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Merge branch 'main' into fix-issue-1746

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yangdx 2025-07-08 18:20:02 +08:00
commit b6ab69e25d
3 changed files with 13 additions and 4 deletions
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2
README-zh.md
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@ -30,7 +30,7 @@
<a href="https://github.com/HKUDS/LightRAG/issues/285"><img src="https://img.shields.io/badge/💬微信群-交流-07c160?style=for-the-badge&logo=wechat&logoColor=white&labelColor=1a1a2e"></a> <a href="https://github.com/HKUDS/LightRAG/issues/285"><img src="https://img.shields.io/badge/💬微信群-交流-07c160?style=for-the-badge&logo=wechat&logoColor=white&labelColor=1a1a2e"></a>
</p> </p>
<p> <p>
<a href="README_zh.md"><img src="https://img.shields.io/badge/🇨🇳中文版-1a1a2e?style=for-the-badge"></a> <a href="README-zh.md"><img src="https://img.shields.io/badge/🇨🇳中文版-1a1a2e?style=for-the-badge"></a>
<a href="README.md"><img src="https://img.shields.io/badge/🇺🇸English-1a1a2e?style=for-the-badge"></a> <a href="README.md"><img src="https://img.shields.io/badge/🇺🇸English-1a1a2e?style=for-the-badge"></a>
</p> </p>
</div> </div>

4
lightrag/operate.py
View file

@ -26,6 +26,7 @@ from .utils import (
get_conversation_turns, get_conversation_turns,
use_llm_func_with_cache, use_llm_func_with_cache,
update_chunk_cache_list, update_chunk_cache_list,
remove_think_tags,
) )
from .base import ( from .base import (
BaseGraphStorage, BaseGraphStorage,
@ -1703,7 +1704,8 @@ async def extract_keywords_only(
result = await use_model_func(kw_prompt, keyword_extraction=True) result = await use_model_func(kw_prompt, keyword_extraction=True)
# 6. Parse out JSON from the LLM response # 6. Parse out JSON from the LLM response
match = re.search(r"\{.*\}", result, re.DOTALL) result = remove_think_tags(result)
match = re.search(r"\{.*?\}", result, re.DOTALL)
if not match: if not match:
logger.error("No JSON-like structure found in the LLM respond.") logger.error("No JSON-like structure found in the LLM respond.")
return [], [] return [], []

11
lightrag/utils.py
View file

@ -1465,6 +1465,11 @@ async def update_chunk_cache_list(
) )
def remove_think_tags(text: str) -> str:
"""Remove <think> tags from the text"""
return re.sub(r"^(<think>.*?</think>|<think>)", "", text, flags=re.DOTALL).strip()
async def use_llm_func_with_cache( async def use_llm_func_with_cache(
input_text: str, input_text: str,
use_llm_func: callable, use_llm_func: callable,
@ -1531,6 +1536,7 @@ async def use_llm_func_with_cache(
kwargs["max_tokens"] = max_tokens kwargs["max_tokens"] = max_tokens
res: str = await use_llm_func(input_text, **kwargs) res: str = await use_llm_func(input_text, **kwargs)
res = remove_think_tags(res)
if llm_response_cache.global_config.get("enable_llm_cache_for_entity_extract"): if llm_response_cache.global_config.get("enable_llm_cache_for_entity_extract"):
await save_to_cache( await save_to_cache(
@ -1557,8 +1563,9 @@ async def use_llm_func_with_cache(
if max_tokens is not None: if max_tokens is not None:
kwargs["max_tokens"] = max_tokens kwargs["max_tokens"] = max_tokens
logger.info(f"Call LLM function with query text lenght: {len(input_text)}") logger.info(f"Call LLM function with query text length: {len(input_text)}")
return await use_llm_func(input_text, **kwargs) res = await use_llm_func(input_text, **kwargs)
return remove_think_tags(res)
def get_content_summary(content: str, max_length: int = 250) -> str: def get_content_summary(content: str, max_length: int = 250) -> str: