Reader

Reader is an AI data ingestion pipeline and web content parser designed to convert websites and documents into clean markdown for use with large language models. It functions as a headless browser content extractor and web-to-markdown converter, transforming URLs and PDF files into structured text formats while removing irrelevant web clutter.

The system optimizes retrieval augmented generation by acting as a search optimizer that retrieves web results and applies re-ranking to improve context relevance. It further enhances content accessibility by using vision models to generate descriptive captions for images and creating vector embeddings for semantic retrieval.

The tool provides broad capabilities for document conversion, web content extraction, and data preprocessing. These include headless browser rendering for JavaScript execution, multi-format conversion of office documents, and bucket-based content caching to reduce latency.

The conversion engine can be deployed as a self-hosted container including all necessary headless browsers and document processors.

Features

Markdown Converters - Transforms web pages and PDF documents into clean markdown syntax for use with large language models.

Vector and AI Data Pipelines - Provides a complete pipeline for fetching web content, generating embeddings, and preparing data for RAG applications.

LLM Context Preparation - Converts unstructured web and document data into clean markdown to provide high-quality context for LLMs.

Retrieval Re-ranking - Applies a secondary scoring model to search results to improve the relevance of retrieved documents for RAG.

Vector Embeddings - Generates numerical vector representations of text and images to enable semantic retrieval.

Document Conversion - Automates the transformation of PDFs and office files into machine-readable text and markdown.

Format Conversion Toolkits - Converts diverse file types including PDFs and office documents into standardized markdown for LLM consumption.

Markdown Content Parsers - Converts websites and documents into clean markdown specifically tailored for use with large language models.

Web Extraction Engines - Retrieves and transforms unstructured web content from URLs into structured formats for automated analysis.

Markdown Conversion Utilities - Converts specific website URLs into structured markdown versions optimized for AI agent consumption.

Web Content Scrapers - Extracts full content from web pages and converts it into structured markdown for AI ingestion.

Markdown Search Converters - Retrieves websites based on search terms and automatically transforms the content into clean markdown.

RAG Pipeline Optimizers - Improving retrieval augmented generation by cleaning web data and re-ranking search results for better model accuracy.

Headless Browsers - Uses automated headless browsers to execute JavaScript and extract the fully rendered DOM of web pages.

Agentic Web Browsing - Enables autonomous agents to fetch and read live website content as structured markdown for real-time interaction.

AI-Generated Captions - Uses multimodal vision models to automatically generate descriptive text captions for images lacking metadata.

Search Result Optimizations - Implements re-ranking logic to improve the relevance of search results for AI context windows.

Container Deployment - Ships the conversion engine as a portable container image for consistent environment execution and deployment.

Self-Hosted Deployment Tools - Allows the conversion engine to be run as a self-hosted container inclusive of all browser and document dependencies.

Content Caching Accelerators - Implements a caching layer using cloud storage buckets to reduce latency and redundant fetching of processed web pages.

Web Crawlers - Utility for transforming URLs into LLM-friendly input streams.

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Features

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