From 95e1b379272eaee5f0756ff3614834deaad78ef2 Mon Sep 17 00:00:00 2001 From: Nova AI Date: Thu, 12 Feb 2026 18:13:12 +0000 Subject: [PATCH] Add GitHub Pages landing page - Obsidian dark theme design - Links to ClawHub, GitHub, and william.mantly.vip - Feature comparison with web-search RAG tools - Responsive layout with terminal UI preview --- docs/index.html | 531 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 531 insertions(+) create mode 100644 docs/index.html diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..42cf4f7 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,531 @@ + + + + + + OpenClaw RAG | Knowledge Base with Semantic Search + + + + + + +
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Local Knowledge with
Semantic Search

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Index your entire OpenClaw workspace — chat history, code, documentation — and search by meaning, not keywords. Fully offline, zero dependencies, complete privacy.

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+ Install on ClawHub + View on GitHub +
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+ $ + python3 rag_query.py "how to send SMS" +
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🔍 Found 3 relevant items:

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📄 Past Conversation (voipms-sms)

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Use voipms_sms_client.py with API endpoint...

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API: https://voip.ms/api/v1/rest.php

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✅ Indexed 2,533 documents from your workspace

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Powerful Features

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Everything you need to build and maintain your knowledge base

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Semantic Search

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Find context by meaning, not just keywords. ChromaDB with all-MiniLM-L6-v2 embeddings automatically understands the intent behind your queries.

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📚
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Multi-Source Indexing

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Index chat sessions, Python/JavaScript code, Markdown docs, skill documentation — everything in your OpenClaw workspace becomes searchable.

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Completely Offline

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No API keys, no external services, no cloud databases. All embeddings and storage live on your machine. Your data never leaves.

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Automatic Integration

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The AI automatically retrieves relevant context from your knowledge base when responding. It "remembers" your past work transparently.

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Type Filtering

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Search by document type — sessions, workspace, skills, memory. Find exactly what you're looking for with precision.

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Blazing Fast

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Indexing at ~1,000 docs/minute, search in under 100ms. The embedding model (79MB) caches locally after first run.

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Why OpenClaw RAG?

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Compare with web-search RAG tools

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Web-Search RAG Tools

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  • Searches the internet
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  • Requires web connection
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  • No knowledge of your code
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  • Can't find past solutions
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  • For research, not memory
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✨ OpenClaw RAG

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  • Indexes YOUR knowledge base
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  • Fully offline after setup
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  • Understands your entire workspace
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  • Remembers your past solutions
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  • For institutional memory & productivity
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Built for Developers, by Developers

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Different use cases for different problems

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🌐 Use Web RAG for Research

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Need current information, multi-engine web searches, or gathering context from the internet? That's what web-search RAG tools do best.

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💾 Use OpenClaw RAG for Memory

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Need to find past solutions, code patterns, decisions, or conversations? That's our specialty — we remember everything you've done.

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🤝 Best of Both Worlds

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Use web-search RAG for research, then save the important findings to your knowledge base. OpenClaw RAG preserves what matters forever.

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