diff --git a/README.md b/README.md
index b2941b2e..7a366c05 100644
--- a/README.md
+++ b/README.md
@@ -132,8 +132,6 @@ cp env.example .env  # Update the .env with your LLM and embedding configuration
 docker compose up
 ```
 
-> Tip: When targeting Google Gemini, set `LLM_BINDING=gemini`, choose a model such as `LLM_MODEL=gemini-flash-latest`, and provide your Gemini key via `LLM_BINDING_API_KEY` (or `GEMINI_API_KEY`). The server now understands this binding out of the box.
-
 > Historical versions of LightRAG docker images can be found here: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag)
 
 ### Install  LightRAG Core
diff --git a/lightrag/api/README.md b/lightrag/api/README.md
index bc21fac4..f62e24d3 100644
--- a/lightrag/api/README.md
+++ b/lightrag/api/README.md
@@ -50,6 +50,7 @@ LightRAG necessitates the integration of both an LLM (Large Language Model) and
 * openai or openai compatible
 * azure_openai
 * aws_bedrock
+* gemini
 
 It is recommended to use environment variables to configure the LightRAG Server. There is an example environment variable file named `env.example` in the root directory of the project. Please copy this file to the startup directory and rename it to `.env`. After that, you can modify the parameters related to the LLM and Embedding models in the `.env` file. It is important to note that the LightRAG Server will load the environment variables from `.env` into the system environment variables each time it starts. **LightRAG Server will prioritize the settings in the system environment variables to .env file**.
 
@@ -72,6 +73,8 @@ EMBEDDING_DIM=1024
 # EMBEDDING_BINDING_API_KEY=your_api_key
 ```
 
+> When targeting Google Gemini, set `LLM_BINDING=gemini`, choose a model such as `LLM_MODEL=gemini-flash-latest`, and provide your Gemini key via `LLM_BINDING_API_KEY` (or `GEMINI_API_KEY`).
+
 * Ollama LLM + Ollama Embedding:
 
 ```