Update bfrs docs and docs order

docs/docs/deep-dive/optimizers/BootstrapFinetune.md

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-sidebar_position: 5
+sidebar_position: 4
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 # teleprompt.BootstrapFinetune

docs/docs/deep-dive/optimizers/Ensemble.md

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-sidebar_position: 3
+sidebar_position: 4
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 # teleprompt.Ensemble

docs/docs/deep-dive/optimizers/LabeledFewShot.md

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-# teleprompt.LabeledFewShot
+# LabeledFewShot
 
 ### Constructor
 

docs/docs/deep-dive/optimizers/bfrs.md

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+---
+sidebar_position: 3
+---
+
+# BootstrapFewShotWithRandomSearch
+
+`BootstrapFewShotWithRandomSearch` is an teleprompter that extends `BootstrapFewShot` by incorporating a random search strategy to optimize the selection of few-shot examples. This teleprompter is useful when you want to explore a variety of example combinations to find the best examples for your model.
+
+## Usage
+
+In terms of API `BootstrapFewShotWithRandomSearch` teleprompter is quite similar to `BootstrapFewShot` with few additional parameters needed. More precisely the parameters are:
+
+- `metric` (callable): The metric function used to evaluate examples during bootstrapping and final evaluation.
+- `teacher_settings` (dict, optional): Settings for the teacher predictor. Defaults to an empty dictionary.
+- `max_bootstrapped_demos` (int, optional): Maximum number of bootstrapped demonstrations per predictor. Defaults to 4.
+- `max_labeled_demos` (int, optional): Maximum number of labeled demonstrations per predictor. Defaults to 16.
+- `max_rounds` (int, optional): Maximum number of bootstrapping rounds. Defaults to 1.
+- `num_candidate_programs` (int): Number of candidate programs to generate during random search. Defaults to 16.
+- `num_threads` (int): Number of threads used for evaluation during random search. Defaults to 6.
+- `max_errors` (int): Maximum errors permitted during evaluation. Halts run with the latest error message. Defaults to 10.
+- `stop_at_score` (float, optional): Score threshold for random search to stop early. Defaults to None.
+- `metric_threshold` (float, optional): Score threshold for the metric to determine a successful example. Defaults to None.
+
+## Working Example
+
+Let's take the example of optimizing a simple CoT pipeline for GSM8k dataset, we'll take the example in [BootstrapFewShot](/docs/deep-dive/optimizers/bootstrap-fewshot.mdx) as our running example for optimizers. We're gonna assume our data and pipeline is same as the on in `BootstrapFewShot` article. So let's start by intializing the optimizer:
+
+```python
+from dspy.teleprompt import BootstrapFewShotWithRandomSearch
+
+teleprompter = BootstrapFewShotWithRandomSearch(
+    metric=gsm8k_metric, 
+    max_bootstrapped_demos=8, 
+    max_labeled_demos=8,
+    num_threads=10,
+    num_candidate_programs=10
+)
+```
+
+Now that we have initialized our teleprompter, let's compile our CoT module with the call to `compile` standard to all optimizers:
+
+```python
+cot_compiled = teleprompter.compile(CoT(), trainset=trainset, valset=devset)
+```
+
+Once the training is done you'll have a more optimized module that you can save or load again for use anytime:
+
+```python
+cot_compiled.save('turbo_gsm8k.json')
+
+# Loading:
+# cot = CoT()
+# cot.load('turbo_gsm8k.json')
+```

docs/docs/deep-dive/optimizers/bootstrap-fewshot.mdx

@@ -15,18 +15,25 @@ We'll be making a basic answer generation pipeline over GSM8K dataset that we sa
 ```python
 import dspy
 
-turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=250)
-dspy.settings.configure(lm=turbo)
+turbo = dspy.LM(model='openai/gpt-3.5-turbo', max_tokens=250)
+dspy.configure(lm=turbo)
 ```
 
 Now that we have the LM client setup it's time to import the train-dev split in `GSM8k` class that DSPy provides us:
 
 ```python
-from dspy.datasets.gsm8k import GSM8K, gsm8k_metric
+import random
+from dspy.datasets import DataLoader
 
-gms8k = GSM8K()
+dl = DataLoader()
 
-trainset, devset = gms8k.train, gms8k.dev
+gsm8k = dl.from_huggingface(
+    "gsm8k",
+    "main",
+    input_keys = ("question",),
+)
+
+trainset, devset = gsm8k['train'], random.sample(gsm8k['test'], 50)
 ```
 
 We'll now define a basic QA inline signature i.e. `question->answer` and pass it to `ChainOfThought` module, that applies necessary addition for CoT style prompting to the Signature.
@@ -64,9 +71,9 @@ Now we have the baseline pipeline ready to use, so let's try using the `Bootstra
 Let's start by importing and initializing our teleprompter, for the metric we'll be using the same `gsm8k_metric` imported and used above:
 
 ```python
-from dspy.teleprompt import BootstrapFewShotWithRandomSearch
+from dspy.teleprompt import BootstrapFewShot
 
-teleprompter = BootstrapFewShotWithRandomSearch(
+teleprompter = BootstrapFewShot(
     metric=gsm8k_metric, 
     max_bootstrapped_demos=8, 
     max_labeled_demos=8,

docs/docs/deep-dive/optimizers/copro.mdx

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-sidebar_position: 2
+sidebar_position: 5
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 import AuthorDetails from '@site/src/components/AuthorDetails';

docs/docs/deep-dive/optimizers/miprov2.md

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+---
+sidebar_position: 6
+---
+
 # `MIPROv2` Optimizer
 
 ## Table of Contents

docs/docusaurus.config.ts

@@ -55,17 +55,17 @@ const config: Config = {
   // Plugins configuration
   plugins: [
     // Additional plugin for API documentation
-    [
-      '@docusaurus/plugin-content-docs',
-      {
-        id: 'api', // Unique identifier for the API docs
-        path: './api', // Path to the API documentation markdown files
-        routeBasePath: '/api/', // URL route for the API documentation section
-        sidebarPath: require.resolve('./sidebars.ts'), // Path to the API sidebar configuration
-        // URL for the "edit this page" feature for the API docs
-        // editUrl: 'https://github.com/stanfordnlp/dspy/tree/main/api',
-      },
-    ],
+    //[
+    //  '@docusaurus/plugin-content-docs',
+    //  {
+    //    id: 'api', // Unique identifier for the API docs
+    //    path: './api', // Path to the API documentation markdown files
+    //    routeBasePath: '/api/', // URL route for the API documentation section
+    //    sidebarPath: require.resolve('./sidebars.ts'), // Path to the API sidebar configuration
+    //    // URL for the "edit this page" feature for the API docs
+    //    // editUrl: 'https://github.com/stanfordnlp/dspy/tree/main/api',
+    //  },
+    //],
   ],
   // Theme configuration
   themeConfig: {