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| """ | |
| Self-hosted text embeddings (fastembed - ONNX runtime, CPU, no API key, no | |
| external network call at runtime) for document RAG: turning uploaded text | |
| into vectors stored in MongoDB Atlas, and turning the user's question into a | |
| vector to search against them. | |
| Same reasoning as Piper for voice: a self-hosted model has no external | |
| service to be rate-limited or blocked by, which matters on a cloud host. | |
| """ | |
| import asyncio | |
| from functools import lru_cache | |
| EMBEDDING_MODEL_NAME = "BAAI/bge-small-en-v1.5" # 384 dimensions, small, fast on CPU | |
| def _get_model(): | |
| from fastembed import TextEmbedding | |
| return TextEmbedding(model_name=EMBEDDING_MODEL_NAME) | |
| def _embed_sync(texts: list[str]) -> list[list[float]]: | |
| model = _get_model() | |
| return [vec.tolist() for vec in model.embed(texts)] | |
| async def embed_texts(texts: list[str]) -> list[list[float]]: | |
| return await asyncio.get_event_loop().run_in_executor(None, _embed_sync, texts) | |
| async def embed_text(text: str) -> list[float]: | |
| vectors = await embed_texts([text]) | |
| return vectors[0] | |