RAG Articles
RAG & vector grounding — chunk stability, Named Entity Recognition, model-ready markdown conversion, and token optimization.
Search Result Source Extraction API: Citation Pipelines
Extract clean text passages with byte offsets, eliminate LLM hallucinations, bypass bot walls, and build citation-ready RAG pipelines with Ollagraph.
RAGData Privacy Audit for AI: What GDPR/CCPA Mean for Web Scraping Pipelines
Scraping web data for AI and RAG does not bypass GDPR or CCPA. Learn how to audit pipelines, scrub PII, enforce provenance, and build compliant scrapers.
AI SearchHow LLMs Index the Web: From Crawler Fetch Queues to Vectorized Knowledge Graphs
Learn the complete indexing pipeline used by LLM crawlers: URL queues, content extraction, chunking, embedding, and vectorized retrieval graphs.
RAGAnswer-First Content Audit: Can AI Extract a Direct Answer From Your Page?
Learn how to run an answer-first content audit testing whether ChatGPT, Perplexity, and Claude can extract direct, quotable answers from your pages.
CitationsCitation Readiness Score: How to Build a Reliable Scoring Model
Learn how to engineer and calculate a Citation Readiness Score (CRS) model to optimize web content for LLM retrieval, RAG grounding, and AI search engines.
AEOAEO Audit Tool: What Should an Answer Engine Optimization Audit Actually Measure?
Learn how to build and deploy an Answer Engine Optimization (AEO) audit tool measuring semantic chunking, RAG similarity, and entity graph alignment.
GuidesIntegrating MCP Web Tools into LangGraph Autonomous Multi-Agent Workflows
Connect LangGraph multi-agent swarms to Ollagraph's Model Context Protocol (MCP) server for stateful, real-time web search, JS scraping, and RAG retrieval.
GuidesReliable Web Data Pipelines: From Crawl to Validated JSON
Learn how to build reliable web data pipelines from crawling through validated JSON with quality gates, observability, and self-healing retries.
GuidesRendering Before Extraction: Reliable Web Data Pipelines
Rendering is the failure-prone layer between fetch and extraction. Learn readiness checks, browser pooling, and quality signals that prevent silent bad data.
GuidesDocument-to-JSON Extraction for Typed Web Records
Learn how to turn unstructured web pages and PDFs into validated, strongly-typed JSON records. Eliminate schema drift and power production AI pipelines.
GuidesE-commerce Data Extraction for Variants, Prices, and Stock
Learn how to extract structured product data, variants, and stock status from e-commerce sites at scale with schema validation and JS rendering.
AI SearchEntity Extraction for AI Agents: Turn Web Pages into Facts
Extract companies, people, and products from web pages with entity resolution and disambiguation so AI agents can act on clean, typed data.
GuidesEvidence-Based Data Extraction: Provenance for Every Field
Learn how to implement field-level provenance tracking in data extraction pipelines with JSON schemas, confidence scoring, and quality gates.
GuidesHow to Extract Structured Data from JavaScript Apps
Use state sniffing, network capture, and schema checks to pull clean records from modern JavaScript apps without brittle scraping.
RAGMarkdown Conversion Quality for AI: Accuracy, Structure, Retrieval
Discover how high-fidelity Markdown conversion preserves headings, tables, and provenance to maximize AI retrieval accuracy and keep citations verifiable.
GuidesStructured Data Extraction API for Validated JSON
Turn messy web pages into validated JSON with schema contracts, evidence packets, and quality gates. Built for trust in downstream systems.
RAGStructured Data Extraction for RAG: Web Pages to Queryable Facts
Learn how structured data extraction turns web pages into queryable entities and relationships, enabling precise, citeable answers for RAG.
GuidesWhy Schema-Driven Extraction Beats CSS Selectors
CSS selectors break when layouts shift. Schema-driven extraction validates every record, reduces silent corruption, and makes failures debuggable.
RAGDocument-to-Markdown Pipelines for AI Knowledge Bases
Build reliable batch conversion pipelines with idempotency, validation gates, and quarantine/replay to keep AI knowledge bases clean.
GuidesHow to Extract Tables from Any Website to JSON
Turn HTML and CSS-based tables into clean JSON, see where DIY parsing breaks, and learn when Ollagraph's API is the safer choice.
RAGStructured Markdown for AI Agents: Better Retrieval and Citations
Preserve headings, lists, tables, and code blocks to stabilize chunks, improve retrieval accuracy, and keep citations deterministic.
RAGHTML to Markdown Boilerplate Removal for Better RAG Retrieval
Strip boilerplate before Markdown conversion to improve RAG retrieval and citations. Deterministic extraction keeps chunks stable.
RAGHTML to Markdown for AI With Links and Source Attribution
Convert HTML into LLM-ready Markdown without losing headings, links, or provenance. Keep citations traceable to the source page.
RAGJavaScript Rendering to Deterministic Markdown for AI Content
Render dynamic pages only when content is ready, then convert them into stable Markdown for reliable AI ingestion. Fail closed when signals are weak.
RAGHow Markdown Formatting Improves RAG Retrieval
Proper Markdown preserves hierarchy, lists, tables, and code so chunks stay stable and retrieval improves. Use a formatting contract before embedding.
RAGOCR to Markdown for RAG: Turn Scans into Citable Evidence
Turn scanned PDFs and images into citation-ready Markdown for RAG with layout recovery, provenance anchors, and reliable evaluation.
RAGPDF to Markdown for RAG: Preserve Tables and Structure
Convert PDFs into AI-ready Markdown that preserves tables, headings, reading order, and citations for stronger RAG retrieval.
RAGXLSX to Markdown for RAG: A Production Conversion Blueprint
Convert Excel workbooks into AI-ready Markdown with deterministic normalization, validation gates, and replay-safe processing for RAG pipelines.