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Use direct embedding_func from hashing_kv (do not by pass maxiumu async control)

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yangdx
2025-02-01 22:12:45 +08:00
parent c98a675b6c
commit c3942077a9
1 changed files with 4 additions and 4 deletions
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8
lightrag/utils.py
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@@ -511,10 +511,10 @@ async def handle_cache(
quantized = min_val = max_val = None quantized = min_val = max_val = None
if is_embedding_cache_enabled: if is_embedding_cache_enabled:
# Use embedding cache # Use embedding cache
embedding_model_func = hashing_kv.global_config["embedding_func"]["func"] current_embedding = await hashing_kv.embedding_func([prompt])
llm_model_func = hashing_kv.global_config.get("llm_model_func") llm_model_func = (
hashing_kv.llm_model_func if hasattr(hashing_kv, "llm_model_func") else None
current_embedding = await embedding_model_func([prompt]) )
quantized, min_val, max_val = quantize_embedding(current_embedding[0]) quantized, min_val, max_val = quantize_embedding(current_embedding[0])
best_cached_response = await get_best_cached_response( best_cached_response = await get_best_cached_response(
hashing_kv, hashing_kv,