Custom model config for RAG tools

src/crewai_tools/adapters/embedchain_adapter.py

@@ -1,12 +1,25 @@
 from typing import Any
+
+from embedchain import App
+
 from crewai_tools.tools.rag.rag_tool import Adapter
 
+
 class EmbedchainAdapter(Adapter):
-    embedchain_app: Any
+    embedchain_app: App
     summarize: bool = False
 
     def query(self, question: str) -> str:
-        result, sources = self.embedchain_app.query(question, citations=True, dry_run=(not self.summarize))
+        result, sources = self.embedchain_app.query(
+            question, citations=True, dry_run=(not self.summarize)
+        )
         if self.summarize:
             return result
         return "\n\n".join([source[0] for source in sources])
+
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        self.embedchain_app.add(*args, **kwargs)

src/crewai_tools/adapters/lancedb_adapter.py

@@ -35,7 +35,7 @@ class LanceDBAdapter(Adapter):
         self._db = lancedb_connect(self.uri)
         self._table = self._db.open_table(self.table_name)
 
-        return super().model_post_init(__context)
+        super().model_post_init(__context)
 
     def query(self, question: str) -> str:
         query = self.embedding_function([question])[0]
@@ -47,3 +47,10 @@ class LanceDBAdapter(Adapter):
         )
         values = [result[self.text_column_name] for result in results]
         return "\n".join(values)
+
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        self._table.add(*args, **kwargs)

src/crewai_tools/tools/base_tool.py

@@ -1,28 +1,47 @@
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Optional, Type
 
-from pydantic import BaseModel, model_validator
+from langchain_core.tools import StructuredTool
+from pydantic import BaseModel, ConfigDict, Field, validator
 from pydantic.v1 import BaseModel as V1BaseModel
 
-from langchain_core.tools import StructuredTool
 
 class BaseTool(BaseModel, ABC):
+    class _ArgsSchemaPlaceholder(V1BaseModel):
+        pass
+
+    model_config = ConfigDict()
+
     name: str
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
     """Used to tell the model how/when/why to use the tool."""
-    args_schema: Optional[Type[V1BaseModel]] = None
+    args_schema: Type[V1BaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
     """The schema for the arguments that the tool accepts."""
     description_updated: bool = False
     """Flag to check if the description has been updated."""
     cache_function: Optional[Callable] = lambda: True
     """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
 
-    @model_validator(mode="after")
-    def _check_args_schema(self):
-        self._set_args_schema()
+    @validator("args_schema", always=True, pre=True)
+    def _default_args_schema(cls, v: Type[V1BaseModel]) -> Type[V1BaseModel]:
+        if not isinstance(v, cls._ArgsSchemaPlaceholder):
+            return v
+
+        return type(
+            f"{cls.__name__}Schema",
+            (V1BaseModel,),
+            {
+                "__annotations__": {
+                    k: v for k, v in cls._run.__annotations__.items() if k != "return"
+                },
+            },
+        )
+
+    def model_post_init(self, __context: Any) -> None:
         self._generate_description()
-        return self
+
+        super().model_post_init(__context)
 
     def run(
         self,
@@ -57,16 +76,20 @@ class BaseTool(BaseModel, ABC):
                 (V1BaseModel,),
                 {
                     "__annotations__": {
-                        k: v for k, v in self._run.__annotations__.items() if k != 'return'
+                        k: v
+                        for k, v in self._run.__annotations__.items()
+                        if k != "return"
                     },
                 },
             )
+
     def _generate_description(self):
         args = []
-        for arg, attribute in self.args_schema.schema()['properties'].items():
-            args.append(f"{arg}: '{attribute['type']}'")
+        for arg, attribute in self.args_schema.schema()["properties"].items():
+            if "type" in attribute:
+                args.append(f"{arg}: '{attribute['type']}'")
 
-        description = self.description.replace('\n', ' ')
+        description = self.description.replace("\n", " ")
         self.description = f"{self.name}({', '.join(args)}) - {description}"
 
 
@@ -93,19 +116,19 @@ def tool(*args):
         def _make_tool(f: Callable) -> BaseTool:
             if f.__doc__ is None:
                 raise ValueError("Function must have a docstring")
+            if f.__annotations__ is None:
+                raise ValueError("Function must have type annotations")
 
