Merge branch 'main' into fix/together-base-url-py

The Python Together LLM and embedder ignored TOGETHER_API_BASE, unlike
their TypeScript siblings, so users behind a gateway or proxy could not
redirect Together traffic. Add together_base_url to a new TogetherConfig
(LLM side) and to BaseEmbedderConfig (embedder side, matching the existing
openai_base_url/huggingface_base_url pattern), and resolve base_url with
explicit config value > TOGETHER_API_BASE env var > hardcoded default in
both mem0/llms/together.py and mem0/embeddings/together.py. Also register
TogetherConfig in LlmFactory.provider_to_class so config dicts routed
through Memory pick it up.

Closes #6587

mem0/configs/embeddings/base.py

@@ -20,6 +20,8 @@ class BaseEmbedderConfig(ABC):
         ollama_base_url: Optional[str] = None,
         # Openai specific
         openai_base_url: Optional[str] = None,
+        # Together specific
+        together_base_url: Optional[str] = None,
         # Huggingface specific
         model_kwargs: Optional[dict] = None,
         huggingface_base_url: Optional[str] = None,
@@ -58,6 +60,8 @@ class BaseEmbedderConfig(ABC):
         :type huggingface_base_url: Optional[str], optional
         :param openai_base_url: Openai base URL to be use, defaults to "https://api.openai.com/v1"
         :type openai_base_url: Optional[str], optional
+        :param together_base_url: Together base URL to be use, defaults to None
+        :type together_base_url: Optional[str], optional
         :param azure_kwargs: key-value arguments for the AzureOpenAI embedding model, defaults a dict inside init
         :type azure_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
         :param http_client_proxies: The proxy server settings used to create self.http_client, defaults to None
@@ -77,6 +81,7 @@ class BaseEmbedderConfig(ABC):
         self.model = model
         self.api_key = api_key
         self.openai_base_url = openai_base_url
+        self.together_base_url = together_base_url
         self.embedding_dims = embedding_dims
 
         # AzureOpenAI specific

mem0/configs/llms/together.py

@@ -0,0 +1,56 @@
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class TogetherConfig(BaseLlmConfig):
+    """
+    Configuration class for Together-specific parameters.
+    Inherits from BaseLlmConfig and adds Together-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # Together-specific parameters
+        together_base_url: Optional[str] = None,
+    ):
+        """
+        Initialize Together configuration.
+
+        Args:
+            model: Together model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: Together API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            together_base_url: Together API base URL, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # Together-specific parameters
+        self.together_base_url = together_base_url

mem0/embeddings/together.py

@@ -13,8 +13,9 @@ class TogetherEmbedding(EmbeddingBase):
 
         self.config.model = self.config.model or "intfloat/multilingual-e5-large-instruct"
         api_key = self.config.api_key or os.getenv("TOGETHER_API_KEY")
+        base_url = self.config.together_base_url or os.getenv("TOGETHER_API_BASE") or "https://api.together.ai/v1"
         self.config.embedding_dims = self.config.embedding_dims or 1024
-        self.client = Together(api_key=api_key)
+        self.client = Together(api_key=api_key, base_url=base_url)
 
     def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
         """

mem0/embeddings/vertexai.py

@@ -8,14 +8,6 @@ from mem0.embeddings.base import EmbeddingBase
 from mem0.utils.gcp_auth import GCPAuthenticator
 
 
-def _max_instances_per_request(model: str) -> int:
-    # Vertex AI's per-request input cap depends on the model family: gemini-embedding-*
-    # accepts exactly one input text per predict() call, while text-embedding-004/005 and
-    # text-multilingual-embedding-002 accept up to 250.
-    # https://docs.cloud.google.com/vertex-ai/docs/samples/aiplatform-sdk-embedding
-    return 1 if model.startswith("gemini-embedding") else 250
-
-
 class VertexAIEmbedding(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config)
@@ -80,9 +72,8 @@ class VertexAIEmbedding(EmbeddingBase):
                 raise ValueError(f"Invalid memory action: {memory_action}")
             embedding_type = self.embedding_types[memory_action]
         all_embeddings = []
-        chunk_size = _max_instances_per_request(self.config.model)
-        for i in range(0, len(texts), chunk_size):
-            chunk = texts[i : i + chunk_size]
+        for i in range(0, len(texts), 250):
+            chunk = texts[i : i + 250]
             inputs = [TextEmbeddingInput(text=t, task_type=embedding_type) for t in chunk]
             results = self.model.get_embeddings(texts=inputs, output_dimensionality=self.config.embedding_dims)
             all_embeddings.extend(r.values for r in results)

