From eb9c988a86571fd41c1ddc68606bc00a1f8faa17 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=82=B9=E6=9F=B3=E7=9F=B3?= Date: Wed, 3 Dec 2025 10:48:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0README=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mcp_center/servers/rag/README.en.md | 23 +++++++++++++++++++++++ mcp_center/servers/rag/README.md | 23 +++++++++++++++++++++++ 2 files changed, 46 insertions(+) create mode 100644 mcp_center/servers/rag/README.en.md create mode 100644 mcp_center/servers/rag/README.md diff --git a/mcp_center/servers/rag/README.en.md b/mcp_center/servers/rag/README.en.md new file mode 100644 index 00000000..149e85f6 --- /dev/null +++ b/mcp_center/servers/rag/README.en.md @@ -0,0 +1,23 @@ +# RAG Knowledge Base Management MCP (Management Control Program) Specification Document + +## 1. Service Introduction + +This service is a comprehensive RAG (Retrieval-Augmented Generation) knowledge base management MCP (Management Control Program) based on SQLite database, FTS5 full-text search, and sqlite-vec vector search technologies. It provides full lifecycle management functions for knowledge bases including **creation, deletion, selection, document import, hybrid search, document management, and database import/export**. It supports multiple document formats such as TXT, DOCX, DOC, and PDF, uses asynchronous batch vectorization processing, and combines keyword search and vector search hybrid search strategies to provide a complete solution for knowledge base construction and intelligent retrieval. The service is adapted to both Chinese and English configurations to meet the needs of different scenarios. + + +## 2. Core Tool Information + +| Tool Name | Tool Function | Core Input Parameters | Key Return Content | +| ---- | ---- | ---- | ---- | +| `create_knowledge_base` | Creates a new knowledge base. A knowledge base is a container for documents, and each knowledge base can have its own chunk_size and embedding configuration | - `kb_name`: Knowledge base name (required, must be unique)
- `chunk_size`: Chunk size in tokens (required, e.g., 512, 1024)
- `embedding_model`: Embedding model name (optional)
- `embedding_endpoint`: Embedding service endpoint URL (optional)
- `embedding_api_key`: Embedding service API Key (optional) | Creation result dictionary (including `kb_id` knowledge base ID, `kb_name` knowledge base name, `chunk_size` chunk size) | +| `delete_knowledge_base` | Deletes the specified knowledge base. Cannot delete the currently active knowledge base. Deleting a knowledge base will cascade delete all documents and chunks under it | - `kb_name`: Knowledge base name (required) | Deletion result dictionary (including `kb_name` deleted knowledge base name) | +| `list_knowledge_bases` | Lists all available knowledge bases. Returns detailed information about all knowledge bases, including the currently selected one | No parameters | Knowledge base list dictionary (including `knowledge_bases` list, `count` number of knowledge bases, `current_kb_id` currently selected knowledge base ID. Each knowledge base contains id, name, chunk_size, embedding_model, created_at, is_current, etc.) | +| `select_knowledge_base` | Selects a knowledge base as the currently active one. After selection, subsequent operations such as document import and search will be performed in this knowledge base | - `kb_name`: Knowledge base name (required) | Selection result dictionary (including `kb_id` knowledge base ID, `kb_name` knowledge base name, `document_count` number of documents in this knowledge base) | +| `import_document` | Imports documents into the currently selected knowledge base. Supports concurrent import of multiple files. Supports TXT, DOCX, DOC, and PDF formats. Documents will be parsed, split into chunks, and vectors will be generated asynchronously in batch and stored in the database | - `file_paths`: List of file paths (absolute paths), supports 1~n files (required)
- `chunk_size`: Chunk size in tokens (optional, defaults to knowledge base's chunk_size) | Import result dictionary (including `total` total number of files, `success_count` number of successfully imported files, `failed_count` number of failed files, `success_files` list of successfully imported files, `failed_files` list of failed files) | +| `search` | Performs hybrid search in the currently selected knowledge base. Combines keyword search (FTS5) and vector search (sqlite-vec), merges results using weighted approach (keyword weight 0.3, vector weight 0.7), deduplicates, reranks using Jaccard similarity, and returns the top-k most relevant results | - `query`: Query text (required)
