Update documentation in README files

env.example

@@ -123,7 +123,7 @@ MAX_PARALLEL_INSERT=2
 
 ###########################################################
 ### LLM Configuration
-### LLM_BINDING type: openai, ollama, lollms, azure_openai
+### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock
 ###########################################################
 ### LLM temperature setting for all llm binding (openai, azure_openai, ollama)
 # TEMPERATURE=1.0

lightrag/api/README-zh.md

@@ -40,6 +40,7 @@ LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效
 * lollms
 * openai 或 openai 兼容
 * azure_openai
+* aws_bedrock
 
 建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。**LightRAG 服务器会优先使用系统环境变量中的设置**。
 
@@ -357,6 +358,7 @@ LightRAG 支持绑定到各种 LLM/嵌入后端：
 * openai 和 openai 兼容
 * azure_openai
 * lollms
+* aws_bedrock
 
 使用环境变量 `LLM_BINDING` 或 CLI 参数 `--llm-binding` 选择 LLM 后端类型。使用环境变量 `EMBEDDING_BINDING` 或 CLI 参数 `--embedding-binding` 选择嵌入后端类型。
 

lightrag/api/README.md

@@ -40,6 +40,7 @@ LightRAG necessitates the integration of both an LLM (Large Language Model) and
 * lollms
 * openai or openai compatible
 * azure_openai
+* aws_bedrock
 
 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**.
 
@@ -360,6 +361,7 @@ LightRAG supports binding to various LLM/Embedding backends:
 * openai & openai compatible
 * azure_openai
 * lollms
+* aws_bedrock
 
 Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select the LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select the Embedding backend type.
 
@@ -459,8 +461,8 @@ You cannot change storage implementation selection after adding documents to Lig
 | --ssl-keyfile         | None          | Path to SSL private key file (required if --ssl is enabled)                                                                     |
 | --top-k               | 50            | Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.                  |
 | --cosine-threshold    | 0.4           | The cosine threshold for nodes and relation retrieval, works with top-k to control the retrieval of nodes and relations.        |
-| --llm-binding         | ollama        | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai)                                                          |
-| --embedding-binding   | ollama        | Embedding binding type (lollms, ollama, openai, azure_openai)                                                                   |
+| --llm-binding         | ollama        | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai, aws_bedrock)                                                          |
+| --embedding-binding   | ollama        | Embedding binding type (lollms, ollama, openai, azure_openai, aws_bedrock)                                                                   |
 | --auto-scan-at-startup| -             | Scan input directory for new files and start indexing                                                                           |
 
 ### Additional Ollama Binding Options