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 ecosystem

OpenAI models in LangChain!

Hugging Face models in LangChain!

Prompt templates

Prompt templates and chaining

Chat prompt templates

Few-shot prompting

Creating the few-shot example set

Building the few-shot prompt template

Implementing few-shot prompting

Introduction to LangChain & Chatbot Mechanics

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.

Sequential chains

Building prompts for sequential chains

Sequential chains with LCEL

Introduction to LangChain agents

What's an agent?

ReAct agents

Custom tools for agents

Defining a function for tool use

Creating custom tools

Integrating custom tools with agents

Chains and Agents

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!

Integrating document loaders

PDF document loaders

CSV document loaders

HTML document loaders

Splitting external data for retrieval

Splitting by character

Recursively splitting by character

Splitting HTML

RAG storage and retrieval using vector databases

Preparing the documents and vector database

Building a retrieval prompt template

Creating a RAG chain

Wrap-up!

Retrieval Augmented Generation (RAG)

Revolutionize your applications by harnessing the power of the LangChain framework for creating applications based on large language models (LLMs)! One of the major challenges for developing applications in the age of generative AI is integrating models, data sources, prompts, and other components from different providers into a single application. The LangChain framework provides a single, unified syntax for putting all these pieces together to allow you to integrate LLMs more seamlessly into your projects. Whether you're a seasoned developer or just getting started, this course will equip you with the knowledge and skills to build dynamic and intelligent applications that leverage the limitless capabilities of LangChain. Join us on this transformative journey and redefine how you create applications powered by language models.

<h2>Foundation for Developing in the LangChain Ecosystem</h2>
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.<br><br>
<h2>Chatbot Creation Methodologies using LangChain</h2>
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.<br><br>
<h2>Data Handling and Retrieval Augmentation Generation (RAG) using LangChain</h2>
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.<br><br>
<h2>Advanced Chain, Tool and Agent Integrations</h2>
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.<br><br>
<h2>Debugging and Performance Metrics</h2>
Finally, become proficient in debugging, optimization, and performance evaluation, ensuring your chatbots are developed for error handling.  Add layers of transparency for troubleshooting.

Introduction to Embeddings with the OpenAI API

Prompt Engineering with the OpenAI API

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

Developing LLM Applications with LangChain

Ingegnere AI associato per sviluppatori

Sviluppare applicazioni AI

Sviluppare applicazioni con LangChain

AI Engineer & LangChain Contributor

RAG development steps

What is a vector database and why do I need it?

Which vector database should I use?

Meet the documents...

Setting up a Chroma vector database

Building a prompt template

Chaining it all together!

Let's practice!

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Retrieval Augmented Generation (RAG) - RAG storage and retrieval using vector databases

RAG storage and retrieval using vector databases

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