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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

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

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

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

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

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

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

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

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

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

Python for LLM Workflows: Tooling and Best Practices

Prerequisites: Intermediate Python skills

Level: Intermediate

What You'll Learn

Environment Setup
Configure a Python development environment suitable for LLM workflow development.
API Integration
Interact effectively with various LLM provider APIs using Python.
Workflow Orchestration
Utilize LangChain to build and manage complex LLM chains and agents.
Data Handling
Employ LlamaIndex for efficient data loading, indexing, and querying in LLM applications.
RAG Implementation
Construct Retrieval-Augmented Generation systems to ground LLM responses in specific data.
Prompt Engineering
Apply effective prompt engineering techniques directly within Python code.
Testing & Evaluation
Implement testing and evaluation strategies specific to LLM-based systems.
Deployment
Understand best practices for packaging, deploying, and monitoring Python LLM applications.

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