tools fix and formatting (#2441)

mem0/client/main.py

@@ -618,18 +618,16 @@ class MemoryClient:
         return response.json()
 
     @api_error_handler
-    def feedback(self, memory_id: str, feedback: Optional[str] = None, feedback_reason: Optional[str] = None) -> Dict[str, str]:
+    def feedback(
+        self, memory_id: str, feedback: Optional[str] = None, feedback_reason: Optional[str] = None
+    ) -> Dict[str, str]:
         VALID_FEEDBACK_VALUES = {"POSITIVE", "NEGATIVE", "VERY_NEGATIVE"}
 
         feedback = feedback.upper() if feedback else None
         if feedback is not None and feedback not in VALID_FEEDBACK_VALUES:
             raise ValueError(f'feedback must be one of {", ".join(VALID_FEEDBACK_VALUES)} or None')
 
-        data = {
+        data = {"memory_id": memory_id, "feedback": feedback, "feedback_reason": feedback_reason}
-            "memory_id": memory_id,
-            "feedback": feedback,
-            "feedback_reason": feedback_reason
-        }
 
         response = self.client.post("/v1/feedback/", json=data)
         response.raise_for_status()
@@ -1019,18 +1017,16 @@ class AsyncMemoryClient:
         return response.json()
 
     @api_error_handler
-    async def feedback(self, memory_id: str, feedback: Optional[str] = None, feedback_reason: Optional[str] = None) -> Dict[str, str]:
+    async def feedback(
+        self, memory_id: str, feedback: Optional[str] = None, feedback_reason: Optional[str] = None
+    ) -> Dict[str, str]:
         VALID_FEEDBACK_VALUES = {"POSITIVE", "NEGATIVE", "VERY_NEGATIVE"}
 
         feedback = feedback.upper() if feedback else None
         if feedback is not None and feedback not in VALID_FEEDBACK_VALUES:
             raise ValueError(f'feedback must be one of {", ".join(VALID_FEEDBACK_VALUES)} or None')
 
-        data = {
+        data = {"memory_id": memory_id, "feedback": feedback, "feedback_reason": feedback_reason}
-            "memory_id": memory_id,
-            "feedback": feedback,
-            "feedback_reason": feedback_reason
-        }
 
         response = await self.async_client.post("/v1/feedback/", json=data)
         response.raise_for_status()

mem0/configs/prompts.py

@@ -208,6 +208,7 @@ Please note to return the IDs in the output from the input IDs only and do not g
         }
 """
 
+
 def get_update_memory_messages(retrieved_old_memory_dict, response_content, custom_update_memory_prompt=None):
     if custom_update_memory_prompt is None:
         global DEFAULT_UPDATE_MEMORY_PROMPT

mem0/embeddings/configs.py

@@ -13,7 +13,16 @@ class EmbedderConfig(BaseModel):
     @field_validator("config")
     def validate_config(cls, v, values):
         provider = values.data.get("provider")
-        if provider in ["openai", "ollama", "huggingface", "azure_openai", "gemini", "vertexai", "together", "lmstudio"]:
+        if provider in [
+            "openai",
+            "ollama",
+            "huggingface",
+            "azure_openai",
+            "gemini",
+            "vertexai",
+            "together",
+            "lmstudio",
+        ]:
             return v
         else:
             raise ValueError(f"Unsupported embedding provider: {provider}")

mem0/embeddings/gemini.py

@@ -28,5 +28,7 @@ class GoogleGenAIEmbedding(EmbeddingBase):
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
-        response = genai.embed_content(model=self.config.model, content=text, output_dimensionality=self.config.embedding_dims)
+        response = genai.embed_content(
+            model=self.config.model, content=text, output_dimensionality=self.config.embedding_dims
+        )
         return response["embedding"]

mem0/embeddings/lmstudio.py

@@ -26,8 +26,4 @@ class LMStudioEmbedding(EmbeddingBase):
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
-        return (
+        return self.client.embeddings.create(input=[text], model=self.config.model).data[0].embedding
-            self.client.embeddings.create(input=[text], model=self.config.model)
-            .data[0]
-            .embedding
-        )

