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
Choosing Your Python Version
As we begin setting up our environment, the first decision point is selecting the right Python version. While Python 3 is the standard, specific minor versions (like 3.9, 3.10, 3.11, 3.12) can have implications for compatibility with the rapidly evolving LLM ecosystem libraries.
Why Python Version Matters
The Python version you choose impacts several aspects of your development workflow:
- Library Compatibility: This is often the most significant factor. Major LLM libraries like LangChain, LlamaIndex, Transformers (from Hugging Face), TensorFlow, and PyTorch specify the Python versions they support. Using an unsupported version can lead to installation failures or unexpected runtime errors. Often, libraries require a minimum version (e.g., Python 3.8+) and may not yet support the absolute latest release immediately.
- Language Features: Newer Python versions introduce useful language features. For instance, structural pattern matching (
matchstatements, introduced in 3.10) or improvements in type hinting can make your code cleaner and more maintainable. While not strictly necessary for LLM workflows, leveraging modern features can enhance development. - Performance: Python's core developers continuously work on performance improvements. Newer versions often execute code faster than older ones, which can be relevant for computationally intensive tasks sometimes found in data preprocessing or model interaction loops.
- Security and Maintenance: Older Python versions eventually reach their End-of-Life (EOL) and stop receiving security updates. Sticking to actively maintained versions is important for security and long-term project stability.
Checking Your Version and Making a Choice
Before proceeding, you can check your currently installed default Python version by opening your terminal or command prompt and running:
python --version
# or sometimes
python3 --version
If you have multiple Python installations, ensure you're checking the one you intend to use for your LLM projects.
So, which version should you pick? Here's a practical approach:
- Consult Library Documentation: Check the documentation for the primary libraries you plan to use (e.g., LangChain, LlamaIndex, OpenAI's Python client). They usually state their supported Python versions clearly.
- Target Recent, Stable Releases: As a general rule, aim for one of the more recent, stable Python versions that are widely supported by the LLM ecosystem. As of late 2023 / early 2024, versions like Python 3.9, 3.10, and 3.11 are often safe bets, offering a good balance of modern features and broad library compatibility. Python 3.12 is becoming increasingly supported.
- Avoid EOL Versions: Do not use versions that are no longer receiving security updates (e.g., Python 3.7 and earlier).
- Be Cautious with Bleeding Edge: While tempting, immediately jumping to the absolute newest Python release (e.g., a recent .0 release or beta versions) might lead to compatibility issues as libraries take time to adapt. It's often wiser to wait a few months after a major Python release for the ecosystem to catch up.
The diagram below illustrates the typical status progression for Python versions.
Python version lifecycle stages. Aim for versions in the "Stable Release" or early "Security Fixes Only" phases for LLM development.
For this course, we recommend using Python 3.9 or newer, ensuring compatibility with the examples and libraries we'll be working with. If you already have a slightly different version installed (e.g., 3.10, 3.11), it will likely work perfectly fine. The most important step is consistency, which we'll address next when discussing virtual environments.
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