Ranking system & chatbot with Langchain, LLM, Vector DB

Build ranking system & stock market pro chatbot using langchain, openai and hugging face model, TensorFlow & vector db
Free tutorial
Rating: 4.1 out of 5 (18 ratings)
1,237 students
36min of on-demand video
English [Auto]

Overview of terms used in this course
Overview of building ranking system using tensor flow, hugging face transformer, pipecone vector db, and neural network
Ranking system POC code walk through
Overview of building a market pro chatbot using langchain, openAI, and in memory vector db
Market pro chatbot code walk through


  • Basic understanding of different AIML tooling and frameworks such as neural work, tensor flow, and langchain


Learn how to create a sophisticated ranking system and a cutting-edge stock market professional chatbot by leveraging the power of advanced AIML tooling and frameworks. In this comprehensive course, you will gain hands-on experience with a variety of state-of-the-art technologies and techniques to build intelligent systems that excel in ranking and stock market analysis.

We will begin by exploring LangChain, a powerful language modeling framework that enables the creation of intelligent conversational agents. You will dive deep into its capabilities and learn how to harness its potential to build robust and context-aware chatbots.

Next, we will delve into the world of machine learning with Tensorflow. Through practical exercises, you will acquire a solid understanding of neural networks and their application in training models for accurate predictions and decision-making in the stock market domain.

To further enhance your AI toolkit, we will introduce you to OpenAI, an industry-leading platform that offers a plethora of cutting-edge machine learning models and APIs. You will explore its vast capabilities and leverage its powerful algorithms to enhance your chatbot's natural language processing capabilities.

Additionally, we will explore the Hugging Face Transformer library, which provides a comprehensive suite of pre-trained models for a wide range of natural language processing tasks. You will learn how to fine-tune these models to create a highly intelligent and contextually aware chatbot.

Furthermore, you will gain expertise in working with Pipecone Vector DB, an efficient and scalable database specifically designed for managing large volumes of vectors. You will learn how to store and query vector-based data, such as embeddings generated by your chatbot, effectively.

Lastly, you will explore InMemory Vector DB, a fast and memory-efficient database solution that enables real-time data access and manipulation. You will learn how to leverage its capabilities to improve the performance of your ranking system and stock market chatbot.

By the end of this course, you will have acquired the skills and knowledge needed to build sophisticated ranking systems and stock market professional chatbots, empowering you to make informed decisions and succeed in the rapidly evolving world of artificial intelligence and finance.

Who this course is for:

  • Everyone wants a hands on experience of building ranking system and openai chatbot. This course provides overview, flow diagram and code walk through. Both use cases are working POC.


AI ML Enthusiast
Eric Rong
  • 3.9 Instructor Rating
  • 30 Reviews
  • 3,304 Students
  • 2 Courses

As a technology enthusiast, I am passionate about learning new things and applying them to improve the quality of our daily lives. One area that I am particularly interested in is Artificial Intelligence and Machine Learning (AIML).

I understand that for people with no technology background, understanding how AIML works can be a daunting task. However, I firmly believe that with the right guidance and approach, anyone can learn how to use AIML technology as a tool to improve their work and daily life.

In my courses, I focus on teaching non-technical users how to understand and use AIML technology. I use a variety of teaching methods, including basic explanations of the technology, pictures, and day-to-day usage examples to help students better understand how to use AIML tools in their daily life.

My goal is to make AIML accessible to everyone, regardless of their technical background. I want my students to feel confident in using these tools and to see the benefits of incorporating AIML into their work and daily life.

Join me on this exciting journey of learning and discover how AIML technology can be a valuable tool for you!

Top companies trust Udemy

Get your team access to Udemy's top 26,000+ courses