-            args_schema = None
-            if f.__annotations__:
-                class_name = "".join(tool_name.split()).title()
-                args_schema = type(
-                    class_name,
-                    (V1BaseModel,),
-                    {
-                        "__annotations__": {
-                            k: v for k, v in f.__annotations__.items() if k != 'return'
-                        },
+            class_name = "".join(tool_name.split()).title()
+            args_schema = type(
+                class_name,
+                (V1BaseModel,),
+                {
+                    "__annotations__": {
+                        k: v for k, v in f.__annotations__.items() if k != "return"
                     },
-                )
+                },
+            )
 
             return Tool(
                 name=tool_name,
@@ -120,4 +143,4 @@ def tool(*args):
         return _make_with_name(args[0].__name__)(args[0])
     if len(args) == 1 and isinstance(args[0], str):
         return _make_with_name(args[0])
-    raise ValueError("Invalid arguments")
+    raise ValueError("Invalid arguments")

src/crewai_tools/tools/code_docs_search_tool/code_docs_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedCodeDocsSearchToolSchema(BaseModel):
-	"""Input for CodeDocsSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the Code Docs content")
+    """Input for CodeDocsSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the Code Docs content",
+    )
+
 
 class CodeDocsSearchToolSchema(FixedCodeDocsSearchToolSchema):
-	"""Input for CodeDocsSearchTool."""
-	docs_url: str = Field(..., description="Mandatory docs_url path you want to search")
+    """Input for CodeDocsSearchTool."""
+
+    docs_url: str = Field(..., description="Mandatory docs_url path you want to search")
+
 
 class CodeDocsSearchTool(RagTool):
-	name: str = "Search a Code Docs content"
-	description: str = "A tool that can be used to semantic search a query from a Code Docs content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = CodeDocsSearchToolSchema
-	docs_url: Optional[str] = None
+    name: str = "Search a Code Docs content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a Code Docs content."
+    )
+    args_schema: Type[BaseModel] = CodeDocsSearchToolSchema
 
-	def __init__(self, docs_url: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if docs_url is not None:
-			self.docs_url = docs_url
-			self.description = f"A tool that can be used to semantic search a query the {docs_url} Code Docs content."
-			self.args_schema = FixedCodeDocsSearchToolSchema
-			self._generate_description()
+    def __init__(self, docs_url: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if docs_url is not None:
+            self.add(docs_url)
+            self.description = f"A tool that can be used to semantic search a query the {docs_url} Code Docs content."
+            self.args_schema = FixedCodeDocsSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		docs_url = kwargs.get('docs_url', self.docs_url)
-		self.app = App()
-		self.app.add(docs_url, data_type=DataType.DOCS_SITE)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.DOCS_SITE
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "docs_url" in kwargs:
+            self.add(kwargs["docs_url"])

src/crewai_tools/tools/csv_search_tool/csv_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedCSVSearchToolSchema(BaseModel):
-	"""Input for CSVSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the CSV's content")
+    """Input for CSVSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the CSV's content",
+    )
+
 
 class CSVSearchToolSchema(FixedCSVSearchToolSchema):
-	"""Input for CSVSearchTool."""
-	csv: str = Field(..., description="Mandatory csv path you want to search")
+    """Input for CSVSearchTool."""
+
+    csv: str = Field(..., description="Mandatory csv path you want to search")
+
 
 class CSVSearchTool(RagTool):
-	name: str = "Search a CSV's content"
-	description: str = "A tool that can be used to semantic search a query from a CSV's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = CSVSearchToolSchema
-	csv: Optional[str] = None
+    name: str = "Search a CSV's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a CSV's content."
+    )
+    args_schema: Type[BaseModel] = CSVSearchToolSchema
 
