README.md

metadata

title: Self-RAG Demo
emoji: 🔄
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

🔄 Self-RAG: Self-Reflective Retrieval-Augmented Generation

State-of-the-art RAG with adaptive retrieval and self-correction

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⚠️ DEMO DISCLAIMER

This is an EDUCATIONAL DEMONSTRATION of Self-RAG concepts with simplified logic.

What This Demo Shows:

• ✅ Concept of reflection tokens ([Retrieve], [Relevant], [Supported])
• ✅ Adaptive retrieval decision-making
• ✅ Self-correction loops visualization
• ✅ Comparison with traditional RAG

What This Demo Does NOT Provide:

• ❌ NOT production-ready - Simplified for education
• ❌ NOT full Self-RAG model - Uses rule-based logic instead of trained model
• ❌ NOT real LLM integration - Demo uses simulated responses
• ❌ NOT actual retrieval - Uses synthetic document set

Use Case:

• Educational demonstration of Self-RAG methodology
• Understanding adaptive retrieval concepts
• Research exploration of reflection-based RAG

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🎯 What is Self-RAG?

Self-RAG is a framework developed in 2023 that improves traditional RAG by:

1. Deciding WHEN to retrieve - Not every query needs retrieval
2. Evaluating retrieved docs - Are they relevant?
3. Checking answer quality - Is answer supported by docs?
4. Self-correcting - Revise answer if not well-supported

Advantages over Traditional RAG:

Feature	Traditional RAG	Self-RAG
Retrieval	Always retrieves	Adaptive (40% fewer)
Relevance Check	No	Yes
Support Verification	No	Yes
Self-Correction	No	Yes
Accuracy	Baseline	+5-15% better
Efficiency	Slower	Faster (fewer retrievals)
Explainability	Low	High (shows reasoning)

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🧠 Reflection Tokens

Self-RAG uses special tokens to control behavior:

1. [Retrieve] / [No Retrieve]

Decision: Should I search for information?

Example:

• Query: "What is 2+2?" → [No Retrieve] (simple math, no need)
• Query: "What was the GDP of Brazil in 2023?" → [Retrieve] (need data)

2. [Relevant] / [Irrelevant]

Evaluation: Are retrieved documents useful?

Example:

• Query: "Marie Curie discoveries"
• Doc: "Marie Curie discovered radium" → [Relevant]
• Doc: "Albert Einstein's theories" → [Irrelevant]

3. [Supported] / [Not Supported]

Verification: Is my answer backed by docs?

Example:

• Answer: "Marie Curie discovered radium in 1898"
• Doc confirms this → [Supported]
• Doc doesn't mention date → [Not Supported] → Revise!

4. [Useful] / [Not Useful]

Quality: Is the answer actually helpful?

Example:

• Answer addresses query completely → [Useful]
• Answer is vague or incomplete → [Not Useful] → Try again!

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🔄 Self-Correction Loop

Query: "When did Marie Curie win her Nobel Prizes?"
    ↓
[Retrieve] - Decides to search documents
    ↓
Retrieve docs about Marie Curie
    ↓
[Relevant] - Doc about Nobel Prizes is relevant
[Irrelevant] - Doc about her childhood is not
    ↓
Generate Answer: "Marie Curie won two Nobel Prizes"
    ↓
[Not Supported] - Too vague! Need dates!
    ↓
SELF-CORRECT → Revise answer
    ↓
Revised Answer: "Marie Curie won Nobel Prizes in 1903 (Physics) and 1911 (Chemistry)"
    ↓
[Supported] - Verified against docs
[Useful] - Complete answer
    ↓
Return final answer ✓

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📊 Performance Benchmarks (From Paper)

Accuracy Improvements:

Dataset	Traditional RAG	Self-RAG	Improvement
PopQA	72.3%	81.5%	+9.2%
PubHealth	83.1%	91.2%	+8.1%
Biography	67.8%	78.4%	+10.6%
Average	74.4%	83.7%	+9.3%

Efficiency Gains:

• 40% fewer retrievals (adaptive decision)
• 25% faster overall (despite self-correction)
• Lower cost (fewer API calls if using external retrieval)

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🚀 Demo Features

1. Interactive Query Testing

Try queries and see:

• Whether Self-RAG decides to retrieve
• Which documents are marked relevant
• If answer is supported
• Self-correction in action

2. Reflection Token Visualization

See the decision-making process:

Step 1: [Retrieve] ✓
Step 2: Retrieved 3 docs
Step 3: [Relevant] Doc 1 ✓, Doc 2 ✗, Doc 3 ✓
Step 4: Generated answer
Step 5: [Not Supported] - Correcting...
Step 6: Revised answer
Step 7: [Supported] ✓ [Useful] ✓

3. Comparison Mode

Compare Traditional RAG vs. Self-RAG side-by-side:

• See quality difference
• Observe retrieval decisions
• Understand when Self-RAG helps most

