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Remove quantized embedding info from LLM cache

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- Delete quantize_embedding function
- Delete dequantize_embedding function
- Remove embedding fields from CacheData
- Update save_to_cache to exclude embedding data
- Clean up unused quantization-related code
This commit is contained in:
yangdx 2025-08-05 17:58:34 +08:00
parent c7d17f13c1
commit cb75e6631e
2 changed files with 0 additions and 57 deletions
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12
lightrag/operate.py
View file

@ -1833,9 +1833,6 @@ async def kg_query(
args_hash=args_hash,
content=response,
prompt=query,
quantized=quantized,
min_val=min_val,
max_val=max_val,
mode=query_param.mode,
cache_type="query",
),
@ -1972,9 +1969,6 @@ async def extract_keywords_only(
args_hash=args_hash,
content=json.dumps(cache_data),
prompt=text,
quantized=quantized,
min_val=min_val,
max_val=max_val,
mode=param.mode,
cache_type="keywords",
),
@ -3105,9 +3099,6 @@ async def naive_query(
args_hash=args_hash,
content=response,
prompt=query,
quantized=quantized,
min_val=min_val,
max_val=max_val,
mode=query_param.mode,
cache_type="query",
),
@ -3231,9 +3222,6 @@ async def kg_query_with_keywords(
args_hash=args_hash,
content=response,
prompt=query,
quantized=quantized,
min_val=min_val,
max_val=max_val,
mode=query_param.mode,
cache_type="query",
),

45
lightrag/utils.py
View file

@ -756,40 +756,6 @@ def cosine_similarity(v1, v2):
return dot_product / (norm1 * norm2)
def quantize_embedding(embedding: np.ndarray | list[float], bits: int = 8) -> tuple:
"""Quantize embedding to specified bits"""
# Convert list to numpy array if needed
if isinstance(embedding, list):
embedding = np.array(embedding)
# Calculate min/max values for reconstruction
min_val = embedding.min()
max_val = embedding.max()
if min_val == max_val:
# handle constant vector
quantized = np.zeros_like(embedding, dtype=np.uint8)
return quantized, min_val, max_val
# Quantize to 0-255 range
scale = (2**bits - 1) / (max_val - min_val)
quantized = np.round((embedding - min_val) * scale).astype(np.uint8)
return quantized, min_val, max_val
def dequantize_embedding(
quantized: np.ndarray, min_val: float, max_val: float, bits=8
) -> np.ndarray:
"""Restore quantized embedding"""
if min_val == max_val:
# handle constant vector
return np.full_like(quantized, min_val, dtype=np.float32)
scale = (max_val - min_val) / (2**bits - 1)
return (quantized * scale + min_val).astype(np.float32)
async def handle_cache(
hashing_kv,
args_hash,
@ -824,9 +790,6 @@ class CacheData:
args_hash: str
content: str
prompt: str
quantized: np.ndarray | None = None
min_val: float | None = None
max_val: float | None = None
mode: str = "default"
cache_type: str = "query"
chunk_id: str | None = None
@ -866,14 +829,6 @@ async def save_to_cache(hashing_kv, cache_data: CacheData):
"return": cache_data.content,
"cache_type": cache_data.cache_type,
"chunk_id": cache_data.chunk_id if cache_data.chunk_id is not None else None,
"embedding": cache_data.quantized.tobytes().hex()
if cache_data.quantized is not None
else None,
"embedding_shape": cache_data.quantized.shape
if cache_data.quantized is not None
else None,
"embedding_min": cache_data.min_val,
"embedding_max": cache_data.max_val,
"original_prompt": cache_data.prompt,
}