-	def __init__(self, csv: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if csv is not None:
-			self.csv = csv
-			self.description = f"A tool that can be used to semantic search a query the {csv} CSV's content."
-			self.args_schema = FixedCSVSearchToolSchema
-			self._generate_description()
+    def __init__(self, csv: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if csv is not None:
+            self.add(csv)
+            self.description = f"A tool that can be used to semantic search a query the {csv} CSV's content."
+            self.args_schema = FixedCSVSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		csv = kwargs.get('csv', self.csv)
-		self.app = App()
-		self.app.add(csv, data_type=DataType.CSV)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.CSV
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "csv" in kwargs:
+            self.add(kwargs["csv"])

src/crewai_tools/tools/directory_search_tool/directory_search_tool.py

@@ -1,42 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.loaders.directory_loader import DirectoryLoader
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedDirectorySearchToolSchema(BaseModel):
-	"""Input for DirectorySearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the directory's content")
+    """Input for DirectorySearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the directory's content",
+    )
+
 
 class DirectorySearchToolSchema(FixedDirectorySearchToolSchema):
-	"""Input for DirectorySearchTool."""
-	directory: str = Field(..., description="Mandatory directory you want to search")
+    """Input for DirectorySearchTool."""
+
+    directory: str = Field(..., description="Mandatory directory you want to search")
+
 
 class DirectorySearchTool(RagTool):
-	name: str = "Search a directory's content"
-	description: str = "A tool that can be used to semantic search a query from a directory's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = DirectorySearchToolSchema
-	directory: Optional[str] = None
+    name: str = "Search a directory's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a directory's content."
+    )
+    args_schema: Type[BaseModel] = DirectorySearchToolSchema
 
-	def __init__(self, directory: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if directory is not None:
-			self.directory = directory
-			self.description = f"A tool that can be used to semantic search a query the {directory} directory's content."
-			self.args_schema = FixedDirectorySearchToolSchema
-			self._generate_description()
+    def __init__(self, directory: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if directory is not None:
+            self.add(directory)
+            self.description = f"A tool that can be used to semantic search a query the {directory} directory's content."
+            self.args_schema = FixedDirectorySearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		directory = kwargs.get('directory', self.directory)
-		loader = DirectoryLoader(config=dict(recursive=True))
-		self.app = App()
-		self.app.add(directory, loader=loader)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["loader"] = DirectoryLoader(config=dict(recursive=True))
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "directory" in kwargs:
+            self.add(kwargs["directory"])

src/crewai_tools/tools/docx_search_tool/docx_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedDOCXSearchToolSchema(BaseModel):
-	"""Input for DOCXSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the DOCX's content")
+    """Input for DOCXSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the DOCX's content",
+    )
+
 
 class DOCXSearchToolSchema(FixedDOCXSearchToolSchema):
-	"""Input for DOCXSearchTool."""
-	docx: str = Field(..., description="Mandatory docx path you want to search")
+    """Input for DOCXSearchTool."""
+
+    docx: str = Field(..., description="Mandatory docx path you want to search")
+
 
 class DOCXSearchTool(RagTool):
-	name: str = "Search a DOCX's content"
-	description: str = "A tool that can be used to semantic search a query from a DOCX's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = DOCXSearchToolSchema
-	docx: Optional[str] = None
+    name: str = "Search a DOCX's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a DOCX's content."
+    )
+    args_schema: Type[BaseModel] = DOCXSearchToolSchema
 
-	def __init__(self, docx: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if docx is not None:
-			self.docx = docx
-			self.description = f"A tool that can be used to semantic search a query the {docx} DOCX's content."
-			self.args_schema = FixedDOCXSearchToolSchema
-			self._generate_description()
+    def __init__(self, docx: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if docx is not None:
+            self.add(docx)
+            self.description = f"A tool that can be used to semantic search a query the {docx} DOCX's content."
+            self.args_schema = FixedDOCXSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		docx = kwargs.get('docx', self.docx)
-		self.app = App()
-		self.app.add(docx, data_type=DataType.DOCX)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.DOCX
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "docx" in kwargs:
+            self.add(kwargs["docx"])

src/crewai_tools/tools/github_search_tool/github_search_tool.py

@@ -1,46 +1,58 @@
-from typing import Optional, Type, List, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, List, Optional, Type
 
