Developing LLM Applications with LangChain

Overview

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Discover how to build AI-powered applications using LLMs, prompts, chains, and agents in LangChain.

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.

Syllabus

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.

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.

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!