
Explore how LangChain links LLMs to data sources, enabling up-to-date, domain-specific answers. Build agents and chains that use prompts, memory, and models to power personal assistants, document Q&A, and chatbots.
OpenAI, a for-profit AI research lab, offers GPT-3, ChatGPT, Codex, DALL-E, and Whisper. It enables use cases from customer support to content creation and coding.
See a Lang Chain demo that shows how to power an LMS using pdf data, chunks, embeddings, and a vector store with open source models like mistral.
Explore LangChain's modules—the models, retrievers, toolkits, document loaders, vector stores, and embedding models—and how the graph framework and LangSmith enable production-ready agent platforms.
Explore how LangChain uses LLMs, chat models, and text embedding models, compare open-source options like Hugging Face, and learn to use prompts and access tokens to build AI apps.
Launch a real-time application with a user interface hosted on Hugging Face spaces, using streamlit, requirements.txt, and api keys to deploy LangChain llm ai apps to production.
Build a simple question answering app by wiring a streamlit UI to an openai model, capturing user input, querying the model, and displaying the answer with a generate button.
Explore chart models, a conversational ai that generates human-like responses from vast text data. Apply chat models to chatbots, virtual assistants, and customer support scenarios.
Explore text embedding models and how word embeddings convert words to numerical vectors that machines can process. See embeddings cluster related words and reveal semantic meaning while enabling dimensionality reduction.
Implement text embeddings in Python by installing LangChain and OpenAI libraries, configuring an API key, and initializing an OpenAI embeddings object to convert text into numerical feature vectors for comparison. Then build a few basic applications to gain comfort developing projects that use embeddings.
Build a similar words finder app using embeddings and FAISS with OpenAI to show related words for inputs like cat and strawberry, built in Visual Studio Code with Streamlit UI.
Learn how prompts steer large language models and shape results. Explore the core components: prompt templates, example selectors, and output parsers, and their use with chart models.
Explore prompt templates with LangChain and OpenAI to craft dynamic prompts for the DaVinci model, using input variables, f-strings, and hands-on implementation walkthrough.
Explore example selectors and few-shot prompts in LangChain to craft robust prompts for OpenAI models, using prompt templates to steer outputs such as summarization, question answering, and entity recognition.
Learn to control prompt length with LangChain's length-based example selector, balancing examples and tokens to reduce costs while improving contextual answers using a few-shot prompt template.
Explore LangChain's output parsers to format LLM results as csv, json, or xml, and learn to implement these formats using prompt templates and format instructions.
Unlock the Power of AI with LangChain: Learn to Create Revolutionary Language-Based Applications
Looking to harness the full potential of AI and revolutionize the world of language-based applications? Look no further than LangChain, the comprehensive course designed to transform you from a novice to an expert in record time.
Gen AI apps and LLM projects!
Dive into hands-on projects that will shape your expertise, including:
Project 1: Construct a dynamic question-answering application with the unparalleled capabilities of LangChain, OpenAI, and Hugging Face Spaces, Google Gemini Pro .
Project 2: Develop an engaging conversational bot using LangChain and OpenAI to deliver an interactive user experience.
Project 3: Create an AI-powered app tailored for children, facilitating the discovery of related classes of objects and fostering educational growth.
Project 4: Build a captivating marketing campaign app that utilizes the persuasive potential of well-crafted sales copy, boosting sales and brand reach.
Project 5: Develop a ChatGPT clone with an added summarization feature, delivering a versatile and invaluable chatbot experience.
Project 6: MCQ Quiz Creator App - Seamlessly create multiple-choice quizzes for your students using LangChain and Pinecone.
Project 7: CSV Data Analysis Toll - Helps you analyze your CSV file by answering your queries about its data.
Project 8: Youtube Script Writing Tool - Effortlessly create compelling YouTube scripts with this user-friendly and efficient script-writing tool.
Project 9 - Support Chat Bot For Your Website - Helps your visitors/customers to find the relevant data or blog links that can be useful to them.
Project 10 - Automatic Ticket Classification Tool - The Automatic Ticket Classification Tool categorizes support tickets based on content to streamline ticket management and response processes.
Project 11 - HR - Resume Screening Assistance - HR project using AI to assist in screening resumes, optimizing the hiring process with smart analysis and recommendations
Project 12 - Email Generator using LLAMA 2- The Email Generator is a tool that automatically creates customized emails, saving time and effort in crafting personalized messages.
Project 13 - Invoice Extraction Bot using LLAMA 2- Invoice Extraction Bot: AI-powered tool that extracts key details from invoices accurately and efficiently. Simplify your data entry process.
Course Content:
In this course, we will explore the capabilities of LangChain, an open-source framework that combines LLMs like GPT-4 with external computation and data sources to build scalable and performant AI applications.
You will gain in-depth knowledge of LangChain components, including LLM wrappers, Chains, and Agents. Additionally, we will delve into embeddings and vector databases, with a focus on Pinecone.
Through a learning-by-doing approach, we will collaboratively build real-world LLM applications using Python, LangChain, and OpenAI, complete with modern web app front-ends developed with Streamlit.
Course Highlights:
Unlock the true potential of LangChain, LLMs, Google Gemini Pro , Chat Models, Prompts, Indexes, Data Connections, Chains, Agents, and Memory.
Harness the power of LLMs and LangChain to develop robust applications, integrating cognitive and information sources with Pinecone.
Explore new horizons and unleash unprecedented possibilities with LangChain and Google Gemini Pro , Pinecone-powered applications.