From 7c5fd51bbd63a04eaeb8cdd4ef16db0b7703c4a3 Mon Sep 17 00:00:00 2001 From: souzatharsis Date: Sun, 24 Nov 2024 10:24:49 -0300 Subject: [PATCH] update --- .../_build/.doctrees/environment.pickle | Bin 756665 -> 800548 bytes .../_build/.doctrees/markdown/intro.doctree | Bin 22519 -> 27825 bytes .../_build/.doctrees/markdown/toc.doctree | Bin 23708 -> 23709 bytes .../notebooks/nondeterminism.doctree | Bin 16271 -> 19321 bytes .../notebooks/output_size_limit.doctree | Bin 74155 -> 78045 bytes .../notebooks/structured_output.doctree | Bin 74511 -> 80567 bytes .../_build/html/_sources/markdown/intro.md | 28 ++++-- .../_sources/notebooks/nondeterminism.ipynb | 9 +- .../notebooks/output_size_limit.ipynb | 13 ++- .../notebooks/structured_output.ipynb | 7 ++ tamingllms/_build/html/markdown/intro.html | 72 +++++++++++---- tamingllms/_build/html/markdown/toc.html | 6 +- .../_build/html/notebooks/nondeterminism.html | 54 ++++++++--- .../html/notebooks/output_size_limit.html | 79 +++++++++++----- .../html/notebooks/structured_output.html | 85 +++++++++++++----- tamingllms/_build/html/objects.inv | 5 +- tamingllms/_build/html/searchindex.js | 2 +- .../markdown/markdown-notebooks.ipynb | 6 +- .../notebooks/nondeterminism.ipynb | 9 +- .../notebooks/output_size_limit.ipynb | 13 ++- .../notebooks/structured_output.ipynb | 7 ++ tamingllms/_toc.yml | 2 + tamingllms/markdown/intro.md | 28 ++++-- tamingllms/notebooks/nondeterminism.ipynb | 9 +- tamingllms/notebooks/output_size_limit.ipynb | 13 ++- tamingllms/notebooks/structured_output.ipynb | 7 ++ 26 files changed, 339 insertions(+), 115 deletions(-) diff --git a/tamingllms/_build/.doctrees/environment.pickle b/tamingllms/_build/.doctrees/environment.pickle index 5b55d7323be42a2eef6f669305c11e0915562e78..de7af0738827a05028970bee40957ceee9756b21 100644 GIT binary patch literal 800548 zcmeFa3%Fd@Q6_3z@1yrywrqJ@v8|(HbsxR_Qf$Y^vLwrTMv`MEmeSg%d!MtrPrux| zThg&Duz?{K*+YPIcuknhs(NYB`~G6XjW^K$;&o9yELN{%gX*PXqgJg1 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z3;rHjU1O(rBCBRXUZmMA+lDe#r`NpNIo*bCBDO!X_1Z9hpYHD|VcIH^iaIaZ&u?dOLU_#Ih_^Gps3^yv#bh0e=4=qH4m!#aP;Lj?%+?aUZ}HshrHLGAR1 zfl_p2SaVwjz0EM!EX8pfsLXZNFUQ6 z0m)P7gFu}w|BXH-(y!w}?x%m!_Q%mjBE=C$AEop$0v?XmfQG92n&x?PVbqW-WeT{3 zfo)+xTNua|2C#*JYhl1z7^oHosD*(6P3W|RfoNd>TIBm_=Pj(`7S?HtLrSIHP)ej= z#)s5TA;Ur$VPw#Ft&l;l@+)M}gLH)qdh1sqgI@Af$e@Sw3K{eyOCf_+cex51bejZh gz=M{H3K_IWQ^=spg^?krggM7tkA88E8jSAzKY<$c{Qv*} diff --git a/tamingllms/_build/html/_sources/markdown/intro.md b/tamingllms/_build/html/_sources/markdown/intro.md index 29886a3..1d64c49 100644 --- a/tamingllms/_build/html/_sources/markdown/intro.md +++ b/tamingllms/_build/html/_sources/markdown/intro.md @@ -1,10 +1,18 @@ # Introduction +```{epigraph} +Too often we enjoy the comfort of opinion without the discomfort of thought. + +-- John F. Kennedy +``` +```{contents} +``` + +## Core Challenges We'll Address In recent years, Large Language Models (LLMs) have emerged as a transformative force in technology, promising to revolutionize how we build products and interact with computers. From ChatGPT to GitHub Copilot, Claude Artifacts, cursor.com, replit, and others, these systems have captured the public imagination and sparked a gold rush of AI-powered applications. However, beneath the surface of this technological revolution lies a complex landscape of challenges that practitioners must navigate. As we'll explore in this book, the engineering effort required to manage these challenges - from handling non-deterministic outputs to preventing hallucinations - cannot be overstated. While the potential of LLM technology remains