Hi there, Llama Fans! 🦙
Welcome to this week's edition of the LlamaIndex newsletter! We're thrilled to share exciting updates, including insights into the AutoRAG framework and strategies for enhancing knowledge graph applications.
🤩 The Highlights:
- In Case You Missed It: week before last we released a New Multilingual Visual Embedding Model: We open-sourced our visual embedding model and training set on Huggingface! Key features include:
- Trained on 5 languages (IT, ES, EN, FR, DE)
- 70% fewer tokens = 3x faster inference for the English-only model
- Matryoshka Representation Learning for flexible dimension reduction
- Full training set is open-source
- Learn more here and check out the model integration.
- Introducing AutoRAG Framework: AutoRAG is a framework for optimizing your RAG pipelines. Key findings include:
- Hybrid retrieval methods often outperform pure vector or BM25 approaches.
- Query expansion isn't always beneficial—context matters.
- Some rerankers may perform worse than no reranking—test before implementing.
- Read the full paper here.
- Agentic Strategies for Knowledge Graphs: Dramatically improve the accuracy of your knowledge graph applications by applying agentic strategies with LlamaIndex workflows! Key insights include:
- Implement agentic strategies for text2cypher using LlamaIndex Workflows.
- Explore multi-step approaches with retry and self-correction mechanisms.
- Check out the full guest post here.
🗺️ LlamaCloud & LlamaParse:
- Building RAG Applications: Learn how to build a RAG application using LlamaParse, LlamaCloud, and AWS Bedrock. This guide covers:
- Efficient document parsing with LlamaParse.
- Managing indices on LlamaCloud.
- Integrating AWS Bedrock's embedding models and LLMs.
- Check it out here.
✨ Framework:
- Agentic Strategies Implementation: Discover how to implement agentic strategies for text2cypher using LlamaIndex workflows. This comprehensive post covers:
- Error checking, retries, and correction mechanisms.
- Benefits of iterative planning for complex queries.
- Read more here.
- AutoRAG Framework Insights: Explore the AutoRAG framework for picking optimal configurations for your RAG pipelines. Key findings include:
- Evaluation techniques and data sets used.
- Read the full paper here.
✍️ Community:
- AI Tour Planner Agent: Build a travel planner agent from scratch with @clusteredbytes using LlamaIndex workflows and SerpAPI. Check it out here.
- Women in AI RAG Hackathon: Join the Women in AI RAG Hackathon in Palo Alto to explore RAG using open-source vector databases. Register now.
- Webinar Recap with Memgraph: Learn how @memgraphdb and LlamaIndex work together to build agentic graph applications. Read the recap or watch the recording here.
- Neomagus LLM x Law Hackathon Winner: Discover how Neomagus won the hackathon with a solution for verifying AI-generated legal information. Read the full post here.
Thank you for being a part of our community! Stay tuned for more updates, and don’t hesitate to reach out with your questions or feedback.
Happy Llama-ing! 🦙
