Developing LLM Applications with LangChain
Discover how to build AI-powered applications using LLMs, prompts, chains, and agents in LangChain.
Comience El Curso Gratis4 Horas13 Videos44 Ejercicios
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Descripción del curso
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.Empresas
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Introduction to LangChain & Chatbot Mechanics
GratuitoWelcome 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 xpLangChain's core components50 xpHugging Face models in LangChain!100 xpOpenAI models in LangChain!100 xpPrompting strategies for chatbots50 xpPrompt templates and chaining100 xpChat prompt templates100 xpManaging chat model memory50 xpIntegrating a chatbot message history100 xpCreating a memory buffer100 xpImplementing a summary memory100 xp - 2
Loading and Preparing External Data for Chatbots
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 xpThird-party 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 xpStoring and retrieving documents100 xpRAG with sources100 xp - 3
LangChain Expression Language (LCEL), 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.
LangChain Expression Language (LCEL)50 xpLCEL for LLM chatbot chains100 xpLCEL for RAG workflows100 xpImplementing functional LangChain chains50 xpSequential chains with LCEL100 xpPassing values between chains100 xpAn introduction to LangChain agents50 xpWhat's an agent?50 xpZero-Shot ReAct agents100 xp - 4
Tools, Troubleshooting, and Evaluation
In the final chapter, you'll give your agents the power to do even more, by designing custom tools and functions for them to use. You'll also learn about how to troubleshoot and evaluate your application to ensure it performs well.
Utilizing tools in LangChain50 xpCreating custom tools100 xpScaling custom tools100 xpFormatting tools as OpenAI functions100 xpTroubleshooting methods for optimization50 xpCallbacks for troubleshooting100 xpReal-time performance monitoring100 xpEvaluating model output in LangChain50 xpBuilt-in evaluation criteria100 xpCustom evaluation criteria100 xpEvaluation chains100 xpWrap-up!50 xp
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Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y másEn las siguientes pistas
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Jonathan Bennion
Ver MasAI Engineer & LangChain Contributor
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