Vectorize

Exposes vector search capabilities through the Model Context Protocol (MCP) standard, enabling Claude and other MCP-compatible clients to perform semantic similarity searches across indexed document collections. Implements MCP resource and tool handlers that translate search queries into vector embeddings and return ranked results with relevance scores, allowing LLM agents to retrieve contextually relevant information without custom API integration code.

Provides a research workflow that indexes local or private documents into a searchable vector store, enabling LLM agents to conduct deep research across proprietary knowledge bases without exposing content to external APIs. Implements document ingestion pipelines that convert various file formats into embeddings and stores them in a local or private vector backend, with MCP tools exposing search and retrieval operations to Claude for iterative research tasks.

Converts diverse file formats (PDF, DOCX, images with OCR, web content, etc.) into clean Markdown output, enabling downstream processing and indexing. Uses format-specific extraction libraries and OCR engines to parse structured and unstructured content, normalizing output to Markdown for consistency across heterogeneous document sources. Integrates with the document indexing pipeline to prepare extracted content for embedding and retrieval.

Splits extracted documents into semantically coherent chunks optimized for embedding and retrieval, using strategies beyond simple token counting (e.g., paragraph boundaries, section headers, semantic similarity). Implements configurable chunking strategies that preserve context and meaning, avoiding splits that break sentences or separate related content, and includes overlap handling to maintain continuity across chunk boundaries for better retrieval performance.

Orchestrates end-to-end document processing: accepts files in multiple formats, extracts content to Markdown, chunks semantically, generates embeddings, and stores in vector database. Implements a configurable pipeline that handles format detection, error recovery, and batch processing, with progress tracking and logging for visibility into ingestion status. Integrates extraction, chunking, and embedding steps into a single workflow accessible via MCP tools.

Abstracts vector database operations behind a unified interface, supporting multiple backends (Vectorize, Pinecone, Weaviate, Milvus, etc.) without changing application code. Implements adapter pattern with backend-specific drivers that handle connection pooling, query translation, and result normalization, allowing seamless switching between providers or multi-backend deployments for redundancy and cost optimization.

Enables filtering search results by document metadata (source, date, author, tags, etc.) before or after vector similarity ranking, allowing precise retrieval of relevant documents within constrained sets. Implements metadata indexing alongside vector embeddings and supports complex filter expressions (AND, OR, range queries) that are evaluated efficiently by the underlying vector database, with fallback to post-retrieval filtering for backends without native metadata support.

Abstracts embedding model selection, allowing users to choose from multiple embedding providers (OpenAI, Hugging Face, local models, etc.) and switch between them without re-indexing. Implements model registry with metadata (dimension, cost, latency, language support) and handles model-specific input preprocessing (tokenization, normalization) and output normalization (dimension alignment, score scaling) to ensure consistency across providers.