diff --git a/docs/open-source/quickstart.mdx b/docs/open-source/quickstart.mdx index 7d6248ed..afd90a28 100644 --- a/docs/open-source/quickstart.mdx +++ b/docs/open-source/quickstart.mdx @@ -250,7 +250,7 @@ Mem0 supports several language models (LLMs) through integration with various [p ## Use Mem0 Platform ```python -from mem0 import Mem0 +from mem0.proxy.main import Mem0 client = Mem0(api_key="m0-xxx") diff --git a/mem0/__init__.py b/mem0/__init__.py index 998a88e6..0433062e 100644 --- a/mem0/__init__.py +++ b/mem0/__init__.py @@ -4,4 +4,3 @@ __version__ = importlib.metadata.version("mem0ai") from mem0.memory.main import Memory # noqa from mem0.client.main import MemoryClient # noqa -from mem0.proxy.main import Mem0 #noqa diff --git a/mem0/client/main.py b/mem0/client/main.py index 5e584b7b..58f1e1a0 100644 --- a/mem0/client/main.py +++ b/mem0/client/main.py @@ -255,7 +255,7 @@ class MemoryClient: capture_client_event("client.delete_users", self) return {"message": "All users, agents, and sessions deleted."} - + def reset(self): """Reset the client. (Not implemented) diff --git a/mem0/configs/base.py b/mem0/configs/base.py index eeb98c2c..3af837b8 100644 --- a/mem0/configs/base.py +++ b/mem0/configs/base.py @@ -7,17 +7,26 @@ from mem0.vector_stores.configs import VectorStoreConfig from mem0.llms.configs import LlmConfig from mem0.embeddings.configs import EmbedderConfig + class MemoryItem(BaseModel): id: str = Field(..., description="The unique identifier for the text data") - memory: str = Field(..., description="The memory deduced from the text data") # TODO After prompt changes from platform, update this + memory: str = Field( + ..., description="The memory deduced from the text data" + ) # TODO After prompt changes from platform, update this hash: Optional[str] = Field(None, description="The hash of the memory") # The metadata value can be anything and not just string. Fix it - metadata: Optional[Dict[str, Any]] = Field(None, description="Additional metadata for the text data") + metadata: Optional[Dict[str, Any]] = Field( + None, description="Additional metadata for the text data" + ) score: Optional[float] = Field( None, description="The score associated with the text data" ) - created_at: Optional[str] = Field(None, description="The timestamp when the memory was created") - updated_at: Optional[str] = Field(None, description="The timestamp when the memory was updated") + created_at: Optional[str] = Field( + None, description="The timestamp when the memory was created" + ) + updated_at: Optional[str] = Field( + None, description="The timestamp when the memory was updated" + ) class MemoryConfig(BaseModel): @@ -36,4 +45,4 @@ class MemoryConfig(BaseModel): history_db_path: str = Field( description="Path to the history database", default=os.path.join(mem0_dir, "history.db"), - ) \ No newline at end of file + ) diff --git a/mem0/configs/embeddings/base.py b/mem0/configs/embeddings/base.py index 79789289..05ad9952 100644 --- a/mem0/configs/embeddings/base.py +++ b/mem0/configs/embeddings/base.py @@ -1,6 +1,7 @@ from abc import ABC from typing import Optional + class BaseEmbedderConfig(ABC): """ Config for Embeddings. @@ -11,12 +12,10 @@ class BaseEmbedderConfig(ABC): model: Optional[str] = None, api_key: Optional[str] = None, embedding_dims: Optional[int] = None, - # Ollama specific ollama_base_url: Optional[str] = None, - # Huggingface specific - model_kwargs: Optional[dict] = None + model_kwargs: Optional[dict] = None, ): """ Initializes a configuration class instance for the Embeddings. @@ -33,7 +32,7 @@ class BaseEmbedderConfig(ABC): :type model_kwargs: Optional[Dict[str, Any]], defaults a dict inside init """ - + self.model = model self.api_key = api_key self.embedding_dims = embedding_dims diff --git a/mem0/configs/llms/base.py b/mem0/configs/llms/base.py index 71855b01..c7b83411 100644 --- a/mem0/configs/llms/base.py +++ b/mem0/configs/llms/base.py @@ -1,6 +1,7 @@ from abc import ABC from typing import Optional + class BaseLlmConfig(ABC): """ Config for LLMs. @@ -14,16 +15,14 @@ class BaseLlmConfig(ABC): max_tokens: int = 3000, top_p: float = 0, top_k: int = 1, - # Openrouter specific models: Optional[list[str]] = None, route: Optional[str] = "fallback", openrouter_base_url: Optional[str] = "https://openrouter.ai/api/v1", site_url: Optional[str] = None, app_name: Optional[str] = None, - # Ollama specific - ollama_base_url: Optional[str] = None + ollama_base_url: Optional[str] = None, ): """ Initializes a configuration class instance for the LLM. @@ -55,7 +54,7 @@ class BaseLlmConfig(ABC): :param ollama_base_url: The base URL of the LLM, defaults to None :type ollama_base_url: Optional[str], optional """ - + self.model = model self.temperature = temperature self.api_key = api_key diff --git a/mem0/configs/vector_stores/chroma.py b/mem0/configs/vector_stores/chroma.py index 86a7ddcf..8054f3b5 100644 --- a/mem0/configs/vector_stores/chroma.py +++ b/mem0/configs/vector_stores/chroma.py @@ -2,15 +2,20 @@ from typing import Optional, ClassVar, Dict, Any from pydantic import BaseModel, Field, model_validator + class ChromaDbConfig(BaseModel): try: from chromadb.api.client import Client except ImportError: - raise ImportError("Chromadb requires extra dependencies. Install with `pip install chromadb`") from None + raise ImportError( + "Chromadb requires extra dependencies. Install with `pip install chromadb`" + ) from None Client: ClassVar[type] = Client collection_name: str = Field("mem0", description="Default name for the collection") - client: Optional[Client] = Field(None, description="Existing ChromaDB client instance") + client: Optional[Client] = Field( + None, description="Existing ChromaDB client instance" + ) path: Optional[str] = Field(None, description="Path to the database directory") host: Optional[str] = Field(None, description="Database connection remote host") port: Optional[int] = Field(None, description="Database connection remote port") @@ -29,9 +34,11 @@ class ChromaDbConfig(BaseModel): input_fields = set(values.keys()) extra_fields = input_fields - allowed_fields if extra_fields: - raise ValueError(f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}") + raise ValueError( + f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}" + ) return values - + model_config = { "arbitrary_types_allowed": True, - } \ No newline at end of file + } diff --git a/mem0/configs/vector_stores/pgvector.py b/mem0/configs/vector_stores/pgvector.py index 41134dfe..839e707f 100644 --- a/mem0/configs/vector_stores/pgvector.py +++ b/mem0/configs/vector_stores/pgvector.py @@ -2,11 +2,14 @@ from typing import Optional, Dict, Any from pydantic import BaseModel, Field, model_validator + class PGVectorConfig(BaseModel): dbname: str = Field("postgres", description="Default name for the database") collection_name: str = Field("mem0", description="Default name for the collection") - embedding_model_dims: Optional[int] = Field(1536, description="Dimensions of the embedding model") + embedding_model_dims: Optional[int] = Field( + 1536, description="Dimensions of the embedding model" + ) user: Optional[str] = Field(None, description="Database user") password: Optional[str] = Field(None, description="Database password") host: Optional[str] = Field(None, description="Database host. Default is localhost") @@ -21,7 +24,7 @@ class PGVectorConfig(BaseModel): if not host and not port: raise ValueError("Both 'host' and 'port' must be provided.") return values - + @model_validator(mode="before") @classmethod def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]: @@ -29,6 +32,7 @@ class PGVectorConfig(BaseModel): input_fields = set(values.keys()) extra_fields = input_fields - allowed_fields if extra_fields: - raise ValueError(f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}") + raise ValueError( + f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}" + ) return values - \ No newline at end of file diff --git