-from embedchain import App
 from embedchain.loaders.github import GithubLoader
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedGithubSearchToolSchema(BaseModel):
-	"""Input for GithubSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the github repo's content")
+    """Input for GithubSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the github repo's content",
+    )
+
 
 class GithubSearchToolSchema(FixedGithubSearchToolSchema):
-	"""Input for GithubSearchTool."""
-	github_repo: str = Field(..., description="Mandatory github you want to search")
-	content_types: List[str] = Field(..., description="Mandatory content types you want to be inlcuded search, options: [code, repo, pr, issue]")
+    """Input for GithubSearchTool."""
+
+    github_repo: str = Field(..., description="Mandatory github you want to search")
+    content_types: List[str] = Field(
+        ...,
+        description="Mandatory content types you want to be inlcuded search, options: [code, repo, pr, issue]",
+    )
+
 
 class GithubSearchTool(RagTool):
-	name: str = "Search a github repo's content"
-	description: str = "A tool that can be used to semantic search a query from a github repo's content."
-	summarize: bool = False
-	gh_token: str = None
-	args_schema: Type[BaseModel] = GithubSearchToolSchema
-	github_repo: Optional[str] = None
-	content_types: List[str]
+    name: str = "Search a github repo's content"
+    description: str = "A tool that can be used to semantic search a query from a github repo's content."
+    summarize: bool = False
+    gh_token: str
+    args_schema: Type[BaseModel] = GithubSearchToolSchema
+    content_types: List[str]
 
-	def __init__(self, github_repo: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if github_repo is not None:
-			self.github_repo = github_repo
-			self.description = f"A tool that can be used to semantic search a query the {github_repo} github repo's content."
-			self.args_schema = FixedGithubSearchToolSchema
-			self._generate_description()
+    def __init__(self, github_repo: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if github_repo is not None:
+            self.add(github_repo)
+            self.description = f"A tool that can be used to semantic search a query the {github_repo} github repo's content."
+            self.args_schema = FixedGithubSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		github_repo = kwargs.get('github_repo', self.github_repo)
-		loader = GithubLoader(config={"token": self.gh_token})
-		app = App()
-		app.add(f"repo:{github_repo} type:{','.join(self.content_types)}", data_type="github", loader=loader)
-		self.app = app
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = "github"
+        kwargs["loader"] = GithubLoader(config={"token": self.gh_token})
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "github_repo" in kwargs:
+            self.add(kwargs["github_repo"])

src/crewai_tools/tools/json_search_tool/json_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedJSONSearchToolSchema(BaseModel):
-	"""Input for JSONSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the JSON's content")
+    """Input for JSONSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the JSON's content",
+    )
+
 
 class JSONSearchToolSchema(FixedJSONSearchToolSchema):
-	"""Input for JSONSearchTool."""
-	json_path: str = Field(..., description="Mandatory json path you want to search")
+    """Input for JSONSearchTool."""
+
+    json_path: str = Field(..., description="Mandatory json path you want to search")
+
 
 class JSONSearchTool(RagTool):
-	name: str = "Search a JSON's content"
-	description: str = "A tool that can be used to semantic search a query from a JSON's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = JSONSearchToolSchema
-	json_path: Optional[str] = None
+    name: str = "Search a JSON's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a JSON's content."
+    )
+    args_schema: Type[BaseModel] = JSONSearchToolSchema
 
-	def __init__(self, json_path: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if json_path is not None:
-			self.json_path = json_path
-			self.description = f"A tool that can be used to semantic search a query the {json} JSON's content."
-			self.args_schema = FixedJSONSearchToolSchema
-			self._generate_description()
+    def __init__(self, json_path: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if json_path is not None:
+            self.add(json_path)
+            self.description = f"A tool that can be used to semantic search a query the {json_path} JSON's content."
+            self.args_schema = FixedJSONSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		json_path = kwargs.get('json_path', self.json_path)
-		self.app = App()
-		self.app.add(json_path, data_type=DataType.JSON)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.JSON
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "json_path" in kwargs:
+            self.add(kwargs["json_path"])

src/crewai_tools/tools/mdx_seach_tool/mdx_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedMDXSearchToolSchema(BaseModel):
-	"""Input for MDXSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the MDX's content")
+    """Input for MDXSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the MDX's content",
+    )
+
 
 class MDXSearchToolSchema(FixedMDXSearchToolSchema):
-	"""Input for MDXSearchTool."""
-	mdx: str = Field(..., description="Mandatory mdx path you want to search")
+    """Input for MDXSearchTool."""
+
+    mdx: str = Field(..., description="Mandatory mdx path you want to search")
+
 
 class MDXSearchTool(RagTool):
-	name: str = "Search a MDX's content"
-	description: str = "A tool that can be used to semantic search a query from a MDX's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = MDXSearchToolSchema
-	mdx: Optional[str] = None
+    name: str = "Search a MDX's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a MDX's content."
+    )
+    args_schema: Type[BaseModel] = MDXSearchToolSchema
 
