Add sambanova provider

docs/components/embedders/models/sambanova.mdx

@@ -0,0 +1,45 @@
+---
+title: SambaNova
+---
+
+To use SambaNova embedding models, set the `SAMBANOVA_API_KEY` environment variable. You can obtain the SambaNova API key from the [SambaNova Platform](https://cloud.sambanova.ai/apis).
+
+### Usage
+
+<Note> The `embedding_model_dims` parameter for `vector_store` should be set to `4096` for SambaNova embedder. </Note>
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["SAMBANOVA_API_KEY"] = "your_api_key"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "sambanova",
+        "config": {
+            "model": "gpt-oss-120b"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring SambaNova embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `gpt-oss-120b` |
+| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `api_key` | The SambaNova API key | `None` |

docs/components/embedders/overview.mdx

@@ -23,6 +23,7 @@ See the list of supported embedders below.
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
   <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
   <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
+  <Card title="SambaNova" href="/components/embedders/models/sambanova"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/models/sambanova.mdx

@@ -0,0 +1,39 @@
+---
+title: SambaNova
+---
+
+To use SambaNova LLM models, you have to set the `SAMBANOVA_API_KEY` environment variable. You can obtain the SambaNova API key from their [Account settings page](https://cloud.sambanova.ai/apis).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["SAMBANOVA_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "sambanova",
+        "config": {
+            "model": "gpt-oss-120b",
+            "temperature": 0.2,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+## Config
+
+All available parameters for the `sambanova` config are present in [Master List of All Params in Config](../config).

docs/components/llms/overview.mdx

@@ -34,6 +34,7 @@ See the list of supported LLMs below.
   <Card title="Sarvam AI" href="/components/llms/models/sarvam" />
   <Card title="LM Studio" href="/components/llms/models/lmstudio" />
   <Card title="Langchain" href="/components/llms/models/langchain" />
+  <Card title="SambaNova" href="/components/llms/models/sambanova" />
 </CardGroup>
 
 ## Structured vs Unstructured Outputs

mem0/embeddings/configs.py

@@ -25,6 +25,7 @@ def validate_config(cls, v, values):
             "langchain",
             "aws_bedrock",
             "fastembed",
+            "sambanova",
         ]:
             return v
         else:

mem0/embeddings/sambanova.py

@@ -0,0 +1,30 @@
+import os
+from typing import Literal, Optional
+
+from sambanova import SambaNova
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
+
+
+class SambaNovaEmbedding(EmbeddingBase):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        self.config.model = self.config.model or "E5-Mistral-7B-Instruct"
+        api_key = self.config.api_key or os.getenv("SAMBANOVA_API_KEY")
+        self.config.embedding_dims = self.config.embedding_dims or 4096
+        self.client = SambaNova(api_key=api_key)
+
+    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
+        """
+        Get the embedding for the given text using SambaNova.
+
+        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.
+        """
+
+        return self.client.embeddings.create(model=self.config.model, input=text).data[0].embedding

mem0/llms/configs.py

@@ -28,6 +28,7 @@ def validate_config(cls, v, values):
             "lmstudio",
             "vllm",
             "langchain",
+            "sambanova",
         ):
             return v
         else:

mem0/llms/sambanova.py

@@ -0,0 +1,88 @@
+import json
+import os
+from typing import Dict, List, Optional
+
+try:
+    from sambanova import SambaNova
+except ImportError:
+    raise ImportError("The 'sambanova' library is required. Please install it using 'pip install sambanova'.")
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.base import LLMBase
+from mem0.memory.utils import extract_json
+
+
+class SambaNovaLLM(LLMBase):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config)
+
+        if not self.config.model:
+            self.config.model = "gpt-oss-120b"
+
+        api_key = self.config.api_key or os.getenv("SAMBANOVA_API_KEY")
+        self.client = SambaNova(api_key=api_key)
+
+    def _parse_response(self, response, tools):
+        """
+        Process the response based on whether tools are used or not.
+
+        Args:
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
+
+        Returns:
+            str or dict: The processed response.
+        """
+        if tools:
+            processed_response = {
+                "content": response.choices[0].message.content,
+                "tool_calls": [],
+            }
+
+            if response.choices[0].message.tool_calls:
+                for tool_call in response.choices[0].message.tool_calls:
+                    processed_response["tool_calls"].append(
+                        {
+                            "name": tool_call.function.name,
+                            "arguments": json.loads(extract_json(tool_call.function.arguments)),
+                        }
+                    )
+
+            return processed_response
+        else:
+            return response.choices[0].message.content
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+    ):
+        """
+        Generate a response based on the given messages using SambaNova.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response. Defaults to "text".
+            tools (list, optional): List of tools that the model can call. Defaults to None.
+            tool_choice (str, optional): Tool choice method. Defaults to "auto".
+
+        Returns:
+            str: The generated response.
+        """
+        params = {
+            "model": self.config.model,
+            "messages": messages,
+            "temperature": self.config.temperature,
+            "max_tokens": self.config.max_tokens,
+            "top_p": self.config.top_p,
+        }
+        if response_format:
+            params["response_format"] = response_format
+        if tools:
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
+
+        response = self.client.chat.completions.create(**params)
+        return self._parse_response(response, tools)

