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

Author: Dev-Khant

docs/changelog/overview.mdx

@@ -6,6 +6,15 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2025-04-07" description="v0.1.84">
+
+**New Features:**
+- **Langchain Embedder:** Added Langchain embedder integration
+
+**Improvements:**
+- **Langchain LLM:** Updated Langchain LLM integration to directly pass the Langchain object LLM
+</Update>
+
 <Update label="2025-04-07" description="v0.1.83">
 
 **Bug Fixes:**

docs/components/embedders/models/langchain.mdx

@@ -0,0 +1,120 @@
+---
+title: LangChain
+---
+
+Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
+
+For a complete list of available embedding models supported by LangChain, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+from langchain_openai import OpenAIEmbeddings
+
+# Set necessary environment variables for your chosen LangChain provider
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Initialize a LangChain embeddings model directly
+openai_embeddings = OpenAIEmbeddings(
+    model="text-embedding-3-small",
+    dimensions=1536
+)
+
+# Pass the initialized model to the config
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": openai_embeddings
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+</CodeGroup>
+
+## Supported LangChain Embedding Providers
+
+LangChain supports a wide range of embedding providers, including:
+
+- OpenAI (`OpenAIEmbeddings`)
+- Cohere (`CohereEmbeddings`)
+- Google (`VertexAIEmbeddings`)
+- Hugging Face (`HuggingFaceEmbeddings`)
+- Sentence Transformers (`HuggingFaceEmbeddings`)
+- Azure OpenAI (`AzureOpenAIEmbeddings`)
+- Ollama (`OllamaEmbeddings`)
+- Together (`TogetherEmbeddings`)
+- And many more
+
+You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available embedding providers, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
+
+## Provider-Specific Configuration
+
+When using LangChain as an embedder provider, you'll need to:
+
+1. Set the appropriate environment variables for your chosen embedding provider
+2. Import and initialize the specific model class you want to use
+3. Pass the initialized model instance to the config
+
+### Examples with Different Providers
+
+#### HuggingFace Embeddings
+
+```python
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Initialize a HuggingFace embeddings model
+hf_embeddings = HuggingFaceEmbeddings(
+    model_name="BAAI/bge-small-en-v1.5",
+    encode_kwargs={"normalize_embeddings": True}
+)
+
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": hf_embeddings
+        }
+    }
+}
+```
+
+#### Ollama Embeddings
+
+```python
+from langchain_ollama import OllamaEmbeddings
+
+# Initialize an Ollama embeddings model
+ollama_embeddings = OllamaEmbeddings(
+    model="nomic-embed-text"
+)
+
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": ollama_embeddings
+        }
+    }
+}
+```
+
+<Note>
+  Make sure to install the necessary LangChain packages and any provider-specific dependencies.
+</Note>
+
+## Config
+
+All available parameters for the `langchain` embedder config are present in [Master List of All Params in Config](../config).

docs/components/embedders/overview.mdx

@@ -23,6 +23,7 @@ See the list of supported embedders below.
   <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
   <Card title="Together" href="/components/embedders/models/together"></Card>
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
+  <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/config.mdx

@@ -109,7 +109,6 @@ Here's a comprehensive list of all parameters that can be used across different
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
     | `lmstudio_base_url`  | Base URL for LM Studio API                    | LM Studio         |
-    | `langchain_provider` | Provider for Langchain                      | Langchain         |
   </Tab>
   <Tab title="TypeScript">
     | Parameter            | Description                                   | Provider          |

docs/components/llms/models/langchain.mdx

@@ -12,19 +12,24 @@ For a complete list of available chat models supported by LangChain, refer to th
 ```python Python
 import os
 from mem0 import Memory
+from langchain_openai import ChatOpenAI
 
 # Set necessary environment variables for your chosen LangChain provider
-# For example, if using OpenAI through LangChain:
 os.environ["OPENAI_API_KEY"] = "your-api-key"
 
+# Initialize a LangChain model directly
+openai_model = ChatOpenAI(
+    model="gpt-4o",
+    temperature=0.2,
+    max_tokens=2000
+)
+
+# Pass the initialized model to the config
 config = {
     "llm": {
         "provider": "langchain",
         "config": {
-            "langchain_provider": "OpenAI",
-            "model": "gpt-4o",
-            "temperature": 0.2,
-            "max_tokens": 2000,
+            "model": openai_model
         }
     }
 }
@@ -53,15 +58,15 @@ LangChain supports a wide range of LLM providers, including:
 - HuggingFace (`HuggingFaceChatEndpoint`)
 - And many more
 
-You can specify any supported provider in the `langchain_provider` parameter. For a complete and up-to-date list of available providers, refer to the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat).
+You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available providers, refer to the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat).
 
 ## Provider-Specific Configuration
 
 When using LangChain as a provider, you'll need to:
 
 1. Set the appropriate environment variables for your chosen LLM provider
-2. Specify the LangChain provider class name in the `langchain_provider` parameter
-3. Include any additional configuration parameters required by the specific provider
+2. Import and initialize the specific model class you want to use
+3. Pass the initialized model instance to the config
 
 <Note>
   Make sure to install the necessary LangChain packages and any provider-specific dependencies.

docs/docs.json

@@ -161,7 +161,8 @@
                           "components/embedders/models/vertexai",
                           "components/embedders/models/gemini",
                           "components/embedders/models/lmstudio",
-                          "components/embedders/models/together"
+                          "components/embedders/models/together",
+                          "components/embedders/models/langchain"
                         ]
                       }
                     ]

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 @@ def __init__(
         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 @@ def __init__(
         :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 @@ def __init__(
 
         # 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 @@ def validate_config(cls, v, values):
             "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)