Add embedder docs and config changes (#1684)

docs/components/embedders.mdx

@@ -0,0 +1,64 @@
+---
+title: 🧩 Embedding models
+---
+
+## Overview
+
+Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
+
+<CardGroup cols={3}>
+    <Card title="OpenAI" href="#openai"></Card>
+    <Card title="Ollama" href="#ollama"></Card>
+</CodeGroup>
+
+> When using `Qdrant` as a vector database, ensure you update the `embedding_model_dims` to match the dimensions of the embedding model you are using.
+
+## OpenAI
+
+To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+Example of how to select the desired embedding model:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key"
+
+config = {
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+## Ollama
+
+You can use embedding models from Ollama to run Mem0 locally.
+
+Here's how to select it:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key"
+
+config = {
+    "embedder": {
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```

docs/mint.json

@@ -69,6 +69,10 @@
           "group": "Vector Database",
           "pages": ["components/vectordb"]
         },
+        {
+          "group": "Embedding Models",
+          "pages": ["components/embedders"]
+        },
         {
           "group": "Features",
           "pages": ["features/openai_compatibility"]

mem0/configs/embeddings/base.py

@@ -9,9 +9,12 @@ class BaseEmbedderConfig(ABC):
     def __init__(
         self,
         model: Optional[str] = None,
+        api_key: Optional[str] = None,
         embedding_dims: Optional[int] = None,
+
         # Ollama specific
-        base_url: Optional[str] = None,
+        ollama_base_url: Optional[str] = None,
+
         # Huggingface specific
         model_kwargs: Optional[dict] = None
     ):
@@ -20,20 +23,23 @@ class BaseEmbedderConfig(ABC):
 
         :param model: Embedding model to use, defaults to None
         :type model: Optional[str], optional
+        :param api_key: API key to be use, defaults to None
+        :type api_key: Optional[str], optional
         :param embedding_dims: The number of dimensions in the embedding, defaults to None
         :type embedding_dims: Optional[int], optional
-        :param base_url: Base URL for the Ollama API, defaults to None
+        :param ollama_base_url: Base URL for the Ollama API, defaults to None
-        :type base_url: Optional[str], optional
+        :type ollama_base_url: Optional[str], optional
         :param model_kwargs: key-value arguments for the huggingface embedding model, defaults a dict inside init
         :type model_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
 
         """
         
         self.model = model
+        self.api_key = api_key
         self.embedding_dims = embedding_dims
 
         # Ollama specific
-        self.base_url = base_url
+        self.ollama_base_url = ollama_base_url
 
         # Huggingface specific
         self.model_kwargs = model_kwargs or {}

mem0/configs/llms/base.py

@@ -10,6 +10,7 @@ class BaseLlmConfig(ABC):
         self,
         model: Optional[str] = None,
         temperature: float = 0,
+        api_key: Optional[str] = None,
         max_tokens: int = 3000,
         top_p: float = 0,
         top_k: int = 1,
@@ -32,6 +33,8 @@ class BaseLlmConfig(ABC):
         :param temperature:  Controls the randomness of the model's output.
         Higher values (closer to 1) make output more random, lower values make it more deterministic, defaults to 0
         :type temperature: float, optional
+        :param api_key: OpenAI API key to be use, defaults to None
+        :type api_key: Optional[str], optional
         :param max_tokens: Controls how many tokens are generated, defaults to 3000
         :type max_tokens: int, optional
         :param top_p: Controls the diversity of words. Higher values (closer to 1) make word selection more diverse,
@@ -39,15 +42,15 @@ class BaseLlmConfig(ABC):
         :type top_p: float, optional
         :param top_k: Controls the diversity of words. Higher values make word selection more diverse, defaults to 0
         :type top_k: int, optional
-        :param models: Controls the Openrouter models used, defaults to None
+        :param models: Openrouter models to use, defaults to None
         :type models: Optional[list[str]], optional
-        :param route: Controls the Openrouter route used, defaults to "fallback"
+        :param route: Openrouter route to be used, defaults to "fallback"
         :type route: Optional[str], optional
-        :param openrouter_base_url: Controls the Openrouter base URL used, defaults to "https://openrouter.ai/api/v1"
+        :param openrouter_base_url: Openrouter base URL to be use, defaults to "https://openrouter.ai/api/v1"
         :type openrouter_base_url: Optional[str], optional
-        :param site_url: Controls the Openrouter site URL used, defaults to None
+        :param site_url: Openrouter site URL to use, defaults to None
         :type site_url: Optional[str], optional
-        :param app_name: Controls the Openrouter app name used, defaults to None
+        :param app_name: Openrouter app name to use, defaults to None
         :type app_name: Optional[str], optional
         :param ollama_base_url: The base URL of the LLM, defaults to None
         :type ollama_base_url: Optional[str], optional
@@ -55,6 +58,7 @@ class BaseLlmConfig(ABC):
         
         self.model = model
         self.temperature = temperature
+        self.api_key = api_key
         self.max_tokens = max_tokens
         self.top_p = top_p
         self.top_k = top_k

mem0/configs/vector_stores/chroma.py

@@ -1,4 +1,4 @@
-from typing import Optional,ClassVar
+from typing import Optional, ClassVar, Dict, Any
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -21,6 +21,17 @@ class ChromaDbConfig(BaseModel):
         if not path and not (host and port):
             raise ValueError("Either 'host' and 'port' or 'path' must be provided.")
         return values
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.model_fields.keys())
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+        if extra_fields:
+            raise ValueError(f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}")
+        return values
     
