Wrap-up!

1. Wrap-up!

Congratulations on completing the course! Let's wrap-up by reviewing what you've learned!

2. LangChain's core components

We started by talking about the core components of LangChain: models, prompts, chains, agents, and document retrievers. We used open source models from Hugging Face, as well as proprietary models from OpenAI.

3. Chains and agents

We learned how to combine models, prompts, and agents using LCEL chains.

4. Retrieval Augmented Generation (RAG)

Finally, we learned how to integrate external data into LLMs, overcoming the limitations of their training data.

5. LangChain Hub

As you continue your LangChain journey into AI application development, the LangChain Hub will be a fantastic resource along the way. The Hub contains a catalog of prompts for a whole range of different tasks. You can search, use, and add to this massive catalog as you progress.

6. LangChain templates

LangChain Templates are also worth investigating. They are sets of code blocks that are ready to use out of the packet, and they cover many of the most common use cases. They may require minor modifications or additions, but they're often the best place to start.

7. The LangChain ecosystem

The core LangChain package that you've seen in this course is only a piece of the full LangChain ecosystem, which also includes LangSmith, LangServe, and LangGraph, as you've also seen in this course. LangSmith is used for troubleshooting and evaluating LLM applications, and LangServe is used for deploying these applications to production, while LangGraph, as you know, is used for multi-agent knowledge graphs. Continue to explore these tools as the ecosystem evolves!

8. Let's practice!

Your journey is only just beginning, so onward!

This exercise is part of the course

Developing LLM Applications with LangChain

IntermediateSkill Level

4.8+

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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.

Exercise 1: The LangChain ecosystem Exercise 2: OpenAI models in LangChain!Exercise 3: Hugging Face models in LangChain!Exercise 4: Prompt templates Exercise 5: Prompt templates and chaining Exercise 6: Chat prompt templates Exercise 7: Few-shot prompting Exercise 8: Creating the few-shot example set Exercise 9: Building the few-shot prompt template Exercise 10: Implementing few-shot prompting

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.

Exercise 1: Sequential chains Exercise 2: Building prompts for sequential chains Exercise 3: Sequential chains with LCEL Exercise 4: Introduction to LangChain agents Exercise 5: What's an agent?Exercise 6: ReAct agents Exercise 7: Custom tools for agents Exercise 8: Defining a function for tool use Exercise 9: Creating custom tools Exercise 10: Integrating custom tools with 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!

Exercise 1: Integrating document loaders Exercise 2: PDF document loaders Exercise 3: CSV document loaders Exercise 4: HTML document loaders Exercise 5: Splitting external data for retrieval Exercise 6: Splitting by character Exercise 7: Recursively splitting by character Exercise 8: Splitting HTML Exercise 9: RAG storage and retrieval using vector databases Exercise 10: Preparing the documents and vector database Exercise 11: Building a retrieval prompt template Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!

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