-	def __init__(self, mdx: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if mdx is not None:
-			self.mdx = mdx
-			self.description = f"A tool that can be used to semantic search a query the {mdx} MDX's content."
-			self.args_schema = FixedMDXSearchToolSchema
-			self._generate_description()
+    def __init__(self, mdx: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if mdx is not None:
+            self.add(mdx)
+            self.description = f"A tool that can be used to semantic search a query the {mdx} MDX's content."
+            self.args_schema = FixedMDXSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		mdx = kwargs.get('mdx', self.mdx)
-		self.app = App()
-		self.app.add(mdx, data_type=DataType.MDX)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.MDX
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "mdx" in kwargs:
+            self.add(kwargs["mdx"])

src/crewai_tools/tools/pdf_search_tool/pdf_search_tool.py

@@ -1,41 +1,51 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedPDFSearchToolSchema(BaseModel):
-	"""Input for PDFSearchTool."""
-	query: str = Field(..., description="Mandatory query you want to use to search the PDF's content")
+    """Input for PDFSearchTool."""
+
+    query: str = Field(
+        ..., description="Mandatory query you want to use to search the PDF's content"
+    )
+
 
 class PDFSearchToolSchema(FixedPDFSearchToolSchema):
-	"""Input for PDFSearchTool."""
-	pdf: str = Field(..., description="Mandatory pdf path you want to search")
+    """Input for PDFSearchTool."""
+
+    pdf: str = Field(..., description="Mandatory pdf path you want to search")
+
 
 class PDFSearchTool(RagTool):
-	name: str = "Search a PDF's content"
-	description: str = "A tool that can be used to semantic search a query from a PDF's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = PDFSearchToolSchema
-	pdf: Optional[str] = None
+    name: str = "Search a PDF's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a PDF's content."
+    )
+    args_schema: Type[BaseModel] = PDFSearchToolSchema
 
-	def __init__(self, pdf: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if pdf is not None:
-			self.pdf = pdf
-			self.description = f"A tool that can be used to semantic search a query the {pdf} PDF's content."
-			self.args_schema = FixedPDFSearchToolSchema
-			self._generate_description()
+    def __init__(self, pdf: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if pdf is not None:
+            self.add(pdf)
+            self.description = f"A tool that can be used to semantic search a query the {pdf} PDF's content."
+            self.args_schema = FixedPDFSearchToolSchema
 
-	def _run(
-		self,
-		query: str,
-		**kwargs: Any,
-	) -> Any:
-		pdf = kwargs.get('pdf', self.pdf)
-		self.app = App()
-		self.app.add(pdf, data_type=DataType.PDF_FILE)
-		return super()._run(query=query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.PDF_FILE
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "pdf" in kwargs:
+            self.add(kwargs["pdf"])

src/crewai_tools/tools/pg_seach_tool/pg_search_tool.py

@@ -1,45 +1,37 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Type
 
-from embedchain import App
 from embedchain.loaders.postgres import PostgresLoader
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
+
 class PGSearchToolSchema(BaseModel):
-	"""Input for PGSearchTool."""
-	search_query: str = Field(..., description="Mandatory semantic search query you want to use to search the database's content")
+    """Input for PGSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory semantic search query you want to use to search the database's content",
+    )
+
 
 class PGSearchTool(RagTool):
-	name: str = "Search a database's table content"
-	description: str = "A tool that can be used to semantic search a query from a database table's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = PGSearchToolSchema
-	db_uri: str = Field(..., description="Mandatory database URI")
-	table_name: str = Field(..., description="Mandatory table name")
-	search_query: str = Field(..., description="Mandatory semantic search query you want to use to search the database's content")
+    name: str = "Search a database's table content"
+    description: str = "A tool that can be used to semantic search a query from a database table's content."
+    args_schema: Type[BaseModel] = PGSearchToolSchema
+    db_uri: str = Field(..., description="Mandatory database URI")
 
