Add langchain embedding, update langchain LLM and version bump -> 0.1.84 (#2510)

mem0/configs/llms/base.py

@@ -13,7 +13,7 @@ class BaseLlmConfig(ABC):
 
     def __init__(
         self,
-        model: Optional[str] = None,
+        model: Optional[Union[str, Dict]] = None,
         temperature: float = 0.1,
         api_key: Optional[str] = None,
         max_tokens: int = 2000,
@@ -41,8 +41,6 @@ class BaseLlmConfig(ABC):
         xai_base_url: Optional[str] = None,
         # LM Studio specific
         lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
-        # Langchain specific
-        langchain_provider: Optional[str] = None,
     ):
         """
         Initializes a configuration class instance for the LLM.
@@ -89,8 +87,6 @@ class BaseLlmConfig(ABC):
         :type xai_base_url: Optional[str], optional
         :param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
         :type lmstudio_base_url: Optional[str], optional
-        :param langchain_provider: Langchain provider to be use, defaults to None
-        :type langchain_provider: Optional[str], optional
         """
 
         self.model = model
@@ -127,6 +123,3 @@ class BaseLlmConfig(ABC):
 
         # LM Studio specific
         self.lmstudio_base_url = lmstudio_base_url
-
-        # Langchain specific
-        self.langchain_provider = langchain_provider

mem0/embeddings/configs.py

@@ -22,6 +22,7 @@ class EmbedderConfig(BaseModel):
             "vertexai",
             "together",
             "lmstudio",
+            "langchain",
         ]:
             return v
         else:

mem0/embeddings/langchain.py

@@ -0,0 +1,36 @@
+import os
+from typing import Literal, Optional
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
+
+try:
+    from langchain.embeddings.base import Embeddings
+except ImportError:
+    raise ImportError("langchain is not installed. Please install it using `pip install langchain`")
+
+
+class LangchainEmbedding(EmbeddingBase):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        if self.config.model is None:
+            raise ValueError("`model` parameter is required")
+
+        if not isinstance(self.config.model, Embeddings):
+            raise ValueError("`model` must be an instance of Embeddings")
+
+        self.langchain_model = self.config.model
+
+    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
+        """
+        Get the embedding for the given text using Langchain.
+
+        Args:
+            text (str): The text to embed.
+            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
+        Returns:
+            list: The embedding vector.
+        """
+
+        return self.langchain_model.embed_query(text)

