Context Lens

Give your AI the ability to understand meaning, not just match keywords.

What is Context Lens?

Context Lens transforms any content into a searchable knowledge base for your AI assistant. This self-contained Model Context Protocol (MCP) server with built-in serverless vector storage (LanceDB) brings semantic search to your conversations. Point it at any content - codebases, documentation, contracts, or text files - and your AI can instantly understand and answer questions about the content.

Traditional keyword search finds files containing specific words. Miss the exact term? Miss the content.

Context Lens understands meaning. Ask about "authentication" and find code about login, credentials, tokens, OAuth, and access control - even if those files never use the word "authentication."

See It In Action

Want to understand how Context-Lens works? Here's the fun part: you can use Context-Lens to learn about Context-Lens.

Demo: Using Claude Desktop with Context-Lens to index and query this repository itself. No git clone, no scrolling through code - just questions and answers.

Why LanceDB?

Context Lens uses LanceDB - a modern, serverless vector database:

• 🆓 Completely Free & Local - No cloud services, API keys, or subscriptions
• ⚡ Zero Infrastructure - Embedded database, just a file on disk
• 🚀 Fast & Efficient - Built on Apache Arrow, optimized for vector search
• 💾 Simple Storage - Single file database, easy to backup or move

Think of it as "SQLite for AI embeddings" - all the power of vector search without the complexity.

Features

• 🔍 Semantic Search - Understand meaning, not just keywords
• 🚀 Zero Setup - No installation, no configuration, no API keys
• 💾 Serverless Storage - Built-in LanceDB, no external database
• 🔒 100% Local & Private - All data stays on your machine
• 📁 Local & GitHub - Index local files or public GitHub repositories
• 🎯 Smart Parsing - Language-aware chunking for better results

Architecture

How It Works

When you add content to Context Lens, it doesn't just dump text into a database. Here's what actually happens:

Smart Reading: Context Lens detects your file type and uses specialized parsers. Python files are analyzed with AST parsing, JSON is parsed structurally, Markdown is split by headers. This preserves the natural structure of your content.

Meaningful Chunks: Instead of arbitrary character limits, content is chunked intelligently - complete functions, logical paragraphs, full sections. Your code never gets split mid-function.

Semantic Vectors: Each chunk is converted to a 384-dimensional vector using a local embedding model. These vectors capture meaning, not just words. "authentication" and "login system" become similar vectors even though they share no words.

Local Storage: Everything goes into LanceDB - a serverless vector database that's just a file on your disk. No cloud services, no API calls, completely private.

Conceptual Search: When you ask a question, it becomes a vector too. Context Lens finds chunks with similar vectors (similar meaning) and ranks them by relevance. You get answers based on concepts, not keyword matching.

Technical Specifications

... View full README on GitHub