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feat: openai embeddings

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This commit is contained in:
Andy Lee
2025-07-17 17:02:47 -07:00
parent 90d9f27383
commit a13c527e39
6 changed files with 311 additions and 49 deletions
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8
packages/leann-core/src/leann/searcher_base.py
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@@ -78,12 +78,14 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
"Cannot use recompute mode without 'embedding_model' in meta.json."
)
embedding_mode = self.meta.get("embedding_mode", "sentence-transformers")
server_started = self.embedding_server_manager.start_server(
port=port,
model_name=self.embedding_model,
passages_file=passages_source_file,
distance_metric=kwargs.get("distance_metric"),
use_mlx=kwargs.get("use_mlx", False),
embedding_mode=embedding_mode,
enable_warmup=kwargs.get("enable_warmup", False),
)
if not server_started:
@@ -120,8 +122,8 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
# Fallback to direct computation
from .api import compute_embeddings
use_mlx = self.meta.get("use_mlx", False)
return compute_embeddings([query], self.embedding_model, use_mlx)
embedding_mode = self.meta.get("embedding_mode", "sentence-transformers")
return compute_embeddings([query], self.embedding_model, embedding_mode)
def _compute_embedding_via_server(self, chunks: list, zmq_port: int) -> np.ndarray:
"""Compute embeddings using the ZMQ embedding server."""