mem0/llms/langchain.py

@@ -1,174 +1,25 @@
 from typing import Dict, List, Optional
-import enum
 
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
 
-# Default import for langchain_community
 try:
-    from langchain_community import chat_models
+    from langchain.chat_models.base import BaseChatModel
 except ImportError:
-    raise ImportError("langchain_community not found. Please install it with `pip install langchain-community`")
-
-# Provider-specific package mapping
-PROVIDER_PACKAGES = {
-    "Anthropic": "langchain_anthropic",
-    "MistralAI": "langchain_mistralai",
-    "Fireworks": "langchain_fireworks",
-    "AzureOpenAI": "langchain_openai",
-    "OpenAI": "langchain_openai",
-    "Together": "langchain_together",
-    "VertexAI": "langchain_google_vertexai",
-    "GoogleAI": "langchain_google_genai",
-    "Groq": "langchain_groq",
-    "Cohere": "langchain_cohere",
-    "Bedrock": "langchain_aws",
-    "HuggingFace": "langchain_huggingface",
-    "NVIDIA": "langchain_nvidia_ai_endpoints",
-    "Ollama": "langchain_ollama",
-    "AI21": "langchain_ai21",
-    "Upstage": "langchain_upstage",
-    "Databricks": "databricks_langchain",
-    "Watsonx": "langchain_ibm",
-    "xAI": "langchain_xai",
-    "Perplexity": "langchain_perplexity",
-}
-
-
-class LangchainProvider(enum.Enum):
-    Abso = "ChatAbso"
-    AI21 = "ChatAI21"
-    Alibaba = "ChatAlibabaCloud"
-    Anthropic = "ChatAnthropic"
-    Anyscale = "ChatAnyscale"
-    AzureAIChatCompletionsModel = "AzureAIChatCompletionsModel"
-    AzureOpenAI = "AzureChatOpenAI"
-    AzureMLEndpoint = "ChatAzureMLEndpoint"
-    Baichuan = "ChatBaichuan"
-    Qianfan = "ChatQianfan"
-    Bedrock = "ChatBedrock"
-    Cerebras = "ChatCerebras"
-    CloudflareWorkersAI = "ChatCloudflareWorkersAI"
-    Cohere = "ChatCohere"
-    ContextualAI = "ChatContextualAI"
-    Coze = "ChatCoze"
-    Dappier = "ChatDappier"
-    Databricks = "ChatDatabricks"
-    DeepInfra = "ChatDeepInfra"
-    DeepSeek = "ChatDeepSeek"
-    EdenAI = "ChatEdenAI"
-    EverlyAI = "ChatEverlyAI"
-    Fireworks = "ChatFireworks"
-    Friendli = "ChatFriendli"
-    GigaChat = "ChatGigaChat"
-    Goodfire = "ChatGoodfire"
-    GoogleAI = "ChatGoogleAI"
-    VertexAI = "VertexAI"
-    GPTRouter = "ChatGPTRouter"
-    Groq = "ChatGroq"
-    HuggingFace = "ChatHuggingFace"
-    Watsonx = "ChatWatsonx"
-    Jina = "ChatJina"
-    Kinetica = "ChatKinetica"
-    Konko = "ChatKonko"
-    LiteLLM = "ChatLiteLLM"
-    LiteLLMRouter = "ChatLiteLLMRouter"
-    Llama2Chat = "Llama2Chat"
-    LlamaAPI = "ChatLlamaAPI"
-    LlamaEdge = "ChatLlamaEdge"
-    LlamaCpp = "ChatLlamaCpp"
-    Maritalk = "ChatMaritalk"
-    MiniMax = "ChatMiniMax"
-    MistralAI = "ChatMistralAI"
-    MLX = "ChatMLX"
-    ModelScope = "ChatModelScope"
-    Moonshot = "ChatMoonshot"
-    Naver = "ChatNaver"
-    Netmind = "ChatNetmind"
-    NVIDIA = "ChatNVIDIA"
-    OCIModelDeployment = "ChatOCIModelDeployment"
-    OCIGenAI = "ChatOCIGenAI"
-    OctoAI = "ChatOctoAI"
-    Ollama = "ChatOllama"
-    OpenAI = "ChatOpenAI"
-    Outlines = "ChatOutlines"
-    Perplexity = "ChatPerplexity"
-    Pipeshift = "ChatPipeshift"
-    PredictionGuard = "ChatPredictionGuard"
-    PremAI = "ChatPremAI"
-    PromptLayerOpenAI = "PromptLayerChatOpenAI"
-    QwQ = "ChatQwQ"
-    Reka = "ChatReka"
-    RunPod = "ChatRunPod"
-    SambaNovaCloud = "ChatSambaNovaCloud"
-    SambaStudio = "ChatSambaStudio"
-    SeekrFlow = "ChatSeekrFlow"
-    SnowflakeCortex = "ChatSnowflakeCortex"
-    Solar = "ChatSolar"
-    SparkLLM = "ChatSparkLLM"
-    Nebula = "ChatNebula"
-    Hunyuan = "ChatHunyuan"
-    Together = "ChatTogether"
-    TongyiQwen = "ChatTongyiQwen"
-    Upstage = "ChatUpstage"
-    Vectara = "ChatVectara"
-    VLLM = "ChatVLLM"
-    VolcEngine = "ChatVolcEngine"
-    Writer = "ChatWriter"
-    xAI = "ChatXAI"
-    Xinference = "ChatXinference"
-    Yandex = "ChatYandex"
-    Yi = "ChatYi"
-    Yuan2 = "ChatYuan2"
-    ZhipuAI = "ChatZhipuAI"
+    raise ImportError("langchain is not installed. Please install it using `pip install langchain`")
 
 
 class LangchainLLM(LLMBase):
     def __init__(self, config: Optional[BaseLlmConfig] = None):
         super().__init__(config)
 
