After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Foundation for Developing in the LangChain Ecosystem
Augment your LLM toolkit with LangChain's ecosystem, enabling seamless integration with OpenAI and Hugging Face models. Discover an open-source framework that optimizes real-world applications and allows you to create sophisticated information retrieval systems unique to your use case.Chatbot Creation Methodologies using LangChain
Utilize LangChain tools to develop chatbots, comparing nuances between HuggingFace's open-source models and OpenAI's closed-source models. Utilize prompt templates for intricate conversations, laying the groundwork for advanced chatbot development.Data Handling and Retrieval Augmentation Generation (RAG) using LangChain
Master tokenization and vector databases for optimized data retrieval, enriching chatbot interactions with a wealth of external information. Utilize RAG memory functions to optimize diverse use cases.Advanced Chain, Tool and Agent Integrations
Utilize the power of chains, tools, agents, APIs, and intelligent decision-making to handle full end-to-end use cases and advanced LLM output handling.Debugging and Performance Metrics
Finally, become proficient in debugging, optimization, and performance evaluation, ensuring your chatbots are developed for error handling. Add layers of transparency for troubleshooting.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Define the steps required to integrate proprietary and open-source LLMs into LangChain, including parameter tuning and prompt templating
- Distinguish between sequential LCEL chains and agent-based workflows when selecting an execution strategy for multi-step tasks
- Evaluate scenarios in which custom tools and ReAct agents in LangGraph improve accuracy or functionality within an LLM solution
- Identify the core LangChain components—models, prompts, chains, agents, and retrievers—used to build LLM applications
- Recognize suitable document loading, chunking, and vector storage techniques that enable Retrieval Augmented Generation
Prerequisites
Curriculum
Course outline
1
Introduction to LangChain & Chatbot Mechanics
Welcome to the LangChain framework for building applications on LLMs! You'll learn about the main components of LangChain, including models, chains, agents, prompts, and parsers. You'll create chatbots using both open-source models from Hugging Face and proprietary models from OpenAI, create prompt templates, and integrate different chatbot memory strategies to manage context and resources during conversations.
- The LangChain ecosystem50 XP
- OpenAI models in LangChain!100 XP
- Hugging Face models in LangChain!100 XP
- Prompt templates50 XP
- Prompt templates and chaining100 XP
- Chat prompt templates100 XP
- Few-shot prompting50 XP
- Creating the few-shot example set100 XP
- Building the few-shot prompt template100 XP
- Implementing few-shot prompting100 XP
2
Chains and Agents
Time to level up your LangChain chains! You'll learn to use the LangChain Expression Language (LCEL) for defining chains with greater flexibility. You'll create sequential chains, where inputs are passed between components to create more advanced applications. You'll also begin to integrate agents, which use LLMs for decision-making.
3
Retrieval Augmented Generation (RAG)
One limitation of LLMs is that they have a knowledge cut-off due to being trained on data up to a certain point. In this chapter, you'll learn to create applications that use Retrieval Augmented Generation (RAG) to integrate external data with LLMs. The RAG workflow contains a few different processes, including splitting data, creating and storing the embeddings using a vector database, and retrieving the most relevant information for use in the application. You'll learn to master the entire workflow!
Developing LLM Applications with LangChain
Course
Complete
