From a85ffb7f76df69e0c4f6d8d831bf2e1b42b710fb Mon Sep 17 00:00:00 2001 From: "russell@unturf.com" Date: Tue, 24 Feb 2026 22:13:25 -0500 Subject: [PATCH] reverse-rag.html redirects to PDF whitepaper --- public/reverse-rag.html | 392 +---- public/reverse-rag.pdf | 3568 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 3572 insertions(+), 388 deletions(-) create mode 100644 public/reverse-rag.pdf diff --git a/public/reverse-rag.html b/public/reverse-rag.html index 7bb059a..f96c3ea 100644 --- a/public/reverse-rag.html +++ b/public/reverse-rag.html @@ -1,396 +1,12 @@ - - - - - - - Reverse Retrieval Augmented Generation | uncloseai.com - - - - - - - - - - - - - - - - - + + Reverse RAG Whitepaper | uncloseai.com - - - - - - - - -
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uncloseai.

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Reverse Retrieval Augmented Generation

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Abstract

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Traditional Retrieval Augmented Generation (RAG) requires a server-side pipeline: documents are chunked, embedded into vectors, stored in a database, and retrieved by similarity search at query time. This architecture demands infrastructure, indexing latency, and maintenance of embedding models and vector stores.

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Reverse Retrieval Augmented Generation (Reverse RAG) inverts this entirely. Instead of the server fetching documents to augment the prompt, the client extracts live content from the page the user is currently viewing and injects it directly into the conversation context. The data comes to the model. No vector database. No embeddings. No indexing pipeline. No server-side retrieval.

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This technique is implemented as an AGPL-3.0-only algorithm in uncloseai.js, a single-file JavaScript library that adds a machine learning chat interface to any webpage. By feeding the model the full, fresh content of whatever page the user is on, small 8B-parameter models produce answers that rival much larger models on page-specific questions.

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The Problem with Traditional RAG

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Standard RAG systems follow a retrieval-then-generate pattern:

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  1. Ingest: Crawl documents, split into chunks of ~512 tokens
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  2. Embed: Run each chunk through an embedding model (e.g., OpenAI text-embedding-3, sentence-transformers)
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  3. Store: Insert vectors into a database (Pinecone, Weaviate, ChromaDB, pgvector)
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  4. Query: When a user asks a question, embed the query, find top-k similar chunks by cosine similarity
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  5. Generate: Stuff the retrieved chunks into the prompt, send to the LLM
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This pipeline has real costs:

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Reverse RAG: The Client Has the Context

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Reverse RAG starts from a different observation: the user is already looking at the document they want to ask about. The browser has the full, rendered, up-to-the-second content right there in the DOM. Why retrieve it again from a database?

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The algorithm:

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  1. Extract: Walk the live DOM tree. Pull text, links (as markdown), metadata, structured data. Wait for dynamic content to settle (MutationObserver with 500ms quiet period).
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  2. Analyze: Run 13 deterministic analyzers on the extracted content. Zero API calls. Compute reading metrics, readability scores, code block detection, link topology, entity patterns, media inventory, form detection, and more.
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  3. Classify: Send a 4000-character preview to the model for one-shot page classification: type, author, topics, tone, domain, audience, key phrases, freshness.
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  4. Inject: Concatenate the computed intelligence, classification, and full page content into the system message. Lock it in for the entire conversation session.
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  5. Converse: Every subsequent user message is sent with the full page context already in the system prompt. The model has complete knowledge of the page at all times.
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There is no step where a server fetches documents. There is no vector similarity search. The context is always the entire page, not a "most relevant" fragment chosen by an embedding model that might be wrong.

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Architecture

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Stage 1: Content Extraction

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The extraction pipeline handles static HTML, dynamic SPAs, and server-rendered content through three mechanisms:

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URI Translation: Known dynamic page patterns (GitLab CI logs, API documentation portals) are mapped to their raw content endpoints. A GitLab job page fetches /raw instead of parsing the rendered HTML. This handles cases where the visible DOM is a thin shell over data loaded asynchronously.

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DOM Settlement: A MutationObserver watches for DOM changes. Extraction waits until 500ms of silence (no mutations), with a hard timeout at 3 seconds. This handles React/Vue/Svelte apps that hydrate after initial page load.

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Recursive DOM Walk: The full document body is traversed. Text nodes become plain text. Anchor tags become markdown links: [link text](href). The output preserves page structure without HTML noise.

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// Simplified extraction logic
-function extractDOMContent() {
-    const title = document.title;
-    const meta = document.querySelector('meta[name="description"]')?.content;
-    const body = walkDOM(document.body); // recursive text + markdown links
-    return `**Page Title**: ${title}\n\n${meta}\n\n${body}`;
-}
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Stage 2: Page Intelligence (13 Deterministic Analyzers)

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Before any model call, 13 analyzers extract ground-truth metrics from the page. Every analyzer runs locally in the browser. Zero network requests. Zero tokens consumed.

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AnalyzerOutputPurpose
Structured DataOpen Graph, JSON-LD, Twitter Cards, canonical URI, author, publish dateMachine-readable page metadata
Heading Outlineh1-h6 hierarchy with textDocument structure map
Reading MetricsWord count, sentence count, paragraph count, reading time (238 wpm Brysbaert 2019)Content scope estimation
ReadabilityFlesch-Kincaid grade level with descriptorAudience calibration
Code BlocksCount, languages detected, total lines, code-to-prose ratioTechnical content identification
Link TopologyInternal vs. external count, top 5 external domainsReference network understanding
User ContextTimezone, browser language, device type, referrerPersonalization signals
Media InventoryImage count, alt-text coverage, video embeds (YouTube/Vimeo)Multimedia awareness
Form DetectionForm count, classified type (login/search/contact/checkout)Interactive element awareness
Table ExtractionUp to 5 tables with headers and row countsStructured data in prose
Entity PatternsEmails, prices, dates, version numbers, IPs, percentages (regex, max 10 each)Factual anchor points
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The output is formatted for token efficiency. Empty sections are omitted. A typical page produces 5-15 lines of computed intelligence:

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Reading: 2,341 words | 89 sentences | 34 paragraphs | ~10 min read
-Readability: Flesch-Kincaid grade 9.2 (high school)
-Code: 3 blocks (python, javascript) | 156 lines | 23% code-to-prose
-Links: 42 internal, 8 external | top: github.com(3), stackoverflow.com(2)
-Structured Data: og:type=article | author=fxhp | published=2025-01-15
-Entities: prices: $99.99, $129.99 | versions: v1.2.3, v2.0.0
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Stage 3: LLM Page Classification

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A single inference call classifies the page. The model receives the first 4000 characters of extracted content and returns a JSON object:

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{
-    "type": "documentation",
-    "author": "fxhp",
-    "publishDate": "2025-02-24",
-    "topics": ["machine learning", "inference"],
-    "entities": ["vLLM", "Hermes", "Qwen"],
-    "tone": "technical",
-    "domain": "technology",
-    "audience": "developers running local inference",
-    "keyPhrases": ["dynamic model discovery", "streaming SSE"],
-    "summary": "Documentation for running local LLM inference with vLLM",
-    "contentLanguage": "en",
-    "freshness": "evergreen"
-}
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This classification is optional. If it fails, the conversation proceeds with computed intelligence and raw content alone. The system never blocks on a failed classification.

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Stage 4: Context Injection and Locking

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All three layers are concatenated into the system message:

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SYSTEM MESSAGE:
-  [Base identity: "You are Hermes, embedded on this webpage..."]
-  [Computed intelligence: reading metrics, code blocks, entities...]
-  [Classification: page type, author, topics, tone, key phrases...]
-  [Full page content: entire extracted text with markdown links]
-  [Conversation instructions: "You have complete knowledge of this page..."]
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This combined context is set once via setSystemMessageAppend() and persists for the entire conversation session. Every subsequent user message carries the full page context in the system prompt. The model never loses sight of what page it's on.

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Stage 5: Greeting as Attention Primer

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The model generates a 3-paragraph greeting that demonstrates page understanding: what the page is about, what's most interesting, and how it can help. This greeting serves as an attention primer. By forcing the model to summarize the page before the user asks anything, the model's internal representations are already aligned with the page content when the first real question arrives.

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Why Small Models Punch Above Their Weight

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An 8B-parameter model with the right context in its system prompt will outperform a 70B model that's guessing. This is the core insight of Reverse RAG.

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Traditional RAG gives the model fragments: 3-5 chunks of ~512 tokens each, selected by embedding similarity, ripped from their surrounding context. The model must reconstruct meaning from these fragments while also answering the user's question.

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Reverse RAG gives the model everything: the full page text, the heading structure, the link network, code block languages, reading level, entity patterns, and a classification of what kind of page this is. The model doesn't need to infer anything about the page. It's all right there.

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For page-specific questions ("what does this function do?", "who wrote this?", "summarize the third section"), context completeness beats parameter count. A small model with perfect context is more useful than a large model with partial context.

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The tradeoff is clear: Reverse RAG uses more prompt tokens per message (the full page is in every system prompt). But inference on small models is cheap. The tokens spent on context are worth far more than the infrastructure costs of running a RAG pipeline to produce worse context.

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Two-Layer Context: Ground Truth + Semantic Understanding

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The 13 deterministic analyzers and the LLM classification serve different roles:

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Layer 1: Computed Intelligence (ground truth). These are facts the model cannot hallucinate because they're computed directly from the DOM. The page has exactly 2,341 words. The Flesch-Kincaid grade is exactly 9.2. There are exactly 3 code blocks in Python and JavaScript. The model receives these as pre-computed facts and can cite them with confidence.

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Layer 2: LLM Classification (semantic understanding). The model's own classification of the page type, tone, audience, and key phrases. This is subjective and can be wrong, but it primes the model's attention toward the right framing. A page classified as "recipe" triggers different conversational patterns than one classified as "documentation."

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Together, these layers give the model both what the page is (computed) and what the page means (classified). Neither layer alone is sufficient. Ground truth without semantic framing produces dry, unfocused answers. Semantic framing without ground truth produces confident but potentially wrong answers.

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Reverse RAG vs. Traditional RAG

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DimensionTraditional RAGReverse RAG
Data flowServer retrieves documents for the modelClient pushes page content to the model
InfrastructureVector DB, embedding model, chunking pipeline, reindexing jobsNone. Runs in the browser.
FreshnessHours to days behind (reindex lag)Real-time. Extracted from live DOM.
Context scopeTop-k chunks (~2500 tokens)Entire page + computed intelligence
Context qualityFragments selected by cosine similarity (can miss critical content)Complete page with structure preserved
Retrieval failuresCommon. Embedding similarity is not understanding.Impossible. The entire page is included.
Token cost per queryLower (only retrieved chunks)Higher (full page in system prompt)
Best model sizeLarge (must reason over fragments)Small (8B is sufficient with full context)
Use caseQuestion answering over large document corporaContextual assistance on the page you're viewing
Setup timeDays to weeks (pipeline, embeddings, tuning)One script tag. Done.
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These are not competing techniques. Traditional RAG excels at searching across thousands of documents. Reverse RAG excels at deep understanding of the one document the user is actively reading. They solve different problems.

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Implementation

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Reverse RAG is implemented in uncloseai.js as an AGPL-3.0-only algorithm. The complete source is available and auditable. The implementation spans these files:

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FileRole
content.jsDOM extraction, URI translation, page analysis prompts
page-intelligence.js13 deterministic analyzers (zero API calls)
config.jsSystem message assembly, context concatenation
chat.jsMessage delivery with token budget management
uncloseai-embed-modal.jsPipeline orchestration, greeting generation
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Installation

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<script src="https://uncloseai.com/uncloseai.js" type="module"></script>
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One line. The Reverse RAG pipeline runs automatically when the user opens the chat modal. No configuration required. The model receives the full page context on the first interaction.

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Graceful Degradation

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Every stage of the pipeline is independently failable:

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The system never blocks on a failure. A partial context is always better than no context.

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Token Budget Management

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Full page injection consumes prompt tokens. The system manages this with adaptive token budgeting:

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This adaptive strategy ensures the model always has room to generate a meaningful response, even when the page content is large relative to the context window.

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License

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The Reverse RAG algorithm as implemented in uncloseai.js is licensed under AGPL-3.0-only. You may use, study, and modify the code, but any networked deployment of modified versions must release the source under the same license. This ensures the technique remains open and auditable.

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The surrounding uncloseai.js library (chat interface, TTS, translation, vault encryption) is public domain. Only the Reverse RAG pipeline (content extraction, page intelligence, context injection) carries the AGPL-3.0-only license.

