[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

pyproject.toml

@@ -72,6 +72,7 @@ extras = [
     "sentence-transformers>=5.0.0",
     "elasticsearch>=8.0.0,<9.0.0",
     "opensearch-py>=2.0.0",
+    "fastembed>=0.3.1",
 ]
 test = [
     "pytest>=8.2.2",

tests/embeddings/test_fastembed_embeddings.py

@@ -0,0 +1,46 @@
+from unittest.mock import Mock, patch
+
+import pytest
+import numpy as np
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+
+try:
+    from mem0.embeddings.fastembed import FastEmbedEmbedding
+except ImportError:
+    pytest.skip("fastembed not installed", allow_module_level=True)
+  
+
+@pytest.fixture
+def mock_fastembed_client():
+    with patch("mem0.embeddings.fastembed.TextEmbedding") as mock_fastembed:
+        mock_client = Mock()
+        mock_fastembed.return_value = mock_client
+        yield mock_client
+
+
+def test_embed_with_jina_model(mock_fastembed_client):
+    config = BaseEmbedderConfig(model="jinaai/jina-embeddings-v2-base-en", embedding_dims=768)
+    embedder = FastEmbedEmbedding(config)
+    
+    mock_embedding = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
+    mock_fastembed_client.embed.return_value = iter([mock_embedding])
+    
+    text = "Sample text to embed."
+    embedding = embedder.embed(text)
+    
+    mock_fastembed_client.embed.assert_called_once_with(text)
+    assert list(embedding) == [0.1, 0.2, 0.3, 0.4, 0.5]
+
+
+def test_embed_removes_newlines(mock_fastembed_client):
+    config = BaseEmbedderConfig(model="jinaai/jina-embeddings-v2-base-en", embedding_dims=768)
+    embedder = FastEmbedEmbedding(config)
+    
+    mock_embedding = np.array([0.7, 0.8, 0.9])
+    mock_fastembed_client.embed.return_value = iter([mock_embedding])
+    
+    text_with_newlines = "Hello\nworld"
+    embedding = embedder.embed(text_with_newlines)
+    
+    mock_fastembed_client.embed.assert_called_once_with("Hello world")
+    assert list(embedding) == [0.7, 0.8, 0.9]