diff --git a/lightrag/llm/openai.py b/lightrag/llm/openai.py
index 66c3bfe4..a2bbfa23 100644
--- a/lightrag/llm/openai.py
+++ b/lightrag/llm/openai.py
@@ -138,6 +138,9 @@ async def openai_complete_if_cache(
     base_url: str | None = None,
     api_key: str | None = None,
     token_tracker: Any | None = None,
+    stream: bool | None = None,
+    timeout: int | None = None,
+    keyword_extraction: bool = False,
     **kwargs: Any,
 ) -> str:
     """Complete a prompt using OpenAI's API with caching support and Chain of Thought (COT) integration.
@@ -167,13 +170,15 @@ async def openai_complete_if_cache(
         api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
         token_tracker: Optional token usage tracker for monitoring API usage.
         enable_cot: Whether to enable Chain of Thought (COT) processing. Default is False.
+        stream: Whether to stream the response. Default is False.
+        timeout: Request timeout in seconds. Default is None.
+        keyword_extraction: Whether to enable keyword extraction mode. When True, triggers
+            special response formatting for keyword extraction. Default is False.
         **kwargs: Additional keyword arguments to pass to the OpenAI API.
             Special kwargs:
             - openai_client_configs: Dict of configuration options for the AsyncOpenAI client.
                 These will be passed to the client constructor but will be overridden by
                 explicit parameters (api_key, base_url).
-            - hashing_kv: Will be removed from kwargs before passing to OpenAI.
-            - keyword_extraction: Will be removed from kwargs before passing to OpenAI.
 
     Returns:
         The completed text (with integrated COT content if available) or an async iterator
@@ -194,7 +199,6 @@ async def openai_complete_if_cache(
 
     # Remove special kwargs that shouldn't be passed to OpenAI
     kwargs.pop("hashing_kv", None)
-    kwargs.pop("keyword_extraction", None)
 
     # Extract client configuration options
     client_configs = kwargs.pop("openai_client_configs", {})
@@ -224,6 +228,12 @@ async def openai_complete_if_cache(
 
     messages = kwargs.pop("messages", messages)
 
+    # Add explicit parameters back to kwargs so they're passed to OpenAI API
+    if stream is not None:
+        kwargs["stream"] = stream
+    if timeout is not None:
+        kwargs["timeout"] = timeout
+
     try:
         # Don't use async with context manager, use client directly
         if "response_format" in kwargs:
@@ -512,7 +522,6 @@ async def openai_complete(
 ) -> Union[str, AsyncIterator[str]]:
     if history_messages is None:
         history_messages = []
-    keyword_extraction = kwargs.pop("keyword_extraction", None)
     if keyword_extraction:
         kwargs["response_format"] = "json"
     model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
@@ -521,6 +530,7 @@ async def openai_complete(
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
+        keyword_extraction=keyword_extraction,
         **kwargs,
     )
 
@@ -535,7 +545,6 @@ async def gpt_4o_complete(
 ) -> str:
     if history_messages is None:
         history_messages = []
-    keyword_extraction = kwargs.pop("keyword_extraction", None)
     if keyword_extraction:
         kwargs["response_format"] = GPTKeywordExtractionFormat
     return await openai_complete_if_cache(
@@ -544,6 +553,7 @@ async def gpt_4o_complete(
         system_prompt=system_prompt,
         history_messages=history_messages,
         enable_cot=enable_cot,
+        keyword_extraction=keyword_extraction,
         **kwargs,
     )
 
@@ -558,7 +568,6 @@ async def gpt_4o_mini_complete(
 ) -> str:
     if history_messages is None:
         history_messages = []
-    keyword_extraction = kwargs.pop("keyword_extraction", None)
     if keyword_extraction:
         kwargs["response_format"] = GPTKeywordExtractionFormat
     return await openai_complete_if_cache(
@@ -567,6 +576,7 @@ async def gpt_4o_mini_complete(
         system_prompt=system_prompt,
         history_messages=history_messages,
         enable_cot=enable_cot,
+        keyword_extraction=keyword_extraction,
         **kwargs,
     )
 
@@ -581,13 +591,13 @@ async def nvidia_openai_complete(
 ) -> str:
     if history_messages is None:
         history_messages = []
-    kwargs.pop("keyword_extraction", None)
     result = await openai_complete_if_cache(
         "nvidia/llama-3.1-nemotron-70b-instruct",  # context length 128k
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
         enable_cot=enable_cot,
+        keyword_extraction=keyword_extraction,
         base_url="https://integrate.api.nvidia.com/v1",
         **kwargs,
     )
@@ -609,6 +619,7 @@ async def openai_embed(
     model: str = "text-embedding-3-small",
     base_url: str | None = None,
     api_key: str | None = None,
+    embedding_dim: int | None = None,
     client_configs: dict[str, Any] | None = None,
     token_tracker: Any | None = None,
 ) -> np.ndarray:
@@ -619,6 +630,12 @@ async def openai_embed(
         model: The OpenAI embedding model to use.
         base_url: Optional base URL for the OpenAI API.
         api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
+        embedding_dim: Optional embedding dimension for dynamic dimension reduction.
+            **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper.
+            Do NOT manually pass this parameter when calling the function directly.
+            The dimension is controlled by the @wrap_embedding_func_with_attrs decorator.
+            Manually passing a different value will trigger a warning and be ignored.
+            When provided (by EmbeddingFunc), it will be passed to the OpenAI API for dimension reduction.
         client_configs: Additional configuration options for the AsyncOpenAI client.
             These will override any default configurations but will be overridden by
             explicit parameters (api_key, base_url).
@@ -638,9 +655,19 @@ async def openai_embed(
     )
 
     async with openai_async_client:
-        response = await openai_async_client.embeddings.create(
-            model=model, input=texts, encoding_format="base64"
-        )
+        # Prepare API call parameters
+        api_params = {
+            "model": model,
+            "input": texts,
+            "encoding_format": "base64",
+        }
+
+        # Add dimensions parameter only if embedding_dim is provided
+        if embedding_dim is not None:
+            api_params["dimensions"] = embedding_dim
+
+        # Make API call
+        response = await openai_async_client.embeddings.create(**api_params)
 
         if token_tracker and hasattr(response, "usage"):
             token_counts = {