Reverting the tools commit (#2404)

Author: parshvadaftari

mem0/llms/anthropic.py

@@ -4,26 +4,14 @@
 try:
     import anthropic
 except ImportError:
-    raise ImportError(
-        "The 'anthropic' library is required. Please install it using 'pip install anthropic'."
-    )
+    raise ImportError("The 'anthropic' library is required. Please install it using 'pip install anthropic'.")
 
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
 
 
 class AnthropicLLM(LLMBase):
-    """
-    A class for interacting with Anthropic's Claude models using the specified configuration.
-    """
-
     def __init__(self, config: Optional[BaseLlmConfig] = None):
-        """
-        Initializes the AnthropicLLM instance with the given configuration.
-
-        Args:
-            config (Optional[BaseLlmConfig]): Configuration settings for the language model.
-        """
         super().__init__(config)
 
         if not self.config.model:
@@ -35,17 +23,23 @@ def __init__(self, config: Optional[BaseLlmConfig] = None):
     def generate_response(
         self,
         messages: List[Dict[str, str]],
-    ) -> str:
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+    ):
         """
-        Generates a response using Anthropic's Claude model based on the provided messages.
+        Generate a response based on the given messages using Anthropic.
 
         Args:
-            messages (List[Dict[str, str]]): A list of dictionaries, each containing a 'role' and 'content' key.
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response. Defaults to "text".
+            tools (list, optional): List of tools that the model can call. Defaults to None.
+            tool_choice (str, optional): Tool choice method. Defaults to "auto".
 
         Returns:
-            str: The generated response from the model.
+            str: The generated response.
         """
-        # Extract system message separately
+        # Separate system message from other messages
         system_message = ""
         filtered_messages = []
         for message in messages:
@@ -62,6 +56,9 @@ def generate_response(
             "max_tokens": self.config.max_tokens,
             "top_p": self.config.top_p,
         }
+        if tools:  # TODO: Remove tools if no issues found with new memory addition logic
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
 
         response = self.client.messages.create(**params)
         return response.content[0].text

mem0/llms/aws_bedrock.py

@@ -4,26 +4,14 @@
 try:
     import boto3
 except ImportError:
-    raise ImportError(
-        "The 'boto3' library is required. Please install it using 'pip install boto3'."
-    )
+    raise ImportError("The 'boto3' library is required. Please install it using 'pip install boto3'.")
 
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
 
 
 class AWSBedrockLLM(LLMBase):
-    """
-    A wrapper for AWS Bedrock's language models, integrating them with the LLMBase class.
-    """
-
     def __init__(self, config: Optional[BaseLlmConfig] = None):
-        """
-        Initializes the AWS Bedrock LLM with the provided configuration.
-
-        Args:
-            config (Optional[BaseLlmConfig]): Configuration object for the model.
-        """
         super().__init__(config)
 
         if not self.config.model:
@@ -37,29 +25,49 @@ def __init__(self, config: Optional[BaseLlmConfig] = None):
 
     def _format_messages(self, messages: List[Dict[str, str]]) -> str:
         """
-        Formats a list of messages into a structured prompt for the model.
+        Formats a list of messages into the required prompt structure for the model.
 
         Args:
-            messages (List[Dict[str, str]]): A list of dictionaries containing 'role' and 'content'.
+            messages (List[Dict[str, str]]): A list of dictionaries where each dictionary represents a message.
+                                            Each dictionary contains 'role' and 'content' keys.
 
         Returns:
             str: A formatted string combining all messages, structured with roles capitalized and separated by newlines.
         """
-        formatted_messages = [
-            f"\n\n{msg['role'].capitalize()}: {msg['content']}" for msg in messages
-        ]
+        formatted_messages = []
+        for message in messages:
+            role = message["role"].capitalize()
+            content = message["content"]
+            formatted_messages.append(f"\n\n{role}: {content}")
+
         return "".join(formatted_messages) + "\n\nAssistant:"
 
-    def _parse_response(self, response) -> str:
+    def _parse_response(self, response, tools) -> str:
         """
-        Extracts the generated response from the API response.
+        Process the response based on whether tools are used or not.
 
         Args:
-            response: The raw response from the AWS Bedrock API.
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
 
         Returns:
-            str: The generated response text.
+            str or dict: The processed response.
         """
+        if tools:
+            processed_response = {"tool_calls": []}
+
+            if response["output"]["message"]["content"]:
+                for item in response["output"]["message"]["content"]:
+                    if "toolUse" in item:
+                        processed_response["tool_calls"].append(
+                            {
+                                "name": item["toolUse"]["name"],
+                                "arguments": item["toolUse"]["input"],
+                            }
+                        )
+
+            return processed_response
+
         response_body = json.loads(response["body"].read().decode())
         return response_body.get("completion", "")
 
@@ -68,21 +76,22 @@ def _prepare_input(
         provider: str,
         model: str,
         prompt: str,
-        model_kwargs: Optional[Dict[str, Any]] = None,
+        model_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Dict[str, Any]:
         """
-        Prepares the input dictionary for the specified provider's model.
+        Prepares the input dictionary for the specified provider's model by mapping and renaming
+        keys in the input based on the provider's requirements.
 
