Feat/local search tool

docs/en/Components/Config.md

@@ -102,6 +102,24 @@ tools:
     url: https://mcp.api-inference.modelscope.net/xxx/sse
     exclude:
       - map_geo
+  # Local codebase / document search (sirchmunk), exposed as the `localsearch` tool
+  localsearch:
+    mcp: false
+    paths:
+      - ./src
+      - ./docs
+    work_path: ./.sirchmunk
+    mode: FAST
+    # Optional: llm_api_key, llm_base_url, llm_model_name (else inherited from `llm`)
+    # When true, a shallow sirchmunk DirectoryScanner run at tool connect injects file titles/previews
+    # into the `localsearch` tool description (default: false)
+    # description_catalog: false
+    # description_catalog_max_files: 120
+    # description_catalog_max_depth: 5
+    # description_catalog_max_chars: 10000
+    # description_catalog_max_preview_chars: 400
+    # description_catalog_cache_ttl_seconds: 300
+    # description_catalog_exclude: []  # extra globs / dir names merged with sirchmunk defaults
 ```
 
 For the complete list of supported tools and custom tools, please refer to [here](./Tools.md)
@@ -167,19 +185,19 @@ In addition to yaml configuration, MS-Agent also supports several additional com
 
 > Any configuration in agent.yaml can be passed in with new values via command line, and also supports reading from environment variables with the same name (case insensitive), for example `--llm.modelscope_api_key xxx-xxx`.
 
-- knowledge_search_paths: Knowledge search paths, comma-separated multiple paths. When provided, automatically enables SirchmunkSearch for knowledge retrieval, with LLM configuration automatically inherited from the `llm` module.
+- knowledge_search_paths: Comma-separated local search paths. Merges into `tools.localsearch.paths` and registers the **`localsearch`** tool (sirchmunk) for on-demand use by the model—not automatic per-turn injection. LLM settings are inherited from the `llm` module unless you set `tools.localsearch.llm_*` fields.
 
 ### Quick Start for Knowledge Search
 
-Use the `--knowledge_search_paths` parameter to quickly enable knowledge search based on local documents:
+Use `--knowledge_search_paths` or define `tools.localsearch` in yaml so the model can call `localsearch` when needed:
 
 ```bash
 # Using default agent.yaml configuration, automatically reuses LLM settings
-ms-agent run --query "How to implement user authentication?" --knowledge_search_paths "./src,./docs"
+ms-agent run --query "How to implement user authentication?" --knowledge_search_paths "/path/to/docs"
 
 # Specify configuration file
 ms-agent run --config /path/to/agent.yaml --query "your question" --knowledge_search_paths "/path/to/docs"
 ```
 
 LLM-related parameters (api_key, base_url, model) are automatically inherited from the `llm` module in the configuration file, no need to configure them repeatedly.
-If you need to use independent LLM configuration in the `knowledge_search` module, you can explicitly configure `knowledge_search.llm_api_key` and other parameters in the yaml.
+For a dedicated sirchmunk LLM, set `tools.localsearch.llm_api_key`, `llm_base_url`, and `llm_model_name` in yaml. Legacy top-level `knowledge_search` with the same keys is still read for backward compatibility.

docs/zh/Components/config.md

@@ -102,6 +102,24 @@ tools:
     url: https://mcp.api-inference.modelscope.net/xxx/sse
     exclude:
       - map_geo
+  # 本地代码库/文档搜索（sirchmunk），对应模型可调用的 `localsearch` 工具
+  localsearch:
+    mcp: false
+    paths:
+      - ./src
+      - ./docs
+    work_path: ./.sirchmunk
+    mode: FAST
+    # 可选：llm_api_key、llm_base_url、llm_model_name（不填则从 `llm` 继承）
+    # 为 true 时，在工具连接阶段用 sirchmunk DirectoryScanner 做浅层扫描，把文件标题/预览写入
+    # `localsearch` 工具 description，便于模型知道本地知识库里大致有哪些内容（默认 false）
+    # description_catalog: false
+    # description_catalog_max_files: 120
+    # description_catalog_max_depth: 5
+    # description_catalog_max_chars: 10000
+    # description_catalog_max_preview_chars: 400
+    # description_catalog_cache_ttl_seconds: 300
+    # description_catalog_exclude: []  # 额外 glob / 目录名，与 sirchmunk 默认排除合并
 ```
 