-	def __init__(self, table_name: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if table_name is not None:
-			self.table_name = table_name
-			self.description = f"A tool that can be used to semantic search a query the {table_name} database table's content."
-			self._generate_description()
-		else:
-			raise('To use PGSearchTool, you must provide a `table_name` argument')
+    def __init__(self, table_name: str, **kwargs):
+        super().__init__(**kwargs)
+        self.add(table_name)
+        self.description = f"A tool that can be used to semantic search a query the {table_name} database table's content."
+        self._generate_description()
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-
-		config = { "url":  self.db_uri }
-		postgres_loader = PostgresLoader(config=config)
-		app = App()
-		app.add(
-				f"SELECT * FROM {self.table_name};",
-				data_type='postgres',
-				loader=postgres_loader
-		)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        table_name: str,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = "postgres"
+        kwargs["loader"] = PostgresLoader(config=dict(url=self.db_uri))
+        super().add(f"SELECT * FROM {table_name};", **kwargs)

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

src/crewai_tools/tools/txt_search_tool/txt_search_tool.py

@@ -1,40 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
+
 class FixedTXTSearchToolSchema(BaseModel):
-	"""Input for TXTSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the txt's content")
+    """Input for TXTSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the txt's content",
+    )
+
 
 class TXTSearchToolSchema(FixedTXTSearchToolSchema):
-	"""Input for TXTSearchTool."""
-	txt: str = Field(..., description="Mandatory txt path you want to search")
+    """Input for TXTSearchTool."""
+
+    txt: str = Field(..., description="Mandatory txt path you want to search")
+
 
 class TXTSearchTool(RagTool):
-	name: str = "Search a txt's content"
-	description: str = "A tool that can be used to semantic search a query from a txt's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = TXTSearchToolSchema
-	txt: Optional[str] = None
+    name: str = "Search a txt's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a txt's content."
+    )
+    args_schema: Type[BaseModel] = TXTSearchToolSchema
 
-	def __init__(self, txt: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if txt is not None:
-			self.txt = txt
-			self.description = f"A tool that can be used to semantic search a query the {txt} txt's content."
-			self.args_schema = FixedTXTSearchToolSchema
-			self._generate_description()
+    def __init__(self, txt: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if txt is not None:
+            self.add(txt)
+            self.description = f"A tool that can be used to semantic search a query the {txt} txt's content."
+            self.args_schema = FixedTXTSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		txt = kwargs.get('txt', self.txt)
-		self.app = App()
-		self.app.add(txt, data_type=DataType.TEXT_FILE)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.TEXT_FILE
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "txt" in kwargs:
+            self.add(kwargs["txt"])

src/crewai_tools/tools/website_search/website_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedWebsiteSearchToolSchema(BaseModel):
-	"""Input for WebsiteSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search a specific website")
+    """Input for WebsiteSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search a specific website",
+    )
+
 
 class WebsiteSearchToolSchema(FixedWebsiteSearchToolSchema):
-	"""Input for WebsiteSearchTool."""
-	website: str = Field(..., description="Mandatory valid website URL you want to search on")
+    """Input for WebsiteSearchTool."""
+
+    website: str = Field(
+        ..., description="Mandatory valid website URL you want to search on"
+    )
+
 
 class WebsiteSearchTool(RagTool):
-	name: str = "Search in a specific website"
-	description: str = "A tool that can be used to semantic search a query from a specific URL content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = WebsiteSearchToolSchema
-	website: Optional[str] = None
+    name: str = "Search in a specific website"
+    description: str = "A tool that can be used to semantic search a query from a specific URL content."
+    args_schema: Type[BaseModel] = WebsiteSearchToolSchema
 
-	def __init__(self, website: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if website is not None:
-			self.website = website
-			self.description = f"A tool that can be used to semantic search a query from {website} website content."
-			self.args_schema = FixedWebsiteSearchToolSchema
-			self._generate_description()
+    def __init__(self, website: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if website is not None:
+            self.add(website)
+            self.description = f"A tool that can be used to semantic search a query from {website} website content."
+            self.args_schema = FixedWebsiteSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		website = kwargs.get('website', self.website)
-		self.app = App()
-		self.app.add(website, data_type=DataType.WEB_PAGE)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.WEB_PAGE
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "website" in kwargs:
+            self.add(kwargs["website"])

src/crewai_tools/tools/xml_search_tool/xml_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedXMLSearchToolSchema(BaseModel):
-	"""Input for XMLSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the XML's content")
+    """Input for XMLSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the XML's content",
+    )
+
 
 class XMLSearchToolSchema(FixedXMLSearchToolSchema):
-	"""Input for XMLSearchTool."""
-	xml: str = Field(..., description="Mandatory xml path you want to search")
+    """Input for XMLSearchTool."""
+
+    xml: str = Field(..., description="Mandatory xml path you want to search")
+
 
 class XMLSearchTool(RagTool):
-	name: str = "Search a XML's content"
-	description: str = "A tool that can be used to semantic search a query from a XML's content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = XMLSearchToolSchema
-	xml: Optional[str] = None
+    name: str = "Search a XML's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a XML's content."
+    )
+    args_schema: Type[BaseModel] = XMLSearchToolSchema
 
