Merge pull request #92 from langbot-app/feat/rerank-model

docs: add rerank model documentation

en/insight/features.mdx

@@ -55,13 +55,16 @@ For platform-specific feature support, please check [this document](/en/insight/
 | [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLM gateway (MaaS) |
 | [TBox (Ant TreasureBox)](https://tbox.cn/) | ✅ | LLMOps platform |
 | [Coze](https://coze.cn) | ✅ | LLMOps platform |
+| [Cohere](https://cohere.com/) | ✅ | Rerank models |
+| [Jina](https://jina.ai/) | ✅ | Rerank models |
+| [Voyage AI](https://voyageai.com/) | ✅ | Rerank models |
 
 ## LLM Application Paradigm Implementation
 
 | Application Paradigm | Status | Remarks |
 | --- | --- | --- |
 | Agent | ✅ |  |
-| RAG | ✅ | Using Chroma as vector database |
+| RAG | ✅ | Using Chroma as vector database, supports Rerank re-scoring |
 | MCP | ✅ | Support Stdio and HTTP protocol |
 
 ## System Extensibility

en/usage/knowledge/readme.mdx

@@ -29,6 +29,17 @@ Go to your pipeline configuration, under the "AI" tab, choose `Local Agent` as t
 
 <img width="400px" src="/images/zh/deploy/knowledge/use_kb.png" alt="use_kb" />
 
+### Configure Reranking (Optional)
+
+After selecting a knowledge base, you can configure a **Rerank Model** below to improve retrieval quality:
+
+- **Rerank Model**: Select a configured rerank model (if none available, add one in [Model Configuration](/en/usage/models/readme) first)
+- **Rerank Top-K**: Number of most relevant results to keep after reranking, default is 5
+
+<Tip>
+Rerank models are optional. When configured, candidate documents from knowledge base retrieval are re-scored by the rerank model, keeping only the top-K most relevant results before passing them to the LLM, significantly reducing noise from irrelevant content.
+</Tip>
+
 <Info>
 LangBot's built-in knowledge base can be used only when the runner is set to `Local Agent`. For other runners, refer to their documentation.
 </Info>

en/usage/models/readme.mdx

@@ -45,3 +45,13 @@ The system has integrated the official embedding model provided by seekdb, no pa
 - On the "Embedding Model" page, select "seekdb-built-in";
 - Click "Save" to use it immediately;
 - Then select this model in your knowledge base to take effect.
+
+### Rerank Model
+
+Rerank models re-score RAG retrieval results to significantly improve the precision of knowledge base answers. When a knowledge base returns multiple candidate documents, the rerank model uses a cross-encoder to precisely score each document's relevance to the query, selecting only the most relevant content.
+
+Enter `Model Name` and select `Model Provider`, then submit. After that, select this model in your pipeline's "AI" configuration.
+
+<Tip>
+Rerank models are optional. Without one, knowledge base results are used in their original retrieval order. With a rerank model configured, results are re-scored and only the top-K most relevant results are kept.
+</Tip>

zh/insight/features.mdx

@@ -55,13 +55,16 @@ LangBot 是一个开源的大语言模型原生即时通信机器人开发平台
 | [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
 | [TBox（蚂蚁百宝箱）](https://tbox.cn/) | ✅ | LLMOps 平台 |
 | [Coze](https://coze.cn) | ✅ | LLMOps 平台 |
+| [Cohere](https://cohere.com/) | ✅ | Rerank 模型 |
+| [Jina](https://jina.ai/) | ✅ | Rerank 模型 |
+| [Voyage AI](https://voyageai.com/) | ✅ | Rerank 模型 |
 
 ## LLM 应用范式实现
 
 | 应用范式 | 状态 | 备注 |
 | --- | --- | --- |
 | Agent | ✅ |  |
-| RAG | ✅ | 使用 Chroma 作为向量数据库 |
+| RAG | ✅ | 使用 Chroma 作为向量数据库，支持 Rerank 重排序 |
 | MCP | ✅ | 支持 Stdio 和 HTTP 两种方式 |
 
 ## 系统扩展性

zh/usage/knowledge/readme.mdx

@@ -29,6 +29,17 @@ LangBot 原生支持 RAG（Retrieval-Augmented Generation，即检索增强生
 
 <img width="400px" src="/images/zh/deploy/knowledge/use_kb.png" alt="use_kb" />
 
+### 配置重排序（可选）
+
+选择知识库后，您可以在下方配置**重排序模型**来提升检索质量：
+
+- **重排序模型**：选择已配置的 Rerank 模型（如未配置，请先到[模型配置](/zh/usage/models/readme)中添加）
+- **重排序保留数量（Top-K）**：重排序后保留的最相关结果数量，默认为 5
+
+<Tip>
+重排序模型是可选的。配置后，知识库检索返回的候选文档会经过重排序模型重新打分，只保留最相关的 Top-K 条，再交给大模型回答，显著减少无关信息干扰。
+</Tip>
+
 <Info>
 仅当运行器为`内置 Agent`时，才可以使用 LangBot 原生知识库，若要在使用其他运行器时使用知识库，请参考所使用的运行器对应产品的文档。
 </Info>

zh/usage/models/readme.mdx

@@ -23,7 +23,8 @@ LangBot Models 是 LangBot 官方提供的模型服务。当您使用 LangBot Sp
 ├── API Keys: [sk-xxx]
 ├── LLM 模型: gpt-4o
 ├── LLM 模型: gpt-4o-mini
-└── 向量模型: text-embedding-3-small
+├── 向量模型: text-embedding-3-small
+└── 重排序模型: rerank-v3.5
 ```
 
 <Tip>
@@ -57,6 +58,16 @@ LangBot Models 是 LangBot 官方提供的模型服务。当您使用 LangBot Sp
 
 ### 使用 seekdb 内置嵌入模型（零配置）
 系统已集成 seekdb 提供的官方嵌入模型，无需填写任何参数。
-- 在“嵌入模型”页面选择「seekdb-内置」；
+- 在”嵌入模型”页面选择「seekdb-内置」；
 - 点击「保存」即可立即使用；
 - 后续在知识库中选择该模型即可生效。
+
+### 重排序模型 (Rerank Model)
+
+重排序模型用于对 RAG 检索结果进行重新评分排序，显著提升知识库回答的精准度。当知识库返回多个候选文档时，重排序模型会使用 cross-encoder 对每个文档与查询的相关性进行精确打分，筛选出最相关的内容。
+
+填入 `模型名称`、选择 `模型供应商`，然后提交即可。之后请在流水线的”AI 能力”配置中选择此模型。
+
+<Tip>
+重排序模型是可选的。不配置时，知识库检索结果将按向量相似度排序直接使用。配置后，检索结果会经过重排序模型重新打分，只保留最相关的 Top-K 条结果。
+</Tip>