         Args:
-            provider (str): The model provider (e.g., "meta", "ai21", "mistral", "cohere", "amazon").
-            model (str): The model identifier.
-            prompt (str): The input prompt.
-            model_kwargs (Optional[Dict[str, Any]]): Additional model parameters.
+            provider (str): The name of the service provider (e.g., "meta", "ai21", "mistral", "cohere", "amazon").
+            model (str): The name or identifier of the model being used.
+            prompt (str): The text prompt to be processed by the model.
+            model_kwargs (Dict[str, Any]): Additional keyword arguments specific to the model's requirements.
 
         Returns:
-            Dict[str, Any]: The prepared input dictionary.
+            Dict[str, Any]: The prepared input dictionary with the correct keys and values for the specified provider.
         """
-        model_kwargs = model_kwargs or {}
+
         input_body = {"prompt": prompt, **model_kwargs}
 
         provider_mappings = {
@@ -110,35 +119,102 @@ def _prepare_input(
                 },
             }
             input_body["textGenerationConfig"] = {
-                k: v
-                for k, v in input_body["textGenerationConfig"].items()
-                if v is not None
+                k: v for k, v in input_body["textGenerationConfig"].items() if v is not None
             }
 
         return input_body
 
-    def generate_response(self, messages: List[Dict[str, str]]) -> str:
+    def _convert_tool_format(self, original_tools):
         """
-        Generates a response using AWS Bedrock based on the provided messages.
+        Converts a list of tools from their original format to a new standardized format.
 
         Args:
-            messages (List[Dict[str, str]]): List of message dictionaries containing 'role' and 'content'.
+            original_tools (list): A list of dictionaries representing the original tools, each containing a 'type' key and corresponding details.
 
         Returns:
-            str: The generated response text.
+            list: A list of dictionaries representing the tools in the new standardized format.
         """
-        prompt = self._format_messages(messages)
-        provider = self.config.model.split(".")[0]
-        input_body = self._prepare_input(
-            provider, self.config.model, prompt, self.model_kwargs
-        )
-        body = json.dumps(input_body)
-
-        response = self.client.invoke_model(
-            body=body,
-            modelId=self.config.model,
-            accept="application/json",
-            contentType="application/json",
-        )
-
-        return self._parse_response(response)
+        new_tools = []
+
+        for tool in original_tools:
+            if tool["type"] == "function":
+                function = tool["function"]
+                new_tool = {
+                    "toolSpec": {
+                        "name": function["name"],
+                        "description": function["description"],
+                        "inputSchema": {
+                            "json": {
+                                "type": "object",
+                                "properties": {},
+                                "required": function["parameters"].get("required", []),
+                            }
+                        },
+                    }
+                }
+
+                for prop, details in function["parameters"].get("properties", {}).items():
+                    new_tool["toolSpec"]["inputSchema"]["json"]["properties"][prop] = {
+                        "type": details.get("type", "string"),
+                        "description": details.get("description", ""),
+                    }
+
+                new_tools.append(new_tool)
+
+        return new_tools
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+    ):
+        """
+        Generate a response based on the given messages using AWS Bedrock.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            tools (list, optional): List of tools that the model can call. Defaults to None.
+            tool_choice (str, optional): Tool choice method. Defaults to "auto".
+
+        Returns:
+            str: The generated response.
+        """
+
+        if tools:
+            # Use converse method when tools are provided
+            messages = [
+                {
+                    "role": "user",
+                    "content": [{"text": message["content"]} for message in messages],
+                }
+            ]
+            inference_config = {
+                "temperature": self.model_kwargs["temperature"],
+                "maxTokens": self.model_kwargs["max_tokens_to_sample"],
+                "topP": self.model_kwargs["top_p"],
+            }
+            tools_config = {"tools": self._convert_tool_format(tools)}
+
+            response = self.client.converse(
+                modelId=self.config.model,
+                messages=messages,
+                inferenceConfig=inference_config,
+                toolConfig=tools_config,
+            )
+        else:
+            # Use invoke_model method when no tools are provided
+            prompt = self._format_messages(messages)
+            provider = self.model.split(".")[0]
+            input_body = self._prepare_input(provider, self.config.model, prompt, **self.model_kwargs)
+            body = json.dumps(input_body)
+
+            response = self.client.invoke_model(
+                body=body,
+                modelId=self.model,
+                accept="application/json",
+                contentType="application/json",
+            )
+
+        return self._parse_response(response, tools)