-	def __init__(self, xml: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if xml is not None:
-			self.xml = xml
-			self.description = f"A tool that can be used to semantic search a query the {xml} XML's content."
-			self.args_schema = FixedXMLSearchToolSchema
-			self._generate_description()
+    def __init__(self, xml: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if xml is not None:
+            self.add(xml)
+            self.description = f"A tool that can be used to semantic search a query the {xml} XML's content."
+            self.args_schema = FixedXMLSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		xml = kwargs.get('xml', self.xml)
-		self.app = App()
-		self.app.add(xml, data_type=DataType.XML)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.XML
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "xml" in kwargs:
+            self.add(kwargs["xml"])

src/crewai_tools/tools/youtube_channel_search_tool/youtube_channel_search_tool.py

@@ -1,43 +1,55 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedYoutubeChannelSearchToolSchema(BaseModel):
-	"""Input for YoutubeChannelSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the Youtube Channels content")
+    """Input for YoutubeChannelSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the Youtube Channels content",
+    )
+
 
 class YoutubeChannelSearchToolSchema(FixedYoutubeChannelSearchToolSchema):
-	"""Input for YoutubeChannelSearchTool."""
-	youtube_channel_handle: str = Field(..., description="Mandatory youtube_channel_handle path you want to search")
+    """Input for YoutubeChannelSearchTool."""
+
+    youtube_channel_handle: str = Field(
+        ..., description="Mandatory youtube_channel_handle path you want to search"
+    )
+
 
 class YoutubeChannelSearchTool(RagTool):
-	name: str = "Search a Youtube Channels content"
-	description: str = "A tool that can be used to semantic search a query from a Youtube Channels content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = YoutubeChannelSearchToolSchema
-	youtube_channel_handle: Optional[str] = None
+    name: str = "Search a Youtube Channels content"
+    description: str = "A tool that can be used to semantic search a query from a Youtube Channels content."
+    args_schema: Type[BaseModel] = YoutubeChannelSearchToolSchema
 
-	def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if youtube_channel_handle is not None:
-			self.youtube_channel_handle = youtube_channel_handle
-			self.description = f"A tool that can be used to semantic search a query the {youtube_channel_handle} Youtube Channels content."
-			self.args_schema = FixedYoutubeChannelSearchToolSchema
-			self._generate_description()
+    def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if youtube_channel_handle is not None:
+            self.add(youtube_channel_handle)
+            self.description = f"A tool that can be used to semantic search a query the {youtube_channel_handle} Youtube Channels content."
+            self.args_schema = FixedYoutubeChannelSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		youtube_channel_handle = kwargs.get('youtube_channel_handle', self.youtube_channel_handle)
-		if not youtube_channel_handle.startswith("@"):
-			youtube_channel_handle = f"@{youtube_channel_handle}"
-		self.app = App()
-		self.app.add(youtube_channel_handle, data_type=DataType.YOUTUBE_CHANNEL)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        youtube_channel_handle: str,
+        **kwargs: Any,
+    ) -> None:
+        if not youtube_channel_handle.startswith("@"):
+            youtube_channel_handle = f"@{youtube_channel_handle}"
+
+        kwargs["data_type"] = DataType.YOUTUBE_CHANNEL
+        super().add(youtube_channel_handle, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "youtube_channel_handle" in kwargs:
+            self.add(kwargs["youtube_channel_handle"])

src/crewai_tools/tools/youtube_video_search_tool/youtube_video_search_tool.py

@@ -1,41 +1,52 @@
-from typing import Optional, Type, Any
-from pydantic.v1 import BaseModel, Field
+from typing import Any, Optional, Type
 
