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

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)