fix readme

KAG_VERSION

@@ -1 +1 @@
-0.5.20241025.1
+0.0.5-beta1

README.md

@@ -24,7 +24,7 @@ kg-solver uses a logical symbol-guided hybrid solving and reasoning engine that
 
 In the context of private knowledge bases, unstructured data, structured information, and business expert experience often coexist. KAG references the DIKW hierarchy to upgrade SPG to a version that is friendly to LLMs. For unstructured data such as news, events, logs, and books, as well as structured data like transactions, statistics, and approvals, along with business experience and domain knowledge rules, KAG employs techniques such as layout analysis, knowledge extraction, property normalization, and semantic alignment to integrate raw business data and expert rules into a unified business knowledge graph.
 
-![KAG Diagram](./_static/images/kag-diag.png)
+![KAG Diagram](./_static/images/kag-diag.jpg)
 
 This makes it compatible with schema-free information extraction and schema-constrained expertise construction on the same knowledge type (e. G., entity type, event type), and supports the cross-index representation between the graph structure and the original text block. This mutual index representation is helpful to the construction of inverted index based on graph structure, and promotes the unified representation and reasoning of logical forms.
 

README_cn.md

@@ -26,7 +26,7 @@ kg-solver 采用逻辑形式引导的混合求解和推理引擎，该引擎包
 
 私域知识库场景，非结构化数据、结构化信息、业务专家经验 往往三者共存，KAG 参考了 DIKW 层次结构，将 SPG 升级为对 LLM 友好的版本。针对新闻、事件、日志、书籍等非结构化数据，交易、统计、审批等结构化数据，业务经验、领域知识等规则，KAG 采用版面分析、知识抽取、属性标化、语义对齐等技术，将原始的业务数据&专家规则融合到统一的业务知识图谱中。
 
-![KAG 示意图](./_static/images/kag-diag.png)
+![KAG 示意图](./_static/images/kag-diag.jpg)
 
 这使得它能够在同一知识类型（如实体类型、事件类型）上兼容无 schema 约束的信息提取和有 schema 约束的专业知识构建，并支持图结构与原始文本块之间的互索引表示。这种互索引表示有助于基于图结构的倒排索引的构建，并促进了逻辑形式的统一表示、推理。