Adding Gemini (#1862)

Makefile

@@ -12,7 +12,8 @@ install:
 
 install_all:
 	poetry install
-	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai
+	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai \
+	                        google-generativeai
 
 # Format code with ruff
 format:

docs/components/embedders/models/gemini.mdx

@@ -0,0 +1,41 @@
+---
+title: Gemini
+---
+
+To use Gemini embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["GOOGLE_API_KEY"] = "key"
+
+config = {
+    "embedder": {
+        "provider": "gemini",
+        "config": {
+            "model": "models/text-embedding-004"
+        }
+    },
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "collection_name": "test",
+            "embedding_model_dims": 768,
+        }
+    },
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Gemini embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `models/text-embedding-004` |

docs/components/embedders/overview.mdx

@@ -13,6 +13,7 @@ See the list of supported embedders below.
   <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
   <Card title="Ollama" href="/components/embedders/models/ollama"></Card>
   <Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
+  <Card title="Gemini" href="/components/embedders/models/gemini"></Card>
   <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
 </CardGroup>
 

docs/mint.json

@@ -125,7 +125,8 @@
                 "components/embedders/models/openai",
                 "components/embedders/models/azure_openai",
                 "components/embedders/models/ollama",
-                "components/embedders/models/huggingface"
+                "components/embedders/models/huggingface",
+                "components/embedders/models/gemini"
               ]
             }
           ]

mem0/embeddings/configs.py

@@ -13,7 +13,7 @@ class EmbedderConfig(BaseModel):
     @field_validator("config")
     def validate_config(cls, v, values):
         provider = values.data.get("provider")
-        if provider in ["openai", "ollama", "huggingface", "azure_openai", "vertexai"]:
+        if provider in ["openai", "ollama", "huggingface", "azure_openai", "gemini", "vertexai"]:
             return v
         else:
             raise ValueError(f"Unsupported embedding provider: {provider}")

mem0/embeddings/gemini.py

@@ -0,0 +1,27 @@
+import os
+from typing import Optional
+import google.generativeai as genai
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
+
+
+class GoogleGenAIEmbedding(EmbeddingBase):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+        if self.config.model is None:
+            self.config.model = "models/text-embedding-004" # embedding-dim = 768
+
+        genai.configure(api_key=self.config.api_key or os.getenv("GOOGLE_API_KEY"))
+
+    def embed(self, text):
+        """
+        Get the embedding for the given text using Google Generative AI.
+        Args:
+            text (str): The text to embed.
+        Returns:
+            list: The embedding vector.
+        """
+        text = text.replace("\n", " ")
+        response = genai.embed_content(model=self.config.model, content=text)
+        return response['embedding']

mem0/utils/factory.py

@@ -41,6 +41,7 @@ class EmbedderFactory:
         "ollama": "mem0.embeddings.ollama.OllamaEmbedding",
         "huggingface": "mem0.embeddings.huggingface.HuggingFaceEmbedding",
         "azure_openai": "mem0.embeddings.azure_openai.AzureOpenAIEmbedding",
+        "gemini": "mem0.embeddings.gemini.GoogleGenAIEmbedding",
     }
 
     @classmethod