mem0/utils/factory.py

@@ -12,10 +12,12 @@
 from mem0.configs.llms.vllm import VllmConfig
 from mem0.configs.rerankers.base import BaseRerankerConfig
 from mem0.configs.rerankers.cohere import CohereRerankerConfig
-from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
-from mem0.configs.rerankers.zero_entropy import ZeroEntropyRerankerConfig
-from mem0.configs.rerankers.llm import LLMRerankerConfig
 from mem0.configs.rerankers.huggingface import HuggingFaceRerankerConfig
+from mem0.configs.rerankers.llm import LLMRerankerConfig
+from mem0.configs.rerankers.sentence_transformer import (
+    SentenceTransformerRerankerConfig,
+)
+from mem0.configs.rerankers.zero_entropy import ZeroEntropyRerankerConfig
 from mem0.embeddings.mock import MockEmbeddings
 
 
@@ -50,6 +52,7 @@ class LlmFactory:
         "lmstudio": ("mem0.llms.lmstudio.LMStudioLLM", LMStudioConfig),
         "vllm": ("mem0.llms.vllm.VllmLLM", VllmConfig),
         "langchain": ("mem0.llms.langchain.LangchainLLM", BaseLlmConfig),
+        "sambanova": ("mem0.llms.sambanova.SambaNovaLLM", BaseLlmConfig),
     }
 
     @classmethod
@@ -146,6 +149,7 @@ class EmbedderFactory:
         "langchain": "mem0.embeddings.langchain.LangchainEmbedding",
         "aws_bedrock": "mem0.embeddings.aws_bedrock.AWSBedrockEmbedding",
         "fastembed": "mem0.embeddings.fastembed.FastEmbedEmbedding",
+        "sambanova": "mem0.embeddings.sambanova.SambaNovaEmbedding",
     }
 
     @classmethod

pyproject.toml

@@ -66,6 +66,7 @@ llms = [
     "vertexai>=0.1.0",
     "google-generativeai>=0.3.0",
     "google-genai>=1.0.0",
+    "sambanova>=1.2.0",
 ]
 extras = [
     "boto3>=1.34.0",

tests/embeddings/test_sambanova_embeddings.py

@@ -0,0 +1,60 @@
+from unittest.mock import Mock, patch
+
+import pytest
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.sambanova import SambaNovaEmbedding
+
+
+@pytest.fixture
+def mock_sambanova_client():
+    with patch("mem0.embeddings.sambanova.SambaNova") as mock_sambanova:
+        mock_client = Mock()
+        mock_sambanova.return_value = mock_client
+        yield mock_client
+
+
+def test_embed_default_model(mock_sambanova_client):
+    config = BaseEmbedderConfig()
+    embedder = SambaNovaEmbedding(config)
+    mock_response = Mock()
+    mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])]
+    mock_sambanova_client.embeddings.create.return_value = mock_response
+
+    result = embedder.embed("Hello world")
+
+    mock_sambanova_client.embeddings.create.assert_called_once_with(
+        input="Hello world", model="E5-Mistral-7B-Instruct"
+    )
+    assert result == [0.1, 0.2, 0.3]
+
+
+def test_embed_without_api_key_env_var(mock_sambanova_client):
+    config = BaseEmbedderConfig(api_key="test_key")
+    embedder = SambaNovaEmbedding(config)
+    mock_response = Mock()
+    mock_response.data = [Mock(embedding=[1.0, 1.1, 1.2])]
+    mock_sambanova_client.embeddings.create.return_value = mock_response
+
+    result = embedder.embed("Testing API key")
+
+    mock_sambanova_client.embeddings.create.assert_called_once_with(
+        input="Testing API key", model="E5-Mistral-7B-Instruct"
+    )
+    assert result == [1.0, 1.1, 1.2]
+
+
+def test_embed_uses_environment_api_key(mock_sambanova_client, monkeypatch):
+    monkeypatch.setenv("SAMBANOVA_API_KEY", "env_key")
+    config = BaseEmbedderConfig()
+    embedder = SambaNovaEmbedding(config)
+    mock_response = Mock()
+    mock_response.data = [Mock(embedding=[1.3, 1.4, 1.5])]
+    mock_sambanova_client.embeddings.create.return_value = mock_response
+
+    result = embedder.embed("Environment key test")
+
+    mock_sambanova_client.embeddings.create.assert_called_once_with(
+        input="Environment key test", model="E5-Mistral-7B-Instruct"
+    )
+    assert result == [1.3, 1.4, 1.5]