The Context-Aware RAG Framework

Your Documents Have Context. Now Your RAG Does Too.
Every chunk remembers. Every query understands. Every answer matters.
60% better accuracy through Anthropic's contextual retrieval methodology
See AutoLlama in Action Read the Research
💬 "It's like RAG finally learned how to read" — Early Adopter

The Context Journey

📄
Original Document
Rich context & connections
→
🔪
Traditional RAG
Chunks without memory
→
😕
Lost Context
"Information exists"
📄
Original Document
Rich context & connections
→
🧠
AutoLlama
Chunks with contextual memory
→
🎯
Perfect Context
"Section 7.3 requires 409A filing within 30 days"

Every Embedding Tells a Story

Because context isn't optional—it's everything

🚀

One-Command Deploy

Get production-ready RAG platform running with just docker compose up. Zero configuration required.

🧠

Anthropic's Contextual Retrieval

Only open-source framework implementing the full contextual retrieval methodology that powers Claude's superior RAG capabilities - delivering 35-67% performance improvement.

🔬

Dual-Stage AI Analysis

Unique two-stage pipeline: Stage 1 extracts 11+ metadata dimensions (sentiment, entities, technical level), Stage 2 adds contextual descriptions before embedding - no other framework does both.

🎯

JavaScript-First RAG

Finally, enterprise-grade RAG for the Node.js ecosystem. While LangChain and LlamaIndex focus on Python, AutoLlama brings cutting-edge retrieval to JavaScript developers.

🏠

Air-Gapped Enterprise

Complete data sovereignty with v2.3.4 Pure Local Mode. Toggle between local air-gapped deployment and cloud services with one click. Enterprise compliance ready: SOC 2, GDPR, HIPAA, ISO 27001.

🛡️

Actually Open Source

No hidden costs, no API limits, no vendor lock-in. Full control over your data with local deployment options. Beat commercial solutions like Vectara and Azure AI Search without the enterprise price tag.

Watch Context Come Alive

See the same query fail in standard RAG and succeed in AutoLlama—the difference is context

❌ Before: Original Chunk

WOMEN IN THE ANCIENT NEAR EAST Women in the Ancient Near East provides a collection of primary sources that further our understanding of women from Mesopotamian and Near Eastern civilizations, from the earliest historical and literary texts in the third millennium BC to the end of Mesopotamian political autonomy in the sixth century BC...
📏 Length: 1,199 characters
⚠️ Status: Raw text chunk without contextual awareness

✅ After: AI Enhanced

AI-GENERATED SUMMARY:
This chunk serves as an introduction to the book "Women in the Ancient Near East," outlining its purpose to provide comprehensive primary sources for understanding women's roles in Mesopotamian civilizations from 3rd millennium BC to 6th century BC.
ENHANCED EMBEDDING INPUT:
Summary + Original Content → Enhanced Context
🎯 Impact: 35-60% better retrieval accuracy
🧠 Model: gpt-4o-mini → text-embedding-3-small

🎯 Contextual Enhancement Value

The enhanced version combines the original chunk with document-aware context, enabling the AI to understand how this specific section relates to the broader document. This results in significantly more accurate semantic search and retrieval compared to traditional RAG systems that embed chunks in isolation.

Chunk Index
47
Status
Completed ✓
Enhancement
Contextual Enabled

See AutoLlama in Action

Real screenshots from the AutoLlama platform—experience the interface that makes contextual RAG effortless

Dashboard
Comprehensive document management with interactiveprocessed documents, real-time processing queue, and intuitive search interface
Main Dashboard
Comprehensive document management with interactiveprocessed documents, real-time processing queue, and intuitive search interface
Analysis
Get comprehensive AI-powered insights into your documents with topic extraction, entity mapping, and RAG readiness scoring.
Document Intelligence Dashboard
Get comprehensive AI-powered insights into your documents with topic extraction, entity mapping, and RAG readiness scoring.
Chunks
Visual representation of processed chunks with completion status and easy navigation to individual chunk details
Chunks Grid View
Visual representation of processed chunks with completion status and easy navigation to individual chunk details
Live Processing
Processing queue showing Llama actively processing items with real-time status updates
Real-Time Processing
Processing queue showing Llama actively processing items with real-time status updates
AI Insights
AI analysis panel displaying extracted topics, entities, sentiment analysis, and contextual processing statistics
AI Analysis Panel
Deep AI insights with topic extraction, entity recognition, sentiment analysis, and processing quality metrics
Search
Ultra-fast BM25 search interface showing contextually enhanced search results with tags, entities, sentiment analysis, and AI-generated metadata
Ultra-Fast Contextual Search
Lightning-fast BM25 search across contextually enhanced content, tags, entities, sentiment, and AI-generated metadata - find exactly what you need instantly
OpenWebUI
OpenWebUI settings panel showing AutoLlama pipeline configuration with URL and API key setup
One-Click Setup
Configure your pipeline with just a URL and API key. AutoLlama handles the rest.
Integration
OpenWebUI interface showing autollama-rag model in dropdown selection menu
Native Integration
AutoLlama appears as 'autollama-rag' in your model dropdown. Switch seamlessly between AI models.
Intelligence
OpenWebUI chat interface showing intelligent query response about Aiolos with contextual understanding and follow-up suggestions
Intelligent Responses
Get precise answers from your documents with contextual understanding and follow-up suggestions.
v2.3.4
AutoLlama v2.3.4 mode toggle interface showing air-gapped local and cloud deployment options with security indicators
One-Click Mode Toggle
Toggle between air-gapped local and cloud deployment modes with visual security indicators and real-time validation
Live Demo
▶️ Hover to Play
Live Mode Switching Demo
Watch the complete workflow: navigate to Settings → Connections and see real-time mode switching between air-gapped and cloud configurations

What You're Seeing

✓ Real-Time Processing
Watch documents transform into intelligent chunks
✓ Visual Analytics
Comprehensive insights and quality metrics
✓ Contextual Intelligence
Every chunk remembers its place in the story
✓ Enterprise Ready
Production interface built for scale

Stop Losing the Plot

Your documents have narratives. Your contracts have dependencies. Your research has connections. AutoLlama preserves them all.

LangChain

105K GitHub stars
Basic metadata extraction
Complex orchestration

Standard embedding methods

LlamaIndex

40K GitHub stars
Advanced indexing
Python ecosystem

No contextual enrichment

AutoLlama

Contextual retrieval ✓
11+ metadata dimensions ✓
JavaScript-first ✓

60% better accuracy

Commercial Solutions

Vectara: $500-10K/mo
Azure AI: $50-5K/mo
AWS Bedrock: Pay-per-use

Vendor lock-in

"For developers who need production-ready RAG that actually works, AutoLlama is the only open-source framework that combines Anthropic's contextual retrieval with comprehensive content analysis."

Why We Built The Context-Aware RAG Framework

We believe documents are more than bags of words. They're structured thoughts, connected ideas, and contextual relationships.

We believe that when you ask a question, you deserve an answer that understands not just the words, but the story they're part of.

We believe context isn't a nice-to-have—it's the difference between information and understanding.

That's why we built AutoLlama.
Experience Understanding