compelling, understanding and addressing the hidden costs and complexities of building reliable LLM-powered systems will enable us to fully harness their transformative impact. -## Core Challenges We'll Address While the capabilities of LLMs are indeed remarkable, the prevailing narrative often glosses over fundamental problems that engineers, product managers, and organizations face when building real-world applications. This book aims to bridge that gap, offering a practical, clear-eyed examination of the pitfalls and challenges in working with LLMs. Throughout this book, we'll tackle the following (non-exhaustive) list of critical challenges: @@ -73,9 +81,15 @@ pip install -r requirements.txt ``` ### 2. API Keys Configuration -Set required API keys: -```bash -export OPENAI_API_KEY=your-openai-key +1. Create a `.env` file in the root directory of the project. +2. Add your API keys and other sensitive information to the `.env` file. For example: + + ``` + OPENAI_API_KEY=your_openai_api_key_here + ``` + +```{warning} +Never share your `.env` file or commit it to version control. It contains sensitive information that should be kept private. ``` ### 3. Code Repository @@ -85,11 +99,11 @@ git clone https://github.com/souzatharsis/tamingllms.git cd tamingllms ``` + + ### Troubleshooting Common Issues - If you encounter API rate limits, consider using smaller examples or implementing retry logic - For package conflicts, try creating a fresh virtual environment or use a package manager like `poetry` - Check the book's repository issues page for known problems and solutions -Now that your environment is set up, let's begin our exploration of LLM challenges. - - +Now that your environment is set up, let's begin our exploration of LLM challenges. \ No newline at end of file diff --git a/tamingllms/_build/html/_sources/notebooks/nondeterminism.ipynb b/tamingllms/_build/html/_sources/notebooks/nondeterminism.ipynb index c379d6f..63acbaa 100644 --- a/tamingllms/_build/html/_sources/notebooks/nondeterminism.ipynb +++ b/tamingllms/_build/html/_sources/notebooks/nondeterminism.ipynb @@ -6,11 +6,18 @@ "source": [ "\n", "# Non-determinism & Evals\n", + "```{epigraph}\n", + "Evals are surprisingly often all you need.\n", "\n", - "One of the most fundamental challenges when building products with Large Language Models (LLMs) is their non-deterministic nature. Unlike traditional software systems where the same input reliably produces the same output, LLMs can generate different responses each time they're queried - even with identical prompts. This characteristic is both a strength and a significant engineering challenge.\n", + "-- Greg Brockman, OpenAI's President\n", + "```\n", + "```{contents}\n", + "```\n", "\n", "## Understanding the Challenge\n", "\n", + "One of the most fundamental challenges when building products with Large Language Models (LLMs) is their non-deterministic nature. Unlike traditional software systems where the same input reliably produces the same output, LLMs can generate different responses each time they're queried - even with identical prompts. This characteristic is both a strength and a significant engineering challenge.\n", + "\n", "### What is Non-determinism in LLMs?\n", "\n", "When you ask ChatGPT or any other LLM the same question multiple times, you'll likely get different responses. This isn't a bug - it's a fundamental feature of how these models work. The \"temperature\" parameter, which controls the randomness of outputs, allows models to be creative and generate diverse responses. However, this same feature makes it incredibly difficult to build reliable, testable systems.