Update README.md

README-zh.md

@@ -636,21 +636,15 @@ rag.insert(["文本1", "文本2",...])
 
 # 带有自定义批量大小配置的批量插入
 rag = LightRAG(
+    ...
     working_dir=WORKING_DIR,
-    addon_params={
-        "insert_batch_size": 4  # 每批处理4个文档
-    }
+    max_parallel_insert = 4
 )
 
 rag.insert(["文本1", "文本2", "文本3", ...])  # 文档将以4个为一批进行处理
 ```
 
-`addon_params`中的`insert_batch_size`参数控制插入过程中每批处理的文档数量。这对于以下情况很有用：
-
-- 管理大型文档集合的内存使用
-- 优化处理速度
-- 提供更好的进度跟踪
-- 如果未指定，默认值为10
+参数 `max_parallel_insert` 用于控制文档索引流水线中并行处理的文档数量。若未指定，默认值为 **2**。建议将该参数设置为 **10 以下**，因为性能瓶颈通常出现在大语言模型（LLM）的处理环节。
 
 </details>
 
@@ -1115,7 +1109,7 @@ rag.clear_cache(modes=["local"])
 | **vector_db_storage_cls_kwargs** | `dict` | 向量数据库的附加参数，如设置节点和关系检索的阈值 | cosine_better_than_threshold: 0.2（默认值由环境变量COSINE_THRESHOLD更改） |
 | **enable_llm_cache** | `bool` | 如果为`TRUE`，将LLM结果存储在缓存中；重复的提示返回缓存的响应 | `TRUE` |
 | **enable_llm_cache_for_entity_extract** | `bool` | 如果为`TRUE`，将实体提取的LLM结果存储在缓存中；适合初学者调试应用程序 | `TRUE` |
-| **addon_params** | `dict` | 附加参数，例如`{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`：设置示例限制、输出语言和文档处理的批量大小 | `example_number: 所有示例, language: English, insert_batch_size: 10` |
+| **addon_params** | `dict` | 附加参数，例如`{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"]}`：设置示例限制、输出语言和文档处理的批量大小 | `example_number: 所有示例, language: English` |
 | **convert_response_to_json_func** | `callable` | 未使用 | `convert_response_to_json` |
 | **embedding_cache_config** | `dict` | 问答缓存的配置。包含三个参数：`enabled`：布尔值，启用/禁用缓存查找功能。启用时，系统将在生成新答案之前检查缓存的响应。`similarity_threshold`：浮点值（0-1），相似度阈值。当新问题与缓存问题的相似度超过此阈值时，将直接返回缓存的答案而不调用LLM。`use_llm_check`：布尔值，启用/禁用LLM相似度验证。启用时，在返回缓存答案之前，将使用LLM作为二次检查来验证问题之间的相似度。 | 默认：`{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
 

README.md

@@ -629,21 +629,15 @@ rag.insert(["TEXT1", "TEXT2",...])
 
 # Batch Insert with custom batch size configuration
 rag = LightRAG(
+    ...
     working_dir=WORKING_DIR,
-    addon_params={
-        "insert_batch_size": 4  # Process 4 documents per batch
-    }
+    max_parallel_insert = 4
 )
 
 rag.insert(["TEXT1", "TEXT2", "TEXT3", ...])  # Documents will be processed in batches of 4
 ```
 
-The `insert_batch_size` parameter in `addon_params` controls how many documents are processed in each batch during insertion. This is useful for:
-
-- Managing memory usage with large document collections
-- Optimizing processing speed
-- Providing better progress tracking
-- Default value is 10 if not specified
+The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.
 
 </details>
 
@@ -1181,7 +1175,7 @@ Valid modes are:
 | **vector_db_storage_cls_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) |
 | **enable_llm_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
 | **enable_llm_cache_for_entity_extract** | `bool` | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application | `TRUE` |
-| **addon_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`: sets example limit, output language, and batch size for document processing | `example_number: all examples, language: English, insert_batch_size: 10` |
+| **addon_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"]}`: sets example limit, entiy/relation extraction output language | `example_number: all examples, language: English` |
 | **convert_response_to_json_func** | `callable` | Not used | `convert_response_to_json` |
 | **embedding_cache_config** | `dict` | Configuration for question-answer caching. Contains three parameters: `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers. `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM. `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |