Vectorize: Precision File Transformation & Chunking

Vectorize: Advanced retrieval meets private deep research – extract, chunk, and transform any file into markdown precision. Your enterprise’s secret weapon."

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About Vectorize

What is Vectorize: Precision File Transformation & Chunking?

Vectorize is a specialized platform designed to streamline advanced file processing through its Model Context Protocol (MCP) server integration. This solution enables precise vector-based document retrieval, text extraction, and automated chunking of files into structured Markdown formats. By leveraging Vectorize’s APIs, users gain access to robust tools for handling complex data workflows, from legal contracts to financial reports, while maintaining high precision and context-aware processing.

How to Use Vectorize: Precision File Transformation & Chunking?

Getting started involves three core steps:

Set up environment variables: Configure your organization ID and API key via VECTORIZE_ORG_ID and VECTORIZE_API_KEY.
Run the MCP server: Use npx to execute the server with required parameters, ensuring dependencies are installed.
Execute operations through JSON-formatted API requests for tasks like document retrieval, text extraction, or generating deep research reports.

Vectorize Features

Key Features of Vectorize: Precision File Transformation & Chunking?

Vectorize excels in:

Context-aware retrieval: Pinpoint relevant documents using semantic search against predefined pipelines.
Universal file processing: Convert PDFs, spreadsheets, and other formats into structured Markdown with automatic chunking.
Private Deep Research: Generate tailored reports by combining user data with web searches, ensuring confidential results.
Modular configuration: Integrate seamlessly with tools like Claude/Windsurf via customizable command-line parameters.

Use Cases of Vectorize: Precision File Transformation & Chunking?

Common applications include:

Automating financial audits by extracting key metrics from thousands of PDF invoices.
Accelerating legal research by chunking case law documents for AI analysis.
Creating real-time market reports by merging internal data with web-derived insights.
Streamlining HR workflows via standardized formatting of employee contracts.

Vectorize FAQ

FAQ from Vectorize: Precision File Transformation & Chunking?

How do I handle large files?
Use base64 encoding for documents over 1MB, specifying the contentType parameter in extraction requests.

Can I customize chunk sizes?
Chunking parameters are currently pipeline-specific; contact support for enterprise customization options.

What happens if API keys are exposed?
Always follow security best practices—restrict access via environment variable management and rotate keys regularly.

Do you support CSV files?
Yes, through the generic text extraction pipeline which preserves table structures in Markdown format.

How is data stored?
Documents are processed in-memory by default; integration with persistent vector databases is available via API extensions.

Content

Vectorize MCP Server

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

Features

Installation

Running with npx

bash export VECTORIZE_ORG_ID=YOUR_ORG_ID export VECTORIZE_API_KEY=YOUR_API_KEY npx -y @vectorize-io/vectorize-mcp-server

Configuration on Claude/Windsurf

json { "mcpServers": { "vectorize": { "command": "npx", "args": ["-y", "@vectorize-io/vectorize-mcp-server"], "env": { "VECTORIZE_ORG_ID": "your-org-id", "VECTORIZE_API_KEY": "your-api-key" } } } }

Tools

Retrieve documents

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

json { "name": "retrieve", "arguments": { "pipeline": "your-pipeline-id", "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):

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

Deep Research

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

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

Development


# Install dependencies

npm install

# Build

npm run build

Contributing

Fork the repository
Create your feature branch
Submit a pull request