\n", diff --git a/tamingllms/_build/html/_sources/notebooks/output_size_limit.ipynb b/tamingllms/_build/html/_sources/notebooks/output_size_limit.ipynb index 1234500..943064d 100644 --- a/tamingllms/_build/html/_sources/notebooks/output_size_limit.ipynb +++ b/tamingllms/_build/html/_sources/notebooks/output_size_limit.ipynb @@ -5,15 +5,21 @@ "metadata": {}, "source": [ "# Output Size Limitations\n", + "```{epigraph}\n", + "Only those who will risk going too far can possibly find out how far one can go.\n", "\n", + "-- T.S. Eliot\n", + "```\n", + "```{contents}\n", + "```\n", "## What are Token Limits?\n", "\n", "Tokens are the basic units that LLMs process text with. A token can be as short as a single character or as long as a complete word. In English, a general rule of thumb is that 1 token ≈ 4 characters or ¾ of a word.\n", "\n", - "The `max_output_tokens` is parameter often available in modern LLMs that determines the maximum length of text that an LLM can generate in a single response. Contrary to what one might expect, the model does not \"summarizes the answer\" such that it does not surpass `max_output_tokens` limit. Instead, it will stop once it reaches this limit, even mid-sentence, i.e. the response may be truncated.\n", - "\n", - "**Table 1: Token Cost and Length Limitation Comparison Across Key Models**\n", + "The `max_output_tokens` is parameter often available in modern LLMs that determines the maximum length of text that an LLM can generate in a single response. {numref}`token-cost-table` shows the `max_output_tokens` for several key models, which typically range between 4096 and 16384 tokens. Contrary to what one might expect, the model does not \"summarizes the answer\" such that it does not surpass `max_output_tokens` limit. Instead, it will stop once it reaches this limit, even mid-sentence, i.e. the response may be truncated.\n", "\n", + "```{table} Token Cost and Length Limitation Comparison Across Key Models\n", + ":name: token-cost-table\n", "| Model | max_output_tokens | max_input_tokens | input_cost_per_token | output_cost_per_token |\n", "|------------------------------|-------------------|------------------|----------------------|-----------------------|\n", "| meta.llama3-2-11b-instruct-v1:0 | 4096 | 128000 | 3.5e-7 | 3.5e-7 |\n", @@ -23,6 +29,7 @@ "| gpt-4o-mini | 16384 | 128000 | 1.5e-7 | 6e-7 |\n", "| gemini/gemini-1.5-flash-002 | 8192 | 1048576 | 7.5e-8 | 3e-7 |\n", "| gemini/gemini-1.5-pro-002 | 8192 | 2097152 | 3.5e-6 | 1.05e-5 |\n", + "```\n", "\n", "## Problem Statement\n", "\n", diff --git a/tamingllms/_build/html/_sources/notebooks/structured_output.ipynb b/tamingllms/_build/html/_sources/notebooks/structured_output.ipynb index 2fbd144..2098da7 100644 --- a/tamingllms/_build/html/_sources/notebooks/structured_output.ipynb +++ b/tamingllms/_build/html/_sources/notebooks/structured_output.ipynb @@ -5,6 +5,13 @@ "metadata": {}, "source": [ "# Wrestling with Structured Output\n", + "```{epigraph}\n", + "In limits, there is freedom. Creativity thrives within structure.\n", + "\n", + "-- Julia B. Cameron\n", + "```\n", + "```{contents}\n", + "```\n", "\n", "## The Structured Output Challenges\n", "\n", diff --git a/tamingllms/_build/html/markdown/intro.html b/tamingllms/_build/html/markdown/intro.html index c855c6c..df516a1 100644 --- a/tamingllms/_build/html/markdown/intro.html +++ b/tamingllms/_build/html/markdown/intro.html @@ -4,7 +4,7 @@ - Introduction + 1. Introduction @@ -38,7 +38,7 @@ - + @@ -154,7 +154,7 @@ @@ -165,7 +165,7 @@ @@ -174,11 +174,37 @@
    -