a/mem0/configs/vector_stores/qdrant.py b/mem0/configs/vector_stores/qdrant.py index 8d716dc8..bb76cac3 100644 --- a/mem0/configs/vector_stores/qdrant.py +++ b/mem0/configs/vector_stores/qdrant.py @@ -1,16 +1,24 @@ from pydantic import BaseModel, Field, model_validator from typing import Optional, ClassVar, Dict, Any + class QdrantConfig(BaseModel): - from qdrant_client import QdrantClient + from qdrant_client import QdrantClient + QdrantClient: ClassVar[type] = QdrantClient collection_name: str = Field("mem0", description="Name of the collection") - embedding_model_dims: Optional[int] = Field(1536, description="Dimensions of the embedding model") - client: Optional[QdrantClient] = Field(None, description="Existing Qdrant client instance") + embedding_model_dims: Optional[int] = Field( + 1536, description="Dimensions of the embedding model" + ) + client: Optional[QdrantClient] = Field( + None, description="Existing Qdrant client instance" + ) host: Optional[str] = Field(None, description="Host address for Qdrant server") port: Optional[int] = Field(None, description="Port for Qdrant server") - path: Optional[str] = Field("/tmp/qdrant", description="Path for local Qdrant database") + path: Optional[str] = Field( + "/tmp/qdrant", description="Path for local Qdrant database" + ) url: Optional[str] = Field(None, description="Full URL for Qdrant server") api_key: Optional[str] = Field(None, description="API key for Qdrant server") on_disk: Optional[bool] = Field(False, description="Enables persistent storage") @@ -38,9 +46,11 @@ class QdrantConfig(BaseModel): input_fields = set(values.keys()) extra_fields = input_fields - allowed_fields if extra_fields: - raise ValueError(f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}") + raise ValueError( + f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}" + ) return values model_config = { "arbitrary_types_allowed": True, - } \ No newline at end of file + } diff --git a/mem0/embeddings/azure_openai.py b/mem0/embeddings/azure_openai.py index 834f5c1f..10c804ec 100644 --- a/mem0/embeddings/azure_openai.py +++ b/mem0/embeddings/azure_openai.py @@ -6,17 +6,18 @@ from openai import AzureOpenAI from mem0.configs.embeddings.base import BaseEmbedderConfig from mem0.embeddings.base import EmbeddingBase + class AzureOpenAIEmbedding(EmbeddingBase): def __init__(self, config: Optional[BaseEmbedderConfig] = None): super().__init__(config) - + if self.config.model is None: self.config.model = "text-embedding-3-small" if self.config.embedding_dims is None: self.config.embedding_dims = 1536 - + api_key = os.getenv("AZURE_OPENAI_API_KEY") or self.config.api_key - self.client = AzureOpenAI(api_key=api_key) + self.client = AzureOpenAI(api_key=api_key) def embed(self, text): """ @@ -30,10 +31,7 @@ class AzureOpenAIEmbedding(EmbeddingBase): """ text = text.replace("\n", " ") return ( - self.client.embeddings.create( - input=[text], - model=self.config.model - ) + self.client.embeddings.create(input=[text], model=self.config.model) .data[0] .embedding ) diff --git a/mem0/embeddings/base.py b/mem0/embeddings/base.py index 2e9ba2a2..ef6800f3 100644 --- a/mem0/embeddings/base.py +++ b/mem0/embeddings/base.py @@ -3,18 +3,20 @@ from abc import ABC, abstractmethod from mem0.configs.embeddings.base import BaseEmbedderConfig + class EmbeddingBase(ABC): """Initialized a base embedding class :param config: Embedding configuration option class, defaults to None :type config: Optional[BaseEmbedderConfig], optional """ + def __init__(self, config: Optional[BaseEmbedderConfig] = None): if config is None: self.config = BaseEmbedderConfig() else: self.config = config - + @abstractmethod def embed(self, text): """ diff --git a/mem0/embeddings/configs.py b/mem0/embeddings/configs.py index 818ebd59..73aa9b30 100644 --- a/mem0/embeddings/configs.py +++ b/mem0/embeddings/configs.py @@ -9,8 +9,7 @@ class EmbedderConfig(BaseModel): default="openai", ) config: Optional[dict] = Field( - description="Configuration for the specific embedding model", - default={} + description="Configuration for the specific embedding model", default={} ) @field_validator("config") @@ -20,4 +19,3 @@ class EmbedderConfig(BaseModel): return v else: raise ValueError(f"Unsupported embedding provider: {provider}") - \ No newline at end of file diff --git a/mem0/embeddings/huggingface.py b/mem0/embeddings/huggingface.py index a2e3bfa1..6bfd5582 100644 --- a/mem0/embeddings/huggingface.py +++ b/mem0/embeddings/huggingface.py @@ -9,19 +9,15 @@ from mem0.embeddings.base import EmbeddingBase class HuggingFaceEmbedding(EmbeddingBase): def __init__(self, config: Optional[BaseEmbedderConfig] = None): super().__init__(config) - + if self.config.model is None: self.config.model = "multi-qa-MiniLM-L6-cos-v1" - - self.model = SentenceTransformer( - self.config.model, - **self.config.model_kwargs - ) + + self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs) if self.config.embedding_dims is None: self.config.embedding_dims = self.model.get_sentence_embedding_dimension() - def embed(self, text): """ Get the embedding for the given text using Hugging Face. diff --git a/mem0/embeddings/ollama.py b/mem0/embeddings/ollama.py index 30d74be8..1298760b 100644 --- a/mem0/embeddings/ollama.py +++ b/mem0/embeddings/ollama.py @@ -6,18 +6,20 @@ from mem0.embeddings.base import EmbeddingBase try: from ollama import Client except ImportError: - raise ImportError("Ollama requires extra dependencies. Install with `pip install ollama`") from None + raise ImportError( + "Ollama requires extra dependencies. Install with `pip install ollama`" + ) from None class OllamaEmbedding(EmbeddingBase): def __init__(self, config: Optional[BaseEmbedderConfig] = None): super().__init__(config) - + if not self.config.model: - self.config.model="nomic-embed-text" + self.config.model = "nomic-embed-text" if not self.config.embedding_dims: - self.config.embedding_dims=512 - + self.config.embedding_dims = 512 + self.client = Client(host=self.config.ollama_base_url) self._ensure_model_exists() diff --git a/mem0/embeddings/openai.py b/mem0/embeddings/openai.py index f878b8c2..a229fc8c 100644 --- a/mem0/embeddings/openai.py +++ b/mem0/embeddings/openai.py @@ -6,10 +6,11 @@ from openai import OpenAI from mem0.configs.embeddings.base import BaseEmbedderConfig from mem0.embeddings.base import EmbeddingBase + class OpenAIEmbedding(EmbeddingBase): def __init__(self, config: Optional[BaseEmbedderConfig] = None): super().__init__(config) - + self.config.model = self.config.model or "text-embedding-3-small" self.config.embedding_dims = self.config.embedding_dims or 1536 @@ -28,10 +29,7 @@ class OpenAIEmbedding(EmbeddingBase): """ text = text.replace("\n", " ") return ( - self.client.embeddings.create( - input=[text], - model=self.config.model - ) + self.client.embeddings.create(input=[text], model=self.config.model) .data[0] .embedding ) diff --git a/mem0/llms/aws_bedrock.py b/mem0/llms/aws_bedrock.py index 96bc88eb..801be578 100644 --- a/mem0/llms/aws_bedrock.py +++ b/mem0/llms/aws_bedrock.py @@ -5,22 +5,30 @@ from typing import Dict, List, Optional, Any try: import boto3 except ImportError: - raise ImportError("AWS Bedrock requires extra dependencies. Install with `pip install boto3`") from None + raise ImportError( + "AWS Bedrock requires extra dependencies. Install with `pip install boto3`" + ) from None from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig -class AWSBedrockLLM(LLMBase): + +class AWSBedrockLLM(LLMBase): def __init__(self, config: Optional[BaseLlmConfig] = None): super().