 支持的完整工具列表，以及自定义工具请参考 [这里](./tools)
@@ -165,13 +183,13 @@ handler: custom_handler
       }
     }
     ```
-- knowledge_search_paths: 知识搜索路径，逗号分隔的多个路径。传入后会自动启用 SirchmunkSearch 进行知识检索，LLM 配置自动从 `llm` 模块复用
+- knowledge_search_paths: 知识搜索路径，逗号分隔。会合并到 `tools.localsearch.paths` 并注册 **`localsearch`** 工具（sirchmunk），由模型按需调用，不再在每轮自动注入上下文；除非配置 `tools.localsearch.llm_*`，否则 LLM 从 `llm` 模块复用
 
 > agent.yaml 中的任意一个配置，都可以使用命令行传入新的值，也支持从同名（大小写不敏感）环境变量中读取，例如 `--llm.modelscope_api_key xxx-xxx`。
 
 ### 知识搜索快速使用
 
-通过 `--knowledge_search_paths` 参数，可以快速启用基于本地文档的知识搜索：
+通过 `--knowledge_search_paths` 或在 yaml 中配置 `tools.localsearch`，启用本地知识搜索（模型按需调用 `localsearch`）：
 
 ```bash
 # 使用默认 agent.yaml 配置，自动复用 LLM 设置
@@ -182,4 +200,4 @@ ms-agent run --config /path/to/agent.yaml --query "你的问题" --knowledge_sea
 ```
 
 LLM 相关参数（api_key, base_url, model）会自动从配置文件的 `llm` 模块继承，无需重复配置。
-如果需要在 `knowledge_search` 模块中使用独立的 LLM 配置，可以在 yaml 中显式配置 `knowledge_search.llm_api_key` 等参数。
+若 sirchmunk 需独立 LLM，可在 yaml 的 `tools.localsearch` 下设置 `llm_api_key`、`llm_base_url`、`llm_model_name`。

ms_agent/agent/agent.yaml

@@ -13,42 +13,7 @@ generation_config:
 
 prompt:
   system: |
-    You are an assistant that helps me complete tasks. You need to follow these instructions:
-
-    1. Analyze whether my requirements need tool-calling. If no tools are needed, you can think directly and provide an answer.
-
-    2. I will give you many tools, some of which are similar. Please carefully analyze which tool you currently need to invoke.
-       * If tools need to be invoked, you must call at least one tool in each round until the requirement is completed.
-       * If you get any useful links or images from the tool calling, output them with your answer as well.
-       * Check carefully the tool result, what it contains, whether it has information you need.
-
-    3. You DO NOT have built-in geocode/coordinates/links. Do not output any fake geocode/coordinates/links. Always query geocode/coordinates/links from tools first!
-
-    4. If you need to complete coding tasks, you need to carefully analyze the original requirements, provide detailed requirement analysis, and then complete the code writing.
-
-    5. This conversation is NOT for demonstration or testing purposes. Answer it as accurately as you can.
-
-    6. Do not call tools carelessly. Show your thoughts **as detailed as possible**.
-
-    7. Respond in the same language the user uses. If the user switches, switch accordingly.
-
-    For requests that require performing a specific task or retrieving information, using the following format:
-    ```
-    The user needs to ...
-    I have analyzed this request in detail and broken it down into the following steps:
-    ...
-    ```
-    If you have tools which may help you to solve problems, follow this format to answer:
-    ```
-    The user needs to ...
-    I have analyzed this request in detail and broken it down into the following steps:
-    ...
-    First, I should use the [Tool Name] because [explain relevance]. The required input parameters are: ...
-    ...
-    I have carefully reviewed the tool's output. The result does/does not fully meet my expectations. Next, I need to ...
-    ```
-
-    **Important: Always respond in the same language the user is using.**
+    you are a helpful assistant.
 
 max_chat_round: 9999