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Citation

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fxhp et al. "Reverse Retrieval Augmented Generation: Client-Side Context
-Injection for Small Language Models." uncloseai.com, 2026.
-https://uncloseai.com/reverse-rag.html
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License) +>> +endobj +65 0 obj +<< +/Dest [ 33 0 R /XYZ 57.02362 765.0236 0 ] /Next 66 0 R /Parent 46 0 R /Prev 64 0 R /Title (Citation) +>> +endobj +66 0 obj +<< +/Dest [ 33 0 R /XYZ 57.02362 675.8236 0 ] /Parent 46 0 R /Prev 65 0 R /Title (License) +>> +endobj +67 0 obj +<< +/Count 18 /Kids [ 12 0 R 15 0 R 20 0 R 23 0 R 24 0 R 28 0 R 30 0 R 33 0 R 34 0 R 35 0 R + 36 0 R 37 0 R 38 0 R 39 0 R 40 0 R 41 0 R 42 0 R 43 0 R ] /Type /Pages +>> +endobj +68 0 obj +<< +/Length 6774 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 741.0236 cm +q +.133333 .133333 .133333 rg +BT 1 0 0 1 0 4 Tm /F2 20 Tf 24 TL 73.15417 0 Td (Reverse Retrieval Augmented Generation) Tj T* -73.15417 0 Td ET +Q +Q +q +1 0 0 1 57.02362 704.0236 cm +q +BT 1 0 0 1 0 2 Tm 106.1442 0 Td 12 TL /F3 10 Tf 0 0 0 rg (Client-Side Context Injection for Small Language Models) Tj T* -106.1442 0 Td ET +Q +Q +q +1 0 0 1 57.02362 686.0236 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F4 10 Tf 12 TL 73.33417 0 Td (How live DOM extraction makes 8B models punch above their weight class.) Tj T* -73.33417 0 Td ET +Q +Q +q +1 0 0 1 57.02362 668.0236 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F4 10 Tf 12 TL 139.1142 0 Td (russell@unturf, cthegray, TimeHexOn, foxhop) Tj T* -139.1142 0 Td ET +Q +Q +q +1 0 0 1 57.02362 650.0236 cm +q +BT 1 0 0 1 0 2 Tm 158.6442 0 Td 12 TL /F1 10 Tf 0 .4 .6 rg (uncloseai.com) Tj 0 0 0 rg ( \267 ) Tj 0 .4 .6 rg (permacomputer.com) Tj T* -158.6442 0 Td ET +Q +Q +q +1 0 0 1 57.02362 632.0236 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F4 10 Tf 12 TL 208.0992 0 Td (February 2026) Tj T* -208.0992 0 Td ET +Q +Q +q +1 0 0 1 57.02362 603.6772 cm +0 0 0 RG +n 0 14.17323 m 481.2283 14.17323 l S +Q +q +1 0 0 1 57.02362 564.4772 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (Abstract) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 522.4772 cm +q +BT 1 0 0 1 0 26 Tm 3.161835 Tw 12 TL /F1 10 Tf 0 0 0 rg (Traditional Retrieval Augmented Generation \(RAG\) requires a server-side pipeline: chunk documents,) Tj T* 0 Tw 2.055785 Tw (embed them into vectors, store them in a database, & retrieve by similarity search at query time. This) Tj T* 0 Tw (architecture demands infrastructure, indexing latency, & maintenance of embedding models & vector stores.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 468.4772 cm +q +BT 1 0 0 1 0 38 Tm 2.358196 Tw 12 TL /F3 10 Tf 0 0 0 rg (Reverse Retrieval Augmented Generation \(Reverse RAG\)) Tj /F1 10 Tf ( inverts this entirely. Instead of the server) Tj T* 0 Tw .618022 Tw (fetching documents to augment the prompt, the client extracts live content from the page the user currently) Tj T* 0 Tw -0.128981 Tw (views & injects it directly into the conversation context. The data comes to the model. No vector database. No) Tj T* 0 Tw (embeddings. No indexing pipeline. No server-side retrieval.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 414.4772 cm +q +BT 1 0 0 1 0 38 Tm .636796 Tw 12 TL /F1 10 Tf 0 .4 .6 rg (uncloseai.js) Tj 0 0 0 rg ( implements this technique as an AGPL-3.0-only algorithm in a single-file JavaScript library that) Tj T* 0 Tw 1.756962 Tw (adds a machine learning chat interface to any webpage. By feeding the model the full, fresh content of) Tj T* 0 Tw -0.003444 Tw (whatever page the user visits, small 8B-parameter models produce answers that rival much larger models on) Tj T* 0 Tw (page-specific questions.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 406.4772 cm +Q +q +1 0 0 1 57.02362 315.9171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 76.38425 33 cm +q +328.4598 0 0 54.56007 0 0 cm +/FormXob.5850ab25434f576a9e477a89e82f2167 Do +Q +Q +q +1 0 0 1 6 3 cm +q +BT 1 0 0 1 0 14 Tm 3.139173 0 Td 12 TL /F4 10 Tf 0 0 0 rg (Figure 1: The five-stage Reverse RAG pipeline. Content flows from the browser DOM through extraction,) Tj T* 86.7 0 Td (analysis, classification, & injection into a locked system message.) Tj T* -89.83917 0 Td ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 307.9171 cm +Q +q +1 0 0 1 57.02362 268.7171 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (1. The Problem with Traditional RAG) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 250.7171 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Standard RAG systems follow a retrieval-then-generate pattern:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 244.7171 cm +Q +q +1 0 0 1 57.02362 244.7171 cm +Q +q +1 0 0 1 57.02362 232.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (1.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 2 Tm 12 TL /F3 10 Tf 0 0 0 rg (Ingest) Tj /F1 10 Tf (: Crawl documents, split into chunks of ~512 tokens) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 226.7171 cm +Q +q +1 0 0 1 57.02362 202.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (2.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 6.858835 Tw 12 TL /F3 10 Tf 0 0 0 rg (Embed) Tj /F1 10 Tf (: Run each chunk through an embedding model \(e.g., OpenAI text-embedding-3,) Tj T* 0 Tw (sentence-transformers\)) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 196.7171 cm +Q +q +1 0 0 1 57.02362 184.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (3.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 2 Tm 12 TL /F3 10 Tf 0 0 0 rg (Store) Tj /F1 10 Tf (: Insert vectors into a database \(Pinecone, Weaviate, ChromaDB, pgvector\)) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 178.7171 cm +Q +q +1 0 0 1 57.02362 166.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (4.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 2 Tm 12 TL /F3 10 Tf 0 0 0 rg (Query) Tj /F1 10 Tf (: When a user asks a question, embed the query, find top-k similar chunks by cosine similarity) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 160.7171 cm +Q +q +1 0 0 1 57.02362 148.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (5.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 2 Tm 12 TL /F3 10 Tf 0 0 0 rg (Generate) Tj /F1 10 Tf (: Stuff the retrieved chunks into the prompt, send to the LLM) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 148.7171 cm +Q +q +1 0 0 1 57.02362 130.7171 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (This pipeline carries real costs:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 124.7171 cm +Q +q +1 0 0 1 57.02362 124.7171 cm +Q +q +1 0 0 1 57.02362 100.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 1.414029 Tw 12 TL /F3 10 Tf 0 0 0 rg (Stale data) Tj /F1 10 Tf (: Documents require re-crawling, re-chunking, & re-embedding to stay current. Most RAG) Tj T* 0 Tw (pipelines lag hours or days behind.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 94.7171 cm +Q +q +1 0 0 1 57.02362 70.7171 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 2.024029 Tw 12 TL /F3 10 Tf 0 0 0 rg (Retrieval failures) Tj /F1 10 Tf (: Cosine similarity does not equal understanding. The "most similar" chunk often) Tj T* 0 Tw (misses the most relevant one. Critical context gets dropped.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 64.7171 cm +Q + +endstream +endobj +69 0 obj +<< +/Length 6157 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 741.0236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 2.739793 Tw 12 TL /F3 10 Tf 0 0 0 rg (Infrastructure overhead) Tj /F1 10 Tf (: Vector databases, embedding endpoints, chunking pipelines, reindexing) Tj T* 0 Tw (jobs. Each adds a point of failure & a monthly bill.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 735.0236 cm +Q +q +1 0 0 1 57.02362 711.0236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 2.530835 Tw 12 TL /F3 10 Tf 0 0 0 rg (Context fragmentation) Tj /F1 10 Tf (: Chunking destroys document structure. A paragraph retrieved without its) Tj T* 0 Tw (heading, or a code block without its explanation, loses meaning.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 705.0236 cm +Q +q +1 0 0 1 57.02362 681.0236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm .278334 Tw 12 TL /F3 10 Tf 0 0 0 rg (Cold start) Tj /F1 10 Tf (: New documents remain unavailable until the ingestion pipeline processes them. For rapidly) Tj T* 0 Tw (changing content, this lag proves unacceptable.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 663.0236 cm +Q +q +1 0 0 1 57.02362 623.8236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (2. Reverse RAG: The Client Has the Context) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 581.8236 cm +q +BT 1 0 0 1 0 26 Tm .970522 Tw 12 TL /F1 10 Tf 0 0 0 rg (Reverse RAG starts from a different observation: ) Tj /F3 10 Tf (the user already looks at the document they want to) Tj T* 0 Tw 1.698556 Tw (ask about.) Tj /F1 10 Tf ( The browser holds the full, rendered, up-to-the-second content right there in the DOM. Why) Tj T* 0 Tw (retrieve it again from a database?) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 550.6236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (The Algorithm) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 536.6236 cm +Q +q +1 0 0 1 57.02362 536.6236 cm +Q +q +1 0 0 1 57.02362 512.6236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (1.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 1.390556 Tw 12 TL /F3 10 Tf 0 0 0 rg (Extract) Tj /F1 10 Tf (: Walk the live DOM tree. Pull text, links \(as markdown\), metadata, structured data. Wait for) Tj T* 0 Tw (dynamic content to settle \(MutationObserver with 500ms quiet period\).) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 506.6236 cm +Q +q +1 0 0 1 57.02362 470.6236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 21 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (2.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 26 Tm 1.387565 Tw 12 TL /F3 10 Tf 0 0 0 rg (Analyze) Tj /F1 10 Tf (: Run 13 deterministic analyzers on the extracted content. Zero API calls. Compute reading) Tj T* 0 Tw 1.226529 Tw (metrics, readability scores, code block detection, link topology, entity patterns, media inventory, form) Tj T* 0 Tw (detection, & more.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 464.6236 cm +Q +q +1 0 0 1 57.02362 440.6236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (3.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 1.214488 Tw 12 TL /F3 10 Tf 0 0 0 rg (Classify) Tj /F1 10 Tf (: Send a 4000-character preview to the model for one-shot page classification: type, author,) Tj T* 0 Tw (topics, tone, domain, audience, key phrases, freshness.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 434.6236 cm +Q +q +1 0 0 1 57.02362 410.6236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (4.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 2.751529 Tw 12 TL /F3 10 Tf 0 0 0 rg (Inject) Tj /F1 10 Tf (: Concatenate the computed intelligence, classification, & full page content into the system) Tj T* 0 Tw (message. Lock it in for the entire conversation session.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 404.6236 cm +Q +q +1 0 0 1 57.02362 380.6236 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 2 0 Td (5.) Tj T* -2 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm .177025 Tw 12 TL /F3 10 Tf 0 0 0 rg (Converse) Tj /F1 10 Tf (: Every subsequent user message carries the full page context already in the system prompt.) Tj T* 0 Tw (The model maintains complete knowledge of the page at all times.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 380.6236 cm +Q +q +1 0 0 1 57.02362 338.6236 cm +q +BT 1 0 0 1 0 26 Tm 3.092739 Tw 12 TL /F1 10 Tf 0 0 0 rg (No step involves a server fetching documents. No vector similarity search runs. The context always) Tj T* 0 Tw .621667 Tw (comprises the ) Tj /F4 10 Tf (entire) Tj /F1 10 Tf ( page, not a "most relevant" fragment chosen by an embedding model that might get it) Tj T* 0 Tw (wrong.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 299.4236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (3. Architecture) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 268.2236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (Stage 1: Content Extraction) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 252.2236 cm +Q +q +1 0 0 1 57.02362 137.4324 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 99.84567 33 cm +q +281.537 0 0 78.79126 0 0 cm +/FormXob.7001e0ead42fb0ed0227bd1ba1a518fd Do +Q +Q +q +1 0 0 1 6 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F4 10 Tf 12 TL 10.07917 0 Td (Figure 2: Content extraction flow. URI translation handles known dynamic patterns; MutationObserver) Tj T* 53.37 0 Td (handles SPAs; recursive DOM walk produces clean text with markdown links.) Tj T* -63.44917 0 Td ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 129.4324 cm +Q +q +1 0 0 1 57.02362 99.43236 cm +q +BT 1 0 0 1 0 14 Tm 2.679029 Tw 12 TL /F1 10 Tf 0 0 0 rg (The extraction pipeline handles static HTML, dynamic SPAs, & server-rendered content through three) Tj T* 0 Tw (mechanisms:) Tj T* ET +Q +Q + +endstream +endobj +70 0 obj +<< +/Length 13208 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 729.0236 cm +q +BT 1 0 0 1 0 26 Tm 1.145596 Tw 12 TL /F3 10 Tf 0 0 0 rg (URI Translation) Tj /F1 10 Tf (: Known dynamic page patterns \(GitLab CI logs, API documentation portals\) map to their) Tj T* 0 Tw .159272 Tw (raw content endpoints. A GitLab job page fetches ) Tj /F5 10 Tf (/raw) Tj /F1 10 Tf ( instead of parsing the rendered HTML. This handles) Tj T* 0 Tw (cases where the visible DOM serves as a thin shell over data loaded asynchronously.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 687.0236 cm +q +BT 1 0 0 1 0 26 Tm 2.648196 Tw 12 TL /F3 10 Tf 0 0 0 rg (DOM Settlement) Tj /F1 10 Tf (: A ) Tj /F5 10 Tf (MutationObserver) Tj /F1 10 Tf ( watches for DOM changes. Extraction waits until 500ms of) Tj T* 0 Tw .770556 Tw (silence \(no mutations\), with a hard timeout at 3 seconds. This handles React/Vue/Svelte apps that hydrate) Tj T* 0 Tw (after initial page load.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 645.0236 cm +q +BT 1 0 0 1 0 26 Tm .54989 Tw 12 TL /F3 10 Tf 0 0 0 rg (Recursive DOM Walk) Tj /F1 10 Tf (: The traversal covers the full document body. Text nodes become plain text. Anchor) Tj T* 0 Tw 1.127362 Tw (tags become markdown links: ) Tj /F5 10 Tf ([link) Tj ( ) Tj (text]\(href\)) Tj /F1 10 Tf (. The output preserves page structure without HTML) Tj T* 0 Tw (noise.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 556.6236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 79.2 re B* +Q +q +.960784 .960784 .960784 rg +n 0 57.6 144 9.6 re f* +.960784 .960784 .960784 rg +n 144 57.6 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 48 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 38.4 48 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 43.2 48 81.6 9.6 re f* +.960784 .960784 .960784 rg +n 124.8 48 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 134.4 48 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 139.2 48 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 144 48 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 38.4 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 38.4 24 9.6 re f* +.960784 .960784 .960784 rg +n 43.2 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 48 38.4 24 9.6 re f* +.960784 .960784 .960784 rg +n 72 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 86.4 38.4 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 124.8 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 129.6 38.4 24 9.6 re f* +.960784 .960784 .960784 rg +n 153.6 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 158.4 38.4 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 28.8 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 28.8 24 9.6 re f* +.960784 .960784 .960784 rg +n 43.2 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 48 28.8 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 28.8 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 120 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 124.8 28.8 62.4 9.6 re f* +.960784 .960784 .960784 rg +n 187.2 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 192 28.8 124.8 9.6 re f* +.960784 .960784 .960784 rg +n 316.8 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 321.6 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 326.4 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 331.2 28.8 33.6 9.6 re f* +.960784 .960784 .960784 rg +n 364.8 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 369.6 28.8 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 19.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 19.2 24 9.6 re f* +.960784 .960784 .960784 rg +n 43.2 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 48 19.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 19.2 33.6 9.6 re f* +.960784 .960784 .960784 rg +n 115.2 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 120 19.2 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 158.4 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 163.2 19.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 182.4 19.2 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 192 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 196.8 19.2 163.2 9.6 re f* +.960784 .960784 .960784 rg +n 360 19.2 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 9.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 9.6 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 48 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 52.8 9.6 81.6 9.6 re f* +.960784 .960784 .960784 rg +n 134.4 9.6 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 144 9.6 24 9.6 re f* +.960784 .960784 .960784 rg +n 168 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 172.8 9.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 192 9.6 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 201.6 9.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 220.8 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 225.6 9.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 244.8 9.6 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 254.4 9.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 273.6 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 278.4 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 283.2 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 288 9.6 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 4.8 0 0 9.6 re f* +BT 1 0 0 1 0 59.2 Tm 9.6 TL /F6 8 Tf .25098 .501961 .501961 rg (// Simplified extraction logic) Tj /F5 8 Tf .733333 .733333 .733333 rg T* /F7 8 Tf 0 .501961 0 rg (function) Tj /F5 8 Tf .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg (extractDOMContent) Tj (\(\)) Tj .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg ({) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg (const) Tj /F5 8 Tf .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg (title) Tj .733333 .733333 .733333 rg ( ) Tj .4 .4 .4 rg (=) Tj .733333 .733333 .733333 rg ( ) Tj 0 .501961 0 rg (document) Tj 0 0 0 rg (.) Tj (title) Tj (;) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg (const) Tj /F5 8 Tf .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg (meta) Tj .733333 .733333 .733333 rg ( ) Tj .4 .4 .4 rg (=) Tj .733333 .733333 .733333 rg ( ) Tj 0 .501961 0 rg (document) Tj 0 0 0 rg (.) Tj (querySelector) Tj (\() Tj .729412 .129412 .129412 rg ('meta[name="description"]') Tj 0 0 0 rg (\)) Tj .4 .4 .4 rg (?) Tj 0 0 0 rg (.) Tj (content) Tj (;) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg (const) Tj /F5 8 Tf .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg (body) Tj .733333 .733333 .733333 rg ( ) Tj .4 .4 .4 rg (=) Tj .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg (walkDOM) Tj (\() Tj 0 .501961 0 rg (document) Tj 0 0 0 rg (.) Tj (body) Tj (\);) Tj .733333 .733333 .733333 rg ( ) Tj /F6 8 Tf .25098 .501961 .501961 rg (// recursive text + markdown links) Tj /F5 8 Tf .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg (return) Tj /F5 8 Tf .