-    class Config:
+    model_config = {
-        arbitrary_types_allowed = True
+        "arbitrary_types_allowed": True,
+    }

mem0/configs/vector_stores/qdrant.py

@@ -1,6 +1,5 @@
-from typing import Optional,ClassVar
-
 from pydantic import BaseModel, Field, model_validator
+from typing import Optional, ClassVar, Dict, Any
 
 class QdrantConfig(BaseModel):
     from qdrant_client import QdrantClient 
@@ -14,10 +13,11 @@ class QdrantConfig(BaseModel):
     path: Optional[str] = Field("/tmp/qdrant", description="Path for local Qdrant database")
     url: Optional[str] = Field(None, description="Full URL for Qdrant server")
     api_key: Optional[str] = Field(None, description="API key for Qdrant server")
-    on_disk: Optional[bool]= Field(False, description="Enables persistant storage")
+    on_disk: Optional[bool] = Field(False, description="Enables persistent storage")
 
     @model_validator(mode="before")
-    def check_host_port_or_path(cls, values):
+    @classmethod
+    def check_host_port_or_path(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         host, port, path, url, api_key = (
             values.get("host"),
             values.get("port"),
@@ -31,5 +31,16 @@ class QdrantConfig(BaseModel):
             )
         return values
 
-    class Config:
+    @model_validator(mode="before")
-        arbitrary_types_allowed = True
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.model_fields.keys())
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+        if extra_fields:
+            raise ValueError(f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}")
+        return values
+
+    model_config = {
+        "arbitrary_types_allowed": True,
+    }

mem0/embeddings/ollama.py

@@ -18,7 +18,7 @@ class OllamaEmbedding(EmbeddingBase):
         if not self.config.embedding_dims:
             self.config.embedding_dims=512
             
-        self.client = Client(host=self.config.base_url)
+        self.client = Client(host=self.config.ollama_base_url)
         self._ensure_model_exists()
 
     def _ensure_model_exists(self):

mem0/embeddings/openai.py

@@ -1,3 +1,4 @@
+import os
 from typing import Optional
 
 from openai import OpenAI
@@ -9,12 +10,11 @@ class OpenAIEmbedding(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config)
     
-        if not self.config.model:
+        self.config.model = self.config.model or "text-embedding-3-small"
-            self.config.model="text-embedding-3-small"
+        self.config.embedding_dims = self.config.embedding_dims or 1536
-        if not self.config.embedding_dims:
-            self.config.embedding_dims=1536
 
-        self.client = OpenAI()
+        api_key = os.getenv("OPENAI_API_KEY") or self.config.api_key
+        self.client = OpenAI(api_key=api_key)
 
     def embed(self, text):
         """

mem0/llms/openai.py

@@ -14,10 +14,11 @@ class OpenAILLM(LLMBase):
         if not self.config.model:
             self.config.model="gpt-4o"
 
-        if os.environ.get("OPENROUTER_API_KEY"):
+        if os.environ.get("OPENROUTER_API_KEY"): # Use OpenRouter
             self.client = OpenAI(api_key=os.environ.get("OPENROUTER_API_KEY"), base_url=self.config.openrouter_base_url)
         else:
-            self.client = OpenAI()
+            api_key = os.getenv("OPENAI_API_KEY") or self.config.api_key
+            self.client = OpenAI(api_key=api_key)
     
     def _parse_response(self, response, tools):
         """

mem0/memory/main.py

@@ -374,6 +374,7 @@ class Memory(MemoryBase):
 
         new_metadata = metadata or {}
         new_metadata["data"] = data
+        new_metadata["hash"] = existing_memory.payload.get("hash")
         new_metadata["created_at"] = existing_memory.payload.get("created_at")
         new_metadata["updated_at"] = datetime.now(pytz.timezone('US/Pacific')).isoformat()
         

mem0/vector_stores/configs.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel, Field, model_validator
 
 class VectorStoreConfig(BaseModel):
     provider: str = Field(
-        description="Provider of the vector store (e.g., 'qdrant', 'chromadb')",
+        description="Provider of the vector store (e.g., 'qdrant', 'chroma')",
         default="qdrant",
     )
     config: Optional[Dict] = Field(

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mem0ai"
-version = "0.0.15"
+version = "0.0.16"
 description = "Long-term memory for AI Agents"
 authors = ["Mem0 <founders@mem0.ai>"]
 exclude = [