"""Base embedder class for MemoVault."""
from abc import ABC, abstractmethod
from typing import Any
class BaseEmbedder(ABC):
"""Base class for all embedding models."""
@abstractmethod
def __init__(self, config: Any):
"""Initialize the embedding model with the given configuration."""
@abstractmethod
def embed(self, texts: list[str]) -> list[list[float]]:
"""Generate embeddings for the given texts.
Args:
texts: List of texts to embed.
Returns:
List of embeddings, each represented as a list of floats.
"""
def embed_one(self, text: str) -> list[float]:
"""Generate an embedding for a single text.
Args:
text: Text to embed.
Returns:
Embedding as a list of floats.
"""
return self.embed([text])[0]
def _truncate_texts(self, texts: list[str], max_tokens: int | None = None) -> list[str]:
"""Truncate texts to fit within max_tokens limit.
Args:
texts: List of texts to truncate.
max_tokens: Maximum number of tokens/characters.
Returns:
List of truncated texts.
"""
if max_tokens is None:
return texts
# Simple character-based truncation (approximation)
# Most models use ~4 chars per token on average
max_chars = max_tokens * 4
return [text[:max_chars] if len(text) > max_chars else text for text in texts]
