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Adding support for translations (#120)

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Add translations support
This commit is contained in:
João Moura
2024-01-12 14:49:36 -03:00
committed by GitHub
parent ea7759b322
commit 8e7772c9c3
12 changed files with 148 additions and 101 deletions
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33
src/crewai/agent.py
View File

@@ -23,6 +23,7 @@ from crewai.agents import (
CrewAgentOutputParser,
ToolsHandler,
)
from crewai.i18n import I18N
from crewai.prompts import Prompts
@@ -54,13 +55,6 @@ class Agent(BaseModel):
role: str = Field(description="Role of the agent")
goal: str = Field(description="Objective of the agent")
backstory: str = Field(description="Backstory of the agent")
llm: Optional[Any] = Field(
default_factory=lambda: ChatOpenAI(
temperature=0.7,
model_name="gpt-4",
),
description="Language model that will run the agent.",
)
memory: bool = Field(
default=True, description="Whether the agent should have memory or not"
)
@@ -85,6 +79,16 @@ class Agent(BaseModel):
cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
default=CacheHandler(), description="An instance of the CacheHandler class."
)
i18n: Optional[I18N] = Field(
default=I18N(), description="Internationalization settings."
)
llm: Optional[Any] = Field(
default_factory=lambda: ChatOpenAI(
temperature=0.7,
model_name="gpt-4",
),
description="Language model that will run the agent.",
)
@field_validator("id", mode="before")
@classmethod
@@ -114,8 +118,8 @@ class Agent(BaseModel):
Output of the agent
"""
if context:
task = "\n".join(
[task, "\nThis is the context you are working with:", context]
task = self.i18n.slice("task_with_context").format(
task=task, context=context
)
tools = tools or self.tools
@@ -148,6 +152,7 @@ class Agent(BaseModel):
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
}
executor_args = {
"i18n": self.i18n,
"tools": self.tools,
"verbose": self.verbose,
"handle_parsing_errors": True,
@@ -160,9 +165,9 @@ class Agent(BaseModel):
)
executor_args["memory"] = summary_memory
agent_args["chat_history"] = lambda x: x["chat_history"]
prompt = Prompts().task_execution_with_memory()
prompt = Prompts(i18n=self.i18n).task_execution_with_memory()
else:
prompt = Prompts().task_execution()
prompt = Prompts(i18n=self.i18n).task_execution()
execution_prompt = prompt.partial(
goal=self.goal,
@@ -170,13 +175,15 @@ class Agent(BaseModel):
backstory=self.backstory,
)
bind = self.llm.bind(stop=["\nObservation"])
bind = self.llm.bind(stop=[self.i18n.slice("observation")])
inner_agent = (
agent_args
| execution_prompt
| bind
| CrewAgentOutputParser(
tools_handler=self.tools_handler, cache=self.cache_handler
tools_handler=self.tools_handler,
cache=self.cache_handler,
i18n=self.i18n,
)
)
self.agent_executor = CrewAgentExecutor(agent=inner_agent, **executor_args)

12
src/crewai/agents/exceptions.py
View File

@@ -1,17 +1,23 @@
from langchain_core.exceptions import OutputParserException
from crewai.i18n import I18N
class TaskRepeatedUsageException(OutputParserException):
"""Exception raised when a task is used twice in a roll."""
i18n: I18N = I18N()
error: str = "TaskRepeatedUsageException"
message: str = "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\n"
message: str
def __init__(self, tool: str, tool_input: str, text: str):
def __init__(self, i18n: I18N, tool: str, tool_input: str, text: str):
self.i18n = i18n
self.text = text
self.tool = tool
self.tool_input = tool_input
self.message = self.message.format(tool=tool, tool_input=tool_input)
self.message = self.i18n.errors("task_repeated_usage").format(
tool=tool, tool_input=tool_input
)
super().__init__(
error=self.error,

15
src/crewai/agents/executor.py
View File

@@ -1,5 +1,4 @@
import time
from textwrap import dedent
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from langchain.agents import AgentExecutor
@@ -12,11 +11,13 @@ from langchain_core.pydantic_v1 import root_validator
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from ..tools.cache_tools import CacheTools
from .cache.cache_hit import CacheHit
from crewai.agents.cache.cache_hit import CacheHit
from crewai.i18n import I18N
from crewai.tools.cache_tools import CacheTools
class CrewAgentExecutor(AgentExecutor):
i18n: I18N = I18N()
iterations: int = 0
max_iterations: Optional[int] = 15
force_answer_max_iterations: Optional[int] = None
@@ -31,13 +32,7 @@ class CrewAgentExecutor(AgentExecutor):
def _force_answer(self, output: AgentAction):
return AgentStep(
action=output,
observation=dedent(
"""\
I've used too many tools for this task.
I'm going to give you my absolute BEST Final answer now and
not use any more tools."""
),
action=output, observation=self.i18n.errors("used_too_many_tools")
)
def _call(

