Added TOC

README.md

@@ -23,6 +23,24 @@
 
 </div>
 
+- [fabric](#fabric)
+  - [What and why](#what-and-why)
+  - [Philosophy](#philosophy)
+    - [Breaking problems into components](#breaking-problems-into-components)
+    - [Too many prompts](#too-many-prompts)
+    - [Our approach to prompting](#our-approach-to-prompting)
+  - [Quickstart](#quickstart)
+    - [1. Just use the Patterns](#1-just-use-the-patterns)
+    - [2. Create your own Fabric Mill (Server)](#2-create-your-own-fabric-mill-server)
+    - [3. The standalone client](#3-the-standalone-client)
+  - [Structure](#structure)
+    - [Components](#components)
+    - [CLI-native](#cli-native)
+    - [Directly calling Patterns](#directly-calling-patterns)
+  - [Examples](#examples)
+  - [Meta](#meta)
+    - [Primary contributors](#primary-contributors)
+
 ## What and why
 
 Since the start of 2023 and GenAI we've seen a massive number of AI applications for accomplishing tasks. It's powerful, but **it's not easy to integrate this functionality into our lives.**
@@ -91,46 +109,57 @@ The most feature-rich way to use Fabric is to use the `fabric` client, which can
 Follow these steps to get the client installed and configured.
 
 1. Navigate to where you want the Fabric project to live on your systemClone the directory to a semi-permanent place on your computer.
+
 ```bash
 # Find a home for Fabric
 cd /where/you/keep/code
 ```
+
 2. Clone the project to your computer.
+
 ```bash
 # Clone Fabric to your computer
 git clone git@github.com:danielmiessler/fabric.git
 ```
+
 3. Enter Fabric's /client directory
+
 ```bash
 # Enter the project and its /client folder
 cd fabric/client
 ```
+
 4. Install the dependencies
+
 ```bash
 # Install the pre-requisites
 pip3 install -r requirements.txt
 ```
+
 5. Add the path to the `fabric` client to your shell
+
 ```bash
 # Tell your shell how to find the `fabric` client
 echo 'alias fabric="/the/path/to/fabric/client" >> .bashrc'
 # Example of ~/.zshrc or ~/.bashrc
 alias fabric="~/Development/fabric/client/fabric"
 ```
+
 6. Restart your shell
+
 ```bash
 # Make sure you can
 echo 'alias fabric="/the/path/to/fabric/client" >> .bashrc'
 # Example
 echo 'alias fabric="~/Development/fabric/client/fabric" >> .zshrc'
 ```
+
 ### Using the `fabric` client
 
 Once you have it all set up, here's how to use it.
 
 1. Check out the options
-`fabric -h`
-
+   `fabric -h`
 
 ```bash
 fabric [-h] [--text TEXT] [--copy] [--output [OUTPUT]] [--stream] [--list]
@@ -153,10 +182,13 @@ options:
                         The pattern (prompt) to use
   --setup               Set up your fabric instance
 ```
+
 2. Set up the client
+
 ```bash
 fabric --setup
 ```
+
 You'll be asked to enter your OpenAI API key, which will be written to `~/.config/fabric/.env`. Patterns will then be downloaded from Github, which will take a few moments.
 
 ### Example commands
@@ -164,13 +196,17 @@ You'll be asked to enter your OpenAI API key, which will be written to `~/.confi
 The client, by default, runs Fabric patterns without needing a server (the Patterns were downloaded during setup). This means the client connects directly to OpenAI using the input given and the Fabric pattern used.
 
 1. Run the `summarize` Pattern based on input from `stdin`. In this case, the body of an article.
+
 ```bash
 pbpaste | fabric -pattern summarize
 ```
+
 2. Run the `analyze_claims` Pattern with the `--stream` option to get immediate and streaming results.
+
 ```bash
 pbpaste | fabric --stream --pattern analyze_claims
 ```
+
 > [!NOTE]  
 > More examples coming in the next few days, including a demo video!
 

client/utils.py

@@ -51,7 +51,7 @@ class Standalone:
             messages = [user_message]
         try:
             stream = self.client.chat.completions.create(
-                model="gpt-4-turbo-preview",
+                model="gpt-4",
                 messages=messages,
                 temperature=0.0,
                 top_p=1,
@@ -98,7 +98,7 @@ class Standalone:
             messages = [user_message]
         try:
             response = self.client.chat.completions.create(
-                model="gpt-4-turbo-preview",
+                model="gpt-4",
                 messages=messages,
                 temperature=0.0,
                 top_p=1,