Disclaimer: This repo was built during the AI Hack Night in Berlin on December 12, 2024.
Number of sentences: 363
The word 'weaviate' is mentioned 9 times in the transcript.
--> actually I checked it's 11 times, so the LLM is wrong here, because we would need an agent for aggregation
Here's an outline of the main points discussed in the podcast:
-
Introduction to Agentic RAG
- Components of an agent: language model, memory, planning, tools
- Difference between vanilla RAG and Agentic RAG
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Planning in Agent Systems
- Discussion of different planning approaches (React, Chain of Thought, Tree of Thoughts)
- Comparison of planning methods and their potential applications
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Multi-Agent Systems
- Structure of multi-agent systems (top-level agent, specialized sub-agents)
- Benefits of using different language models for different roles
- Comparison to DSPY framework
-
Memory in Agent Systems
- Short-term vs. long-term memory
- Discussion of Letta framework for updating agent memory
-
Evaluation and Observability in AI Systems
- Tools for observability (Arises, Telemetry, Phoenix)
- Challenges with using LLMs as judges
- Ensembling and sampling multiple inferences for more robust results
-
Developer Experience for Building Agents
- Challenges in defining tools and functions
- Potential for low-code/no-code solutions
-
Generative Feedback Loops (GFLs)
- Role of agents in GFLs
- Long-running processes for content generation
- Integration with external tools and data sources
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Human-in-the-Loop Considerations
- Importance of human oversight in autonomous systems
- Balancing AI autonomy with human control
-
Future Directions
- Potential for storing and reusing intermediate outputs in complex tasks
- AI-native businesses leveraging generative feedback loops
The podcast concludes with a mention of an upcoming event in New York City where these topics will be further discussed.
🚀 Exciting times in AI! Just wrapped up an incredible podcast on Agentic RAG with @Erica Cardenas from Weaviate. Here are the key takeaways:
1️⃣ Agentic RAG is revolutionizing how we interact with data, going beyond traditional retrieval-augment-generate pipelines.
2️⃣ Multi-agent systems are the future, allowing for specialized roles and more efficient problem-solving.
3️⃣ The importance of short-term and long-term memory in AI agents can't be overstated.
4️⃣ Evaluation and observability tools are crucial for building trust and understanding in AI systems.
5️⃣ Generative Feedback Loops (GFLs) are opening up new possibilities for AI-driven data creation and enhancement.
🔥 Hot take: The next big leap in AI won't just be about better models, but smarter ways to combine and orchestrate them!
👥 Want to dive deeper? Join us next Tuesday in NYC at the Google Pier 57 building to discuss Agentic RAG and the future of AI!
#AI #AgenticRAG #MachineLearning #FutureOfTech #Weaviate
What's your take on Agentic RAG? Drop your thoughts below! 👇
🎵 On the first day of AI, Weaviate gave to me: An agentic RAG system with glee!
On the second day of AI, Weaviate gave to me: Two function calls, And an agentic RAG system with glee!
On the third day of AI, Weaviate gave to me: Three planning steps, Two function calls, And an agentic RAG system with glee!
On the fourth day of AI, Weaviate gave to me: Four multi-agents, Three planning steps, Two function calls, And an agentic RAG system with glee!
On the fifth day of AI, Weaviate gave to me: Five eval tools! Four multi-agents, Three planning steps, Two function calls, And an agentic RAG system with glee!
On the sixth day of AI, Weaviate gave to me: Six loops a-generating, Five eval tools! Four multi-agents, Three planning steps, Two function calls, And an agentic RAG system with glee! 🎵