__init__(config) if not self.config.model: - self.config.model="anthropic.claude-3-5-sonnet-20240620-v1:0" - self.client = boto3.client("bedrock-runtime", region_name=os.environ.get("AWS_REGION"), aws_access_key_id=os.environ.get("AWS_ACCESS_KEY"), aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")) + self.config.model = "anthropic.claude-3-5-sonnet-20240620-v1:0" + self.client = boto3.client( + "bedrock-runtime", + region_name=os.environ.get("AWS_REGION"), + aws_access_key_id=os.environ.get("AWS_ACCESS_KEY"), + aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY"), + ) self.model_kwargs = { - "temperature": self.config.temperature, - "max_tokens_to_sample": self.config.max_tokens, - "top_p": self.config.top_p + "temperature": self.config.temperature, + "max_tokens_to_sample": self.config.max_tokens, + "top_p": self.config.top_p, } def _format_messages(self, messages: List[Dict[str, str]]) -> str: @@ -28,7 +36,7 @@ class AWSBedrockLLM(LLMBase): Formats a list of messages into the required prompt structure for the model. Args: - messages (List[Dict[str, str]]): A list of dictionaries where each dictionary represents a message. + messages (List[Dict[str, str]]): A list of dictionaries where each dictionary represents a message. Each dictionary contains 'role' and 'content' keys. Returns: @@ -36,12 +44,12 @@ class AWSBedrockLLM(LLMBase): """ formatted_messages = [] for message in messages: - role = message['role'].capitalize() - content = message['content'] + 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, tools) -> str: """ Process the response based on whether tools are used or not. @@ -54,72 +62,76 @@ class AWSBedrockLLM(LLMBase): str or dict: The processed response. """ if tools: - processed_response = { - "tool_calls": [] - } - + 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"] - }) - + 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', '') - + + response_body = json.loads(response["body"].read().decode()) + return response_body.get("completion", "") + def _prepare_input( - self, - provider: str, - model: str, - prompt: str, - model_kwargs: Optional[Dict[str, Any]] = {}, - ) -> Dict[str, Any]: + self, + provider: str, + model: str, + prompt: str, + model_kwargs: Optional[Dict[str, Any]] = {}, + ) -> Dict[str, Any]: """ - Prepares the input dictionary for the specified provider's model by mapping and renaming - keys in the input based on the provider's requirements. + 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 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. + Args: + 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 with the correct keys and values for the specified provider. + Returns: + Dict[str, Any]: The prepared input dictionary with the correct keys and values for the specified provider. """ input_body = {"prompt": prompt, **model_kwargs} - + provider_mappings = { "meta": {"max_tokens_to_sample": "max_gen_len"}, "ai21": {"max_tokens_to_sample": "maxTokens", "top_p": "topP"}, "mistral": {"max_tokens_to_sample": "max_tokens"}, "cohere": {"max_tokens_to_sample": "max_tokens", "top_p": "p"}, } - + if provider in provider_mappings: for old_key, new_key in provider_mappings[provider].items(): if old_key in input_body: input_body[new_key] = input_body.pop(old_key) - + if provider == "cohere" and "cohere.command-r" in model: input_body["message"] = input_body.pop("prompt") - + if provider == "amazon": input_body = { "inputText": prompt, "textGenerationConfig": { "maxTokenCount": model_kwargs.get("max_tokens_to_sample"), "topP": model_kwargs.get("top_p"), - "temperature": model_kwargs.get("temperature") - } + "temperature": model_kwargs.get("temperature"), + }, } - input_body["textGenerationConfig"] = {k: v for k, v in input_body["textGenerationConfig"].items() if v is not None} - + input_body["textGenerationConfig"] = { + k: v + for k, v in input_body["textGenerationConfig"].items() + if v is not None + } + return input_body def _convert_tool_format(self, original_tools): @@ -133,32 +145,34 @@ class AWSBedrockLLM(LLMBase): list: A list of dictionaries representing the tools in the new standardized format. """ new_tools = [] - + for tool in original_tools: - if tool['type'] == 'function': - function = tool['function'] + if tool["type"] == "function": + function = tool["function"] new_tool = { "toolSpec": { - "name": function['name'], - "description": function['description'], + "name": function["name"], + "description": function["description"], "inputSchema": { "json": { "type": "object", "properties": {}, - "required": function['parameters'].get('required', []) + "required": function["parameters"].get("required", []), } - } + }, } } - - for prop, details in function['parameters'].get('properties', {}).items(): + + 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', '') + "type": details.get("type", "string"), + "description": details.get("description", ""), } - + new_tools.append(new_tool) - + return new_tools def generate_response( @@ -181,28 +195,39 @@ class AWSBedrockLLM(LLMBase): 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"]} + 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 + 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) + 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' + accept="application/json", + contentType="application/json", ) return self._parse_response(response, tools) diff --git a/mem0/llms/azure_openai.py b/mem0/llms/azure_openai.py index bb249cff..0998a03a 100644 --- a/mem0/llms/azure_openai.py +++ b/mem0/llms/azure_openai.py @@ -6,13 +6,14 @@ from openai import AzureOpenAI from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig + class AzureOpenAILLM(LLMBase): def __init__(self, config: Optional[BaseLlmConfig] = None): super().__init__(config) # Model name should match the custom deployment name chosen for it. if not self.config.model: - self.config.model="gpt-4o" + self.config.model = "gpt-4o" self.client = AzureOpenAI() def _parse_response(self, response, tools): @@ -29,21 +30,22 @@ class AzureOpenAILLM(LLMBase): if tools: processed_response = { "content": response.choices[0].message.content, - "tool_calls": [] + "tool_calls": [], } if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: - processed_response["tool_calls"].append({ - "name": tool_call.function.name, - "arguments": json.loads(tool_call.function.arguments) - }) + processed_response["tool_calls"].append( + { + "name": tool_call.function.name, + "arguments": json.loads(tool_call.function.arguments), + } + ) return processed_response else: return response.choices[0].message.content - def generate_response( self, messages: List[Dict[str, str]], @@ -64,11 +66,11 @@ class AzureOpenAILLM(LLMBase): str: The generated response. """ params = { - "model": self.config.model, - "messages": messages, - "temperature": self.config.temperature, - "max_tokens": self.config.max_tokens, - "top_p": self.config.top_p + "model": self.config.model, + "messages": messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "top_p": self.config.top_p, } if response_format: params["response_format"] = response_format diff --git a/mem0/llms/configs.py b/mem0/llms/configs.py index 1bef9b36..04048ccf 100644 --- a/mem0/llms/configs.py +++ b/mem0/llms/configs.py @@ -14,8 +14,15 @@ class LlmConfig(BaseModel): @field_validator("config") def validate_config(cls, v, values): provider = values.data.get("provider") - if provider in ("openai", "ollama", "groq", "together", "aws_bedrock", "litellm", "azure_openai"): + if provider in ( + "openai", + "ollama", + "groq", + "together", + "aws_bedrock", + "litellm", + "azure_openai", + ): return v else: raise ValueError(f"Unsupported LLM provider: {provider}") - \ No newline at end of file diff --git a/mem0/llms/groq.py b/mem0/llms/groq.py index bd81f57b..d66d92a9 100644 --- a/mem0/llms/groq.py +++ b/mem0/llms/groq.py @@ -4,7 +4,9 @@ from typing import Dict, List, Optional try: from groq import Groq except ImportError: - raise ImportError("Groq requires extra dependencies. Install with `pip install groq`") from None + raise ImportError( + "Groq requires extra dependencies. Install with `pip install groq`" + ) from None from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig @@ -15,7 +17,7 @@ class GroqLLM(LLMBase): super().