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Remove deprecated embedding cache logic

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This commit is contained in:
yangdx
2025-04-28 18:51:43 +08:00
parent 5a393e563e
commit 2d59ac1ecb
1 changed files with 1 additions and 37 deletions
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38
lightrag/utils.py
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@@ -826,46 +826,10 @@ async def handle_cache(
if mode != "default": # handle cache for all type of query if mode != "default": # handle cache for all type of query
if not hashing_kv.global_config.get("enable_llm_cache"): if not hashing_kv.global_config.get("enable_llm_cache"):
return None, None, None, None return None, None, None, None
# TODO: deprecated (PostgreSQL cache not implemented yet)
# Get embedding cache configuration
embedding_cache_config = hashing_kv.global_config.get(
"embedding_cache_config",
{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False},
)
is_embedding_cache_enabled = embedding_cache_config["enabled"]
use_llm_check = embedding_cache_config.get("use_llm_check", False)
quantized = min_val = max_val = None
if is_embedding_cache_enabled: # Use embedding simularity to match cache
current_embedding = await hashing_kv.embedding_func([prompt])
llm_model_func = hashing_kv.global_config.get("llm_model_func")
quantized, min_val, max_val = quantize_embedding(current_embedding[0])
best_cached_response = await get_best_cached_response(
hashing_kv,
current_embedding[0],
similarity_threshold=embedding_cache_config["similarity_threshold"],
mode=mode,
use_llm_check=use_llm_check,
llm_func=llm_model_func if use_llm_check else None,
original_prompt=prompt,
cache_type=cache_type,
)
if best_cached_response is not None:
logger.debug(f"Embedding cached hit(mode:{mode} type:{cache_type})")
return best_cached_response, None, None, None
else:
# if caching keyword embedding is enabled, return the quantized embedding for saving it latter
logger.debug(f"Embedding cached missed(mode:{mode} type:{cache_type})")
return None, quantized, min_val, max_val
else: # handle cache for entity extraction else: # handle cache for entity extraction
if not hashing_kv.global_config.get("enable_llm_cache_for_entity_extract"): if not hashing_kv.global_config.get("enable_llm_cache_for_entity_extract"):
return None, None, None, None return None, None, None, None
# Here is the conditions of code reaching this point:
# 1. All query mode: enable_llm_cache is True and embedding simularity is not enabled
# 2. Entity extract: enable_llm_cache_for_entity_extract is True
if exists_func(hashing_kv, "get_by_mode_and_id"): if exists_func(hashing_kv, "get_by_mode_and_id"):
mode_cache = await hashing_kv.get_by_mode_and_id(mode, args_hash) or {} mode_cache = await hashing_kv.get_by_mode_and_id(mode, args_hash) or {}
else: else:
@@ -1440,7 +1404,7 @@ async def use_llm_func_with_cache(
Args: Args:
input_text: Input text to send to LLM input_text: Input text to send to LLM
use_llm_func: LLM function to call use_llm_func: LLM function with higher priority
llm_response_cache: Cache storage instance llm_response_cache: Cache storage instance
max_tokens: Maximum tokens for generation max_tokens: Maximum tokens for generation
history_messages: History messages list history_messages: History messages list