Merge pull request #1129 from MichaelDecent/v0.6.0_release

feat: add new components in swarmauri

pkgs/base/swarmauri_base/ocrs/OCRBase.py

@@ -0,0 +1,71 @@
+from abc import abstractmethod
+from typing import Optional, List, Literal
+from pydantic import ConfigDict, model_validator, Field
+
+from swarmauri_core.llms.IPredict import IPredict
+from swarmauri_core.ComponentBase import ComponentBase, ResourceTypes
+
+
+@ComponentBase.register_model()
+class OCRBase(IPredict, ComponentBase):
+    allowed_models: List[str] = []
+    resource: Optional[str] = Field(default=ResourceTypes.OCR.value, frozen=True)
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
+    type: Literal["OCRBase"] = "OCRBase"
+
+    @model_validator(mode="after")
+    @classmethod
+    def _validate_name_in_allowed_models(cls, values):
+        name = values.name
+        allowed_models = values.allowed_models
+        if name and name not in allowed_models:
+            raise ValueError(
+                f"Model name {name} is not allowed. Choose from {allowed_models}"
+            )
+        return values
+
+    def add_allowed_model(self, model: str) -> None:
+        """
+        Add a new model to the list of allowed models.
+
+        Raises:
+            ValueError: If the model is already in the allowed models list.
+        """
+        if model in self.allowed_models:
+            raise ValueError(f"Model '{model}' is already allowed.")
+        self.allowed_models.append(model)
+
+    def remove_allowed_model(self, model: str) -> None:
+        """
+        Remove a model from the list of allowed models.
+
+        Raises:
+            ValueError: If the model is not in the allowed models list.
+        """
+        if model not in self.allowed_models:
+            raise ValueError(f"Model '{model}' is not in the allowed models list.")
+        self.allowed_models.remove(model)
+
+    @abstractmethod
+    def predict(self, *args, **kwargs):
+        raise NotImplementedError("predict() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def apredict(self, *args, **kwargs):
+        raise NotImplementedError("apredict() not implemented in subclass yet.")
+
+    @abstractmethod
+    def stream(self, *args, **kwargs):
+        raise NotImplementedError("stream() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def astream(self, *args, **kwargs):
+        raise NotImplementedError("astream() not implemented in subclass yet.")
+
+    @abstractmethod
+    def batch(self, *args, **kwargs):
+        raise NotImplementedError("batch() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def abatch(self, *args, **kwargs):
+        raise NotImplementedError("abatch() not implemented in subclass yet.")

pkgs/base/swarmauri_base/stt/STTBase.py

@@ -0,0 +1,71 @@
+from abc import abstractmethod
+from typing import Optional, List, Literal
+from pydantic import ConfigDict, model_validator, Field
+
+from swarmauri_core.llms.IPredict import IPredict
+from swarmauri_core.ComponentBase import ComponentBase, ResourceTypes
+
+
+@ComponentBase.register_model()
+class STTBase(IPredict, ComponentBase):
+    allowed_models: List[str] = []
+    resource: Optional[str] = Field(default=ResourceTypes.STT.value, frozen=True)
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
+    type: Literal["STTBase"] = "STTBase"
+
+    @model_validator(mode="after")
+    @classmethod
+    def _validate_name_in_allowed_models(cls, values):
+        name = values.name
+        allowed_models = values.allowed_models
+        if name and name not in allowed_models:
+            raise ValueError(
+                f"Model name {name} is not allowed. Choose from {allowed_models}"
+            )
+        return values
+
+    def add_allowed_model(self, model: str) -> None:
+        """
+        Add a new model to the list of allowed models.
+
+        Raises:
+            ValueError: If the model is already in the allowed models list.
+        """
+        if model in self.allowed_models:
+            raise ValueError(f"Model '{model}' is already allowed.")
+        self.allowed_models.append(model)
+
+    def remove_allowed_model(self, model: str) -> None:
+        """
+        Remove a model from the list of allowed models.
+
+        Raises:
+            ValueError: If the model is not in the allowed models list.
+        """
+        if model not in self.allowed_models:
+            raise ValueError(f"Model '{model}' is not in the allowed models list.")
+        self.allowed_models.remove(model)
+
+    @abstractmethod
+    def predict(self, *args, **kwargs):
+        raise NotImplementedError("predict() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def apredict(self, *args, **kwargs):
+        raise NotImplementedError("apredict() not implemented in subclass yet.")
+
+    @abstractmethod
+    def stream(self, *args, **kwargs):
+        raise NotImplementedError("stream() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def astream(self, *args, **kwargs):
+        raise NotImplementedError("astream() not implemented in subclass yet.")
+
+    @abstractmethod
+    def batch(self, *args, **kwargs):
+        raise NotImplementedError("batch() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def abatch(self, *args, **kwargs):
+        raise NotImplementedError("abatch() not implemented in subclass yet.")

