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Perf:Remove Useless Convert When BGE Embedding (#8816)

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### What problem does this PR solve?

FlagModel internal support returns as numpy

### Type of change
- [x] Performance Improvement
This commit is contained in:
Stephen Hu
2025-07-14 14:02:48 +08:00
committed by GitHub
parent dc068bbd1e
commit 5383e254c4
1 changed files with 6 additions and 3 deletions
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9
rag/llm/embedding_model.py
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@ -104,10 +104,13 @@ class DefaultEmbedding(Base):
token_count = 0 token_count = 0
for t in texts: for t in texts:
token_count += num_tokens_from_string(t) token_count += num_tokens_from_string(t)
ress = [] ress = None
for i in range(0, len(texts), batch_size): for i in range(0, len(texts), batch_size):
ress.extend(self._model.encode(texts[i : i + batch_size]).tolist()) if ress is None:
return np.array(ress), token_count ress = self._model.encode(texts[i : i + batch_size], convert_to_numpy=True)
else:
ress = np.concatenate((ress, self._model.encode(texts[i : i + batch_size], convert_to_numpy=True)), axis=0)
return ress, token_count
def encode_queries(self, text: str): def encode_queries(self, text: str):
token_count = num_tokens_from_string(text) token_count = num_tokens_from_string(text)