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contextquery-api / app /services /embeddings.py

test(eval): add RAGAS gate + retrieval evaluation (recall@5, MRR) (#16)

49206fb unverified 5 days ago

688 Bytes

from fastembed import TextEmbedding
from app.config import settings
from app.services.groq_service import AiUnavailable

# 768-dim model — matches the existing vector(768) column in Postgres.
# ONNX-based, no Torch dependency. Loads in ~50-200 MB of memory.
_model = TextEmbedding(model_name="BAAI/bge-base-en-v1.5")


def embed_texts(texts: list[str], batch_size: int = 32) -> list[list[float]]:
    try:
        # FastEmbed returns a generator of numpy arrays. Convert to plain lists.
        vectors = list(_model.embed(texts, batch_size=batch_size))
        return [v.tolist() for v in vectors]
    except Exception as exc:
        raise AiUnavailable("provider", str(exc)) from exc

	from fastembed import TextEmbedding
	from app.config import settings
	from app.services.groq_service import AiUnavailable

	# 768-dim model — matches the existing vector(768) column in Postgres.
	# ONNX-based, no Torch dependency. Loads in ~50-200 MB of memory.
	_model = TextEmbedding(model_name="BAAI/bge-base-en-v1.5")


	def embed_texts(texts: list[str], batch_size: int = 32) -> list[list[float]]:
	try:
	# FastEmbed returns a generator of numpy arrays. Convert to plain lists.
	vectors = list(_model.embed(texts, batch_size=batch_size))
	return [v.tolist() for v in vectors]
	except Exception as exc:
	raise AiUnavailable("provider", str(exc)) from exc