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""" |
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Base Embedding Provider - Abstract Interface for Semantic Grounding |
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""" |
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from abc import ABC, abstractmethod |
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from typing import List, Dict, Any, Optional |
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import time |
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class EmbeddingProvider(ABC): |
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"""Abstract base class for embedding providers.""" |
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def __init__(self, config: Optional[Dict[str, Any]] = None): |
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self.config = config or {} |
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self.provider_id = self.__class__.__name__ |
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self.created_at = time.time() |
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@abstractmethod |
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def embed_text(self, text: str) -> List[float]: |
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"""Generate embedding vector for a single text.""" |
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pass |
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@abstractmethod |
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def embed_batch(self, texts: List[str]) -> List[List[float]]: |
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"""Generate embedding vectors for multiple texts.""" |
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pass |
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@abstractmethod |
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def get_dimension(self) -> int: |
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"""Get the dimension of embedding vectors.""" |
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pass |
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def calculate_similarity(self, embedding1: List[float], embedding2: List[float]) -> float: |
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"""Calculate cosine similarity between two embeddings.""" |
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import math |
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dot_product = sum(a * b for a, b in zip(embedding1, embedding2)) |
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magnitude1 = math.sqrt(sum(a * a for a in embedding1)) |
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magnitude2 = math.sqrt(sum(b * b for b in embedding2)) |
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if magnitude1 == 0 or magnitude2 == 0: |
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return 0.0 |
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return dot_product / (magnitude1 * magnitude2) |
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def get_provider_info(self) -> Dict[str, Any]: |
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"""Get provider metadata.""" |
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return { |
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"provider_id": self.provider_id, |
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"dimension": self.get_dimension(), |
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"created_at": self.created_at, |
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"config_keys": list(self.config.keys()), |
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} |
