fix bug

examples/lightrag_hf_demo.py

@@ -16,11 +16,13 @@ rag = LightRAG(
     llm_model_func=hf_model_complete,  
     llm_model_name='meta-llama/Llama-3.1-8B-Instruct',
     embedding_func=EmbeddingFunc(
-        tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"),
-        embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"),
         embedding_dim=384,
         max_token_size=5000,
-        func=hf_embedding
+        func=lambda texts: hf_embedding(
+            texts, 
+            tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"),
+            embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+        )
     ),
 )
 

examples/lightrag_openai_demo.py

@@ -5,7 +5,7 @@ from lightrag import LightRAG, QueryParam
 from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
 from transformers import AutoModel,AutoTokenizer
 
-WORKING_DIR = "/home/zrguo/code/myrag/agriculture"
+WORKING_DIR = "./dickens"
 
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG, QueryParam
 
-__version__ = "0.0.4"
+__version__ = "0.0.5"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/llm.py

@@ -141,11 +141,6 @@ async def openai_embedding(texts: list[str]) -> np.ndarray:
     return np.array([dp.embedding for dp in response.data])
 
 
-
-@wrap_embedding_func_with_attrs(
-    embedding_dim=384,
-    max_token_size=5000,
-)
 async def hf_embedding(texts: list[str], tokenizer, embed_model) -> np.ndarray:
     input_ids = tokenizer(texts, return_tensors='pt', padding=True, truncation=True).input_ids
     with torch.no_grad():