__init__(config) if not self.config.model: - self.config.model="llama3-70b-8192" + self.config.model = "llama3-70b-8192" self.client = Groq() def _parse_response(self, response, tools): @@ -32,16 +34,18 @@ class GroqLLM(LLMBase): if tools: processed_response = { "content": response.choices[0].message.content, - "tool_calls": [] + "tool_calls": [], } - + if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: - processed_response["tool_calls"].append({ - "name": tool_call.function.name, - "arguments": json.loads(tool_call.function.arguments) - }) - + processed_response["tool_calls"].append( + { + "name": tool_call.function.name, + "arguments": json.loads(tool_call.function.arguments), + } + ) + return processed_response else: return response.choices[0].message.content @@ -66,11 +70,11 @@ class GroqLLM(LLMBase): str: The generated response. """ params = { - "model": self.config.model, - "messages": messages, - "temperature": self.config.temperature, - "max_tokens": self.config.max_tokens, - "top_p": self.config.top_p + "model": self.config.model, + "messages": messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "top_p": self.config.top_p, } if response_format: params["response_format"] = response_format diff --git a/mem0/llms/litellm.py b/mem0/llms/litellm.py index 4cd2ddd3..f3b32169 100644 --- a/mem0/llms/litellm.py +++ b/mem0/llms/litellm.py @@ -1,7 +1,12 @@ import json from typing import Dict, List, Optional -import litellm +try: + import litellm +except ImportError: + raise ImportError( + "litellm requires extra dependencies. Install with `pip install litellm`" + ) from None from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig @@ -12,8 +17,8 @@ class LiteLLM(LLMBase): super().__init__(config) if not self.config.model: - self.config.model="gpt-4o" - + self.config.model = "gpt-4o" + def _parse_response(self, response, tools): """ Process the response based on whether tools are used or not. @@ -28,16 +33,18 @@ class LiteLLM(LLMBase): if tools: processed_response = { "content": response.choices[0].message.content, - "tool_calls": [] + "tool_calls": [], } - + if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: - processed_response["tool_calls"].append({ - "name": tool_call.function.name, - "arguments": json.loads(tool_call.function.arguments) - }) - + processed_response["tool_calls"].append( + { + "name": tool_call.function.name, + "arguments": json.loads(tool_call.function.arguments), + } + ) + return processed_response else: return response.choices[0].message.content @@ -62,14 +69,16 @@ class LiteLLM(LLMBase): str: The generated response. """ if not litellm.supports_function_calling(self.config.model): - raise ValueError(f"Model '{self.config.model}' in litellm does not support function calling.") + raise ValueError( + f"Model '{self.config.model}' in litellm does not support function calling." + ) params = { - "model": self.config.model, - "messages": messages, - "temperature": self.config.temperature, - "max_tokens": self.config.max_tokens, - "top_p": self.config.top_p + "model": self.config.model, + "messages": messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "top_p": self.config.top_p, } if response_format: params["response_format"] = response_format diff --git a/mem0/llms/ollama.py b/mem0/llms/ollama.py index f91870bf..144c0718 100644 --- a/mem0/llms/ollama.py +++ b/mem0/llms/ollama.py @@ -3,28 +3,31 @@ from typing import Dict, List, Optional try: from ollama import Client except ImportError: - raise ImportError("Ollama requires extra dependencies. Install with `pip install ollama`") from None + raise ImportError( + "Ollama requires extra dependencies. Install with `pip install ollama`" + ) from None from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig + class OllamaLLM(LLMBase): def __init__(self, config: Optional[BaseLlmConfig] = None): super().__init__(config) if not self.config.model: - self.config.model="llama3.1:70b" + self.config.model = "llama3.1:70b" self.client = Client(host=self.config.ollama_base_url) self._ensure_model_exists() def _ensure_model_exists(self): """ Ensure the specified model exists locally. If not, pull it from Ollama. - """ + """ local_models = self.client.list()["models"] if not any(model.get("name") == self.config.model for model in local_models): self.client.pull(self.config.model) - + def _parse_response(self, response, tools): """ Process the response based on whether tools are used or not. @@ -38,20 +41,22 @@ class OllamaLLM(LLMBase): """ if tools: processed_response = { - "content": response['message']['content'], - "tool_calls": [] + "content": response["message"]["content"], + "tool_calls": [], } - - if response['message'].get('tool_calls'): - for tool_call in response['message']['tool_calls']: - processed_response["tool_calls"].append({ - "name": tool_call["function"]["name"], - "arguments": tool_call["function"]["arguments"] - }) - + + if response["message"].get("tool_calls"): + for tool_call in response["message"]["tool_calls"]: + processed_response["tool_calls"].append( + { + "name": tool_call["function"]["name"], + "arguments": tool_call["function"]["arguments"], + } + ) + return processed_response else: - return response['message']['content'] + return response["message"]["content"] def generate_response( self, @@ -73,13 +78,13 @@ class OllamaLLM(LLMBase): str: The generated response. """ params = { - "model": self.config.model, - "messages": messages, + "model": self.config.model, + "messages": messages, "options": { - "temperature": self.config.temperature, - "num_predict": self.config.max_tokens, - "top_p": self.config.top_p - } + "temperature": self.config.temperature, + "num_predict": self.config.max_tokens, + "top_p": self.config.top_p, + }, } if response_format: params["format"] = response_format @@ -87,4 +92,4 @@ class OllamaLLM(LLMBase): params["tools"] = tools response = self.client.chat(**params) - return self._parse_response(response, tools) \ No newline at end of file + return self._parse_response(response, tools) diff --git a/mem0/llms/openai.py b/mem0/llms/openai.py index f15424eb..e2e33f18 100644 --- a/mem0/llms/openai.py +++ b/mem0/llms/openai.py @@ -7,19 +7,23 @@ from openai import OpenAI from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig + class OpenAILLM(LLMBase): def __init__(self, config: Optional[BaseLlmConfig] = None): super().__init__(config) if not self.config.model: - self.config.model="gpt-4o" + self.config.model = "gpt-4o" - if os.environ.get("OPENROUTER_API_KEY"): # Use OpenRouter - self.client = OpenAI(api_key=os.environ.get("OPENROUTER_API_KEY"), base_url=self.config.openrouter_base_url) + if os.environ.get("OPENROUTER_API_KEY"): # Use OpenRouter + self.client = OpenAI( + api_key=os.environ.get("OPENROUTER_API_KEY"), + base_url=self.config.openrouter_base_url, + ) else: api_key = os.getenv("OPENAI_API_KEY") or self.config.api_key self.client = OpenAI(api_key=api_key) - + def _parse_response(self, response, tools): """ Process the response based on whether tools are used or not. @@ -34,16 +38,18 @@ class OpenAILLM(LLMBase): if tools: processed_response = { "content": response.choices[0].message.content, - "tool_calls": [] + "tool_calls": [], } - + if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: - processed_response["tool_calls"].append({ - "name": tool_call.function.name, - "arguments": json.loads(tool_call.function.arguments) - }) - + processed_response["tool_calls"].append( + { + "name": tool_call.function.name, + "arguments": json.loads(tool_call.function.arguments), + } + ) + return processed_response else: return response.choices[0].message.content @@ -68,11 +74,11 @@ class OpenAILLM(LLMBase): str: The generated response. """ params = { - "model": self.config.model, - "messages": messages, - "temperature": self.config.temperature, - "max_tokens": self.config.max_tokens, - "top_p": self.config.top_p + "model": self.config.model, + "messages": messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "top_p": self.config.top_p, } if os.getenv("OPENROUTER_API_KEY"): @@ -81,14 +87,14 @@ class OpenAILLM(LLMBase): openrouter_params["models"] = self.config.models openrouter_params["route"] = self.config.route params.pop("model") - + if self.config.site_url and self.config.app_name: - extra_headers={ - "HTTP-Referer": self.config.site_url, - "X-Title": self.config.app_name - } - openrouter_params["extra_headers"] = extra_headers - + extra_headers = { + "HTTP-Referer": self.config.site_url, + "X-Title": self.config.app_name, + } + openrouter_params["extra_headers"] = extra_headers + params.update(**openrouter_params) if response_format: diff --git a/mem0/llms/together.py b/mem0/llms/together.py index 333b47a8..4dbc9ae6 100644 --- a/mem0/llms/together.py +++ b/mem0/llms/together.py @@ -4,19 +4,22 @@ from typing import Dict, List, Optional try: from together import Together except ImportError: - raise ImportError("Together requires extra dependencies. Install with `pip install together`") from None + raise ImportError( + "Together requires extra dependencies. Install with `pip install together`" + ) from None from mem0.llms.base import LLMBase from mem0.configs.llms.base import BaseLlmConfig + class TogetherLLM(LLMBase): def __init__(self, config: Optional[BaseLlmConfig] = None): super().__init__(config) if not self.config.model: - self.config.model="mistralai/Mixtral-8x7B-Instruct-v0.1" + self.config.model = "mistralai/Mixtral-8x7B-Instruct-v0.1" self.client = Together() - + def _parse_response(self, response, tools): """ Process the response based on whether tools are used or not. @@ -31,16 +34,18 @@ class TogetherLLM(LLMBase): if tools: processed_response = { "content": response.choices[0].message.content, - "tool_calls": [] + "tool_calls": [], } - + if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: - processed_response["tool_calls"].append({ - "name": tool_call.function.name, - "arguments": json.loads(tool_call.function.arguments) - }) - + processed_response["tool_calls"].append( + { + "name": tool_call.function.name, + "arguments": json.loads(tool_call.function.arguments), + } + ) + return processed_response else: return response.choices[0].message.content @@ -65,11 +70,11 @@ class TogetherLLM(LLMBase): str: The generated response. """ params = { - "model": self.config.model, - "messages": messages, - "temperature": self.config.temperature, - "max_tokens": self.config.max_tokens, - "top_p": self.config.top_p + "model": self.config.model, + "messages": messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "top_p": self.config.top_p, } if response_format: params["response_format"] = response_format diff --git a/mem0/memory/main.py b/mem0/memory/main.py index 18af3894..64443e95 100644 --- a/mem0/memory/main.py +++ b/mem0/memory/main.py @@ -28,12 +28,16 @@ setup_config() class Memory(MemoryBase): def __init__(self, config: MemoryConfig = MemoryConfig()): self.config = config - self.embedding_model = EmbedderFactory.create(self.config.embedder.provider, self.config.embedder.config) - self.vector_store = VectorStoreFactory.create(self.config.vector_store.provider, self.config.vector_store.config) + self.embedding_model = EmbedderFactory.create( + self.config.embedder.provider, self.config.embedder.config + ) + self.vector_store = VectorStoreFactory.create( + self.config.vector_store.provider, self.config.vector_store.config + ) self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config) self.db = SQLiteManager(self.config.history_db_path) self.collection_name = self.config.vector_store.config.collection_name - + capture_event("mem0.init", self) @classmethod @@ -171,9 +175,13 @@ class Memory(MemoryBase): memory = self.vector_store.get(vector_id=memory_id) if not memory: return None - - filters = {key: memory.payload[key] for key in ["user_id", "agent_id", "run_id"] if memory.payload.get(key)} - + + filters = { + key: memory.payload[key] + for key in ["user_id", "agent_id", "run_id"] + if memory.payload.get(key) + } + # Prepare base memory item memory_item = MemoryItem( id=memory.id, @@ -182,15 +190,25 @@ class Memory(MemoryBase): created_at=memory.payload.get("created_at"), updated_at=memory.payload.get("updated_at"), ).model_dump(exclude={"score"}) - + # Add metadata if there are additional keys - excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at"} - additional_metadata = {k: v for k, v in memory.payload.items() if k not in excluded_keys} + excluded_keys = { + "user_id", + "agent_id", + "run_id", + "hash", + "data", + "created_at", + "updated_at", + } + additional_metadata = { + k: v for k, v in memory.payload.items() if k not in excluded_keys + } if additional_metadata: memory_item["metadata"] = additional_metadata - + result = {**memory_item, **filters} - + return result def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100): @@ -211,7 +229,15 @@ class Memory(MemoryBase): capture_event("mem0.get_all", self, {"filters": len(filters), "limit": limit}) memories = self.vector_store.list(filters=filters, limit=limit) - excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at"} + excluded_keys = { + "user_id", + "agent_id", + "run_id", + "hash", + "data", + "created_at", + "updated_at", + } return [ { **MemoryItem( @@ -221,9 +247,22 @@ class Memory(MemoryBase): created_at=mem.payload.get("created_at"), updated_at=mem.payload.get("updated_at"), ).model_dump(exclude={"score"}), - **{key: mem.payload[key] for key in ["user_id", "agent_id", "run_id"] if key in mem.payload}, - **({"metadata": {k: v for k, v in mem.payload.items() if k not in excluded_keys}} - if any(k for k in mem.payload if k not in excluded_keys) else {}) + **{ + key: mem.payload[key] + for key in ["user_id", "agent_id", "run_id"] + if key in mem.payload + }, + **( + { + "metadata": { + k: v + for k, v in mem.payload.items() + if k not in excluded_keys + } + } + if any(k for k in mem.payload if k not in excluded_keys) + else {} + ), } for mem in memories[0] ] @@ -255,9 +294,19 @@ class Memory(MemoryBase): capture_event("mem0.search", self, {"filters": len(filters), "limit": limit}) embeddings = self.embedding_model.embed(query) - memories = self.vector_store.search(query=embeddings, limit=limit, filters=filters) + memories = self.vector_store.search( + query=embeddings, limit=limit, filters=filters + ) - excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at"} + excluded_keys = { + "user_id", + "agent_id", + "run_id", + "hash", + "data", + "created_at", + "updated_at", + } return [ { @@ -269,9 +318,22 @@ class Memory(MemoryBase): updated_at=mem.payload.get("updated_at"), score=mem.score, ).model_dump(), - **{key: mem.payload[key] for key in ["user_id", "agent_id", "run_id"] if key in mem.payload}, - **({"metadata": {k: v for k, v in mem.payload.items() if k not in excluded_keys}} - if any(k for k in mem.payload if k not in excluded_keys) else {}) + **{ + key: mem.payload[key] + for key in ["user_id", "agent_id", "run_id"] + if key in mem.payload + }, + **( + { + "metadata": { + k: v + for k, v in mem.payload.items() + if k not in excluded_keys + } + } + if any(k for k in mem.payload if k not in excluded_keys) + else {} + ), } for mem in memories ] @@ -289,7 +351,7 @@ class Memory(MemoryBase): """ capture_event("mem0.update", self, {"memory_id": memory_id}) self._update_memory_tool(memory_id, data) - return {'message': 'Memory updated successfully!'} + return {"message": "Memory updated successfully!"} def delete(self, memory_id): """ @@ -300,7 +362,7 @@ class Memory(MemoryBase): """ capture_event("mem0.delete", self, {"memory_id": memory_id}) self._delete_memory_tool(memory_id) - return {'message': 'Memory deleted successfully!'} + return {"message": "Memory deleted successfully!"} def delete_all(self, user_id=None, agent_id=None, run_id=None): """ @@ -328,7 +390,7 @@ class Memory(MemoryBase): memories = self.vector_store.list(filters=filters)[0] for memory in memories: self._delete_memory_tool(memory.id) - return {'message': 'Memories deleted successfully!'} + return {"message": "Memories deleted successfully!"