Mohamed Sherif E.
Before 365:
Data Science Intern
After 365:
AI Engineer at Quality Standards | Information Technology – QSIT
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Online Course
trending topic
Build Chat Applications with OpenAI and LangChain
with
Hristina Hristova
Gain cutting-edge AI skills: Master the LangChain framework to build and deploy real-world AI applications
3916 students already have enrolled
Skill level:
Advanced
Duration:
7 hours
-
Lessons (4 hours)
-
Practice exams (3 hours)
-
Projects (8 hours)
- Lessons (4 hours)
- Practice exams (3 hours)
- Projects (8 hours)
CPE credits:
8
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master LangChain to integrate applications with powerful LLMs.
- Learn to connect to OpenAI’s language and embedding models.
- Develop prompt engineering skills for optimal AI response.
- Apply Retrieval‑Augmented Generation (RAG) with a knowledge base.
- Leverage rare AI engineering skills to enhance career prospects.
Topics & tools
openai modelslangchain frameworkchat prompt templatesfew-shot promptingenvironment variablesopenai api keyconversation memoryoutput parsersretrieval augmented generationlangchain expression languageagent toolingaiLangChain
Your instructor
Head of Data Content at 365 Data Science | Theoretical Physicist | Educator in Physics, Mathematics, and Programming
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Theoretical and Mathematical Physics
Course OVERVIEW
Description
CPE Credits: 8
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
In this Build Chat Applications with OpenAI and LangChain course, we’ll unravel the LangChain framework that has gained massive popularity in AI. One of the components in this ever-growing ecosystem is the LangChain Python library, which we’ll use to leverage OpenAI’s potent language models and build the foundations for developing intriguing LLM-powered applications in our LangChain course.
We’ll employ the power of the LangChain Expression Language to create stateful, context-aware, reasoning chatbots that remember past conversations, answer questions about unseen data, and take action to solve more complex problems. This course is part of our advanced deep learning courses, focusing on practical applications of AI.
Prerequisites
- Python (version 3.8 or later), OpenAI API key, LangChain library, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
- Intermediate Python skills are required.
- Basic concepts in natural language processing (NLP) is helpful but not mandatory.
Advanced preparation
Curriculum
51 lessons
1 project
4 exams
- 2. Tokens, Models, and Prices9 MinThe smallest building block of a language model is the token. The collection of all unique tokens represents the vocabulary of a language model. In this section, we’ll discuss how a token is defined in the context of OpenAI’s language models and how tokens determine a model’s price. Additionally, we’ll spend a few minutes presenting the different models OpenAI offers. Understanding token management is vital for optimizing the use of OpenAI models.
- 3. Setting Up the Environment13 MinThis section lays the groundwork for the course’s practical segment. We’ll cover creating a separate virtual environment with its own Python version and package set. Next, we’ll explain API key management and how to acquire an OpenAI API key—enabling our projects to use OpenAI’s expansive language models. Lastly, we’ll demonstrate setting this API key as an environment variable.
- 4. The OpenAI API17 MinBefore discussing the LangChain framework, we’ll first explore the OpenAI API basics—using various roles in prompts, setting the model temperature to adjust response randomness, limiting completion tokens, and streaming responses. This will later serve as a steppingstone towards understanding LangChain’s integration with OpenAI.
- 5. Model Inputs38 MinWe now enter the realm of LangChain, starting with model inputs. We’ll distinguish between prompts, prompt templates, and chat prompt templates and explore the few-shot prompting—a technique that enhances model responses by training on tailored question-answer examples. We’ll also introduce the invoke() method—essential for the upcoming LangChain Expression Language section.
- 6. Output Parsers8 MinAs we’ve seen in previous sections, the output from a language model in LangChain comes as an AI message. This format, however, often doesn’t come in handy as we can’t pass it to other methods or applications. Therefore, LangChain offers what is known as output parsers—making it possible to parse an AI message to a different object: a string, a CSV, a DateTime object, or whatnot.
- 7. LangChain Expression Language (LCEL)52 MinWe now arrive at the crux of our practice journey—a discussion allowing us to build the most essential objects in LangChain: the chains. The foundational idea behind the LangChain Expression Language is that the output of one chain component serves as the input to the next one, making the chains modular objects that can be quickly composed and combined. Additionally, we’ll discuss Runnable objects—the type of objects that can be entered as chain components.
- 8. Retrieval Augmented Generation (RAG)86 MinIn this course section, we address retrieval augmented generation—a technique that efficiently supplies context-specific information to a model. This process involves loading and segmenting personal data, embedding these segments, storing them in a vector store, and selectively retrieving and delivering only the relevant segments to the model. We’ll start with a theoretical discussion on this topic, then apply our knowledge to develop a basic 365 Q&A chatbot using transcripts from two 365 lectures.How to Integrate Custom Data into an LLMIntroduction to RAGIntroduction to Document Loading and SplittingIntroduction to Document EmbeddingIntroduction to Document Storing, Retrieval, and GenerationIndexing: Document Loading with PyPDFLoaderIndexing: Document Loading with Docx2txtLoaderIndexing: Document Splitting with Character Text Splitter (Theory)Indexing: Document Splitting with Character Text Splitter (Code Along)Indexing: Document Splitting with Markdown Header Text SplitterIndexing: Text Embedding with OpenAIIndexing: Creating a Chroma Vector StoreIndexing: Inspecting and Managing Documents in a Vector StoreRetrieval: Similarity SearchRetrieval: Maximal Marginal Relevance SearchRetrieval: Vector Store-Backed RetrieverGeneration: Stuffing DocumentsGeneration: Generating a ResponsePractice exam
- 9. Course project and exam570 Min
Free preview
1.1 Introduction to the Course
5 min
1.2 Business Applications of LangChain
5 min
1.3 What Makes LangChain Powerful?
5 min
1.4 What Does the Course Cover?
6 min
2.1 Tokens
6 min
2.2 Models and Prices
3 min
9 in 10
people walk away career-ready
with practical data and AI skills.
9 in 10
of our graduates landed a new AI & data job
after enrollment
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ACCREDITED certificates
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