Add ollama embeddings (#1634)

mem0/configs/llms/base.py

@@ -12,6 +12,8 @@ class BaseLlmConfig(ABC):
         temperature: float = 0,
         max_tokens: int = 3000,
         top_p: float = 1,
+
+        # Ollama specific
         base_url: Optional[str] = None
     ):
         """
@@ -35,4 +37,6 @@ class BaseLlmConfig(ABC):
         self.temperature = temperature
         self.max_tokens = max_tokens
         self.top_p = top_p
-        self.base_url = base_url
+
+        # Ollama specific
+        self.base_url = base_url

mem0/embeddings/base.py

@@ -1,7 +1,21 @@
+from typing import Optional
 from abc import ABC, abstractmethod
 
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+
 
 class EmbeddingBase(ABC):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        """Initialize a base LLM class
+
+        :param config: Embedder configuration option class, defaults to None
+        :type config: Optional[BaseEmbedderConfig], optional
+        """
+        if config is None:
+            self.config = BaseEmbedderConfig()
+        else:
+            self.config = config
+
     @abstractmethod
     def embed(self, text):
         """

mem0/embeddings/ollama.py

@@ -1,20 +1,33 @@
-import ollama
+from typing import Optional
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
 
+try:
+    from ollama import Client
+except ImportError:
+    raise ImportError("Ollama requires extra dependencies. Install with `pip install ollama`") from None
+
 
 class OllamaEmbedding(EmbeddingBase):
-    def __init__(self, model="nomic-embed-text"):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
-        self.model = model
+        super().__init__(config)
+    
+        if not self.config.model:
+            self.config.model="nomic-embed-text"
+        if not self.config.embedding_dims:
+            self.config.embedding_dims=512
+            
+        self.client = Client(host=self.config.base_url)
         self._ensure_model_exists()
-        self.dims = 512
 
     def _ensure_model_exists(self):
         """
         Ensure the specified model exists locally. If not, pull it from Ollama.
         """
-        model_list = [m["name"] for m in ollama.list()["models"]]
+        local_models = self.client.list()["models"]
-        if not any(m.startswith(self.model) for m in model_list):
+        if not any(model.get("name") == self.config.model for model in local_models):
-            ollama.pull(self.model)
+            self.client.pull(self.config.model)
 
     def embed(self, text):
         """
@@ -26,5 +39,5 @@ class OllamaEmbedding(EmbeddingBase):
         Returns:
             list: The embedding vector.
         """
-        response = ollama.embeddings(model=self.model, prompt=text)
+        response = self.client.embeddings(model=self.config.model, prompt=text)
         return response["embedding"]

mem0/embeddings/openai.py

@@ -1,13 +1,20 @@
+from typing import Optional
 from openai import OpenAI
 
+from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
 
 
 class OpenAIEmbedding(EmbeddingBase):
-    def __init__(self, model="text-embedding-3-small"):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+    
+        if not self.config.model:
+            self.config.model="text-embedding-3-small"
+        if not self.config.embedding_dims:
+            self.config.embedding_dims=1536
+
         self.client = OpenAI()
-        self.model = model
-        self.dims = 1536
 
     def embed(self, text):
         """
@@ -21,7 +28,7 @@ class OpenAIEmbedding(EmbeddingBase):
         """
         text = text.replace("\n", " ")
         return (
-            self.client.embeddings.create(input=[text], model=self.model)
+            self.client.embeddings.create(input=[text], model=self.config.model)
             .data[0]
             .embedding
         )

mem0/memory/telemetry.py

@@ -48,7 +48,7 @@ telemetry = AnonymousTelemetry(
 def capture_event(event_name, memory_instance, additional_data=None):
     event_data = {
         "collection": memory_instance.collection_name,
-        "vector_size": memory_instance.embedding_model.dims,
+        "vector_size": memory_instance.embedding_model.config.embedding_dims,
         "history_store": "sqlite",
         "vector_store": f"{memory_instance.vector_store.__class__.__module__}.{memory_instance.vector_store.__class__.__name__}",
         "llm": f"{memory_instance.llm.__class__.__module__}.{memory_instance.llm.__class__.__name__}",

mem0/utils/factory.py

@@ -33,8 +33,7 @@ class LlmFactory:
 class EmbedderFactory:
     provider_to_class = {
         "openai": "mem0.embeddings.openai.OpenAIEmbedding",
-        "ollama": "mem0.embeddings.ollama.OllamaEmbedding",
+        "ollama": "mem0.embeddings.ollama.OllamaEmbedding"
-        "huggingface": "mem0.embeddings.huggingface.HuggingFaceEmbedding"
     }
 
     @classmethod

mem0/vector_stores/configs.py

@@ -73,8 +73,12 @@ class VectorStoreConfig(BaseModel):
         
         if isinstance(v, dict):
             if provider == "qdrant":
+                if "path" not in v:
+                    v["path"] = "/tmp/qdrant"
                 return QdrantConfig(**v)
             elif provider == "chromadb":
+                if "path" not in v:
+                    v["path"] = "/tmp/chromadb"
                 return ChromaDbConfig(**v)
         
         return v