Investment Statement MCP Server

"""Embedding generation for semantic search.""" class EmbeddingGenerator: """Generate embeddings for text using sentence-transformers. Uses lazy loading to defer the slow sentence_transformers import until embeddings are actually needed. """ def __init__(self, model_name: str = "all-MiniLM-L6-v2"): """Initialize embedding model configuration. Args: model_name: Name of sentence-transformers model """ self.model_name = model_name self._model = None @property def model(self): """Lazy-load the sentence transformer model.""" if self._model is None: from sentence_transformers import SentenceTransformer self._model = SentenceTransformer(self.model_name) return self._model def encode(self, text: str) -> list[float]: """Generate embedding for a single text. Args: text: Input text Returns: Embedding vector as list of floats """ embedding = self.model.encode(text) return embedding.tolist() def encode_batch(self, texts: list[str]) -> list[list[float]]: """Generate embeddings for multiple texts. Args: texts: List of input texts Returns: List of embedding vectors """ embeddings = self.model.encode(texts) return [emb.tolist() for emb in embeddings] def get_dimension(self) -> int: """Get embedding dimension. Returns: Dimension of embedding vectors """ return self.model.get_sentence_embedding_dimension()