} def history(self, memory_id): """ @@ -350,14 +412,16 @@ class Memory(MemoryBase): metadata = metadata or {} metadata["data"] = data metadata["hash"] = hashlib.md5(data.encode()).hexdigest() - metadata["created_at"] = datetime.now(pytz.timezone('US/Pacific')).isoformat() + metadata["created_at"] = datetime.now(pytz.timezone("US/Pacific")).isoformat() self.vector_store.insert( vectors=[embeddings], ids=[memory_id], payloads=[metadata], ) - self.db.add_history(memory_id, None, data, "ADD", created_at=metadata["created_at"]) + self.db.add_history( + memory_id, None, data, "ADD", created_at=metadata["created_at"] + ) return memory_id def _update_memory_tool(self, memory_id, data, metadata=None): @@ -368,15 +432,17 @@ class Memory(MemoryBase): new_metadata["data"] = data new_metadata["hash"] = existing_memory.payload.get("hash") new_metadata["created_at"] = existing_memory.payload.get("created_at") - new_metadata["updated_at"] = datetime.now(pytz.timezone('US/Pacific')).isoformat() - - if "user_id" in existing_memory.payload: + new_metadata["updated_at"] = datetime.now( + pytz.timezone("US/Pacific") + ).isoformat() + + if "user_id" in existing_memory.payload: new_metadata["user_id"] = existing_memory.payload["user_id"] if "agent_id" in existing_memory.payload: new_metadata["agent_id"] = existing_memory.payload["agent_id"] if "run_id" in existing_memory.payload: new_metadata["run_id"] = existing_memory.payload["run_id"] - + embeddings = self.embedding_model.embed(data) self.vector_store.update( vector_id=memory_id, @@ -384,7 +450,14 @@ class Memory(MemoryBase): payload=new_metadata, ) logging.info(f"Updating memory with ID {memory_id=} with {data=}") - self.db.add_history(memory_id, prev_value, data, "UPDATE", created_at=new_metadata["created_at"], updated_at=new_metadata["updated_at"]) + self.db.add_history( + memory_id, + prev_value, + data, + "UPDATE", + created_at=new_metadata["created_at"], + updated_at=new_metadata["updated_at"], + ) def _delete_memory_tool(self, memory_id): logging.info(f"Deleting memory with {memory_id=}") diff --git a/mem0/memory/setup.py b/mem0/memory/setup.py index ed54ae17..2973b072 100644 --- a/mem0/memory/setup.py +++ b/mem0/memory/setup.py @@ -20,12 +20,12 @@ def setup_config(): def get_user_id(): config_path = os.path.join(mem0_dir, "config.json") if not os.path.exists(config_path): - return "anonymous_user" - + return "anonymous_user" + try: with open(config_path, "r") as config_file: config = json.load(config_file) user_id = config.get("user_id") return user_id - except: + except Exception: return "anonymous_user" diff --git a/mem0/memory/storage.py b/mem0/memory/storage.py index 33b1bf76..126df85d 100644 --- a/mem0/memory/storage.py +++ b/mem0/memory/storage.py @@ -7,12 +7,14 @@ class SQLiteManager: self.connection = sqlite3.connect(db_path, check_same_thread=False) self._migrate_history_table() self._create_history_table() - + def _migrate_history_table(self): with self.connection: cursor = self.connection.cursor() - cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='history'") + cursor.execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name='history'" + ) table_exists = cursor.fetchone() is not None if table_exists: @@ -22,15 +24,15 @@ class SQLiteManager: # Define the expected schema expected_schema = { - 'id': 'TEXT', - 'memory_id': 'TEXT', - 'old_memory': 'TEXT', - 'new_memory': 'TEXT', - 'new_value': 'TEXT', - 'event': 'TEXT', - 'created_at': 'DATETIME', - 'updated_at': 'DATETIME', - 'is_deleted': 'INTEGER' + "id": "TEXT", + "memory_id": "TEXT", + "old_memory": "TEXT", + "new_memory": "TEXT", + "new_value": "TEXT", + "event": "TEXT", + "created_at": "DATETIME", + "updated_at": "DATETIME", + "is_deleted": "INTEGER", } # Check if the schemas are the same @@ -38,7 +40,8 @@ class SQLiteManager: # Rename the old table cursor.execute("ALTER TABLE history RENAME TO old_history") - cursor.execute(""" + cursor.execute( + """ CREATE TABLE IF NOT EXISTS history ( id TEXT PRIMARY KEY, memory_id TEXT, @@ -50,20 +53,22 @@ class SQLiteManager: updated_at DATETIME, is_deleted INTEGER ) - """) + """ + ) # Copy data from the old table to the new table - cursor.execute(""" + cursor.execute( + """ INSERT INTO history (id, memory_id, old_memory, new_memory, new_value, event, created_at, updated_at, is_deleted) SELECT id, memory_id, prev_value, new_value, new_value, event, timestamp, timestamp, is_deleted FROM old_history - """) + """ + ) cursor.execute("DROP TABLE old_history") self.connection.commit() - def _create_history_table(self): with self.connection: self.connection.execute( @@ -82,7 +87,16 @@ class SQLiteManager: """ ) - def add_history(self, memory_id, old_memory, new_memory, event, created_at = None, updated_at = None, is_deleted=0): + def add_history( + self, + memory_id, + old_memory, + new_memory, + event, + created_at=None, + updated_at=None, + is_deleted=0, + ): with self.connection: self.connection.execute( """ diff --git a/mem0/proxy/main.py b/mem0/proxy/main.py index b1586706..76942b1f 100644 --- a/mem0/proxy/main.py +++ b/mem0/proxy/main.py @@ -1,18 +1,26 @@ import httpx from typing import Optional, List, Union import threading -import litellm +try: + import litellm +except ImportError: + raise ImportError( + "litellm requires extra dependencies. Install with `pip install litellm`" + ) from None + +from mem0.memory.telemetry import capture_client_event from mem0 import Memory, MemoryClient from mem0.configs.prompts import MEMORY_ANSWER_PROMPT + class Mem0: def __init__( - self, - config: Optional[dict] = None, - api_key: Optional[str] = None, - host: Optional[str] = None - ): + self, + config: Optional[dict] = None, + api_key: Optional[str] = None, + host: Optional[str] = None, + ): if api_key: self.mem0_client = MemoryClient(api_key, host) else: @@ -77,13 +85,21 @@ class Completions: raise ValueError("One of user_id, agent_id, run_id must be provided") if not litellm.supports_function_calling(model): - raise ValueError(f"Model '{model}' does not support function calling. Please use a model that supports function calling.") + raise ValueError( + f"Model '{model}' does not support function calling. Please use a model that supports function calling." + ) prepared_messages = self._prepare_messages(messages) if prepared_messages[-1]["role"] == "user": - self._async_add_to_memory(messages, user_id, agent_id, run_id, metadata, filters) - relevant_memories = self._fetch_relevant_memories(messages, user_id, agent_id, run_id, filters, limit) - prepared_messages[-1]["content"] = self._format_query_with_memories(messages, relevant_memories) + self._async_add_to_memory( + messages, user_id, agent_id, run_id, metadata, filters + ) + relevant_memories = self._fetch_relevant_memories( + messages, user_id, agent_id, run_id, filters, limit + ) + prepared_messages[-1]["content"] = self._format_query_with_memories( + messages, relevant_memories + ) response = litellm.completion( model=model, @@ -114,9 +130,9 @@ class Completions: base_url=base_url, api_version=api_version, api_key=api_key, - model_list=model_list + model_list=model_list, ) - + capture_client_event("mem0.chat.create", self) return response def _prepare_messages(self, messages: List[dict]) -> List[dict]: @@ -125,7 +141,9 @@ class Completions: messages[0]["content"] = MEMORY_ANSWER_PROMPT return messages - def _async_add_to_memory(self, messages, user_id, agent_id, run_id, metadata, filters): + def _async_add_to_memory( + self, messages, user_id, agent_id, run_id, metadata, filters + ): def add_task(): self.mem0_client.add( messages=messages, @@ -135,11 +153,16 @@ class Completions: metadata=metadata, filters=filters, ) + threading.Thread(target=add_task, daemon=True).start() - def _fetch_relevant_memories(self, messages, user_id, agent_id, run_id, filters, limit): + def _fetch_relevant_memories( + self, messages, user_id, agent_id, run_id, filters, limit + ): # Currently, only pass the last 6 messages to the search API to prevent long query - message_input = [f"{message['role']}: {message['content']}" for message in messages][-6:] + message_input = [ + f"{message['role']}: {message['content']}" for message in messages + ][-6:] # TODO: Make it better by summarizing the past conversation return self.mem0_client.search( query="\n".join(message_input), diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py index d3397567..db1fcbbe 100644 --- a/mem0/utils/factory.py +++ b/mem0/utils/factory.py @@ -3,6 +3,7 @@ import importlib from mem0.configs.llms.base import BaseLlmConfig from mem0.configs.embeddings.base import BaseEmbedderConfig + def load_class(class_type): module_path, class_name = class_type.rsplit(".", 1) module = importlib.import_module(module_path) @@ -29,7 +30,8 @@ class LlmFactory: return llm_instance(base_config) else: raise ValueError(f"Unsupported Llm provider: {provider_name}") - + + class EmbedderFactory: provider_to_class = { "openai": "mem0.embeddings.openai.OpenAIEmbedding", @@ -47,12 +49,13 @@ class EmbedderFactory: return embedder_instance(base_config) else: raise ValueError(f"Unsupported Embedder provider: {provider_name}") - + + class VectorStoreFactory: provider_to_class = { "qdrant": "mem0.vector_stores.qdrant.Qdrant", "chroma": "mem0.vector_stores.chroma.ChromaDB", - "pgvector": "mem0.vector_stores.pgvector.PGVector" + "pgvector": "mem0.vector_stores.pgvector.PGVector", } @classmethod diff --git a/mem0/vector_stores/chroma.py b/mem0/vector_stores/chroma.py index c06a0bda..b75b0c91 100644 --- a/mem0/vector_stores/chroma.py +++ b/mem0/vector_stores/chroma.py @@ -7,14 +7,16 @@ try: import chromadb from chromadb.config import Settings except ImportError: - raise ImportError("Chromadb requires extra dependencies. Install with `pip install chromadb`") from None + raise ImportError( + "Chromadb requires extra dependencies. Install with `pip install chromadb`" + ) from None from mem0.vector_stores.base import VectorStoreBase class OutputData(BaseModel): id: Optional[str] # memory id - score: Optional[float] # distance + score: Optional[float] # distance payload: Optional[Dict] # metadata @@ -25,7 +27,7 @@ class ChromaDB(VectorStoreBase): client: Optional[chromadb.Client] = None, host: Optional[str] = None, port: Optional[int] = None, - path: Optional[str] = None + path: Optional[str] = None, ): """ Initialize the Chromadb vector store. @@ -68,7 +70,7 @@ class ChromaDB(VectorStoreBase): Returns: List[OutputData]: Parsed output data. """ - keys = ['ids', 'distances', 'metadatas'] + keys = ["ids", "distances", "metadatas"] values = [] for key in keys: @@ -78,14 +80,24 @@ class ChromaDB(VectorStoreBase): values.append(value) ids, distances, metadatas = values - max_length = max(len(v) for v in values if isinstance(v, list) and v is not None) + max_length = max( + len(v) for v in values if isinstance(v, list) and v is not None + ) result = [] for i in range(max_length): entry = OutputData( id=ids[i] if isinstance(ids, list) and ids and i < len(ids) else None, - score=distances[i] if isinstance(distances, list) and distances and i < len(distances) else None, - payload=metadatas[i] if isinstance(metadatas, list) and metadatas and i < len(metadatas) else None, + score=( + distances[i] + if isinstance(distances, list) and distances and i < len(distances) + else None + ), + payload=( + metadatas[i] + if isinstance(metadatas, list) and metadatas and i < len(metadatas) + else None + ), ) result.append(entry) @@ -114,7 +126,12 @@ class ChromaDB(VectorStoreBase): ) return collection - def insert(self, vectors: List[list], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None): + def insert( + self, + vectors: List[list], + payloads: Optional[List[Dict]] = None, + ids: Optional[List[str]] = None, + ): """ Insert vectors into a collection. @@ -125,7 +142,9 @@ class ChromaDB(VectorStoreBase): """ self.collection.add(ids=ids, embeddings=vectors, metadatas=payloads) - def search(self, query: List[list], limit: int = 5, filters: Optional[Dict] = None) -> List[OutputData]: + def search( + self, query: List[list], limit: int = 5, filters: Optional[Dict] = None + ) -> List[OutputData]: """ Search for similar vectors. @@ -137,7 +156,9 @@ class ChromaDB(VectorStoreBase): Returns: List[OutputData]: Search results. """ - results = self.collection.query(query_embeddings=query, where=filters, n_results=limit) + results = self.collection.query( + query_embeddings=query, where=filters, n_results=limit + ) final_results = self._parse_output(results) return final_results @@ -150,7 +171,12 @@ class ChromaDB(VectorStoreBase): """ self.collection.delete(ids=vector_id) - def update(self, vector_id: str, vector: Optional[List[float]] = None, payload: Optional[Dict] = None): + def update( + self, + vector_id: str, + vector: Optional[List[float]] = None, + payload: Optional[Dict] = None, + ): """ Update a vector and its payload. @@ -184,8 +210,8 @@ class ChromaDB(VectorStoreBase): return self.client.list_collections() def delete_col(self): - """ - Delete a collection. + """ + Delete a collection. """ self.client.delete_collection(name=self.collection_name) @@ -198,7 +224,9 @@ class ChromaDB(VectorStoreBase): """ return self.client.get_collection(name=self.collection_name) - def list(self, filters: Optional[Dict] = None, limit: int = 100) -> List[OutputData]: + def list( + self, filters: Optional[Dict] = None, limit: int = 100 + ) -> List[OutputData]: """ List all vectors in a collection. diff --git a/mem0/vector_stores/configs.py b/mem0/vector_stores/configs.py index 6308afb3..fc6b102e 100644 --- a/mem0/vector_stores/configs.py +++ b/mem0/vector_stores/configs.py @@ -1,31 +1,34 @@ from typing import Optional, Dict from pydantic import BaseModel, Field, model_validator + class VectorStoreConfig(BaseModel): provider: str = Field( description="Provider of the vector store (e.g., 'qdrant', 'chroma')", default="qdrant", ) config: Optional[Dict] = Field( - description="Configuration for the specific vector store", - default=None + description="Configuration for the specific vector store", default=None ) _provider_configs: Dict[str, str] = { "qdrant": "QdrantConfig", "chroma": "ChromaDbConfig", - "pgvector": "PGVectorConfig" + "pgvector": "PGVectorConfig", } @model_validator(mode="after") - def validate_and_create_config(self) -> 'VectorStoreConfig': + def validate_and_create_config(self) -> "VectorStoreConfig": provider = self.provider config = self.config if provider not in self._provider_configs: raise ValueError(f"Unsupported vector store provider: {provider}") - module = __import__(f"mem0.configs.vector_stores.{provider}", fromlist=[self._provider_configs[provider]]) + module = __import__( + f"mem0.configs.vector_stores.{provider}", + fromlist=[self._provider_configs[provider]], + ) config_class = getattr(module, self._provider_configs[provider]) if config is None: @@ -40,4 +43,4 @@ class VectorStoreConfig(BaseModel): config["path"] = f"/tmp/{provider}" self.config = config_class(**config) - return self \ No newline at end of file + return self diff --git a/mem0/vector_stores/pgvector.py b/mem0/vector_stores/pgvector.py index 66ffa926..ad92624e 100644 --- a/mem0/vector_stores/pgvector.py +++ b/mem0/vector_stores/pgvector.py @@ -1,16 +1,19 @@ import json -from typing import Optional, List, Dict, Any +from typing import Optional, List from pydantic import BaseModel try: import psycopg2 from psycopg2.extras import execute_values except ImportError: - raise ImportError("PGVector requires extra dependencies. Install with `pip install psycopg2`") from None + raise ImportError( + "PGVector requires extra dependencies. Install with `pip install psycopg2`" + ) from None from mem0.vector_stores.base import VectorStoreBase + class OutputData(BaseModel): id: Optional[str] score: Optional[float] @@ -19,14 +22,7 @@ class OutputData(BaseModel): class PGVector(VectorStoreBase): def __init__( - self, - dbname, - collection_name, - embedding_model_dims, - user, - password, - host, - port + self, dbname, collection_name, embedding_model_dims, user, password, host, port ): """ Initialize the PGVector database. @@ -43,18 +39,14 @@ class PGVector(VectorStoreBase): self.collection_name = collection_name self.conn = psycopg2.connect( - dbname=dbname, - user=user, - password=password, - host=host, - port=port + dbname=dbname, user=user, password=password, host=host, port=port ) self.cur = self.conn.cursor() collections = self.list_cols() if collection_name not in collections: self.create_col(embedding_model_dims) - + def create_col(self, embedding_model_dims): """ Create a new collection (table in PostgreSQL). @@ -63,16 +55,18 @@ class PGVector(VectorStoreBase): name (str): Name of the collection. embedding_model_dims (int, optional): Dimension of the embedding vector. """ - self.cur.execute(f""" + self.cur.execute( + f""" CREATE TABLE IF NOT EXISTS {self.collection_name} ( id UUID PRIMARY KEY, vector vector({embedding_model_dims}), payload JSONB ); - """) + """ + ) self.conn.commit() - def insert(self, vectors, payloads = None, ids = None): + def insert(self, vectors, payloads=None, ids=None): """ Insert vectors into a collection. @@ -83,11 +77,18 @@ class PGVector(VectorStoreBase): """ json_payloads = [json.dumps(payload) for payload in payloads] - data = [(id, vector, payload) for id, vector, payload in zip(ids, vectors, json_payloads)] - execute_values(self.cur, f"INSERT INTO {self.collection_name} (id, vector, payload) VALUES %s", data) + data = [ + (id, vector, payload) + for id, vector, payload in zip(ids, vectors, json_payloads) + ] + execute_values( + self.cur, + f"INSERT INTO {self.collection_name} (id, vector, payload) VALUES %s", + data, + ) self.conn.commit() - def search(self, query, limit = 5, filters = None): + def search(self, query, limit=5, filters=None): """ Search for similar vectors. @@ -104,21 +105,28 @@ class PGVector(VectorStoreBase): if filters: for k, v in filters.items(): - filter_conditions.append(f"payload->>%s = %s") + filter_conditions.append("payload->>%s = %s") filter_params.extend([k, str(v)]) - filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else "" + filter_clause = ( + "WHERE " + " AND ".join(filter_conditions) if filter_conditions else "" + ) - self.cur.execute(f""" + self.cur.execute( + f""" SELECT id, vector <-> %s::vector AS distance, payload FROM {self.collection_name} {filter_clause} ORDER BY distance LIMIT %s - """, (query, *filter_params, limit)) + """, + (query, *filter_params, limit), + ) results = self.cur.fetchall() - return [OutputData(id=str(r[0]), score=float(r[1]), payload=r[2]) for r in results] + return [ + OutputData(id=str(r[0]), score=float(r[1]), payload=r[2]) for r in results + ] def delete(self, vector_id): """ @@ -127,10 +135,12 @@ class PGVector(VectorStoreBase): Args: vector_id (str): ID of the vector to delete. """ - self.cur.execute(f"DELETE FROM {self.collection_name} WHERE id = %s", (vector_id,)) + self.cur.execute( + f"DELETE FROM {self.collection_name} WHERE id = %s", (vector_id,) + ) self.conn.commit() - def update(self, vector_id, vector = None, payload = None): + def update(self, vector_id, vector=None, payload=None): """ Update a vector and its payload. @@ -140,9 +150,15 @@ class PGVector(VectorStoreBase): payload (Dict, optional): Updated payload. """ if vector: - self.cur.execute(f"UPDATE {self.collection_name} SET vector = %s WHERE id = %s", (vector, vector_id)) + self.cur.execute( + f"UPDATE {self.collection_name} SET vector = %s WHERE id = %s", + (vector, vector_id), + ) if payload: - self.cur.execute(f"UPDATE {self.collection_name} SET payload = %s WHERE id = %s", (psycopg2.extras.Json(payload), vector_id)) + self.cur.execute( + f"UPDATE {self.collection_name} SET payload = %s WHERE id = %s", + (psycopg2.extras.Json(payload), vector_id), + ) self.conn.commit() def get(self, vector_id) -> OutputData: @@ -155,7 +171,10 @@ class PGVector(VectorStoreBase): Returns: OutputData: Retrieved vector. """ - self.cur.execute(f"SELECT id, vector, payload FROM {self.collection_name} WHERE id = %s", (vector_id,)) + self.cur.execute( + f"SELECT id, vector, payload FROM {self.collection_name} WHERE id = %s", + (vector_id,), + ) result = self.cur.fetchone() if not result: return None @@ -168,11 +187,13 @@ class PGVector(VectorStoreBase): Returns: List[str]: List of collection names. """ - self.cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'") + self.cur.execute( + "SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'" + ) return [row[0] for row in self.cur.fetchall()] def delete_col(self): - """ Delete a collection. """ + """Delete a collection.""" self.cur.execute(f"DROP TABLE IF EXISTS {self.collection_name}") self.conn.commit() @@ -183,22 +204,21 @@ class PGVector(VectorStoreBase): Returns: Dict[str, Any]: Collection information. """ - self.cur.execute(f""" + self.cur.execute( + f""" SELECT table_name, (SELECT COUNT(*) FROM {self.collection_name}) as row_count, (SELECT pg_size_pretty(pg_total_relation_size('{self.collection_name}'))) as total_size FROM information_schema.tables WHERE table_schema = 'public' AND table_name = %s - """, (self.collection_name,)) + """, + (self.collection_name,), + ) result = self.cur.fetchone() - return { - "name": result[0], - "count": result[1], - "size": result[2] - } + return {"name": result[0], "count": result[1], "size": result[2]} - def list(self, filters = None, limit = 100): + def list(self, filters=None, limit=100): """ List all vectors in a collection. @@ -214,10 +234,12 @@ class PGVector(VectorStoreBase): if filters: for k, v in filters.items(): - filter_conditions.append(f"payload->>%s = %s") + filter_conditions.append("payload->>%s = %s") filter_params.extend([k, str(v)]) - filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else "" + filter_clause = ( + "WHERE " + " AND ".join(filter_conditions) if filter_conditions else "" + ) query = f""" SELECT id, vector, payload @@ -235,7 +257,7 @@ class PGVector(VectorStoreBase): """ Close the database connection when the object is deleted. """ - if hasattr(self, 'cur'): + if hasattr(self, "cur"): self.cur.close() - if hasattr(self, 'conn'): - self.conn.close() \ No newline at end of file + if hasattr(self, "conn"): + self.conn.close() diff --git a/mem0/vector_stores/qdrant.py b/mem0/vector_stores/qdrant.py index 9743874f..25064336 100644 --- a/mem0/vector_stores/qdrant.py +++ b/mem0/vector_stores/qdrant.py @@ -28,7 +28,7 @@ class Qdrant(VectorStoreBase): path: str = None, url: str = None, api_key: str = None, - on_disk: bool = False + on_disk: bool = False, ): """ Initialize the Qdrant vector store. @@ -60,13 +60,15 @@ class Qdrant(VectorStoreBase): if not on_disk: if os.path.exists(path) and os.path.isdir(path): shutil.rmtree(path) - + self.client = QdrantClient(**params) - + self.collection_name = collection_name self.create_col(embedding_model_dims, on_disk) - def create_col(self, vector_size: int, on_disk: bool, distance: Distance = Distance.COSINE): + def create_col( + self, vector_size: int, on_disk: bool, distance: Distance = Distance.COSINE + ): """ Create a new collection. @@ -79,12 +81,16 @@ class Qdrant(VectorStoreBase): response = self.list_cols() for collection in response.collections: if collection.name == self.collection_name: - logging.debug(f"Collection {self.collection_name} already exists. Skipping creation.") + logging.debug( + f"Collection {self.collection_name} already exists. Skipping creation." + ) return self.client.create_collection( collection_name=self.collection_name, - vectors_config=VectorParams(size=vector_size, distance=distance, on_disk=on_disk), + vectors_config=VectorParams( + size=vector_size, distance=distance, on_disk=on_disk + ), ) def insert(self, vectors: list, payloads: list = None, ids: list = None): @@ -202,7 +208,7 @@ class Qdrant(VectorStoreBase): return self.client.get_collections() def delete_col(self): - """ Delete a collection. 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