Merge pull request #444 from davidleon/fix/lazy_import

lightrag/lightrag.py

@@ -48,18 +48,25 @@ from .storage import (
 
 
 def lazy_external_import(module_name: str, class_name: str):
-    """Lazily import an external module and return a class from it."""
+    """Lazily import a class from an external module based on the package of the caller."""
 
-    def import_class():
+    # Get the caller's module and package
+    import inspect
+
+    caller_frame = inspect.currentframe().f_back
+    module = inspect.getmodule(caller_frame)
+    package = module.__package__ if module else None
+
+    def import_class(*args, **kwargs):
         import importlib
 
         # Import the module using importlib
-        module = importlib.import_module(module_name)
+        module = importlib.import_module(module_name, package=package)
 
-        # Get the class from the module
-        return getattr(module, class_name)
+        # Get the class from the module and instantiate it
+        cls = getattr(module, class_name)
+        return cls(*args, **kwargs)
 
-    # Return the import_class function itself, not its result
     return import_class
 
 

lightrag/llm.py

@@ -64,6 +64,7 @@ async def openai_complete_if_cache(
         AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
     )
     kwargs.pop("hashing_kv", None)
+    kwargs.pop("keyword_extraction", None)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})

lightrag/storage.py

@@ -107,10 +107,16 @@ class NanoVectorDBStorage(BaseVectorStorage):
             embeddings = await f
             embeddings_list.append(embeddings)
         embeddings = np.concatenate(embeddings_list)
-        for i, d in enumerate(list_data):
-            d["__vector__"] = embeddings[i]
-        results = self._client.upsert(datas=list_data)
-        return results
+        if len(embeddings) == len(list_data):
+            for i, d in enumerate(list_data):
+                d["__vector__"] = embeddings[i]
+            results = self._client.upsert(datas=list_data)
+            return results
+        else:
+            # sometimes the embedding is not returned correctly. just log it.
+            logger.error(
+                f"embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}"
+            )
 
     async def query(self, query: str, top_k=5):
         embedding = await self.embedding_func([query])

lightrag/utils.py

@@ -17,6 +17,17 @@ import tiktoken
 
 from lightrag.prompt import PROMPTS
 
+
+class UnlimitedSemaphore:
+    """A context manager that allows unlimited access."""
+
+    async def __aenter__(self):
+        pass
+
+    async def __aexit__(self, exc_type, exc, tb):
+        pass
+
+
 ENCODER = None
 
 logger = logging.getLogger("lightrag")
@@ -42,9 +53,17 @@ class EmbeddingFunc:
     embedding_dim: int
     max_token_size: int
     func: callable
+    concurrent_limit: int = 16
+
+    def __post_init__(self):
+        if self.concurrent_limit != 0:
+            self._semaphore = asyncio.Semaphore(self.concurrent_limit)
+        else:
+            self._semaphore = UnlimitedSemaphore()
 
     async def __call__(self, *args, **kwargs) -> np.ndarray:
-        return await self.func(*args, **kwargs)
+        async with self._semaphore:
+            return await self.func(*args, **kwargs)
 
 
 def locate_json_string_body_from_string(content: str) -> Union[str, None]: