chore(docs): add shell syntax highlighting and fix typos (promptfoo#953)

* fix hylighting

* fix typos

* revert

* add sh to non-eval commands

* fix

* reintroduce to all non-eval commands

* fix

README.md

@@ -66,7 +66,7 @@ This will create some placeholders in your current directory: `prompts.txt` and
 
 After editing the prompts and variables to your liking, run the eval command to kick off an evaluation:
 
-```sh
+```
 npx promptfoo@latest eval
 ```
 
@@ -190,7 +190,7 @@ npx promptfoo view
 
 In [this example](https://github.com/typpo/promptfoo/tree/main/examples/assistant-cli), we evaluate whether adding adjectives to the personality of an assistant bot affects the responses:
 
-```sh
+```
 npx promptfoo eval -p prompts.txt -r openai:gpt-3.5-turbo -t tests.csv
 ```
 
@@ -210,7 +210,7 @@ You can also output a nice [spreadsheet](https://docs.google.com/spreadsheets/d/
 
 In the [next example](https://github.com/typpo/promptfoo/tree/main/examples/gpt-3.5-vs-4), we evaluate the difference between GPT 3 and GPT 4 outputs for a given prompt:
 
-```sh
+```
 npx promptfoo eval -p prompts.txt -r openai:gpt-3.5-turbo openai:gpt-4 -o output.html
 ```
 

examples/google-vertex/README.md

@@ -1,12 +1,12 @@
 To call Vertex AI models in Node, you'll need to install Google's official auth client as a peer dependency:
 
-```
+```sh
 npm i google-auth-library
 ```
 
 Make sure the Vertex AI API is enabled for the relevant project in Google Cloud. Then, ensure that you've selected that project in the gcloud cli:
 
-```
+```sh
 gcloud config set project PROJECT_ID
 ```
 

examples/langchain-python/README.md

@@ -4,7 +4,7 @@ To run it, first create a virtual env and install the requirements:
 
 Then activate the virtual env.
 
-```
+```sh
 python3 -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt

examples/phi-vs-llama/README.md

@@ -1,6 +1,6 @@
 To get started:
 
-```
+```sh
 ollama pull llama3
 ollama pull phi3
 ```

examples/tool-use/README.md

@@ -10,7 +10,7 @@ Note that the function and tool syntax differ slightly between the two providers
 
 The configuration for this example is specified in `promptfooconfig.yaml`. To run the example, execute the following command in your terminal:
 
-```sh
+```
 promptfoo eval
 ```
 

site/README.md

@@ -4,22 +4,22 @@ This website is built using [Docusaurus 2](https://docusaurus.io/), a modern sta
 
 ### Installation
 
-```
-$ yarn
+```sh
+npm install
 ```
 
 ### Local Development
 
-```
-$ yarn start
+```sh
+npm start
 ```
 
 This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
 
 ### Build
 
-```
-$ yarn build
+```sh
+npm run build
 ```
 
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
+This command generates static content into the `build` directory and can be served using any static content hosting service.

site/docs/configuration/datasets.md

@@ -38,21 +38,21 @@ Dataset generation uses your prompts and any existing test cases to generate new
 
 Run the command in the same directory as your config:
 
-```
+```sh
 promptfoo generate dataset
 ```
 
 This will output the `tests` YAML to your terminal.
 
 If you want to write the new dataset to a file:
 
-```
+```sh
 promptfoo generate dataset -o tests.yaml
 ```
 
 Or if you want to edit the existing config in-place:
 
-```
+```sh
 promptfoo generate dataset -w
 ```
 
@@ -71,6 +71,6 @@ You can customize the dataset generation process by providing additional options
 
 For example:
 
-```bash
+```sh
 promptfoo generate dataset --config path_to_config.yaml --output path_to_output.yaml --instructions "Consider edge cases related to international travel"
 ```

site/docs/configuration/expected-outputs/index.md

@@ -426,7 +426,7 @@ And create a list of assertions (`asserts.yaml`):
 
 Then run the eval command:
 
-```sh
+```
 promptfoo eval --assertions asserts.yaml --model-outputs outputs.json
 ```
 

site/docs/configuration/guide.md

@@ -548,25 +548,25 @@ For detailed information on the config structure, see [Configuration Reference](
 
 If you have multiple sets of tests, it helps to split them into multiple config files. Use the `--config` or `-c` parameter to run each individual config:
 
-```sh
+```
 promptfoo eval -c usecase1.yaml
 ```
 
 and
 
-```sh
+```
 promptfoo eval -c usecase2.yaml
 ```
 
 You can run multiple configs at the same time, which will combine them into a single eval. For example:
 
-```sh
+```
 promptfoo eval -c my_configs/*
 ```
 
 or
 
-```sh
+```
 promptfoo eval -c config1.yaml -c config2.yaml -c config3.yaml
 ```
 

site/docs/configuration/telemetry.md

@@ -15,7 +15,7 @@ No additional information is collected. The above list is exhaustive.
 
 To disable telemetry, set the following environment variable:
 
-```bash
+```sh
 PROMPTFOO_DISABLE_TELEMETRY=1
 ```
 
