Embedding Providers

pip install remina-memory[openai]

"embedder": {
    "provider": "openai",
    "config": {
        "api_key": None,  # Uses OPENAI_API_KEY env
        "model": "text-embedding-3-small",
        "dimensions": 1536,
        "base_url": None,
    }
}

pip install remina-memory[gemini]

"embedder": {
    "provider": "gemini",
    "config": {
        "api_key": None,  # Uses GOOGLE_API_KEY env
        "model": "models/text-embedding-004",
        "dimensions": 768,
    }
}

pip install remina-memory[cohere]

"embedder": {
    "provider": "cohere",
    "config": {
        "api_key": None,  # Uses COHERE_API_KEY env
        "model": "embed-english-v3.0",
    }
}

pip install remina-memory[ollama]

"embedder": {
    "provider": "ollama",
    "config": {
        "base_url": "http://localhost:11434",
        "model": "nomic-embed-text",
    }
}

curl -fsSL https://ollama.com/install.sh | sh
ollama pull nomic-embed-text

pip install remina-memory[huggingface]

"embedder": {
    "provider": "huggingface",
    "config": {
        "model": "sentence-transformers/all-MiniLM-L6-v2",
        "device": "cpu",
    }
}

class EmbeddingBase(ABC):
    def embed(self, text: str) -> List[float]
    def embed_batch(self, texts: List[str]) -> List[List[float]]
    @property
    def dimensions(self) -> int
    @property
    def model_name(self) -> str