mem0/embeddings/openai.py

@@ -17,16 +17,16 @@ class OpenAIEmbedding(EmbeddingBase):
 
         api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")
         base_url = (
-                self.config.openai_base_url
+            self.config.openai_base_url
-                or os.getenv("OPENAI_API_BASE")
+            or os.getenv("OPENAI_API_BASE")
-                or os.getenv("OPENAI_BASE_URL")
+            or os.getenv("OPENAI_BASE_URL")
-                or "https://api.openai.com/v1"
+            or "https://api.openai.com/v1"
         )
         if os.environ.get("OPENAI_API_BASE"):
             warnings.warn(
                 "The environment variable 'OPENAI_API_BASE' is deprecated and will be removed in the 0.1.80. "
                 "Please use 'OPENAI_BASE_URL' instead.",
-                DeprecationWarning
+                DeprecationWarning,
             )
 
         self.client = OpenAI(api_key=api_key, base_url=base_url)
@@ -42,4 +42,8 @@ class OpenAIEmbedding(EmbeddingBase):
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
-        return self.client.embeddings.create(input=[text], model=self.config.model, dimensions = self.config.embedding_dims).data[0].embedding
+        return (
+            self.client.embeddings.create(input=[text], model=self.config.model, dimensions=self.config.embedding_dims)
+            .data[0]
+            .embedding
+        )

mem0/llms/azure_openai_structured.py

@@ -1,4 +1,3 @@
-import json
 import os
 from typing import Dict, List, Optional
 
@@ -36,6 +35,8 @@ class AzureOpenAIStructuredLLM(LLMBase):
         self,
         messages: List[Dict[str, str]],
         response_format: Optional[str] = None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
     ) -> str:
         """
         Generate a response based on the given messages using Azure OpenAI.

mem0/llms/base.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import Dict, List, Optional
 
 from mem0.configs.llms.base import BaseLlmConfig
 
@@ -17,12 +17,14 @@ class LLMBase(ABC):
             self.config = config
 
     @abstractmethod
-    def generate_response(self, messages):
+    def generate_response(self, messages, tools: Optional[List[Dict]] = None, tool_choice: str = "auto"):
         """
         Generate a response based on the given messages.
 
         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.

mem0/llms/lmstudio.py

@@ -10,7 +10,10 @@ class LMStudioLLM(LLMBase):
     def __init__(self, config: Optional[BaseLlmConfig] = None):
         super().__init__(config)
 
-        self.config.model = self.config.model or "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf"
+        self.config.model = (
+            self.config.model
+            or "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf"
+        )
         self.config.api_key = self.config.api_key or "lm-studio"
 
         self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
@@ -20,7 +23,7 @@ class LMStudioLLM(LLMBase):
         messages: List[Dict[str, str]],
         response_format: dict = {"type": "json_object"},
         tools: Optional[List[Dict]] = None,
-        tool_choice: str = "auto"
+        tool_choice: str = "auto",
     ):
         """
         Generate a response based on the given messages using LM Studio.
@@ -39,7 +42,7 @@ class LMStudioLLM(LLMBase):
             "messages": messages,
             "temperature": self.config.temperature,
             "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p
+            "top_p": self.config.top_p,
         }
         if response_format:
             params["response_format"] = response_format

mem0/llms/openai.py

@@ -1,3 +1,4 @@
+import json
 import os
 import warnings
 from typing import Dict, List, Optional
@@ -34,7 +35,7 @@ class OpenAILLM(LLMBase):
                 warnings.warn(
                     "The environment variable 'OPENAI_API_BASE' is deprecated and will be removed in the 0.1.80. "
                     "Please use 'OPENAI_BASE_URL' instead.",
-                    DeprecationWarning
+                    DeprecationWarning,
                 )
 
             self.client = OpenAI(api_key=api_key, base_url=base_url)

mem0/llms/openai_structured.py

@@ -1,4 +1,3 @@
-import json
 import os
 from typing import Dict, List, Optional
 
@@ -23,6 +22,8 @@ class OpenAIStructuredLLM(LLMBase):
         self,
         messages: List[Dict[str, str]],
         response_format: Optional[str] = None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
     ) -> str:
         """
         Generate a response based on the given messages using OpenAI.