4. Example Queries

Pre-loaded examples showing:

• Simple queries (no retrieval needed)
• Complex queries (retrieval + correction)
• Ambiguous queries (multiple iterations)

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🎓 Educational Value

For Students:

• Learn advanced RAG techniques
• Understand decision-making in AI systems
• See self-correction in action
• Explore metacognition in LLMs

For Researchers:

• Prototype adaptive retrieval strategies
• Test verification mechanisms
• Explore self-evaluation approaches
• Generate research hypotheses

For Developers:

• Understand production RAG challenges
• Learn quality control methods
• See explainable AI techniques
• Evaluate cost-accuracy tradeoffs

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🔬 Scientific Foundation

Original Paper:

Asai et al. (2023) "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection"

• Venue: arXiv preprint (later accepted to top conferences)
• Institution: University of Washington, AI2, Meta AI
• Impact: Widely cited, adopted in production systems

Key Innovation:

• Train LLM to generate reflection tokens
• End-to-end trainable (not rule-based)
• Significantly outperforms traditional RAG

Paper Link: https://arxiv.org/abs/2310.11511

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💡 Use Cases

1. Question Answering

Benefit: More accurate, verifiable answers

Example:

• Medical Q&A: Must be supported by sources
• Legal Q&A: Citations critical
• Educational: Teach students to verify

2. Fact Verification

Benefit: Automatic source checking

Example:

• News verification
• Academic writing assistance
• Compliance checking

3. Research Assistance

Benefit: Knows when it needs more info

Example:

• Literature review
• Technical documentation
• Scientific queries

4. Customer Support

Benefit: Reduces hallucinations

Example:

• Product documentation Q&A
• Troubleshooting guides
• Policy explanations

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🔧 Implementation Notes

This Demo:

• Rule-based logic (simplified)
• Synthetic documents (pre-defined)
• Simulated LLM (not real model)
• Educational purpose

Production Self-RAG:

• Trained model with reflection tokens
• Real vector database retrieval
• Actual LLM (GPT-4, Claude, open-source)
• Scalable infrastructure

To Implement Production:

1. Fine-tune LLM with reflection token data
2. Integrate vector database (Pinecone, Qdrant, etc.)
3. Add real document corpus
4. Implement error handling
5. Monitor quality metrics

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📈 When to Use Self-RAG

Use Self-RAG When:

✅ Accuracy is critical (medical, legal, financial)
✅ Sources must be verifiable
✅ Cost of wrong answers is high
✅ Need explainability (why this answer?)
✅ Document quality varies

Traditional RAG is OK When:

⚠️ All queries need retrieval (no decision needed)
⚠️ Document quality is uniformly high
⚠️ Speed more important than accuracy
⚠️ Simpler system preferred

Consider Both:

💡 Use Traditional RAG as baseline
💡 Add Self-RAG for critical paths
💡 A/B test to measure improvement

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⚖️ Ethical Considerations

Appropriate Use:

• ✅ Educational demonstrations
• ✅ Research prototyping
• ✅ Understanding adaptive RAG concepts

Production Deployment:

• ⚠️ Validate on your specific data
• ⚠️ Monitor for failure modes
• ⚠️ Have human oversight for critical applications
• ⚠️ Document limitations clearly

Privacy:

• ✅ This demo: No data collection
• ⚠️ Production: Consider where documents are stored
• ⚠️ Consider retrieval logs (sensitive queries?)

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🔮 Future Directions

Research Areas:

• Multi-hop reasoning with Self-RAG
• Cross-lingual Self-RAG
• Multimodal reflection (images, videos)
• Federated Self-RAG (privacy-preserving)

Engineering Improvements:

• Faster inference (token generation overhead)
• Better reflection token training
• Hybrid approaches (rules + learned)
• Integration with graph-based retrieval

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📚 References

Primary:

1. Asai et al. (2023) "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection"
   • arXiv:2310.11511

Related Work:

2. Lewis et al. (2020) "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" - NeurIPS
3. Lazaridou et al. (2022) "Internet-augmented language models through few-shot prompting for open-domain question answering"

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💬 Community

Discussions:

• Share your Self-RAG implementations
• Discuss reflection token strategies
• Compare with other RAG approaches
• Research collaboration opportunities

Contributing:

• Improve demo examples
• Add new query types
• Better visualization
• Bug reports

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📄 License

MIT License - Educational and research use

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🙏 Acknowledgments

• Asai et al. for Self-RAG methodology
• University of Washington, AI2, Meta AI for research
• Hugging Face for hosting infrastructure

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📧 Contact

Author: Demetrios Chiuratto Agourakis
Institution: São Leopoldo Mandic Medical School
GitHub: @Agourakis82
ORCID: 0009-0001-8671-8878

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🔄 Self-reflection makes AI systems smarter, more reliable, and more trustworthy.

Made with ❤️ for adaptive and explainable AI 🔄