Langchain In Action: Develop Llm-Powered Applications
10
October
2024
Langchain In Action: Develop Llm-Powered Applications
Last updated 7/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 1.30 GB | Duration: 3h 51m
From the Basics of LLMs to Production-Grade Microservice Architecture with Kubernetes
What you'll learn
Master LangChain from basics to advanced features
Understand and implement Retrieval Augmented Generation (RAG) using VectorStores
Learn about the creation and use of powerful Autonomous Agents.
Grasp the functionalities and applications of the Indexing API.
Explore the LangSmith Platform for production ready application
Learn about Microservice architecture in the context of large language model (LLM) applications.
Learn about the new LangChain Expression Language with the Runnable Interface
Requirements
Intermediate Python Skills (OOP, Datatypes, Functions, modules etc.)
Helpful: Terminal and Docker knowledge
Description
This course provides an in-depth exploration into LangChain, a framework pivotal for developing generative AI applications. Aimed at both beginners and experienced practitioners in the AI world, the course starts with the fundamentals, such as the basic usage of the OpenAI API, progressively delving into the more intricate aspects of LangChain.You'll learn about the intricacies of input and output mechanisms in LangChain and how to craft effective prompt templates for OpenAI models. The course takes you through the critical components of LangChain, such as Chains, Callbacks, and Memory, teaching you to create interactive and context-aware AI systems.Midway, the focus shifts to advanced concepts like Retrieval Augmented Generation (RAG) and the creation of Autonomous Agents, enriching your understanding of intelligent system design. Topics like Hybrid Search, Indexing API, and LangSmith will be covered, highlighting their roles in enhancing the efficiency and functionality of AI applications.Toward the end, the course integrates theory with practical skills, introducing Microservice Architecture in large language model (LLM) applications and the LangChain Expression Language. This ensures not only a theoretical understanding of the concepts but also their practical applications.This course is tailored for individuals with a foundational knowledge of Python, aiming to build or enhance their expertise in AI. The structured curriculum ensures a comprehensive grasp of LangChain, from basic concepts to complex applications, preparing you for the future of generative AI.
Who this course is for:
Python Developers, AI Enthusiats
For More Courses Visit & Bookmark Your Preferred Language Blog
From Here: - - - - - - - -
What you'll learn
Master LangChain from basics to advanced features
Understand and implement Retrieval Augmented Generation (RAG) using VectorStores
Learn about the creation and use of powerful Autonomous Agents.
Grasp the functionalities and applications of the Indexing API.
Explore the LangSmith Platform for production ready application
Learn about Microservice architecture in the context of large language model (LLM) applications.
Learn about the new LangChain Expression Language with the Runnable Interface
Requirements
Intermediate Python Skills (OOP, Datatypes, Functions, modules etc.)
Helpful: Terminal and Docker knowledge
Description
This course provides an in-depth exploration into LangChain, a framework pivotal for developing generative AI applications. Aimed at both beginners and experienced practitioners in the AI world, the course starts with the fundamentals, such as the basic usage of the OpenAI API, progressively delving into the more intricate aspects of LangChain.You'll learn about the intricacies of input and output mechanisms in LangChain and how to craft effective prompt templates for OpenAI models. The course takes you through the critical components of LangChain, such as Chains, Callbacks, and Memory, teaching you to create interactive and context-aware AI systems.Midway, the focus shifts to advanced concepts like Retrieval Augmented Generation (RAG) and the creation of Autonomous Agents, enriching your understanding of intelligent system design. Topics like Hybrid Search, Indexing API, and LangSmith will be covered, highlighting their roles in enhancing the efficiency and functionality of AI applications.Toward the end, the course integrates theory with practical skills, introducing Microservice Architecture in large language model (LLM) applications and the LangChain Expression Language. This ensures not only a theoretical understanding of the concepts but also their practical applications.This course is tailored for individuals with a foundational knowledge of Python, aiming to build or enhance their expertise in AI. The structured curriculum ensures a comprehensive grasp of LangChain, from basic concepts to complex applications, preparing you for the future of generative AI.
Who this course is for:
Python Developers, AI Enthusiats
For More Courses Visit & Bookmark Your Preferred Language Blog
From Here: - - - - - - - -
Note:
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