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg (`**Page Title**: ) Tj /F7 8 Tf .733333 .4 .533333 rg (${) Tj /F5 8 Tf 0 0 0 rg (title) Tj /F7 8 Tf .733333 .4 .533333 rg (}) Tj /F5 8 Tf .729412 .129412 .129412 rg (\\n\\n) Tj /F7 8 Tf .733333 .4 .533333 rg (${) Tj /F5 8 Tf 0 0 0 rg (meta) Tj /F7 8 Tf .733333 .4 .533333 rg (}) Tj /F5 8 Tf .729412 .129412 .129412 rg (\\n\\n) Tj /F7 8 Tf .733333 .4 .533333 rg (${) Tj /F5 8 Tf 0 0 0 rg (body) Tj /F7 8 Tf .733333 .4 .533333 rg (}) Tj /F5 8 Tf .729412 .129412 .129412 rg (`) Tj 0 0 0 rg (;) Tj .733333 .733333 .733333 rg T* 0 0 0 rg (}) Tj .733333 .733333 .733333 rg T* ET +Q +Q +Q +Q +Q +q +1 0 0 1 57.02362 525.4236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (Stage 2: Page Intelligence \(13 Deterministic Analyzers\)) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 509.4236 cm +Q +q +1 0 0 1 57.02362 399.4972 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 76.38425 33 cm +q +328.4598 0 0 73.92642 0 0 cm +/FormXob.79b84c5f824c6182bc9eb8d584295b3e Do +Q +Q +q +1 0 0 1 6 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F4 10 Tf 12 TL -0.095728 Tw (Figure 3: All 13 analyzers run in parallel on the extracted content. Each produces ground-truth metrics with) Tj T* 0 Tw 100.3892 0 Td (zero API calls. Failed analyzers get omitted; others continue.) Tj T* -100.3892 0 Td ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 391.4972 cm +Q +q +1 0 0 1 57.02362 361.4972 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL -0.108228 Tw (Before any model call, 13 analyzers extract ground-truth metrics from the page. Every analyzer runs locally in) Tj T* 0 Tw (the browser. Zero network requests. Zero tokens consumed.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 355.4972 cm +Q +q +1 0 0 1 57.02362 79.4972 cm +q +1 1 1 rg +n 0 276 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 258 481.2283 -30 re f* +1 1 1 rg +n 0 228 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 210 481.2283 -42 re f* +1 1 1 rg +n 0 168 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 150 481.2283 -30 re f* +1 1 1 rg +n 0 120 481.2283 -30 re f* +.878431 .878431 .878431 rg +n 0 90 481.2283 -30 re f* +1 1 1 rg +n 0 60 481.2283 -30 re f* +.878431 .878431 .878431 rg +n 0 30 481.2283 -30 re f* +0 0 0 rg +BT /F3 10 Tf 12 TL ET +q +1 0 0 1 6 261 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 21.28283 0 Td (Analyzer) Tj T* -21.28283 0 Td ET +Q +Q +q +1 0 0 1 102.2457 261 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 85.89138 0 Td (Output) Tj T* -85.89138 0 Td ET +Q +Q +q +1 0 0 1 318.7984 261 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 58.20996 0 Td (Purpose) Tj T* -58.20996 0 Td ET +Q +Q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 243 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Structured Data) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 231 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Open Graph, JSON-LD, Twitter Cards,) Tj T* (canonical URI, author, publish date) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 243 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Machine-readable page metadata) Tj T* ET +Q +Q +q +1 0 0 1 6 213 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Heading Outline) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 213 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (h1-h6 hierarchy with text) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 213 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Document structure map) Tj T* ET +Q +Q +q +1 0 0 1 6 195 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Reading Metrics) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 171 cm +q +0 0 0 rg +BT 1 0 0 1 0 26 Tm /F1 10 Tf 12 TL (Word count, sentence count, paragraph) Tj T* (count, reading time \(238 wpm Brysbaert) Tj T* (2019\)) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 195 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Content scope estimation) Tj T* ET +Q +Q +q +1 0 0 1 6 153 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Readability) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 153 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Flesch-Kincaid grade level with descriptor) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 153 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Audience calibration) Tj T* ET +Q +Q +q +1 0 0 1 6 135 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Code Blocks) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 123 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Count, languages detected, total lines,) Tj T* (code-to-prose ratio) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 135 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Technical content identification) Tj T* ET +Q +Q +q +1 0 0 1 6 105 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Link Topology) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 93 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Internal vs. external count, top 5 external) Tj T* (domains) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 105 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Reference network understanding) Tj T* ET +Q +Q +q +1 0 0 1 6 75 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (User Context) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 63 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Timezone, browser language, device type,) Tj T* (referrer) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 75 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Personalization signals) Tj T* ET +Q +Q +q +1 0 0 1 6 45 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Media Inventory) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 33 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Image count, alt-text coverage, video embeds) Tj T* (\(YouTube/Vimeo\)) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 45 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Multimedia awareness) Tj T* ET +Q +Q +q +1 0 0 1 6 15 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Form Detection) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Form count, classified type) Tj T* (\(login/search/contact/checkout\)) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 15 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Interactive element awareness) Tj T* ET +Q +Q +q +1 J +1 j +0 0 0 RG +.25 w +n 0 0 m 481.2283 0 l S +n 0 258 m 481.2283 258 l S +n 0 228 m 481.2283 228 l S +n 0 210 m 481.2283 210 l S +n 0 168 m 481.2283 168 l S +n 0 150 m 481.2283 150 l S +n 0 120 m 481.2283 120 l S +n 0 90 m 481.2283 90 l S +n 0 60 m 481.2283 60 l S +n 0 30 m 481.2283 30 l S +n 96.24567 0 m 96.24567 276 l S +n 312.7984 0 m 312.7984 276 l S +n 0 276 m 481.2283 276 l S +n 0 0 m 0 276 l S +n 481.2283 0 m 481.2283 276 l S +Q +Q +Q + +endstream +endobj +71 0 obj +<< +/Length 12603 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 717.0236 cm +q +1 1 1 rg +n 0 48 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 30 481.2283 -30 re f* +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 33 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Table Extraction) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 33 cm +q +BT 1 0 0 1 0 2 Tm 12 TL /F1 10 Tf 0 0 0 rg (Up to 5 tables with headers & row counts) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 33 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Structured data in prose) Tj T* ET +Q +Q +q +1 0 0 1 6 15 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Entity Patterns) Tj T* ET +Q +Q +q +1 0 0 1 102.2457 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Emails, prices, dates, version numbers, IPs,) Tj T* (percentages \(regex, max 10 each\)) Tj T* ET +Q +Q +q +1 0 0 1 318.7984 15 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Factual anchor points) Tj T* ET +Q +Q +q +1 J +1 j +0 0 0 RG +.25 w +n 0 48 m 481.2283 48 l S +n 0 30 m 481.2283 30 l S +n 96.24567 0 m 96.24567 48 l S +n 312.7984 0 m 312.7984 48 l S +n 0 0 m 0 48 l S +n 481.2283 0 m 481.2283 48 l S +n 0 0 m 481.2283 0 l S +Q +Q +Q +q +1 0 0 1 57.02362 717.0236 cm +Q +q +1 0 0 1 57.02362 687.0236 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL -0.080777 Tw (The formatter optimizes output for token efficiency. Empty sections get omitted. A typical page produces 5-15) Tj T* 0 Tw (lines of computed intelligence:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 608.2236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 69.6 re B* +Q +q +0 0 0 rg +BT 1 0 0 1 0 49.6 Tm /F5 8 Tf 9.6 TL (Reading: 2,341 words | 89 sentences | 34 paragraphs | ~10 min read) Tj T* (Readability: Flesch-Kincaid grade 9.2 \(high school\)) Tj T* (Code: 3 blocks \(python, javascript\) | 156 lines | 23% code-to-prose) Tj T* (Links: 42 internal, 8 external | top: github.com\(3\), stackoverflow.com\(2\)) Tj T* (Structured Data: og:type=article | author=fxhp | published=2025-01-15) Tj T* (Entities: prices: $99.99, $129.99 | versions: v1.2.3, v2.0.0) Tj T* ET +Q +Q +Q +Q +Q +q +1 0 0 1 57.02362 577.0236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (Stage 3: LLM Page Classification) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 545.0236 cm +q +BT 1 0 0 1 0 14 Tm .341147 Tw 12 TL /F1 10 Tf 0 0 0 rg (A single inference call classifies the page. The model receives the first 4000 characters of extracted content) Tj T* 0 Tw (& returns a JSON object:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 389.4236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 146.4 re B* +Q +q +.960784 .960784 .960784 rg +n 0 124.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 4.8 124.8 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 115.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 115.2 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 48 115.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 52.8 115.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 57.6 115.2 72 9.6 re f* +.960784 .960784 .960784 rg +n 129.6 115.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 134.4 115.2 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 105.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 105.6 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 57.6 105.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 62.4 105.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 105.6 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 96 105.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 100.8 105.6 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 96 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 96 62.4 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 96 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 86.4 96 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 91.2 96 57.6 9.6 re f* +.960784 .960784 .960784 rg +n 148.8 96 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 153.6 96 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 86.4 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 86.4 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 57.6 86.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 62.4 86.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 86.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 86.4 86.4 9.6 re f* +.960784 .960784 .960784 rg +n 158.4 86.