mem0/llms/together.py

@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 try:
     from together import Together
@@ -8,19 +8,40 @@ except ImportError:
     raise ImportError("The 'together' library is required. Please install it using 'pip install together'.")
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.together import TogetherConfig
 from mem0.llms.base import LLMBase
 from mem0.memory.utils import extract_json
 
 
 class TogetherLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, TogetherConfig, Dict]] = None):
+        # Convert to TogetherConfig if needed
+        if config is None:
+            config = TogetherConfig()
+        elif isinstance(config, dict):
+            config = TogetherConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, TogetherConfig):
+            # Convert BaseLlmConfig to TogetherConfig
+            config = TogetherConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client_proxies,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
             self.config.model = "MiniMaxAI/MiniMax-M3"
 
         api_key = self.config.api_key or os.getenv("TOGETHER_API_KEY")
-        self.client = Together(api_key=api_key)
+        base_url = self.config.together_base_url or os.getenv("TOGETHER_API_BASE") or "https://api.together.ai/v1"
+        self.client = Together(api_key=api_key, base_url=base_url)
 
     def _parse_response(self, response, tools):
         """

mem0/utils/factory.py

@@ -13,6 +13,7 @@ from mem0.configs.llms.lmstudio import LMStudioConfig
 from mem0.configs.llms.minimax import MinimaxConfig
 from mem0.configs.llms.ollama import OllamaConfig
 from mem0.configs.llms.openai import OpenAIConfig
+from mem0.configs.llms.together import TogetherConfig
 from mem0.configs.llms.vllm import VllmConfig
 from mem0.configs.llms.xai import XAIConfig
 from mem0.configs.rerankers.base import BaseRerankerConfig
@@ -43,7 +44,7 @@ class LlmFactory:
         "ollama": ("mem0.llms.ollama.OllamaLLM", OllamaConfig),
         "openai": ("mem0.llms.openai.OpenAILLM", OpenAIConfig),
         "groq": ("mem0.llms.groq.GroqLLM", BaseLlmConfig),
-        "together": ("mem0.llms.together.TogetherLLM", BaseLlmConfig),
+        "together": ("mem0.llms.together.TogetherLLM", TogetherConfig),
         "aws_bedrock": ("mem0.llms.aws_bedrock.AWSBedrockLLM", AWSBedrockConfig),
         "litellm": ("mem0.llms.litellm.LiteLLM", BaseLlmConfig),
         "azure_openai": ("mem0.llms.azure_openai.AzureOpenAILLM", AzureOpenAIConfig),

tests/embeddings/test_together_embeddings.py

@@ -85,3 +85,29 @@ def test_explicit_config_overrides_defaults(mock_together_client):
     mock_together_client.embeddings.create.return_value = Mock(data=[Mock(embedding=[0.0] * 768)])
     embedder.embed("hello")
     mock_together_client.embeddings.create.assert_called_once_with(model="BAAI/bge-base-en-v1.5", input="hello")
+
+
+def test_together_embedding_base_url(monkeypatch):
+    # case1: default config uses Together's official base url
+    monkeypatch.delenv("TOGETHER_API_BASE", raising=False)
+    config = BaseEmbedderConfig(model=DEFAULT_MODEL, embedding_dims=DEFAULT_EMBEDDING_DIMS, api_key="api_key")
+    embedder = TogetherEmbedding(config)
+    assert str(embedder.client.base_url) == "https://api.together.ai/v1/"
+
+    # case2: with env variable TOGETHER_API_BASE
+    provider_base_url = "https://api.provider.com/v1/"
+    monkeypatch.setenv("TOGETHER_API_BASE", provider_base_url)
+    config = BaseEmbedderConfig(model=DEFAULT_MODEL, embedding_dims=DEFAULT_EMBEDDING_DIMS, api_key="api_key")
+    embedder = TogetherEmbedding(config)
+    assert str(embedder.client.base_url) == provider_base_url
+
+    # case3: with config.together_base_url (explicit config beats env var)
+    config_base_url = "https://api.config.com/v1/"
+    config = BaseEmbedderConfig(
+        model=DEFAULT_MODEL,
+        embedding_dims=DEFAULT_EMBEDDING_DIMS,
+        api_key="api_key",
+        together_base_url=config_base_url,
+    )
+    embedder = TogetherEmbedding(config)
+    assert str(embedder.client.base_url) == config_base_url

