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Developing LLM Applications with LangChain
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2,750 XP
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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.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.- 1
Introduction to LangChain & Chatbot Mechanics
FreeWelcome 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 xpOpenAI models in LangChain!100 xpHugging Face models in LangChain!100 xpPrompt templates50 xpPrompt templates and chaining100 xpChat prompt templates100 xpFew-shot prompting50 xpCreating the few-shot example set100 xpBuilding the few-shot prompt template100 xpImplementing 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.
Sequential chains50 xpBuilding prompts for sequential chains100 xpSequential chains with LCEL100 xpIntroduction to LangChain agents50 xpWhat's an agent?50 xpReAct agents100 xpCustom tools for agents50 xpDefining a function for tool use100 xpCreating custom tools100 xpIntegrating custom tools with agents100 xp - 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!
Integrating document loaders50 xpPDF document loaders100 xpCSV document loaders100 xpHTML document loaders100 xpSplitting external data for retrieval50 xpSplitting by character100 xpRecursively splitting by character100 xpSplitting HTML100 xpRAG storage and retrieval using vector databases50 xpPreparing the documents and vector database100 xpBuilding a retrieval prompt template100 xpCreating a RAG chain100 xpWrap-up!50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.collaborators
Jonathan BennionAI Engineer & LangChain Contributor
Bay area based. Pulling together algorithms while on distance runs. 9 years in data science and ML (ex-Facebook, Disney, Amazon, Google, EA) with 1 intensive year in AI Engineering for enterprise use cases with companies such as Fox Corporation. Created Logical Fallacy chain in LangChain and contributor to DeepEval.
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