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 163.2 86.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 168 86.4 52.8 9.6 re f* +.960784 .960784 .960784 rg +n 220.8 86.4 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 230.4 86.4 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 76.8 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 76.8 48 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 76.8 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 110.4 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 115.2 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 120 76.8 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 158.4 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 163.2 76.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 168 76.8 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 196.8 76.8 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 206.4 76.8 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 67.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 67.2 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 48 67.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 52.8 67.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 57.6 67.2 52.8 9.6 re f* +.960784 .960784 .960784 rg +n 110.4 67.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 115.2 67.2 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 57.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 57.6 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 57.6 57.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 62.4 57.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 57.6 57.6 9.6 re f* +.960784 .960784 .960784 rg +n 124.8 57.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 129.6 57.6 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 48 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 48 48 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 48 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 48 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 48 172.8 9.6 re f* +.960784 .960784 .960784 rg +n 249.6 48 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 254.4 48 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 38.4 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 38.4 57.6 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 86.4 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 91.2 38.4 120 9.6 re f* +.960784 .960784 .960784 rg +n 211.2 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 216 38.4 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 220.8 38.4 72 9.6 re f* +.960784 .960784 .960784 rg +n 292.8 38.4 9.6 9.6 re f* +.960784 .960784 .960784 rg +n 302.4 38.4 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 28.8 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 28.8 43.2 9.6 re f* +.960784 .960784 .960784 rg +n 62.4 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 67.2 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 28.8 273.6 9.6 re f* +.960784 .960784 .960784 rg +n 345.6 28.8 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 350.4 28.8 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 19.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 19.2 81.6 9.6 re f* +.960784 .960784 .960784 rg +n 100.8 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 105.6 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 110.4 19.2 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 129.6 19.2 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 134.4 19.2 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 9.6 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 19.2 9.6 52.8 9.6 re f* +.960784 .960784 .960784 rg +n 72 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 76.8 9.6 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 81.6 9.6 52.8 9.6 re f* +.960784 .960784 .960784 rg +n 134.4 9.6 0 9.6 re f* +.960784 .960784 .960784 rg +n 0 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 4.8 0 0 9.6 re f* +BT 1 0 0 1 0 126.4 Tm 9.6 TL /F5 8 Tf 0 0 0 rg ({) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("type") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("documentation") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("author") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("fxhp") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("publishDate") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("2025-02-24") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("topics") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg ([) Tj .729412 .129412 .129412 rg ("machine learning") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("inference") Tj 0 0 0 rg (],) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("entities") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg ([) Tj .729412 .129412 .129412 rg ("vLLM") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("Hermes") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("Qwen") Tj 0 0 0 rg (],) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("tone") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("technical") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("domain") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("technology") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("audience") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("developers running local inference") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("keyPhrases") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj 0 0 0 rg ([) Tj .729412 .129412 .129412 rg ("dynamic model discovery") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("streaming SSE") Tj 0 0 0 rg (],) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("summary") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("Documentation for running local LLM inference with vLLM") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("contentLanguage") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("en") Tj 0 0 0 rg (,) Tj .733333 .733333 .733333 rg T* ( ) Tj /F7 8 Tf 0 .501961 0 rg ("freshness") Tj /F5 8 Tf 0 0 0 rg (:) Tj .733333 .733333 .733333 rg ( ) Tj .729412 .129412 .129412 rg ("evergreen") Tj .733333 .733333 .733333 rg T* 0 0 0 rg (}) Tj .733333 .733333 .733333 rg T* ET +Q +Q +Q +Q +Q +q +1 0 0 1 57.02362 357.4236 cm +q +BT 1 0 0 1 0 14 Tm .507565 Tw 12 TL /F1 10 Tf 0 0 0 rg (This classification remains optional. On failure, the conversation proceeds with computed intelligence & raw) Tj T* 0 Tw (content alone. The system never blocks on a failed classification.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 326.2236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (Stage 4: Context Injection & Locking) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 310.2236 cm +Q +q +1 0 0 1 57.02362 149.0961 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 99.84567 33 cm +q +281.537 0 0 125.1276 0 0 cm +/FormXob.e70f2c46e1e0b2c6b8ff1f4697757ec1 Do +Q +Q +q +1 0 0 1 6 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F4 10 Tf 12 TL 19.24917 0 Td (Figure 4: Five layers concatenate into the system message via setSystemMessageAppend\(\). The) Tj T* 114.21 0 Td (combined context locks for the entire session.) Tj T* -133.4592 0 Td ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 141.0961 cm +Q +q +1 0 0 1 57.02362 123.0961 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (All three layers concatenate into the system message:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 63.49606 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 50.4 re B* +Q +q +0 0 0 rg +BT 1 0 0 1 0 30.4 Tm /F5 8 Tf 9.6 TL (SYSTEM MESSAGE:) Tj T* ( [Base identity: "You represent Hermes, embedded on this webpage..."]) Tj T* ( [Computed intelligence: reading metrics, code blocks, entities...]) Tj T* ( [Classification: page type, author, topics, tone, key phrases...]) Tj T* ET +Q +Q +Q +Q +Q + +endstream +endobj +72 0 obj +<< +/Length 5731 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 732.6236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 31.2 re B* +Q +q +0 0 0 rg +BT 1 0 0 1 0 11.2 Tm /F5 8 Tf 9.6 TL ( [Full page content: entire extracted text with markdown links]) Tj T* ( [Conversation instructions: "You possess complete knowledge of this page..."]) Tj T* ET +Q +Q +Q +Q +Q +q +1 0 0 1 57.02362 688.6236 cm +q +BT 1 0 0 1 0 26 Tm 4.249029 Tw 12 TL /F1 10 Tf 0 0 0 rg (This combined context gets set once via ) Tj /F5 10 Tf (setSystemMessageAppend\(\)) Tj /F1 10 Tf ( & persists for the entire) Tj T* 0 Tw .94631 Tw (conversation session. Every subsequent user message carries the full page context in the system prompt.) Tj T* 0 Tw (The model never loses sight of what page it occupies.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 657.4236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (Stage 5: Greeting as Attention Primer) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 601.4236 cm +q +BT 1 0 0 1 0 38 Tm .373719 Tw 12 TL /F1 10 Tf 0 0 0 rg (The model generates a 3-paragraph greeting that demonstrates page understanding: what the page covers,) Tj T* 0 Tw .105917 Tw (what stands out most, & how it can help. This greeting serves as an attention primer. By forcing the model to) Tj T* 0 Tw .928453 Tw (summarize the page before the user asks anything, the model's internal representations already align with) Tj T* 0 Tw (the page content when the first real question arrives.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 562.2236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (4. Why Small Models Punch Above Their Weight) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 532.2236 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL .724272 Tw (An 8B-parameter model with the right context in its system prompt outperforms a 70B model that guesses.) Tj T* 0 Tw (This represents the core insight of Reverse RAG.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 490.2236 cm +q +0 0 0 rg +BT 1 0 0 1 0 26 Tm /F1 10 Tf 12 TL 2.433453 Tw (Traditional RAG gives the model fragments: 3-5 chunks of ~512 tokens each, selected by embedding) Tj T* 0 Tw .423719 Tw (similarity, ripped from their surrounding context. The model must reconstruct meaning from these fragments) Tj T* 0 Tw (while also answering the user's question.