

@@ -243,13 +243,18 @@ class Task(BaseModel):
tools = tools or self.tools or []
self.processed_by_agents.add(agent.role)
print("====================================================")
print("context", self.prompt_context)
print("context", agent.role)
print("context", context)
result = agent.execute_task(
task=self,
context=context,
tools=tools,
)
print("result", result)
print("====================================================")
pydantic_output, json_output = self._export_output(result)
task_output = TaskOutput(
@@ -276,7 +281,9 @@ class Task(BaseModel):
content = (
json_output
if json_output
else pydantic_output.model_dump_json() if pydantic_output else result
else pydantic_output.model_dump_json()
if pydantic_output
else result
)
self._save_file(content)

1
src/crewai/tools/agent_tools.py
View File

@@ -1,5 +1,4 @@
from langchain.tools import StructuredTool
from crewai.agents.agent_builder.utilities.base_agent_tool import BaseAgentTools

39
src/crewai/tools/tool_output_parser.py
View File

@@ -1,39 +0,0 @@
import json
from typing import Any, List
import regex
from langchain.output_parsers import PydanticOutputParser
from langchain_core.exceptions import OutputParserException
from langchain_core.outputs import Generation
from pydantic import ValidationError
class ToolOutputParser(PydanticOutputParser):
"""Parses the function calling of a tool usage and it's arguments."""
def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
result[0].text = self._transform_in_valid_json(result[0].text)
json_object = super().parse_result(result)
try:
return self.pydantic_object.parse_obj(json_object)
except ValidationError as e:
name = self.pydantic_object.__name__
msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
raise OutputParserException(msg, llm_output=json_object)
def _transform_in_valid_json(self, text) -> str:
text = text.replace("```", "").replace("json", "")
json_pattern = r"\{(?:[^{}]|(?R))*\}"
matches = regex.finditer(json_pattern, text)
for match in matches:
try:
# Attempt to parse the matched string as JSON
json_obj = json.loads(match.group())
# Return the first successfully parsed JSON object
json_obj = json.dumps(json_obj)
return str(json_obj)
except json.JSONDecodeError:
# If parsing fails, skip to the next match
continue
return text

7
src/crewai/translations/en.json
View File

@@ -15,9 +15,12 @@
"final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
"format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
"task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
"expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary.",
"expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
"human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
"getting_input": "This is the agent's final answer: {final_answer}\nPlease provide feedback: "
"getting_input": "This is the agent's final answer: {final_answer}\nPlease provide feedback: ",
"summarizer_system_message": "You are a helpful assistant that summarizes text.",
"sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
"summary": "This is a summary of our conversation so far:\n{merged_summary}"
},
"errors": {
"force_final_answer_error": "I can't keep going, this was the best I could do.\n {formatted_answer.text}",

5
src/crewai/utilities/converter.py
View File

@@ -213,9 +213,8 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
def is_gpt(llm: Any) -> bool:
from langchain_openai import ChatOpenAI
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
"""Return if llm provided is of gpt from openai."""
return "gpt" in str(llm).lower()
def create_converter(

3
src/crewai/utilities/evaluators/crew_evaluator_handler.py
View File

@@ -4,7 +4,6 @@ from crewai.agent import Agent
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.telemetry import Telemetry
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
from rich.box import HEAVY_EDGE
from rich.console import Console
@@ -51,7 +50,7 @@ class CrewEvaluator:
),
backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
verbose=False,
llm=ChatOpenAI(model=self.openai_model_name),
llm=self.openai_model_name,
)
def _evaluation_task(

4
src/crewai/utilities/planning_handler.py
View File

@@ -1,6 +1,4 @@
from typing import Any, List, Optional
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
from crewai.agent import Agent
@@ -27,7 +25,7 @@ class CrewPlanner:
self.tasks = tasks
if planning_agent_llm is None:
self.planning_agent_llm = ChatOpenAI(model="gpt-4o-mini")
self.planning_agent_llm = "gpt-4o-mini"
else:
self.planning_agent_llm = planning_agent_llm

22
src/crewai/utilities/prompts.py
View File

@@ -20,18 +20,24 @@ class Prompts(BaseModel):
slices.append("tools")
else:
slices.append("no_tools")
system = self._build_prompt(slices)
slices.append("task")
if not self.system_template and not self.prompt_template:
return self._build_prompt(slices)
return {
"system": system,
"user": self._build_prompt(["task"]),
"prompt": self._build_prompt(slices),
}
else:
return self._build_prompt(
slices,
self.system_template,
self.prompt_template,
self.response_template,
)
return {
"prompt": self._build_prompt(
slices,
self.system_template,
self.prompt_template,
self.response_template,
)
}
def _build_prompt(
self,

7
src/crewai/utilities/token_counter_callback.py
View File

@@ -11,10 +11,7 @@ class TokenCalcHandler(CustomLogger):
return
self.token_cost_process.sum_successful_requests(1)
print("*** response_obj", response_obj)
self.token_cost_process.sum_prompt_tokens(
response_obj["usage"]["prompt_tokens"]
)
self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
self.token_cost_process.sum_completion_tokens(
response_obj["usage"]["completion_tokens"]
response_obj["usage"].completion_tokens
)