Merge pull request #116 from Dormiveglia-elf/hotfix/embedding-dim

[hotfix-#75][embedding] Fix the potential embedding problem

examples/lightrag_openai_compatible_demo.py

@@ -34,6 +34,13 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
     )
 
 
+async def get_embedding_dim():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    embedding_dim = embedding.shape[1]
+    return embedding_dim
+
+
 # function test
 async def test_funcs():
     result = await llm_model_func("How are you?")
@@ -43,14 +50,18 @@ async def test_funcs():
     print("embedding_func: ", result)
 
 
-asyncio.run(test_funcs())
+# asyncio.run(test_funcs())
 
+async def main():
+    try:
+        embedding_dimension = await get_embedding_dim()
+        print(f"Detected embedding dimension: {embedding_dimension}")
 
         rag = LightRAG(
             working_dir=WORKING_DIR,
             llm_model_func=llm_model_func,
             embedding_func=EmbeddingFunc(
-        embedding_dim=4096, max_token_size=8192, func=embedding_func
+                embedding_dim=embedding_dimension, max_token_size=8192, func=embedding_func
             ),
         )
 
@@ -77,3 +88,8 @@ print(
         print(
             rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
         )
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    asyncio.run(main())