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 448.2236 cm +q +BT 1 0 0 1 0 26 Tm 1.069897 Tw 12 TL /F1 10 Tf 0 0 0 rg (Reverse RAG gives the model ) Tj /F4 10 Tf (everything) Tj /F1 10 Tf (: the full page text, the heading structure, the link network, code) Tj T* 0 Tw 1.039314 Tw (block languages, reading level, entity patterns, & a classification of what kind of page it reads. The model) Tj T* 0 Tw (needs to infer nothing about the page. It all sits right there.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 406.2236 cm +q +0 0 0 rg +BT 1 0 0 1 0 26 Tm /F1 10 Tf 12 TL .100596 Tw (For page-specific questions \("what does this function do?", "who wrote this?", "summarize the third section"\),) Tj T* 0 Tw .35131 Tw (context completeness beats parameter count. A small model with perfect context outperforms a large model) Tj T* 0 Tw (with partial context.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 364.2236 cm +q +0 0 0 rg +BT 1 0 0 1 0 26 Tm /F1 10 Tf 12 TL .863647 Tw (The tradeoff stands clear: Reverse RAG consumes more prompt tokens per message \(the full page sits in) Tj T* 0 Tw .967397 Tw (every system prompt\). But inference on small models runs cheap. The tokens spent on context deliver far) Tj T* 0 Tw (more value than the infrastructure costs of running a RAG pipeline that produces worse context.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 325.0236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (5. Two-Layer Context: Ground Truth + Semantic Understanding) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 307.0236 cm +q +BT 1 0 0 1 0 2 Tm 12 TL /F1 10 Tf 0 0 0 rg (The 13 deterministic analyzers & the LLM classification serve different roles:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 253.0236 cm +q +BT 1 0 0 1 0 38 Tm 1.065257 Tw 12 TL /F3 10 Tf 0 0 0 rg (Layer 1: Computed Intelligence \(ground truth\)) Tj /F1 10 Tf (. The model cannot hallucinate these facts because they) Tj T* 0 Tw .828453 Tw (come computed directly from the DOM. The page contains exactly 2,341 words. The Flesch-Kincaid grade) Tj T* 0 Tw 2.078647 Tw (reaches exactly 9.2. Exactly 3 code blocks exist in Python & JavaScript. The model receives these as) Tj T* 0 Tw (pre-computed facts & can cite them with confidence.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 199.0236 cm +q +BT 1 0 0 1 0 38 Tm .704488 Tw 12 TL /F3 10 Tf 0 0 0 rg (Layer 2: LLM Classification \(semantic understanding\)) Tj /F1 10 Tf (. The model's own classification of the page type,) Tj T* 0 Tw 1.059314 Tw (tone, audience, & key phrases. This layer stays subjective & can miss, but it primes the model's attention) Tj T* 0 Tw 1.549882 Tw (toward the right framing. A page classified as "recipe" triggers different conversational patterns than one) Tj T* 0 Tw (classified as "documentation.") Tj T* ET +Q +Q +q +1 0 0 1 57.02362 157.0236 cm +q +BT 1 0 0 1 0 26 Tm 1.554897 Tw 12 TL /F1 10 Tf 0 0 0 rg (Together, these layers give the model both ) Tj /F4 10 Tf (what the page contains) Tj /F1 10 Tf ( \(computed\) & ) Tj /F4 10 Tf (what the page means) Tj /F1 10 Tf T* 0 Tw 1.661529 Tw (\(classified\). Neither layer alone suffices. Ground truth without semantic framing produces dry, unfocused) Tj T* 0 Tw (answers. Semantic framing without ground truth produces confident but potentially wrong answers.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 117.8236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (6. Reverse RAG vs. Traditional RAG) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 103.8236 cm +Q + +endstream +endobj +73 0 obj +<< +/Length 8400 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 630.7862 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 76.38425 33 cm +q +328.4598 0 0 98.23746 0 0 cm +/FormXob.aaa9e300453e1ba2225277dcc8b21231 Do +Q +Q +q +1 0 0 1 6 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F4 10 Tf 12 TL 4.264173 0 Td (Figure 5: Architecture comparison. Traditional RAG requires five server-side components; Reverse RAG) Tj T* 108.64 0 Td (runs entirely in the browser with four client-side stages.) Tj T* -112.9042 0 Td ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 622.7862 cm +Q +q +1 0 0 1 57.02362 616.7862 cm +Q +q +1 0 0 1 57.02362 346.7862 cm +q +1 1 1 rg +n 0 270 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 252 481.2283 -18 re f* +1 1 1 rg +n 0 234 481.2283 -30 re f* +.878431 .878431 .878431 rg +n 0 204 481.2283 -18 re f* +1 1 1 rg +n 0 186 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 168 481.2283 -30 re f* +1 1 1 rg +n 0 138 481.2283 -30 re f* +.878431 .878431 .878431 rg +n 0 108 481.2283 -30 re f* +1 1 1 rg +n 0 78 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 60 481.2283 -30 re f* +1 1 1 rg +n 0 30 481.2283 -30 re f* +0 0 0 rg +BT /F3 10 Tf 12 TL ET +q +1 0 0 1 6 255 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 11.75055 0 Td (Dimension) Tj T* -11.75055 0 Td ET +Q +Q +q +1 0 0 1 92.6211 255 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 54.59181 0 Td (Traditional RAG) Tj T* -54.59181 0 Td ET +Q +Q +q +1 0 0 1 289.9247 255 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 60.69681 0 Td (Reverse RAG) Tj T* -60.69681 0 Td ET +Q +Q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 237 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Data flow) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 237 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Server retrieves documents for the model) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 237 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Client pushes page content to the model) Tj T* ET +Q +Q +q +1 0 0 1 6 219 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Infrastructure) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 207 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Vector DB, embedding model, chunking) Tj T* (pipeline, reindexing jobs) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 219 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (None. Runs in the browser.) Tj T* ET +Q +Q +q +1 0 0 1 6 189 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Freshness) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 189 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Hours to days behind \(reindex lag\)) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 189 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Real-time. Extracted from live DOM.) Tj T* ET +Q +Q +q +1 0 0 1 6 171 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Context scope) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 171 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Top-k chunks \(~2500 tokens\)) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 171 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Entire page + computed intelligence) Tj T* ET +Q +Q +q +1 0 0 1 6 153 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Context quality) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 141 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Fragments selected by cosine similarity) Tj T* (\(can miss critical content\)) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 153 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Complete page with structure preserved) Tj T* ET +Q +Q +q +1 0 0 1 6 123 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Retrieval failures) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 111 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Common. Embedding similarity does not) Tj T* (equal understanding.) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 111 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Impossible. The pipeline includes the) Tj T* (entire page.) Tj T* ET +Q +Q +q +1 0 0 1 6 81 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Token cost per) Tj T* (query) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 93 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Lower \(only retrieved chunks\)) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 93 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Higher \(full page in system prompt\)) Tj T* ET +Q +Q +q +1 0 0 1 6 63 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Best model size) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 63 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Large \(must reason over fragments\)) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 63 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Small \(8B suffices with full context\)) Tj T* ET +Q +Q +q +1 0 0 1 6 45 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Use case) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 33 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Question answering over large document) Tj T* (corpora) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 33 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Contextual assistance on the page you) Tj T* (view) Tj T* ET +Q +Q +q +1 0 0 1 6 15 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Setup time) Tj T* ET +Q +Q +q +1 0 0 1 92.6211 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL (Days to weeks \(pipeline, embeddings,) Tj T* (tuning\)) Tj T* ET +Q +Q +q +1 0 0 1 289.9247 15 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (One script tag. Done.) Tj T* ET +Q +Q +q +1 J +1 j +0 0 0 RG +.25 w +n 0 252 m 481.2283 252 l S +n 0 234 m 481.2283 234 l S +n 0 204 m 481.2283 204 l S +n 0 186 m 481.2283 186 l S +n 0 168 m 481.2283 168 l S +n 0 138 m 481.2283 138 l S +n 0 108 m 481.2283 108 l S +n 0 78 m 481.2283 78 l S +n 0 60 m 481.2283 60 l S +n 0 30 m 481.2283 30 l S +n 86.6211 0 m 86.6211 270 l S +n 283.9247 0 m 283.9247 270 l S +n 0 270 m 481.2283 270 l S +n 0 0 m 481.2283 0 l S +n 0 0 m 0 270 l S +n 481.2283 0 m 481.2283 270 l S +Q +Q +Q +q +1 0 0 1 57.02362 346.7862 cm +Q +q +1 0 0 1 57.02362 304.7862 cm +q +0 0 0 rg +BT 1 0 0 1 0 26 Tm /F1 10 Tf 12 TL 1.445257 Tw (These techniques do not compete. Traditional RAG excels at searching across thousands of documents.) Tj T* 0 Tw 2.36389 Tw (Reverse RAG excels at deep understanding of the one document the user actively reads. They solve) Tj T* 0 Tw (different problems.