[feat add]FastEmbed embedding for local embeddings (#3552)

mem0/embeddings/configs.py

@@ -24,6 +24,7 @@ class EmbedderConfig(BaseModel):
             "lmstudio",
             "langchain",
             "aws_bedrock",
+            "fastembed",
         ]:
             return v
         else:

mem0/embeddings/fastembed.py

@@ -0,0 +1,29 @@
+from typing import Optional, Literal
+
+from mem0.embeddings.base import EmbeddingBase
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+
+try:
+    from fastembed import TextEmbedding
+except ImportError:
+    raise ImportError("FastEmbed is not installed.  Please install it using `pip install fastembed`")
+
+class FastEmbedEmbedding(EmbeddingBase):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        self.config.model = self.config.model or "thenlper/gte-large"
+        self.dense_model = TextEmbedding(model_name = self.config.model)
+
+    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
+        """
+        Convert the text to embeddings using FastEmbed running in the Onnx runtime
+        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.
+        """
+        text = text.replace("\n", " ")
+        embeddings = list(self.dense_model.embed(text))
+        return embeddings[0]

mem0/utils/factory.py

@@ -145,6 +145,7 @@ class EmbedderFactory:
         "lmstudio": "mem0.embeddings.lmstudio.LMStudioEmbedding",
         "langchain": "mem0.embeddings.langchain.LangchainEmbedding",
         "aws_bedrock": "mem0.embeddings.aws_bedrock.AWSBedrockEmbedding",
+        "fastembed": "mem0.embeddings.fastembed.FastEmbedEmbedding",
     }
 
     @classmethod