Custom embedder

Any class with name, dimensions, embed(text), and embed_batch(texts) satisfies the Embedder protocol. No inheritance required.


from typing import Any
from loomflow.memory.vector import VectorMemory
 
class CohereEmbedder:
    name: str = "embed-english-v3.0"
    dimensions: int = 1024
 
    def __init__(self, api_key: str) -> None:
        import cohere
        self._client = cohere.AsyncClient(api_key)
 
    async def embed(self, text: str) -> list[float]:
        result = await self._client.embed(
            texts=[text],
            model=self.name,
            input_type="search_document",
        )
        return list(result.embeddings[0])
 
    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
        result = await self._client.embed(
            texts=texts,
            model=self.name,
            input_type="search_document",
        )
        return [list(e) for e in result.embeddings]
 
 
memory = VectorMemory(embedder=CohereEmbedder(api_key="..."))

Same shape works for Voyage, sentence-transformers, or any other embedding provider. The framework only consumes the protocol surface.