-        provider = self.config.langchain_provider
-        if provider not in LangchainProvider.__members__:
-            raise ValueError(f"Invalid provider: {provider}")
-        model_name = LangchainProvider[provider].value
+        if self.config.model is None:
+            raise ValueError("`model` parameter is required")
 
-        try:
-            # Check if this provider needs a specialized package
-            if provider in PROVIDER_PACKAGES:
-                if provider == "Anthropic": # Special handling for Anthropic with Pydantic v2
-                    try:
-                        from langchain_anthropic import ChatAnthropic
-                        model_class = ChatAnthropic
-                    except ImportError:
-                        raise ImportError("langchain_anthropic not found. Please install it with `pip install langchain-anthropic`")
-                else:
-                    package_name = PROVIDER_PACKAGES[provider]
-                    try:
-                            # Import the model class directly from the package
-                        module_path = f"{package_name}"
-                        model_class = __import__(module_path, fromlist=[model_name])
-                        model_class = getattr(model_class, model_name)
-                    except ImportError:
-                        raise ImportError(
-                            f"Package {package_name} not found. " f"Please install it with `pip install {package_name}`"
-                        )
-                    except AttributeError:
-                        raise ImportError(f"Model {model_name} not found in {package_name}")
-            else:
-                # Use the default langchain_community module
-                if not hasattr(chat_models, model_name):
-                    raise ImportError(f"Provider {provider} not found in langchain_community.chat_models")
+        if not isinstance(self.config.model, BaseChatModel):
+            raise ValueError("`model` must be an instance of BaseChatModel")
 
-                model_class = getattr(chat_models, model_name)
-
-            # Initialize the model with relevant config parameters
-            self.langchain_model = model_class(
-                model=self.config.model,
-                temperature=self.config.temperature,
-                max_tokens=self.config.max_tokens
-            )
-        except (ImportError, AttributeError, ValueError) as e:
-            raise ImportError(f"Error setting up langchain model for provider {provider}: {str(e)}")
+        self.langchain_model = self.config.model
 
     def generate_response(
         self,

mem0/memory/main.py

@@ -623,14 +623,13 @@ class Memory(MemoryBase):
         capture_event("mem0._create_memory", self, {"memory_id": memory_id})
         return memory_id
 
-    def _create_procedural_memory(self, messages, metadata=None, llm=None, prompt=None):
+    def _create_procedural_memory(self, messages, metadata=None, prompt=None):
         """
         Create a procedural memory
 
         Args:
             messages (list): List of messages to create a procedural memory from.
             metadata (dict): Metadata to create a procedural memory from.
-            llm (BaseChatModel, optional): LLM class to use for generating procedural memories. Defaults to None. Useful when user is using LangChain ChatModel.
             prompt (str, optional): Prompt to use for the procedural memory creation. Defaults to None.
         """
         try:
@@ -650,12 +649,7 @@ class Memory(MemoryBase):
         ]
 
         try:
-            if llm is not None:
-                parsed_messages = convert_to_messages(parsed_messages)
-                response = llm.invoke(input=parsed_messages)
-                procedural_memory = response.content
-            else:
-                procedural_memory = self.llm.generate_response(messages=parsed_messages)
+            procedural_memory = self.llm.generate_response(messages=parsed_messages)
         except Exception as e:
             logger.error(f"Error generating procedural memory summary: {e}")
             raise

mem0/utils/factory.py

@@ -50,6 +50,7 @@ class EmbedderFactory:
         "vertexai": "mem0.embeddings.vertexai.VertexAIEmbedding",
         "together": "mem0.embeddings.together.TogetherEmbedding",
         "lmstudio": "mem0.embeddings.lmstudio.LMStudioEmbedding",
+        "langchain": "mem0.embeddings.langchain.LangchainEmbedding",
     }
 
     @classmethod