gmakstutis/LightRAG
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

style: fix linting and trailing whitespace

Browse source
This commit is contained in:
captainmirk 2025-12-03 11:36:06 +00:00
parent 896e203574
commit c806694379
1 changed files with 11 additions and 7 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

18
lightrag/lightrag.py
View file

@ -525,7 +525,7 @@ class LightRAG:
_print_config = ",\n ".join([f"{k} = {v}" for k, v in global_config.items()]) _print_config = ",\n ".join([f"{k} = {v}" for k, v in global_config.items()])
logger.debug(f"LightRAG init with param:\n {_print_config}\n") logger.debug(f"LightRAG init with param:\n {_print_config}\n")
# Init Embedding # Init Embedding
# Step 1: Capture max_token_size and embedding_dim before applying decorator # Step 1: Capture max_token_size and embedding_dim before applying decorator
embedding_max_token_size = None embedding_max_token_size = None
if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): if self.embedding_func and hasattr(self.embedding_func, "max_token_size"):
@ -675,14 +675,14 @@ class LightRAG:
return return
meta_file = os.path.join(self.working_dir, "lightrag_meta.json") meta_file = os.path.join(self.working_dir, "lightrag_meta.json")
if os.path.exists(meta_file): if os.path.exists(meta_file):
with open(meta_file, "r", encoding="utf-8") as f: with open(meta_file, "r", encoding="utf-8") as f:
try: try:
meta_data = json.load(f) meta_data = json.load(f)
saved_dim = meta_data.get("embedding_dim") saved_dim = meta_data.get("embedding_dim")
saved_model = meta_data.get("embedding_model_func", "unknown") saved_model = meta_data.get("embedding_model_func", "unknown")
if saved_dim and saved_dim != self.embedding_dim: if saved_dim and saved_dim != self.embedding_dim:
raise ValueError( raise ValueError(
f"Embedding dimension mismatch! " f"Embedding dimension mismatch! "
@ -691,18 +691,22 @@ class LightRAG:
f"Please clear the '{self.working_dir}' directory or switch back to the original model." f"Please clear the '{self.working_dir}' directory or switch back to the original model."
) )
except json.JSONDecodeError: except json.JSONDecodeError:
logger.warning(f"Could not parse {meta_file}. Skipping dimension check.") logger.warning(
f"Could not parse {meta_file}. Skipping dimension check."
)
else: else:
# First run: Save the metadata # First run: Save the metadata
meta_data = { meta_data = {
"embedding_dim": self.embedding_dim, "embedding_dim": self.embedding_dim,
"embedding_model_func": self.embedding_func.__class__.__name__ if self.embedding_func else "unknown", "embedding_model_func": self.embedding_func.__class__.__name__
"created_at": str(os.path.abspath(self.working_dir)) if self.embedding_func
else "unknown",
"created_at": str(os.path.abspath(self.working_dir)),
} }
# Ensure directory exists # Ensure directory exists
if not os.path.exists(self.working_dir): if not os.path.exists(self.working_dir):
os.makedirs(self.working_dir) os.makedirs(self.working_dir)
with open(meta_file, "w", encoding="utf-8") as f: with open(meta_file, "w", encoding="utf-8") as f:
json.dump(meta_data, f, indent=4) json.dump(meta_data, f, indent=4)