    Introduction

    +

    1. Introduction

    +
    +

    Too often we enjoy the comfort of opinion without the discomfort of thought.

    +

    —John F. Kennedy

    +
    + +
    +

    1.1. Core Challenges We’ll Address

    In recent years, Large Language Models (LLMs) have emerged as a transformative force in technology, promising to revolutionize how we build products and interact with computers. From ChatGPT to GitHub Copilot, Claude Artifacts, cursor.com, replit, and others, these systems have captured the public imagination and sparked a gold rush of AI-powered applications. However, beneath the surface of this technological revolution lies a complex landscape of challenges that practitioners must navigate.

    As we’ll explore in this book, the engineering effort required to manage these challenges - from handling non-deterministic outputs to preventing hallucinations - cannot be overstated. While the potential of LLM technology remains compelling, understanding and addressing the hidden costs and complexities of building reliable LLM-powered systems will enable us to fully harness their transformative impact.

    -
    -

    Core Challenges We’ll Address

    While the capabilities of LLMs are indeed remarkable, the prevailing narrative often glosses over fundamental problems that engineers, product managers, and organizations face when building real-world applications. This book aims to bridge that gap, offering a practical, clear-eyed examination of the pitfalls and challenges in working with LLMs.

    Throughout this book, we’ll tackle the following (non-exhaustive) list of critical challenges:

      @@ -190,7 +216,7 @@

      Core Challenges We’ll Address -

      A Note on Perspective

      +

      1.2. A Note on Perspective

      While this book takes a critical look at LLM limitations, our goal is not to discourage their use but to enable more robust and reliable implementations. By understanding these challenges upfront, you’ll be better equipped to build systems that leverage LLMs effectively while avoiding common pitfalls.

      The current discourse around LLMs tends toward extremes—either uncritical enthusiasm or wholesale dismissal. This book takes a different approach:

        @@ -200,7 +226,7 @@

        A Note on Perspective

    -

    A Practical Approach

    +

    1.3. A Practical Approach

    This book takes a hands-on approach to these challenges, providing:

    • Concrete Python examples that you can run and modify

    • @@ -211,7 +237,7 @@

      A Practical Approach

    -

    Who This Book Is For

    +

    1.4. Who This Book Is For

    This book is designed for:

    • Software Engineers building LLM-powered applications

    • @@ -221,7 +247,7 @@

      Who This Book Is For

    -

    Prerequisites

    +

    1.5. Prerequisites

    To make the most of this book, you should have:

    • Basic Python programming experience

    • @@ -230,10 +256,10 @@

      Prerequisites -

      Setting Up Your Environment

      +

      1.6. Setting Up Your Environment

      Before diving into the examples in this book, you’ll need to set up your development environment. Here’s how to get started:

      -

      1. Python Environment Setup

      +

      1.6.1. 1. Python Environment Setup

      # Create and activate a virtual environment
       python -m venv llm-book-env
       source llm-book-env/bin/activate  # On Windows, use: llm-book-env\Scripts\activate
      @@ -244,14 +270,22 @@ 

      1. Python Environment Setup -

      2. API Keys Configuration

      -

      Set required API keys:

      -
      export OPENAI_API_KEY=your-openai-key
      +

      1.6.2. 2. API Keys Configuration

      +
        +
      1. Create a .env file in the root directory of the project.

      2. +
      3. Add your API keys and other sensitive information to the .env file. For example:

        +
        OPENAI_API_KEY=your_openai_api_key_here
         
        +
      4. +
      +
      +

      Warning

      +

      Never share your .env file or commit it to version control. It contains sensitive information that should be kept private.

      +

      -

      3. Code Repository

      +

      1.6.3. 3. Code Repository

      Clone the book’s companion repository:

      git clone https://github.com/souzatharsis/tamingllms.git
       cd tamingllms
      @@ -259,7 +293,7 @@ 

      3. Code Repository -

      Troubleshooting Common Issues

      +

      1.6.4. Troubleshooting Common Issues

      • If you encounter API rate limits, consider using smaller examples or implementing retry logic

      • For package conflicts, try creating a fresh virtual environment or use a package manager like poetry

      • @@ -299,7 +333,7 @@

        Troubleshooting Common Issues Output Size Limitations → + title="next chapter">2. Output Size Limitations →