## Knowledgeable Agents with FalkorDB Graph RAG

AG2 v0.5—Structured Output & GraphRAG: Transform LLM Responses and Knowledge - YouTube

**TL;DR:** * We introduce a new ability for AG2 agents, Graph RAG with FalkorDB, providing the power of knowledge graphs * Structured outputs, using OpenAI models, provide strict adherence to data models to improve reliability and agentic flows * Nested chats are now available with a Swarm

### FalkorDB Graph RAG

Typically, RAG uses vector databases, which store information as embeddings, mathematical representations of data points. When a query is received, it's also converted into an embedding, and the vector database retrieves the most similar embeddings based on distance metrics.

Graph-based RAG, on the other hand, leverages graph databases, which represent knowledge as a network of interconnected entities and relationships. When a query is received, Graph RAG traverses the graph to find relevant information based on the query's structure and semantics.

## EcoAssistant - Using LLM Assistants More Accurately and Affordably

**TL;DR:** * Introducing the **EcoAssistant**, which is designed to solve user queries more accurately and affordably. * We show how to let the LLM assistant agent leverage external API to solve user query. * We show how to reduce the cost of using GPT models via **Assistant Hierarchy**. * We show how to leverage the idea of Retrieval-augmented Generation (RAG) to improve the success rate via **Solution Demonstration**.

### EcoAssistant

In this blog, we introduce the **EcoAssistant**, a system built upon AutoGen with the goal of solving user queries more accurately and affordably.

## Retrieval-Augmented Generation (RAG) Applications with AutoGen

_Last update: August 14, 2024; AutoGen version: v0.2.35_

**TL;DR:** * We introduce **RetrieveUserProxyAgent**, RAG agents of AutoGen that allows retrieval-augmented generation, and its basic usage. * We showcase customizations of RAG agents, such as customizing the embedding function, the text split function and vector database. * We also showcase two advanced usage of RAG agents, integrating with group chat and building a Chat application with Gradio.