PDF Knowledgebase MCP Server

"""Abstract base class for embedding services.""" from abc import ABC, abstractmethod from typing import Dict, List class EmbeddingService(ABC): """Abstract base class for embedding services.""" @abstractmethod async def initialize(self) -> None: """Initialize the embedding service.""" pass @abstractmethod async def generate_embeddings(self, texts: List[str]) -> List[List[float]]: """Generate embeddings for a list of texts. Args: texts: List of text strings to embed. Returns: List of embedding vectors. """ pass @abstractmethod async def generate_embedding(self, text: str) -> List[float]: """Generate embedding for a single text. Args: text: Text string to embed. Returns: Embedding vector. """ pass @abstractmethod def get_embedding_dimension(self) -> int: """Get the dimension of embeddings for the current model. Returns: Embedding dimension. """ pass @abstractmethod async def test_connection(self) -> bool: """Test the connection to the embedding service. Returns: True if connection is successful, False otherwise. """ pass @abstractmethod def get_model_info(self) -> Dict: """Get information about the current embedding model. Returns: Dictionary with model information. """ pass async def estimate_cost(self, texts: List[str]) -> float: """Estimate the cost of embedding a list of texts. Args: texts: List of text strings. Returns: Estimated cost in USD (0 for local models). """ return 0.0 # Default to 0 for local models