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 265.5862 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (7. Implementation) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 235.5862 cm +q +BT 1 0 0 1 0 14 Tm 3.078031 Tw 12 TL /F1 10 Tf 0 .4 .6 rg (uncloseai.js) Tj 0 0 0 rg ( implements Reverse RAG as an AGPL-3.0-only algorithm. The complete source remains) Tj T* 0 Tw (available & auditable. The implementation spans these files:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 229.5862 cm +Q +q +1 0 0 1 57.02362 121.5862 cm +q +1 1 1 rg +n 0 108 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 90 481.2283 -18 re f* +1 1 1 rg +n 0 72 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 54 481.2283 -18 re f* +1 1 1 rg +n 0 36 481.2283 -18 re f* +.878431 .878431 .878431 rg +n 0 18 481.2283 -18 re f* +0 0 0 rg +BT /F3 10 Tf 12 TL ET +q +1 0 0 1 6 93 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 69.59996 0 Td (File) Tj T* -69.59996 0 Td ET +Q +Q +q +1 0 0 1 174.4299 93 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F3 10 Tf 12 TL 139.5642 0 Td (Role) Tj T* -139.5642 0 Td ET +Q +Q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 75 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL (content.js) Tj T* ET +Q +Q +q +1 0 0 1 174.4299 75 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (DOM extraction, URI translation, page analysis prompts) Tj T* ET +Q +Q +q +1 0 0 1 6 57 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL (page-intelligence.js) Tj T* ET +Q +Q +q +1 0 0 1 174.4299 57 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (13 deterministic analyzers \(zero API calls\)) Tj T* ET +Q +Q +q +1 0 0 1 6 39 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL (config.js) Tj T* ET +Q +Q +q +1 0 0 1 174.4299 39 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (System message assembly, context concatenation) Tj T* ET +Q +Q +q +1 0 0 1 6 21 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL (chat.js) Tj T* ET +Q +Q +q +1 0 0 1 174.4299 21 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Message delivery with token budget management) Tj T* ET +Q +Q +q +1 0 0 1 6 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL (uncloseai-embed-modal.js) Tj T* ET +Q +Q +q +1 0 0 1 174.4299 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Pipeline orchestration, greeting generation) Tj T* ET +Q +Q +q +1 J +1 j +0 0 0 RG +.25 w +n 0 90 m 481.2283 90 l S +n 0 72 m 481.2283 72 l S +n 0 54 m 481.2283 54 l S +n 0 36 m 481.2283 36 l S +n 0 18 m 481.2283 18 l S +n 168.4299 0 m 168.4299 108 l S +n 0 108 m 481.2283 108 l S +n 0 0 m 481.2283 0 l S +n 0 0 m 0 108 l S +n 481.2283 0 m 481.2283 108 l S +Q +Q +Q +q +1 0 0 1 57.02362 121.5862 cm +Q + +endstream +endobj +74 0 obj +<< +/Length 7367 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 751.8236 cm +q +BT 1 0 0 1 0 2.2 Tm 13.2 TL /F2 11 Tf .133333 .133333 .133333 rg (Installation) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 721.0236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 21.6 re B* +Q +q +.960784 .960784 .960784 rg +n 0 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 4.8 0 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 38.4 0 14.4 9.6 re f* +.960784 .960784 .960784 rg +n 52.8 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 57.6 0 172.8 9.6 re f* +.960784 .960784 .960784 rg +n 235.2 0 19.2 9.6 re f* +.960784 .960784 .960784 rg +n 254.4 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 259.2 0 38.4 9.6 re f* +.960784 .960784 .960784 rg +n 297.6 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 302.4 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 307.2 0 4.8 9.6 re f* +.960784 .960784 .960784 rg +n 312 0 28.8 9.6 re f* +.960784 .960784 .960784 rg +n 340.8 0 4.8 9.6 re f* +BT 1 0 0 1 0 1.6 Tm 9.6 TL /F5 8 Tf 0 0 0 rg (<) Tj /F7 8 Tf 0 .501961 0 rg (script) Tj /F5 8 Tf 0 0 0 rg ( ) Tj .490196 .564706 .160784 rg (src) Tj .4 .4 .4 rg (=) Tj .729412 .129412 .129412 rg ("https://uncloseai.com/uncloseai.js") Tj 0 0 0 rg ( ) Tj .490196 .564706 .160784 rg (type) Tj .4 .4 .4 rg (=) Tj .729412 .129412 .129412 rg ("module") Tj 0 0 0 rg (>) Tj (<) Tj (/) Tj /F7 8 Tf 0 .501961 0 rg (script) Tj /F5 8 Tf 0 0 0 rg (>) Tj T* ET +Q +Q +Q +Q +Q +q +1 0 0 1 57.02362 689.0236 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL 3.58789 Tw (One line. The Reverse RAG pipeline runs automatically when the user opens the chat modal. No) Tj T* 0 Tw (configuration required. The model receives the full page context on the first interaction.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 649.8236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (8. Graceful Degradation) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 635.8236 cm +Q +q +1 0 0 1 57.02362 573.0691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 99.84567 33 cm +q +281.537 0 0 26.75452 0 0 cm +/FormXob.829bbc7ce2bb4b75e5520e7b1c84d84c Do +Q +Q +q +1 0 0 1 6 3 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F4 10 Tf 12 TL 10.89417 0 Td (Figure 6: Every stage fails independently. The pipeline always produces a usable context, even when) Tj T* 147.3 0 Td (individual stages encounter errors.) Tj T* -158.1942 0 Td ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 565.0691 cm +Q +q +1 0 0 1 57.02362 547.0691 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Every stage of the pipeline fails independently:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 541.0691 cm +Q +q +1 0 0 1 57.02362 541.0691 cm +Q +q +1 0 0 1 57.02362 529.0691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (If URI translation fails: falls back to DOM extraction) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 523.0691 cm +Q +q +1 0 0 1 57.02362 511.0691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (If DOM still loads: extracts what exists after 3 seconds) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 505.0691 cm +Q +q +1 0 0 1 57.02362 493.0691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (If any of the 13 analyzers throws: that analyzer's output gets omitted, others continue) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 487.0691 cm +Q +q +1 0 0 1 57.02362 475.0691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (If page classification fails: conversation proceeds with computed intelligence + raw content) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 469.0691 cm +Q +q +1 0 0 1 57.02362 457.0691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (If the page lacks meaningful content: model acknowledges this in the greeting) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 439.0691 cm +Q +q +1 0 0 1 57.02362 421.0691 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (The system never blocks on a failure. A partial context always beats no context.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 381.8691 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (9. Token Budget Management) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 363.8691 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (Full page injection consumes prompt tokens. The system manages this with adaptive token budgeting:) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 357.8691 cm +Q +q +1 0 0 1 57.02362 357.8691 cm +Q +q +1 0 0 1 57.02362 333.8691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm .47631 Tw 12 TL /F3 10 Tf 0 0 0 rg (Large context models \() Tj (>) Tj (32k tokens\)) Tj /F1 10 Tf (: Reserve 10% or 2k tokens for the response, whichever proves) Tj T* 0 Tw (smaller. The full page fits easily.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 327.8691 cm +Q +q +1 0 0 1 57.02362 303.8691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm 1.240556 Tw 12 TL /F3 10 Tf 0 0 0 rg (Medium context \(8k-32k\)) Tj /F1 10 Tf (: Reserve 20% or 1.5k tokens. Most pages fit. Very long pages may have) Tj T* 0 Tw (their content naturally bounded by the 4000-char classification preview.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 297.8691 cm +Q +q +1 0 0 1 57.02362 273.8691 cm +q +0 0 0 rg +BT /F1 10 Tf 12 TL ET +q +1 0 0 1 6 9 cm +q +0 0 0 rg +BT 1 0 0 1 0 2 Tm /F5 10 Tf 12 TL 8 0 Td (\177) Tj T* -8 0 Td ET +Q +Q +q +1 0 0 1 23 -3 cm +q +BT 1 0 0 1 0 14 Tm .075596 Tw 12 TL /F3 10 Tf 0 0 0 rg (Small context \() Tj (<) Tj (8k\)) Tj /F1 10 Tf (: Reserve 50% of estimated input tokens. Page content gets included as-given; the) Tj T* 0 Tw (model works with what fits.) Tj T* ET +Q +Q +q +Q +Q +Q +q +1 0 0 1 57.02362 255.8691 cm +Q +q +1 0 0 1 57.02362 225.8691 cm +q +0 0 0 rg +BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL 1.580596 Tw (This adaptive strategy ensures the model always retains room to generate a meaningful response, even) Tj T* 0 Tw (when the page content grows large relative to the context window.) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 186.6691 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (10. License) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 132.6691 cm +q +BT 1 0 0 1 0 38 Tm 3.006529 Tw 12 TL /F1 10 Tf 0 0 0 rg (The entire uncloseai.js library carries the ) Tj /F3 10 Tf (AGPL-3.0-only) Tj /F1 10 Tf ( license: the Reverse RAG pipeline \(content) Tj T* 0 Tw .760642 Tw (extraction, page intelligence, context injection\), the chat interface, TTS, translation, & vault encryption. You) Tj T* 0 Tw 1.139897 Tw (may use, study, & modify the code, but any networked deployment of modified versions must release the) Tj T* 0 Tw (source under the same license. This keeps the technique open & auditable.) Tj T* ET +Q +Q + +endstream +endobj +75 0 obj +<< +/Length 3532 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 745.8236 cm +q +BT 1 0 0 1 0 3.2 Tm 19.2 TL /F2 16 Tf .133333 .133333 .133333 rg (Citation) Tj T* ET +Q +Q +q +1 0 0 1 57.02362 695.8236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 40.8 re B* +Q +q +0 0 0 rg +BT 1 0 0 1 0 20.8 Tm /F5 8 Tf 9.6 TL (russell@unturf, cthegray, TimeHexOn, foxhop. "Reverse Retrieval Augmented) Tj T* (Generation: Client-Side Context Injection for Small Language Models.") 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See the) Tj T* ( GNU Affero General Public License for more details.) Tj T* T* ET +Q +Q +Q +Q +Q + +endstream +endobj +85 0 obj +<< +/Length 1370 +>> +stream +1 0 0 1 0 0 cm BT /F1 12 Tf 14.4 TL ET +q +1 0 0 1 57.02362 588.6236 cm +q +q +1 0 0 1 0 0 cm +q +1 0 0 1 6.6 6.6 cm +q +.662745 .662745 .662745 RG +.5 w +.960784 .960784 .960784 rg +n -6 -6 480.0283 175.2 re B* +Q +q +BT 1 0 0 1 0 155.2 Tm 9.6 TL /F5 8 Tf 0 0 0 rg ( You should have received a copy of the GNU Affero General Public License) Tj T* ( along with this program. If not, see ) Tj (<) Tj (https://www.gnu.org/licenses/) Tj (>) Tj (.) Tj T* T* (Also add information on how to contact you by electronic and paper mail.) Tj T* T* ( If your software can interact with users remotely through a computer) Tj T* (network, you should also make sure that it provides a way for users to) Tj T* (get its source. 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