Building Agentic AI Apps: Vector Database Interaction & Docker Deployment
- Authors
- Published on
- Published on
In this riveting episode by Krish Naik, the stage is set for the development of an agentic AI application that delves into the realm of Vector databases. The mission? To create a platform where agents can seamlessly interact with these databases to extract precise responses. As the narrative unfolds, viewers are urged to rally behind the channel, with a target of 1200 likes and 150 comments to fuel the fire for more exhilarating content.
The plot thickens as the protagonist encounters a hurdle involving open API keys, swiftly maneuvering through viewer feedback to incorporate suggested models and enhance the application's functionality. The journey takes a thrilling turn towards the creation of a PDF assistant, where agents are poised to engage with Vector databases, starting with the formidable PG Vector. Docker emerges as a key player in this saga, facilitating the seamless running of the Vector database and the extraction and storage of PDF content.
With an air of anticipation, Krish Naik navigates viewers through the meticulous setup of environment variables, the strategic importation of essential libraries such as assistant and PG assistant storage, and the crucial initialization of the PDF assistant file. The knowledge base comes to life through the integration of PDF URLs, while the Vector database stands ready to house the extracted PDF content. The assistant is meticulously crafted, with parameters fine-tuned to enable seamless program execution, including the exploration of chat history and knowledge base search functionalities. The stage is set, the players are in position, and the curtain rises as the program is set in motion using typer to run the PDF assistant.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch 2-Building Multi Agentic AI RAG With Vector Database on Youtube
Viewer Reactions for 2-Building Multi Agentic AI RAG With Vector Database
PgAssistantStorage can be used with the changed path
Importance of search_knowledge = True parameter for the agent to answer user questions
Steps to check the psql table in docker
Positive feedback on the tutorial videos
Request for a video on using multi AI agents using Autogen and deploying in a cloud environment
Appreciation for clear and easy-to-follow tutorials on AI topics
Request for more details on vector databases and data storage
Request for a video on getting text and images as a response
Request for code in the format of a flask API
Troubleshooting issues with OpenAI creds and changing to GROQ model
Related Articles
Mastering Model Context Protocol: Connecting Service Providers with LLMs
Join Krish Naik in exploring the Model Context Protocol (MCP) in a detailed tutorial. Discover the significance of MCP in streamlining communication between service providers and LLMs. Get ready for a practical demonstration using the lang chain framework to connect to various MCP servers.
Google's A2A Protocol: Revolutionizing AI Communication for Efficient Collaboration
Google's new Agent to Agent (A2A) protocol revolutionizes AI communication, enabling secure collaboration among agents. Supported by 50+ tech partners, A2A streamlines tasks like booking flights and hotels, promising efficient multi-agent systems for the future.
Revolutionize Python Project Management with UV: Rust-Powered Speed!
Discover UV, a lightning-fast Python package manager written in Rust. UV outpaces competitors like poetry and pip sync with 10-100 times faster speeds. Simplify project management and enjoy seamless compatibility on MacOS, Linux, and Windows. Experience the game-changing efficiency of UV today!
Decoding Model Context Protocol (MCP): Enhancing AI Integration
Krish Naik explores the Model Context Protocol (MCP), a game-changer in AI communication. Learn how MCP streamlines LLM integration with tools, enhancing AI capabilities.