Agentic RAG with Memory || Phidata and Qdrant

Building an advanced Agentic Retrieval-Augmented Generation (RAG) system with integrated memory capabilities can significantly enhance AI-driven applications by providing more accurate and contextually relevant responses. Leveraging Qdrant as a vector database alongside Phidata's agentic framework facilitates efficient data retrieval and processing.

Phidata is a versatile framework that enables developers to build AI agents equipped with memory, knowledge, tools, and reasoning capabilities, facilitating the creation of sophisticated, autonomous systems.

Qdrant is a high-performance vector database designed for efficient similarity search and management of high-dimensional data, making it ideal for applications requiring rapid and accurate retrieval of vector embeddings.

Reach out to me:
------------------------
------------------------

Timestamp

00:00 Introduction
00:30 Architecture diagram
03:03 Phidata - Agent Framework
04:05 Code- Knowledge Base
05:43 Qdrant
10:34 Code- Agentic RAG
14:46 User query

Agentic RAG, Vector Database, Semantic Search, AI Memory Management, Intelligent Information Retrieval, Phidata Framework, Qdrant Vector Search, Contextual AI, Machine Learning Architecture, Advanced Information Processing, Adaptive AI Systems, Intelligent Agent Framework, Semantic Vector Matching, Knowledge Retention Mechanisms

#agents #ai #generativeai #llm