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In this course, you will :
- Learn how to use Langchain in Python.
- Discover how to create Langchain Agents.
- Learn about embeddings and how to use a vector store in Langchain.
- Learn about large language models (LLMs) and embeddings.
- Discover how to connect Langchain to the OpenAI API suite.
Syllabus :
1. Langchain Models
- Different Types of Supported Models
- Working with LLM Models
- Chat Models In Langchain
- What Are Embeddings?
- Using OpenAI Text Embeddings to Analyze Sentiment
- Google Colab Notebook For Langchain Models
2. Prompting & Parsing In Langchain
- Prompting Best Practices - Formatting, Few Shot Prompting, & CoT
- Using Langchain's Built-in Prompt Templates
- Output Parsers in Langchain
- Google Colab Notebook for Prompt Templates & Output Parsers
3. Memory, Chaining, & Indexes
- Managing Chatbot Memory in Langchain
- What is Chaining?
- How To Build Chains in Langchain
- Langchain Document Loaders & Vectorstores
4. Langchain Agents
- What are Langchain Agents?
- Working With Langchain Agents
- Building An Arxiv Summarizer Agent