tests/embeddings/test_vertexai_embeddings.py

@@ -163,9 +163,7 @@ def test_invalid_memory_action(mock_text_embedding_model, mock_config):
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_embed_batch_single_call(mock_text_embedding_model, mock_os_environ, mock_config):
-    # text-embedding-005 accepts up to 250 inputs per request, so 2 texts fit in one call.
-    # (gemini-embedding-* only accepts 1 input per request; see test_embed_batch_default_model_sends_one_text_per_call.)
-    mock_config.return_value.model = "text-embedding-005"
+    mock_config.return_value.model = "gemini-embedding-001"
     mock_config.return_value.embedding_dims = 256
 
     config = mock_config()
@@ -200,9 +198,7 @@ def test_embed_batch_empty_list(mock_text_embedding_model, mock_os_environ, mock
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_embed_batch_count_mismatch_raises(mock_text_embedding_model, mock_os_environ, mock_config):
-    # Use a 250-cap model so both texts land in a single chunk/call, keeping the mismatch
-    # (1 embedding returned for that one call) triggered by that single call.
-    mock_config.return_value.model = "text-embedding-005"
+    mock_config.return_value.model = "gemini-embedding-001"
     mock_config.return_value.embedding_dims = 256
 
     config = mock_config()
@@ -265,8 +261,8 @@ def test_embed_batch_invalid_memory_action_raises(mock_text_embedding_model, moc
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_embed_batch_chunking_triggers_two_api_calls(mock_text_embedding_model, mock_os_environ, mock_config):
-    """text-embedding-005 accepts up to 250 inputs/request: 300 texts -> 2 calls (chunks of 250 and 50)."""
-    mock_config.return_value.model = "text-embedding-005"
+    """300 texts must produce exactly 2 get_embeddings calls (chunks of 250 and 50)."""
+    mock_config.return_value.model = "gemini-embedding-001"
     mock_config.return_value.embedding_dims = 256
 
     config = mock_config()
@@ -282,25 +278,3 @@ def test_embed_batch_chunking_triggers_two_api_calls(mock_text_embedding_model,
 
     assert mock_text_embedding_model.from_pretrained.return_value.get_embeddings.call_count == 2
     assert len(result) == 300
-
-
-@patch("mem0.embeddings.vertexai.TextEmbeddingModel")
-def test_embed_batch_default_model_sends_one_text_per_call(mock_text_embedding_model, mock_os_environ, mock_config):
-    """gemini-embedding-001 (default) accepts 1 input/request: 300 texts -> 300 calls of 1 text each."""
-    mock_config.return_value.model = "gemini-embedding-001"
-    mock_config.return_value.embedding_dims = 256
-
-    config = mock_config()
-    embedder = VertexAIEmbedding(config)
-
-    def make_single_response(texts, output_dimensionality):
-        assert len(texts) == 1
-        return [Mock(values=[0.1, 0.2]) for _ in texts]
-
-    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.side_effect = make_single_response
-
-    texts = [f"text {i}" for i in range(300)]
-    result = embedder.embed_batch(texts)
-
-    assert mock_text_embedding_model.from_pretrained.return_value.get_embeddings.call_count == 300
-    assert len(result) == 300

tests/llms/test_together.py

@@ -3,6 +3,7 @@ from unittest.mock import Mock, patch
 import pytest
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.together import TogetherConfig
 from mem0.llms.together import TogetherLLM
 
 
@@ -14,6 +15,38 @@ def mock_together_client():
         yield mock_client
 
 
+def test_together_llm_base_url(monkeypatch):
+    # case1: default config uses Together's official base url
+    monkeypatch.delenv("TOGETHER_API_BASE", raising=False)
+    config = BaseLlmConfig(
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
+    )
+    llm = TogetherLLM(config)
+    assert str(llm.client.base_url) == "https://api.together.ai/v1/"
+
+    # case2: with env variable TOGETHER_API_BASE
+    provider_base_url = "https://api.provider.com/v1/"
+    monkeypatch.setenv("TOGETHER_API_BASE", provider_base_url)
+    config = TogetherConfig(
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
+    )
+    llm = TogetherLLM(config)
+    assert str(llm.client.base_url) == provider_base_url
+
+    # case3: with config.together_base_url (explicit config beats env var)
+    config_base_url = "https://api.config.com/v1/"
+    config = TogetherConfig(
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+        api_key="api_key",
+        together_base_url=config_base_url,
+    )
+    llm = TogetherLLM(config)
+    assert str(llm.client.base_url) == config_base_url
+
+
 def test_generate_response_without_tools(mock_together_client):
     config = BaseLlmConfig(model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0)
     llm = TogetherLLM(config)