- `top_k`: Number of results to return (optional, default from config, usually 5) | Search result dictionary (including `chunks` chunk list, `count` number of results. Each chunk contains id, doc_id, content, tokens, chunk_index, doc_name, score, etc.) | +| `list_documents` | Lists all documents in the currently selected knowledge base. Returns detailed information about the documents | No parameters | Document list dictionary (including `documents` document list, `count` number of documents. Each document contains id, name, file_path, file_type, chunk_size, created_at, updated_at, etc.) | +| `delete_document` | Deletes the specified document from the currently selected knowledge base. Deleting a document will cascade delete all chunks of that document | - `doc_name`: Document name (required) | Deletion result dictionary (including `doc_name` deleted document name) | +| `update_document` | Updates the document's chunk_size and re-parses the document. Will delete existing chunks, re-split the document using the new chunk_size, and asynchronously generate new vectors in batch | - `doc_name`: Document name (required)
- `chunk_size`: New chunk size in tokens (required) | Update result dictionary (including `doc_id` document ID, `doc_name` document name, `chunk_count` new number of chunks, `chunk_size` new chunk size) | +| `export_database` | Exports the entire kb.db database file to the specified path | - `export_path`: Export path (absolute path, required) | Export result dictionary (including `source_path` source database path, `export_path` export path) | +| `import_database` | Imports a .db database file and merges its contents into kb.db. Import will automatically handle name conflicts by adding timestamps to knowledge base and document names | - `source_db_path`: Source database file path (absolute path, required) | Import result dictionary (including `source_path` source database path, `imported_kb_count` number of imported knowledge bases, `imported_doc_count` number of imported documents) | + diff --git a/mcp_center/servers/rag/README.md b/mcp_center/servers/rag/README.md new file mode 100644 index 00000000..d5a6def4 --- /dev/null +++ b/mcp_center/servers/rag/README.md @@ -0,0 +1,23 @@ +# RAG知识库管理MCP（管理控制程序）规范文档 + +## 一、服务介绍 + +本服务是一款功能完整的RAG（检索增强生成）知识库管理MCP（管理控制程序），基于SQLite数据库、FTS5全文检索、sqlite-vec向量检索等技术，实现对知识库的**创建、删除、选择、文档导入、混合检索、文档管理、数据库导入导出**等全生命周期管理功能。支持TXT、DOCX、DOC、PDF等多种文档格式，采用异步批量向量化处理，结合关键词检索和向量检索的混合搜索策略，为知识库构建和智能检索提供完整的解决方案，适配中文与英文双语言配置，满足不同场景下的使用需求。 + + +## 二、核心工具信息 + +| 工具名称 | 工具功能 | 核心输入参数 | 关键返回内容 | +| ---- | ---- | ---- | ---- | +| `create_knowledge_base` | 创建一个新的知识库，知识库是文档的容器，每个知识库可以有自己的chunk_size和embedding配置 | - `kb_name`：知识库名称（必填，必须唯一）
- `chunk_size`：chunk大小，单位token（必填，例如512、1024）
- `embedding_model`：向量化模型名称（可选）
- `embedding_endpoint`：向量化服务端点URL（可选）
- `embedding_api_key`：向量化服务API Key（可选） | 创建结果字典（含`kb_id`知识库ID、`kb_name`知识库名称、`chunk_size`chunk大小） | +| `delete_knowledge_base` | 删除指定的知识库，不能删除当前正在使用的知识库，删除知识库会级联删除该知识库下的所有文档和chunks | - `kb_name`：知识库名称（必填） | 删除结果字典（含`kb_name`已删除的知识库名称） | +| `list_knowledge_bases` | 列出所有可用的知识库，返回所有知识库的详细信息，包括当前选中的知识库 | 无参数 | 知识库列表字典（含`knowledge_bases`知识库列表、`count`知识库数量、`current_kb_id`当前选中的知识库ID，每个知识库包含id、name、chunk_size、embedding_model、created_at、is_current等字段） | +| `select_knowledge_base` | 选择一个知识库作为当前使用的知识库，选择后，后续的文档导入、查询等操作都会在该知识库中进行 | - `kb_name`：知识库名称（必填） | 选择结果字典（含`kb_id`知识库ID、`kb_name`知识库名称、`document_count`该知识库下的文档数量） | +| `import_document` | 导入文档到当前选中的知识库，支持多文件并发导入，支持TXT、DOCX、DOC、PDF格式，文档会被解析、切分为chunks，并异步批量生成向量存储到数据库中 | - `file_paths`：文件路径列表（绝对路径），支持1~n个文件，为list形式（必填）
- `chunk_size`：chunk大小，单位token（可选，默认使用知识库的chunk_size） | 导入结果字典（含`total`总文件数、`success_count`成功导入的文件数、`failed_count`失败的文件数、`success_files`成功导入的文件列表、`failed_files`失败的文件列表） | +| `search` | 在当前选中的知识库中进行混合检索，结合关键词检索（FTS5）和向量检索（sqlite-vec），使用加权方式合并结果（关键词权重0.3，向量权重0.7），去重后使用Jaccard相似度重排序，返回最相关的top-k个结果 | - `query`：查询文本（必填）
- `top_k`：返回数量（可选，默认从配置读取，通常为5） | 检索结果字典（含`chunks`chunk列表、`count`结果数量，每个chunk包含id、doc_id、content、tokens、chunk_index、doc_name、score等字段） | +| `list_documents` | 查看当前选中的知识库下的所有文档列表，返回文档的详细信息 | 无参数 | 文档列表字典（含`documents`文档列表、`count`文档数量，每个文档包含id、name、file_path、file_type、chunk_size、created_at、updated_at等字段） | +| `delete_document` | 删除当前选中的知识库下的指定文档，删除文档会级联删除该文档的所有chunks | - `doc_name`：文档名称（必填） | 删除结果字典（含`doc_name`已删除的文档名称） | +| `update_document` | 修改文档的chunk_size并重新解析文档，会删除原有的chunks，使用新的chunk_size重新切分文档，并异步批量生成新的向量 | - `doc_name`：文档名称（必填）
- `chunk_size`：新的chunk大小，单位token（必填） | 修改结果字典（含`doc_id`文档ID、`doc_name`文档名称、`chunk_count`新的chunk数量、`chunk_size`新的chunk大小） | +| `export_database` | 导出整个kb.db数据库文件到指定路径 | - `export_path`：导出路径（绝对路径，必填） | 导出结果字典（含`source_path`源数据库路径、`export_path`导出路径） | +| `import_database` | 导入一个.db数据库文件，将其中的内容合并到kb.db中，导入时会自动处理重名冲突，为知识库和文档名称添加时间戳 | - `source_db_path`：源数据库文件路径（绝对路径，必填） | 导入结果字典（含`source_path`源数据库路径、`imported_kb_count`导入的知识库数量、`imported_doc_count`导入的文档数量） | + -- Gitee