Custom model config for RAG tools

src/crewai_tools/tools/rag/README.md

@@ -48,9 +48,6 @@ rag_tool = RagTool().from_directory('path/to/your/directory')
 
 # Example: Loading from a web page
 rag_tool = RagTool().from_web_page('https://example.com')
-
-# Example: Loading from an Embedchain configuration
-rag_tool = RagTool().from_embedchain('path/to/your/config.json')
 ```
 
 ## **Contribution**
@@ -61,4 +58,4 @@ Contributions to RagTool and the broader CrewAI tools ecosystem are welcome. To
 
 RagTool is open-source and available under the MIT license.
 
-Thank you for considering RagTool for your knowledge base needs. Your contributions and feedback are invaluable to making RagTool even better.
+Thank you for considering RagTool for your knowledge base needs. Your contributions and feedback are invaluable to making RagTool even better.

src/crewai_tools/tools/rag/rag_tool.py

@@ -1,38 +1,71 @@
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional
+from typing import Any
 
-from pydantic.v1 import BaseModel, ConfigDict
+from pydantic import BaseModel, Field, model_validator
 
 from crewai_tools.tools.base_tool import BaseTool
 
 
 class Adapter(BaseModel, ABC):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    class Config:
+        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):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    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: Optional[Adapter] = None
-    app: Optional[Any] = None
+    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):
+            from embedchain import App
+
+            from crewai_tools.adapters.embedchain_adapter import EmbedchainAdapter
+
+            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,
+        **kwargs: Any,
     ) -> Any:
-        from crewai_tools.adapters.embedchain_adapter import EmbedchainAdapter
-        self.adapter = EmbedchainAdapter(embedchain_app=self.app, summarize=self.summarize)
+        self._before_run(query, **kwargs)
+
         return f"Relevant Content:\n{self.adapter.query(query)}"
 
-    def from_embedchain(self, config_path: str):
-        from embedchain import App
-        from crewai_tools.adapters.embedchain_adapter import EmbedchainAdapter
-
-        app = App.from_config(config_path=config_path)
-        adapter = EmbedchainAdapter(embedchain_app=app)
-        return RagTool(name=self.name, description=self.description, adapter=adapter)
+    def _before_run(self, query, **kwargs):
+        pass