Add max_token_size parameter to embedding function decorators

- Add max_token_size=8192 to all embed funcs
- Move siliconcloud to deprecated folder
- Import wrap_embedding_func_with_attrs
- Update EmbeddingFunc docstring
- Fix langfuse import type annotation

lightrag/llm/bedrock.py

@@ -16,6 +16,7 @@ from tenacity import (
 )
 
 import sys
+from lightrag.utils import wrap_embedding_func_with_attrs
 
 if sys.version_info < (3, 9):
     from typing import AsyncIterator
@@ -253,7 +254,7 @@ async def bedrock_complete(
     return result
 
 
-# @wrap_embedding_func_with_attrs(embedding_dim=1024)
+@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
 # @retry(
 #     stop=stop_after_attempt(3),
 #     wait=wait_exponential(multiplier=1, min=4, max=10),

lightrag/llm/gemini.py

@@ -453,7 +453,7 @@ async def gemini_model_complete(
     )
 
 
-@wrap_embedding_func_with_attrs(embedding_dim=1536)
+@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=2048)
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),

lightrag/llm/hf.py

@@ -26,6 +26,7 @@ from lightrag.exceptions import (
 )
 import torch
 import numpy as np
+from lightrag.utils import wrap_embedding_func_with_attrs
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
@@ -141,6 +142,7 @@ async def hf_model_complete(
     return result
 
 
+@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
 async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray:
     # Detect the appropriate device
     if torch.cuda.is_available():

lightrag/llm/jina.py

@@ -58,7 +58,7 @@ async def fetch_data(url, headers, data):
             return data_list
 
 
-@wrap_embedding_func_with_attrs(embedding_dim=2048)
+@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),

lightrag/llm/llama_index_impl.py

@@ -174,7 +174,7 @@ async def llama_index_complete(
     return result
 
 
-@wrap_embedding_func_with_attrs(embedding_dim=1536)
+@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),

lightrag/llm/lollms.py

@@ -26,6 +26,10 @@ from lightrag.exceptions import (
 from typing import Union, List
 import numpy as np
 
+from lightrag.utils import (
+    wrap_embedding_func_with_attrs,
+)
+
 
 @retry(
     stop=stop_after_attempt(3),
@@ -134,6 +138,7 @@ async def lollms_model_complete(
     )
 
 
+@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
 async def lollms_embed(
     texts: List[str], embed_model=None, base_url="http://localhost:9600", **kwargs
 ) -> np.ndarray:

lightrag/llm/nvidia_openai.py

@@ -33,7 +33,7 @@ from lightrag.utils import (
 import numpy as np
 
 
-@wrap_embedding_func_with_attrs(embedding_dim=2048)
+@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),

lightrag/llm/ollama.py

@@ -25,7 +25,10 @@ from lightrag.api import __api_version__
 
 import numpy as np
 from typing import Optional, Union
-from lightrag.utils import logger
+from lightrag.utils import (
+    wrap_embedding_func_with_attrs,
+    logger,
+)
 
 
 _OLLAMA_CLOUD_HOST = "https://ollama.com"
@@ -169,6 +172,7 @@ async def ollama_model_complete(
     )
 
 
+@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
 async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray:
     api_key = kwargs.pop("api_key", None)
     if not api_key:

lightrag/llm/openai.py

@@ -47,7 +47,7 @@ try:
 
     # Only enable Langfuse if both keys are configured
     if langfuse_public_key and langfuse_secret_key:
-        from langfuse.openai import AsyncOpenAI
+        from langfuse.openai import AsyncOpenAI  # type: ignore[import-untyped]
 
         LANGFUSE_ENABLED = True
         logger.info("Langfuse observability enabled for OpenAI client")
@@ -604,7 +604,7 @@ async def nvidia_openai_complete(
     return result
 
 
-@wrap_embedding_func_with_attrs(embedding_dim=1536)
+@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),

lightrag/utils.py

@@ -355,7 +355,7 @@ class TaskState:
 class EmbeddingFunc:
     embedding_dim: int
     func: callable
-    max_token_size: int | None = None  # deprecated keep it for compatible only
+    max_token_size: int | None = None  # Token limit for the embedding model
     send_dimensions: bool = (
         False  # Control whether to send embedding_dim to the function
     )