-from embedchain import App
 from embedchain.models.data_type import DataType
+from pydantic.v1 import BaseModel, Field
 
 from ..rag.rag_tool import RagTool
 
 
 class FixedYoutubeVideoSearchToolSchema(BaseModel):
-	"""Input for YoutubeVideoSearchTool."""
-	search_query: str = Field(..., description="Mandatory search query you want to use to search the Youtube Video content")
+    """Input for YoutubeVideoSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the Youtube Video content",
+    )
+
 
 class YoutubeVideoSearchToolSchema(FixedYoutubeVideoSearchToolSchema):
-	"""Input for YoutubeVideoSearchTool."""
-	youtube_video_url: str = Field(..., description="Mandatory youtube_video_url path you want to search")
+    """Input for YoutubeVideoSearchTool."""
+
+    youtube_video_url: str = Field(
+        ..., description="Mandatory youtube_video_url path you want to search"
+    )
+
 
 class YoutubeVideoSearchTool(RagTool):
-	name: str = "Search a Youtube Video content"
-	description: str = "A tool that can be used to semantic search a query from a Youtube Video content."
-	summarize: bool = False
-	args_schema: Type[BaseModel] = YoutubeVideoSearchToolSchema
-	youtube_video_url: Optional[str] = None
+    name: str = "Search a Youtube Video content"
+    description: str = "A tool that can be used to semantic search a query from a Youtube Video content."
+    args_schema: Type[BaseModel] = YoutubeVideoSearchToolSchema
 
-	def __init__(self, youtube_video_url: Optional[str] = None, **kwargs):
-		super().__init__(**kwargs)
-		if youtube_video_url is not None:
-			self.youtube_video_url = youtube_video_url
-			self.description = f"A tool that can be used to semantic search a query the {youtube_video_url} Youtube Video content."
-			self.args_schema = FixedYoutubeVideoSearchToolSchema
-			self._generate_description()
+    def __init__(self, youtube_video_url: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if youtube_video_url is not None:
+            self.add(youtube_video_url)
+            self.description = f"A tool that can be used to semantic search a query the {youtube_video_url} Youtube Video content."
+            self.args_schema = FixedYoutubeVideoSearchToolSchema
 
-	def _run(
-		self,
-		search_query: str,
-		**kwargs: Any,
-	) -> Any:
-		youtube_video_url = kwargs.get('youtube_video_url', self.youtube_video_url)
-		self.app = App()
-		self.app.add(youtube_video_url, data_type=DataType.YOUTUBE_VIDEO)
-		return super()._run(query=search_query)
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        kwargs["data_type"] = DataType.YOUTUBE_VIDEO
+        super().add(*args, **kwargs)
+
+    def _before_run(
+        self,
+        query: str,
+        **kwargs: Any,
+    ) -> Any:
+        if "youtube_video_url" in kwargs:
+            self.add(kwargs["youtube_video_url"])

tests/tools/rag/rag_tool_test.py

@@ -0,0 +1,43 @@
+import os
+from tempfile import NamedTemporaryFile
+from typing import cast
+from unittest import mock
+
+from pytest import fixture
+
+from crewai_tools.adapters.embedchain_adapter import EmbedchainAdapter
+from crewai_tools.tools.rag.rag_tool import RagTool
+
+
+@fixture(autouse=True)
+def mock_embedchain_db_uri():
+    with NamedTemporaryFile() as tmp:
+        uri = f"sqlite:///{tmp.name}"
+        with mock.patch.dict(os.environ, {"EMBEDCHAIN_DB_URI": uri}):
+            yield
+
+
+def test_custom_llm_and_embedder():
+    class MyTool(RagTool):
+        pass
+
+    tool = MyTool(
+        config=dict(
+            llm=dict(
+                provider="openai",
+                config=dict(model="gpt-3.5-custom"),
+            ),
+            embedder=dict(
+                provider="openai",
+                config=dict(model="text-embedding-3-custom"),
+            ),
+        )
+    )
+    assert tool.adapter is not None
+    assert isinstance(tool.adapter, EmbedchainAdapter)
+
+    adapter = cast(EmbedchainAdapter, tool.adapter)
+    assert adapter.embedchain_app.llm.config.model == "gpt-3.5-custom"
+    assert (
+        adapter.embedchain_app.embedding_model.config.model == "text-embedding-3-custom"
+    )