
Introduction of Langchain
Build a graphical user interface to chat with your csv file using LangChain and OpenAI, with a Streamlit interface for uploading data and querying a pandas data frame agent.
Learn to chat with json files using a json agent and OpenAI, leveraging json toolkit and json spec to parse and answer questions about API documentation.
Learn how to integrate Google search with a LangChain agent using the Google search API and OpenAI for filtering, by building and running a search tool.
learn how to save OpenAI chat message history to AWS DynamoDB using LangChain, including configuring the database, creating a table, and persisting user and system messages.
Explore how to solve programming problems with a LangChain agent and the Python repl tool, generating reasoning and source code for LeetCode and Hackerrank questions.
Learn how to use a file callback handler with Langchain to log API usage, token costs, and results to a file, with a practical agent setup and Google search tools.
Learn how to enable the verbose flag in LangChain agents to format inputs and outputs for clearer debugging and understand how it differs from debug logs.
Learn to chat with an Excel file using LangChain by loading the data with the unstructured Excel loader, creating a vector store, and building a retrieval QA chain with OpenAI.
Learn to extract insights from documents using langchain document loaders, loading various file types with a directory loader, and accessing document content and source metadata to derive insights.
Learn how to load HTML files with Langchain document loaders, using unstructured and BeautifulSoup HTML loaders, and load multiple files from a directory for fast HTML content.
learn how to load PDF files in LangChain using the Pi PDF loader, load pages as documents with metadata, and optionally extract images.
Develop an end-to-end Streamlit app that loads YouTube videos via LangChain's YouTube loader, splits long transcripts, and summarizes them into bullet points using OpenAI.
Learn to build a multimodal rag cooking assistant using LangChain and OpenAI Vision, combining text, images, and tables from PDFs with a vector store and Streamlit interface.
Embark on a transformative journey into the cutting-edge domain of language models and Python-based chain tools with our expansive and immersive course. This curriculum is thoughtfully designed to unlock the full potential of LangChain, enabling learners to craft, deploy, and amplify the reach of applications powered by artificial intelligence. Catering to both AI novices and individuals keen on elevating their data engineering capabilities, our course stands as a beacon of hands-on learning, offering a rich tapestry of interactive tutorials, projects steeped in real-world applicability, and profound insights aimed at fostering a deep-rooted proficiency in LangChain technology.
Dive deep into the essence of AI and machine learning, navigating the complexities of Python programming tailored explicitly for the manipulation and deployment of sophisticated language models. From the foundational pillars of LangChain to the nuanced art of engineering scalable AI solutions, this course unfolds as a meticulously crafted educational journey, poised to equip learners with critical skills and comprehensive knowledge.
As participants progress, they will be introduced to the intricate workings of language processing tools, exploring the synergy between Python’s versatility and LangChain’s robust framework. Engage with meticulously crafted modules that guide you through the nuances of developing AI-driven applications, emphasizing practical implementation and theoretical understanding. The course encapsulates a holistic learning experience, ensuring learners not only grasp the theoretical underpinnings of LangChain technology but also apply this knowledge through tangible projects, thereby bridging the gap between conceptual learning and real-world application.
By the culmination of this course, learners will have navigated through an extensive array of topics, from the initial setup and configuration of LangChain environments to advanced topics such as natural language processing, data analysis, and the ethical considerations of AI deployment. This journey promises to transform beginners into adept practitioners capable of leveraging LangChain to address complex challenges, innovate within their respective fields, and contribute to the evolution of AI and data engineering. Step into a world where technology meets creativity, and emerge as a visionary ready to shape the future of artificial intelligence.