向量化Mcp伺服器

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什麼是 Vectorize MCP 伺服器？

Vectorize MCP 伺服器是一個開源專案，旨在促進在 Vectorize 生態系統內數據的管理和處理。它作為一個後端伺服器，處理與數據操作、存儲和檢索相關的各種任務，對於使用 Vectorize 技術的開發者來說，是一個不可或缺的組件。

Vectorize MCP 伺服器的特點

數據管理：高效地管理和處理大型數據集。
API 集成：通過強大的 API 與其他服務無縫集成，允許靈活的數據處理。
開源：作為一個開源專案，鼓勵社區貢獻和透明度。
可擴展性：設計上可以根據需求擴展，無論是小型專案還是大型企業解決方案。
文檔：提供全面的文檔，以幫助開發者快速入門並有效利用伺服器。

如何使用 Vectorize MCP 伺服器

安裝：首先從 GitHub 克隆倉庫，並按照文檔中提供的安裝說明進行操作。
配置：根據您的專案需求配置伺服器設置。這可能包括設置數據庫連接和 API 密鑰。
API 使用：利用提供的 API 端點與伺服器互動。您可以執行數據檢索、更新和刪除等操作。
測試：進行全面測試，以確保伺服器在您的應用中按預期運行。
部署：一旦一切設置和測試完成，將伺服器部署到您的生產環境中。

常見問題解答

問：Vectorize MCP 伺服器是免費使用的嗎？

答：是的，它是一個開源專案，根據 MIT 許可證免費使用。

問：我可以為 Vectorize MCP 伺服器做貢獻嗎？

答：當然可以！歡迎貢獻。您可以在 GitHub 倉庫上提交問題、功能請求或拉取請求。

問：我可以在哪裡找到文檔？

答：文檔可在 docs.vectorize.io/api/api-mcp-server 獲得。

問：支持哪些編程語言？

答：伺服器主要使用與 Vectorize 生態系統兼容的語言構建，但可以與任何能夠發送 HTTP 請求的語言集成。

問：我該如何報告錯誤？

答：您可以通過在 GitHub 倉庫上打開問題來報告錯誤，提供盡可能多的細節，以幫助開發者解決問題。

詳細

Vectorize MCP Server

A Model Context Protocol (MCP) server implementation that integrates with Vectorize for advanced Vector retrieval and text extraction.

Installation

Running with npx

export VECTORIZE_ORG_ID=YOUR_ORG_ID
export VECTORIZE_TOKEN=YOUR_TOKEN
export VECTORIZE_PIPELINE_ID=YOUR_PIPELINE_ID

npx -y @vectorize-io/vectorize-mcp-server@latest

VS Code Installation

For one-click installation, click one of the install buttons below:

Manual Installation

For the quickest installation, use the one-click install buttons at the top of this section.

To install manually, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "org_id",
        "description": "Vectorize Organization ID"
      },
      {
        "type": "promptString",
        "id": "token",
        "description": "Vectorize Token",
        "password": true
      },
      {
        "type": "promptString",
        "id": "pipeline_id",
        "description": "Vectorize Pipeline ID"
      }
    ],
    "servers": {
      "vectorize": {
        "command": "npx",
        "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],
        "env": {
          "VECTORIZE_ORG_ID": "${input:org_id}",
          "VECTORIZE_TOKEN": "${input:token}",
          "VECTORIZE_PIPELINE_ID": "${input:pipeline_id}"
        }
      }
    }
  }
}

Optionally, you can add the following to a file called .vscode/mcp.json in your workspace to share the configuration with others:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "org_id",
      "description": "Vectorize Organization ID"
    },
    {
      "type": "promptString",
      "id": "token",
      "description": "Vectorize Token",
      "password": true
    },
    {
      "type": "promptString",
      "id": "pipeline_id",
      "description": "Vectorize Pipeline ID"
    }
  ],
  "servers": {
    "vectorize": {
      "command": "npx",
      "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],
      "env": {
        "VECTORIZE_ORG_ID": "${input:org_id}",
        "VECTORIZE_TOKEN": "${input:token}",
        "VECTORIZE_PIPELINE_ID": "${input:pipeline_id}"
      }
    }
  }
}

Configuration on Claude/Windsurf/Cursor/Cline

{
  "mcpServers": {
    "vectorize": {
      "command": "npx",
      "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],
      "env": {
        "VECTORIZE_ORG_ID": "your-org-id",
        "VECTORIZE_TOKEN": "your-token",
        "VECTORIZE_PIPELINE_ID": "your-pipeline-id"
      }
    }
  }
}

Tools

Retrieve documents

Perform vector search and retrieve documents (see official API):

{
  "name": "retrieve",
  "arguments": {
    "question": "Financial health of the company",
    "k": 5
  }
}

Text extraction and chunking (Any file to Markdown)

Extract text from a document and chunk it into Markdown format (see official API):

{
  "name": "extract",
  "arguments": {
    "base64document": "base64-encoded-document",
    "contentType": "application/pdf"
  }
}

Deep Research

Generate a Private Deep Research from your pipeline (see official API):

{
  "name": "deep-research",
  "arguments": {
    "query": "Generate a financial status report about the company",
    "webSearch": true
  }
}

Development

npm install
npm run dev

Release

Change the package.json version and then:

git commit -am "x.y.z"
git tag x.y.z
git push origin
git push origin --tags

Contributing

Fork the repository
Create your feature branch
Submit a pull request

{ "mcpServers": { "vectorize-mcp-server": { "command": "docker", "args": [ "run", "-i", "--rm", "ghcr.io/metorial/mcp-container--vectorize-io--vectorize-mcp-server--vectorize-mcp-server", "node dist/index.js" ], "env": { "VECTORIZE_ORG_ID": "vectorize-org-id", "VECTORIZE_TOKEN": "vectorize-token", "VECTORIZE_PIPELINE_ID": "vectorize-pipeline-id" } } } }

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