15
src/crewai/agents/output_parser.py
View File

@@ -4,9 +4,10 @@ from typing import Union
from langchain.agents.output_parsers import ReActSingleInputOutputParser
from langchain_core.agents import AgentAction, AgentFinish
from .cache import CacheHandler, CacheHit
from .exceptions import TaskRepeatedUsageException
from .tools_handler import ToolsHandler
from crewai.agents.cache import CacheHandler, CacheHit
from crewai.agents.exceptions import TaskRepeatedUsageException
from crewai.agents.tools_handler import ToolsHandler
from crewai.i18n import I18N
FINAL_ANSWER_ACTION = "Final Answer:"
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
@@ -46,6 +47,7 @@ class CrewAgentOutputParser(ReActSingleInputOutputParser):
tools_handler: ToolsHandler
cache: CacheHandler
i18n: I18N
def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
FINAL_ANSWER_ACTION in text
@@ -65,10 +67,13 @@ class CrewAgentOutputParser(ReActSingleInputOutputParser):
}
if usage == last_tool_usage:
raise TaskRepeatedUsageException(
tool=action, tool_input=tool_input, text=text
text=text,
tool=action,
tool_input=tool_input,
i18n=self.i18n,
)
if result := self.cache.read(action, tool_input):
if self.cache.read(action, tool_input):
action = AgentAction(action, tool_input, text)
return CacheHit(action=action, cache=self.cache)

6
src/crewai/crew.py
View File

@@ -16,6 +16,7 @@ from pydantic_core import PydanticCustomError
from crewai.agent import Agent
from crewai.agents.cache import CacheHandler
from crewai.i18n import I18N
from crewai.process import Process
from crewai.task import Task
from crewai.tools.agent_tools import AgentTools
@@ -44,6 +45,10 @@ class Crew(BaseModel):
config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
cache_handler: Optional[InstanceOf[CacheHandler]] = Field(default=CacheHandler())
id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
language: str = Field(
default="en",
description="Language used for the crew, defaults to English.",
)
@field_validator("id", mode="before")
@classmethod
@@ -99,6 +104,7 @@ class Crew(BaseModel):
"""Starts the crew to work on its assigned tasks."""
for agent in self.agents:
agent.cache_handler = self.cache_handler
agent.i18n = I18N(language=self.language)
if self.process == Process.sequential:
return self._sequential_loop()

45
src/crewai/i18n.py Normal file
View File

@@ -0,0 +1,45 @@
import json
import os
from typing import Dict, Optional
from pydantic import BaseModel, Field, PrivateAttr, ValidationError, model_validator
class I18N(BaseModel):
_translations: Optional[Dict[str, str]] = PrivateAttr()
language: Optional[str] = Field(
default="en",
description="Language used to load translations",
)
@model_validator(mode="after")
def load_translation(self) -> "I18N":
"""Load translations from a JSON file based on the specified language."""
try:
dir_path = os.path.dirname(os.path.realpath(__file__))
prompts_path = os.path.join(dir_path, f"translations/{self.language}.json")
with open(prompts_path, "r") as f:
self._translations = json.load(f)
except FileNotFoundError:
raise ValidationError(
f"Trasnlation file for language '{self.language}' not found."
)
except json.JSONDecodeError:
raise ValidationError(f"Error decoding JSON from the prompts file.")
return self
def slice(self, slice: str) -> str:
return self.retrieve("slices", slice)
def errors(self, error: str) -> str:
return self.retrieve("errors", error)
def tools(self, error: str) -> str:
return self.retrieve("tools", error)
def retrieve(self, kind, key):
try:
return self._translations[kind].get(key)
except:
raise ValidationError(f"Translation for '{kind}':'{key}' not found.")

37
src/crewai/prompts.py
View File

@@ -1,36 +1,15 @@
import json
import os
from typing import ClassVar, Dict, Optional
from typing import ClassVar
from langchain.prompts import PromptTemplate
from pydantic import BaseModel, Field, PrivateAttr, ValidationError, model_validator
from pydantic import BaseModel, Field
from .i18n import I18N
class Prompts(BaseModel):
"""Manages and generates prompts for a generic agent with support for different languages."""
_prompts: Optional[Dict[str, str]] = PrivateAttr()
language: Optional[str] = Field(
default="en",
description="Language of the prompts.",
)
@model_validator(mode="after")
def load_prompts(self) -> "Prompts":
"""Load prompts from a JSON file based on the specified language."""
try:
dir_path = os.path.dirname(os.path.realpath(__file__))
prompts_path = os.path.join(dir_path, f"prompts/{self.language}.json")
with open(prompts_path, "r") as f:
self._prompts = json.load(f)["slices"]
except FileNotFoundError:
raise ValidationError(
f"Prompt file for language '{self.language}' not found."
)
except json.JSONDecodeError:
raise ValidationError(f"Error decoding JSON from the prompts file.")
return self
i18n: I18N = Field(default=I18N())
SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
@@ -48,10 +27,6 @@ class Prompts(BaseModel):
def _build_prompt(self, components: [str]) -> str:
"""Constructs a prompt string from specified components."""
prompt_parts = [
self._prompts[component]
for component in components
if component in self._prompts
]
prompt_parts = [self.i18n.slice(component) for component in components]
prompt_parts.append(self.SCRATCHPAD_SLICE)
return PromptTemplate.from_template("".join(prompt_parts))

8
src/crewai/prompts/en.json
View File

@@ -1,8 +0,0 @@
{
"slices": {
"task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
"memory": "This is the summary of your work so far:\n{chat_history}",
"role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
"tools": "TOOLS:\n------\nYou have access to the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]"
}
}

37
src/crewai/tools/agent_tools.py
View File

@@ -1,45 +1,34 @@
from textwrap import dedent
from typing import List
from typing import List, Optional
from langchain.tools import Tool
from pydantic import BaseModel, Field
from crewai.agent import Agent
from crewai.i18n import I18N
class AgentTools(BaseModel):
"""Default tools around agent delegation"""
agents: List[Agent] = Field(description="List of agents in this crew.")
i18n: Optional[I18N] = Field(
default=I18N(), description="Internationalization settings."
)
def tools(self):
return [
Tool.from_function(
func=self.delegate_work,
name="Delegate work to co-worker",
description=dedent(
f"""\
Useful to delegate a specific task to one of the
following co-workers: [{', '.join([agent.role for agent in self.agents])}].
The input to this tool should be a pipe (|) separated text of length
three, representing the co-worker you want to ask it to (one of the options),
the task and all actual context you have for the task.
For example, `coworker|task|context`.
"""
description=self.i18n.tools("delegate_work").format(
coworkers=", ".join([agent.role for agent in self.agents])
),
),
Tool.from_function(
func=self.ask_question,
name="Ask question to co-worker",
description=dedent(
f"""\
Useful to ask a question, opinion or take from on
of the following co-workers: [{', '.join([agent.role for agent in self.agents])}].
The input to this tool should be a pipe (|) separated text of length
three, representing the co-worker you want to ask it to (one of the options),
the question and all actual context you have for the question.
For example, `coworker|question|context`.
"""
description=self.i18n.tools("ask_question").format(
coworkers=", ".join([agent.role for agent in self.agents])
),
),
]
@@ -57,10 +46,10 @@ class AgentTools(BaseModel):
try:
agent, task, context = command.split("|")
except ValueError:
return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context\n"
return self.i18n.errors("agent_tool_missing_param")
if not agent or not task or not context:
return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
return self.i18n.errors("agent_tool_missing_param")
agent = [
available_agent
@@ -69,7 +58,9 @@ class AgentTools(BaseModel):
]
if not agent:
return f"\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {', '.join([agent.role for agent in self.agents])}.\n"
return self.i18n.errors("agent_tool_unexsiting_coworker").format(
coworkers=", ".join([agent.role for agent in self.agents])
)
agent = agent[0]
return agent.execute_task(task, context)

20
src/crewai/translations/en.json Normal file
View File

@@ -0,0 +1,20 @@
{
"slices": {
"observation": "\nObservation",
"task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
"memory": "This is the summary of your work so far:\n{chat_history}",
"role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
"tools": "TOOLS:\n------\nYou have access to the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]",
"task_with_context": "{task}\nThis is the context you're working with:\n{context}"
},
"errors": {
"used_too_many_tools": "I've used too many tools for this task. I'm going to give you my absolute BEST Final answer now and not use any more tools.",
"agent_tool_missing_param": "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n",
"agent_tool_unexsiting_coworker": "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {coworkers}.\n",
"task_repeated_usage": "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\n"
},
"tools": {
"delegate_work": "Useful to delegate a specific task to one of the following co-workers: {coworkers}.\nThe input to this tool should be a pipe (|) separated text of length 3 (three), representing the co-worker you want to ask it to (one of the options), the task and all actual context you have for the task.\nFor example, `coworker|task|context`.",
"ask_question": "Useful to ask a question, opinion or take from on of the following co-workers: {coworkers}.\nThe input to this tool should be a pipe (|) separated text of length 3 (three), representing the co-worker you want to ask it to (one of the options), the question and all actual context you have for the question.\n For example, `coworker|question|context`."
}
}

19
tests/agent_test.py
View File

@@ -240,10 +240,15 @@ def test_agent_moved_on_after_max_iterations():
allow_delegation=False,
)
output = agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
assert (
output == "I have used the tool multiple times and the final answer remains 42."
)
with patch.object(
CrewAgentExecutor, "_force_answer", wraps=agent.agent_executor._force_answer
) as private_mock:
output = agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
assert (
output
== "I have used the tool multiple times and the final answer remains 42."
)
private_mock.assert_called_once()

2
tests/agent_tools/agent_tools_test.py
View File

@@ -48,7 +48,7 @@ def test_delegate_work_with_wrong_input():
assert (
result
== "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context\n"
== "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
)