Python for LLM Workflows: Tooling and Best Practices
Chapter 1: Introduction to LLM Workflows with Python
What are LLM Workflows?
Common Components of an LLM Application
Why Python for LLM Development?
Overview of the Python LLM Ecosystem
Course Structure and Goals
Quiz for Chapter 1
Chapter 2: Setting Up Your Python Environment for LLM Development
Choosing Your Python Version
Virtual Environments (venv, conda)
Managing Dependencies with pip and requirements.txt
Essential Libraries Installation
Setting Up API Keys Securely
Development Environment Configuration Practice
Quiz for Chapter 2
Chapter 3: Interacting with LLM APIs using Python
Understanding LLM APIs
Making API Requests with the requests Library
Handling API Responses and Errors
Using Official Python Client Libraries
Authentication Methods
Rate Limiting and Cost Management Considerations
Practice: Querying an LLM API
Quiz for Chapter 3
Chapter 4: Core LLM Workflow Libraries: LangChain Fundamentals
Introduction to LangChain
Core Concepts: Models, Prompts, Output Parsers
Working with Different LLM Providers in LangChain
Creating and Using Prompt Templates
Parsing LLM Output Structures
Hands-on Practical: Building a Simple LangChain Application
Quiz for Chapter 4
Chapter 5: Advanced LangChain: Chains and Agents
Understanding Chains for Sequential Operations
Building Custom Chains
Introduction to Agents: LLMs as Reasoning Engines
Available Tools for Agents
Creating a Basic Agent
Debugging Chains and Agents
Practice: Implementing a Multi-Step Chain
Quiz for Chapter 5
Chapter 6: Data Handling for LLMs: LlamaIndex Basics
Introduction to LlamaIndex
Loading Data (Documents, Web Pages)
Indexing Data for Efficient Retrieval
Understanding Nodes and Indexes
Querying Your Indexed Data
Hands-on Practical: Indexing and Querying Documents
Quiz for Chapter 6
Chapter 7: Building Retrieval-Augmented Generation (RAG) Systems
Retrieval-Augmented Generation Concepts
Integrating LlamaIndex/LangChain for RAG
Vector Stores and Embeddings Overview
Setting Up a Basic Vector Store
Constructing a RAG Pipeline
Evaluating RAG Performance Metrics
Practice: Creating a Simple RAG Application
Quiz for Chapter 7
Chapter 8: Prompt Engineering Techniques in Python
Principles of Effective Prompting
Few-Shot Prompting Techniques
Structuring Prompts for Complex Tasks
Using Python for Dynamic Prompt Generation
Techniques for Reducing Hallucinations
Iterative Prompt Refinement
Practice: Developing and Testing Prompts
Quiz for Chapter 8
Chapter 9: Testing and Evaluating LLM Applications
Challenges in Testing LLM Systems
Unit Testing Components
Integration Testing Workflows
Evaluation Strategies: Metrics and Human Feedback
Using Frameworks for Evaluation
Logging and Monitoring LLM Interactions
Practice: Setting Up Basic Tests for an LLM Chain
Quiz for Chapter 9
Chapter 10: Deployment and Operational Best Practices
Packaging Your Python LLM Application
Containerization with Docker
Choosing a Deployment Strategy
API Endpoint Creation (FastAPI, Flask)
Monitoring Deployed Applications
Version Control and CI/CD for LLM Projects
Operational Considerations and Maintenance
Quiz for Chapter 10
Core Concepts: Models, Prompts, Output Parsers
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- LangChain Documentation: Core Components & LangChain Expression Language (LCEL), LangChain Development Team, Accessed 2024 (LangChain) - Official documentation covering the fundamental building blocks of LangChain workflows, including models, prompts, and output parsers, and how they combine using LCEL.
- Language Models are Few-Shot Learners, Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2005.14165 - Groundbreaking paper demonstrating the capabilities of large language models, particularly their sensitivity to prompts and ability to perform tasks with few examples, laying the groundwork for prompt engineering.
- Prompt Engineering Guide, Learn Prompting Community, Accessed 2024 - A comprehensive community-driven resource for understanding and applying prompt engineering techniques, highly relevant to effectively guiding LLM responses.