Files
crewAI/src/crewai_tools/tools/rag/rag_tool.py
Lucas Gomide 8d9cee45f2 feat: attempt to make embedchain optional (#450)
* fix: attempt to make embedchain optional

* fix: drop pydantic_settings dependency

* fix: ensure the package is importable without any extra dependency

After making embedchain option many packages were unstalled which caused errors in some tools due to failing import directives
2025-09-12 13:35:17 -04:00

71 lines
2.0 KiB
Python

import portalocker
from abc import ABC, abstractmethod
from typing import Any
from pydantic import BaseModel, ConfigDict, Field, model_validator
from crewai.tools import BaseTool
class Adapter(BaseModel, ABC):
model_config = ConfigDict(arbitrary_types_allowed=True)
@abstractmethod
def query(self, question: str) -> str:
"""Query the knowledge base with a question and return the answer."""
@abstractmethod
def add(
self,
*args: Any,
**kwargs: Any,
) -> None:
"""Add content to the knowledge base."""
class RagTool(BaseTool):
class _AdapterPlaceholder(Adapter):
def query(self, question: str) -> str:
raise NotImplementedError
def add(self, *args: Any, **kwargs: Any) -> None:
raise NotImplementedError
name: str = "Knowledge base"
description: str = "A knowledge base that can be used to answer questions."
summarize: bool = False
adapter: Adapter = Field(default_factory=_AdapterPlaceholder)
config: dict[str, Any] | None = None
@model_validator(mode="after")
def _set_default_adapter(self):
if isinstance(self.adapter, RagTool._AdapterPlaceholder):
try:
from embedchain import App
except ImportError:
raise ImportError("embedchain is not installed. Please install it with `pip install crewai-tools[embedchain]`")
from crewai_tools.adapters.embedchain_adapter import EmbedchainAdapter
with portalocker.Lock("crewai-rag-tool.lock", timeout=10):
app = App.from_config(config=self.config) if self.config else App()
self.adapter = EmbedchainAdapter(
embedchain_app=app, summarize=self.summarize
)
return self
def add(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.adapter.add(*args, **kwargs)
def _run(
self,
query: str,
) -> str:
return f"Relevant Content:\n{self.adapter.query(query)}"