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yangdx
2025-04-21 00:25:26 +08:00
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README-zh.md
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@@ -74,7 +74,7 @@ pip install lightrag-hku
**有关LightRAG服务器的更多信息，请参阅[LightRAG服务器](./lightrag/api/README.md)。** **有关LightRAG服务器的更多信息，请参阅[LightRAG服务器](./lightrag/api/README.md)。**
## 使用LightRAG Core ### 使用LightRAG Core
LightRAG核心功能的示例代码请参见`examples`目录。您还可参照[视频](https://www.youtube.com/watch?v=g21royNJ4fw)视频完成环境配置。若已持有OpenAI API密钥，可以通过以下命令运行演示代码： LightRAG核心功能的示例代码请参见`examples`目录。您还可参照[视频](https://www.youtube.com/watch?v=g21royNJ4fw)视频完成环境配置。若已持有OpenAI API密钥，可以通过以下命令运行演示代码：
@@ -89,6 +89,10 @@ curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_d
python examples/lightrag_openai_demo.py python examples/lightrag_openai_demo.py
``` ```
如需流式响应示例的实现代码，请参阅 `examples/lightrag_openai_compatible_demo.py`。运行前，请确保根据需求修改示例代码中的LLM及嵌入模型配置。
**注意事项**：在运行demo程序的时候需要注意，不同的测试程序可能使用的是不同的embedding模型，更换不同的embeding模型的时候需要把清空数据目录（`./dickens`），否则层序执行会出错。如果你想保留LLM缓存，可以在清除数据目录是保留`kv_store_llm_response_cache.json`文件。
## 查询 ## 查询
使用以下Python代码片段（在脚本中）初始化LightRAG并执行查询： 使用以下Python代码片段（在脚本中）初始化LightRAG并执行查询：
@@ -817,7 +821,7 @@ rag = LightRAG(
create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype)); create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties); CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx; ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
-- 如有必要可以删除 -- 如有必要可以删除
drop INDEX entity_p_idx; drop INDEX entity_p_idx;
drop INDEX vertex_p_idx; drop INDEX vertex_p_idx;

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README.md
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@@ -125,6 +125,10 @@ curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_d
python examples/lightrag_openai_demo.py python examples/lightrag_openai_demo.py
``` ```
For a streaming response implementation example, please see `examples/lightrag_openai_compatible_demo.py`. Prior to execution, ensure you modify the sample code’s LLM and embedding configurations accordingly.
**Note**: When running the demo program, please be aware that different test scripts may use different embedding models. If you switch to a different embedding model, you must clear the data directory (`./dickens`); otherwise, the program may encounter errors. If you wish to retain the LLM cache, you can preserve the `kv_store_llm_response_cache.json` file while clearing the data directory.
## Query ## Query
Use the below Python snippet (in a script) to initialize LightRAG and perform queries: Use the below Python snippet (in a script) to initialize LightRAG and perform queries:
@@ -838,7 +842,7 @@ For production level scenarios you will most likely want to leverage an enterpri
create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype)); create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties); CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx; ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
-- drop if necessary -- drop if necessary
drop INDEX entity_p_idx; drop INDEX entity_p_idx;
drop INDEX vertex_p_idx; drop INDEX vertex_p_idx;