pkgs/base/swarmauri_base/tts/TTSBase.py

@@ -0,0 +1,71 @@
+from abc import abstractmethod
+from typing import Optional, List, Literal
+from pydantic import ConfigDict, model_validator, Field
+
+from swarmauri_core.llms.IPredict import IPredict
+from swarmauri_core.ComponentBase import ComponentBase, ResourceTypes
+
+
+@ComponentBase.register_model()
+class TTSBase(IPredict, ComponentBase):
+    allowed_models: List[str] = []
+    resource: Optional[str] = Field(default=ResourceTypes.TTS.value, frozen=True)
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
+    type: Literal["TTSBase"] = "TTSBase"
+
+    @model_validator(mode="after")
+    @classmethod
+    def _validate_name_in_allowed_models(cls, values):
+        name = values.name
+        allowed_models = values.allowed_models
+        if name and name not in allowed_models:
+            raise ValueError(
+                f"Model name {name} is not allowed. Choose from {allowed_models}"
+            )
+        return values
+
+    def add_allowed_model(self, model: str) -> None:
+        """
+        Add a new model to the list of allowed models.
+
+        Raises:
+            ValueError: If the model is already in the allowed models list.
+        """
+        if model in self.allowed_models:
+            raise ValueError(f"Model '{model}' is already allowed.")
+        self.allowed_models.append(model)
+
+    def remove_allowed_model(self, model: str) -> None:
+        """
+        Remove a model from the list of allowed models.
+
+        Raises:
+            ValueError: If the model is not in the allowed models list.
+        """
+        if model not in self.allowed_models:
+            raise ValueError(f"Model '{model}' is not in the allowed models list.")
+        self.allowed_models.remove(model)
+
+    @abstractmethod
+    def predict(self, *args, **kwargs):
+        raise NotImplementedError("predict() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def apredict(self, *args, **kwargs):
+        raise NotImplementedError("apredict() not implemented in subclass yet.")
+
+    @abstractmethod
+    def stream(self, *args, **kwargs):
+        raise NotImplementedError("stream() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def astream(self, *args, **kwargs):
+        raise NotImplementedError("astream() not implemented in subclass yet.")
+
+    @abstractmethod
+    def batch(self, *args, **kwargs):
+        raise NotImplementedError("batch() not implemented in subclass yet.")
+
+    @abstractmethod
+    async def abatch(self, *args, **kwargs):
+        raise NotImplementedError("abatch() not implemented in subclass yet.")

pkgs/core/swarmauri_core/ocrs/IPredict.py

@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod
+
+class IPredict(ABC):
+    """
+    Interface focusing on the basic properties and settings essential for defining models.
+    """
+
+    @abstractmethod
+    def predict(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def apredict(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    def stream(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def astream(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    def batch(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def abatch(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass

pkgs/core/swarmauri_core/sst/IPredict.py

@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod
+
+class IPredict(ABC):
+    """
+    Interface focusing on the basic properties and settings essential for defining models.
+    """
+
+    @abstractmethod
+    def predict(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def apredict(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    def stream(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def astream(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    def batch(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def abatch(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass

pkgs/core/swarmauri_core/tts/IPredict.py

@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod
+
+class IPredict(ABC):
+    """
+    Interface focusing on the basic properties and settings essential for defining models.
+    """
+
+    @abstractmethod
+    def predict(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def apredict(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    def stream(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def astream(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    def batch(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass
+
+    @abstractmethod
+    async def abatch(self, *args, **kwargs) -> any:
+        """
+        Generate predictions based on the input data provided to the model.
+        """
+        pass

pkgs/standards/swarmauri_standard/swarmauri_standard/agents/ToolAgent.py

@@ -2,21 +2,21 @@
 from pydantic import ConfigDict
 from typing import Any, Optional, Union, Dict, Literal
 
+from swarmauri_base.tool_llms.ToolLLMBase import ToolLLMBase
 from swarmauri_standard.messages.HumanMessage import HumanMessage
 from swarmauri_base.llms.LLMBase import LLMBase
 from swarmauri_base.agents.AgentBase import AgentBase
 from swarmauri_base.agents.AgentConversationMixin import AgentConversationMixin
 from swarmauri_base.agents.AgentToolMixin import AgentToolMixin
 from swarmauri_base.toolkits.ToolkitBase import ToolkitBase
 from swarmauri_base.conversations.ConversationBase import ConversationBase
-
 from swarmauri_core.messages import IMessage
 from swarmauri_core.ComponentBase import SubclassUnion, ComponentBase
 
 
 @ComponentBase.register_type(AgentBase, "ToolAgent")
 class ToolAgent(AgentToolMixin, AgentConversationMixin, AgentBase):
-    llm: SubclassUnion[LLMBase]
+    llm: SubclassUnion[ToolLLMBase]
     toolkit: SubclassUnion[ToolkitBase]
     conversation: SubclassUnion[ConversationBase]  # 🚧  Placeholder
     model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)