mem0/llms/xai.py

@@ -18,13 +18,21 @@ class XAILLM(LLMBase):
         base_url = self.config.xai_base_url or os.getenv("XAI_API_BASE") or "https://api.x.ai/v1"
         self.client = OpenAI(api_key=api_key, base_url=base_url)
 
-    def generate_response(self, messages: List[Dict[str, str]], response_format=None):
+    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 XAI.
 
         Args:
             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.

mem0/memory/graph_memory.py

@@ -12,11 +12,14 @@ try:
 except ImportError:
     raise ImportError("rank_bm25 is not installed. Please install it using pip install rank-bm25")
 
-from mem0.graphs.tools import (DELETE_MEMORY_STRUCT_TOOL_GRAPH,
+from mem0.graphs.tools import (
-                               DELETE_MEMORY_TOOL_GRAPH,
+    DELETE_MEMORY_STRUCT_TOOL_GRAPH,
-                               EXTRACT_ENTITIES_STRUCT_TOOL,
+    DELETE_MEMORY_TOOL_GRAPH,
-                               EXTRACT_ENTITIES_TOOL, RELATIONS_STRUCT_TOOL,
+    EXTRACT_ENTITIES_STRUCT_TOOL,
-                               RELATIONS_TOOL)
+    EXTRACT_ENTITIES_TOOL,
+    RELATIONS_STRUCT_TOOL,
+    RELATIONS_TOOL,
+)
 from mem0.graphs.utils import EXTRACT_RELATIONS_PROMPT, get_delete_messages
 from mem0.utils.factory import EmbedderFactory, LlmFactory
 

mem0/vector_stores/pinecone.py

@@ -8,7 +8,9 @@ try:
     from pinecone import Pinecone, PodSpec, ServerlessSpec
     from pinecone.data.dataclasses.vector import Vector
 except ImportError:
-    raise ImportError("Pinecone requires extra dependencies. Install with `pip install pinecone pinecone-text`") from None
+    raise ImportError(
+        "Pinecone requires extra dependencies. Install with `pip install pinecone pinecone-text`"
+    ) from None
 
 from mem0.vector_stores.base import VectorStoreBase
 
@@ -34,7 +36,7 @@ class PineconeDB(VectorStoreBase):
         hybrid_search: bool,
         metric: str,
         batch_size: int,
-        extra_params: Optional[Dict[str, Any]]
+        extra_params: Optional[Dict[str, Any]],
     ):
         """
         Initialize the Pinecone vector store.
@@ -199,7 +201,9 @@ class PineconeDB(VectorStoreBase):
 
         return pinecone_filter
 
-    def search(self, query: str, vectors: List[float], limit: int = 5, filters: Optional[Dict] = None) -> List[OutputData]:
+    def search(
+        self, query: str, vectors: List[float], limit: int = 5, filters: Optional[Dict] = None
+    ) -> List[OutputData]:
         """
         Search for similar vectors.
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mem0ai"
-version = "0.1.76"
+version = "0.1.77"
 description = "Long-term memory for AI Agents"
 authors = ["Mem0 <founders@mem0.ai>"]
 exclude = [

tests/test_main.py

@@ -140,7 +140,7 @@ def test_search(memory_instance, version, enable_graph):
         assert result["results"][0]["score"] == 0.9
 
     memory_instance.vector_store.search.assert_called_once_with(
-        query=[0.1, 0.2, 0.3], limit=100, filters={"user_id": "test_user"}
+        query="test query", vectors=[0.1, 0.2, 0.3], limit=100, filters={"user_id": "test_user"}
     )
     memory_instance.embedding_model.embed.assert_called_once_with("test query", "search")