@@ -25,6 +25,6 @@ The CLI checks NPM's package registry for updates. If there is a newer version a
 
 To disable, set:
 
-```bash
+```sh
 PROMPTFOO_DISABLE_UPDATE=1
 ```

site/docs/getting-started.md

@@ -46,7 +46,7 @@ This will create a `promptfooconfig.yaml` file in your current directory.
 
    - **OpenAI**: if testing with an OpenAI model, you'll need to set the `OPENAI_API_KEY` environment variable (see [OpenAI provider docs](/docs/providers/openai) for more info):
 
-     ```bash
+     ```sh
      export OPENAI_API_KEY=sk-abc123
      ```
 
@@ -195,7 +195,7 @@ Have a look at the setup and full output [here](https://github.com/promptfoo/pro
 
 You can also output a nice [spreadsheet](https://docs.google.com/spreadsheets/d/1nanoj3_TniWrDl1Sj-qYqIMD6jwm5FBy15xPFdUTsmI/edit?usp=sharing), [JSON](https://github.com/typpo/promptfoo/blob/main/examples/simple-cli/output.json), YAML, or an HTML file:
 
-```bash
+```
 npx promptfoo@latest eval -o output.html
 ```
 
@@ -214,7 +214,7 @@ providers: [openai:gpt-3.5-turbo, openai:gpt-4]
 
 A simple `npx promptfoo@latest eval` will run the example. Also note that you can override parameters directly from the command line. For example, this command:
 
-```bash
+```
 npx promptfoo@latest eval -p prompts.txt -r openai:gpt-3.5-turbo openai:gpt-4 -o output.html
 ```
 

site/docs/guides/azure-vs-openai.md

@@ -28,7 +28,7 @@ Before we get started, you need the following:
 
 Additionally, make sure you have the following environment variables set:
 
-```bash
+```sh
 OPENAI_API_KEY='...'
 AZURE_OPENAI_API_KEY='...'
 ```
@@ -37,7 +37,7 @@ AZURE_OPENAI_API_KEY='...'
 
 Create a new directory for your comparison project and initialize it:
 
-```bash
+```sh
 npx promptfoo@latest init openai-azure-comparison
 ```
 
@@ -91,7 +91,7 @@ tests:
 
 Execute the comparison using the `promptfoo eval` command:
 
-```bash
+```
 npx promptfoo@latest eval --no-cache
 ```
 

site/docs/guides/claude-vs-gpt.md

@@ -123,7 +123,7 @@ The `assert` blocks allow you to automatically check the model outputs for expec
 
 With your configuration complete, you can kick off the evaluation:
 
-```sh
+```
 npx promptfoo@latest eval
 ```
 
@@ -141,7 +141,7 @@ This will display a comparison view showing how Claude 3 and GPT-4 performed on
 
 You can also output the raw results data to a file:
 
-```sh
+```
 npx promptfoo@latest eval -o results.json
 ```
 

site/docs/guides/cohere-command-r-benchmark.md

@@ -23,7 +23,7 @@ The end result is a side-by-side comparison view that looks like this:
 
 Create a new promptfoo project:
 
-```bash
+```sh
 npx promptfoo@latest init cohere-benchmark
 cd cohere-benchmark
 ```
@@ -41,7 +41,7 @@ providers:
 
 Set the API keys:
 
-```bash
+```sh
 export COHERE_API_KEY=your_cohere_key
 export OPENAI_API_KEY=your_openai_key
 export ANTHROPIC_API_KEY=your_anthropic_key
@@ -109,13 +109,13 @@ tests:
 
 Run the benchmark:
 
-```bash
+```
 npx promptfoo@latest eval
 ```
 
 And view the results:
 
-```bash
+```sh
 npx promptfoo@latest view
 ```
 

site/docs/guides/compare-llama2-vs-gpt.md

@@ -22,7 +22,7 @@ This guide assumes that you have promptfoo [installed](/docs/installation). It a
 
 Initialize a new directory `llama-gpt-comparison` that will contain our prompts and test cases:
 
-```
+```sh
 npx promptfoo@latest init llama-gpt-comparison
 ```
 
@@ -202,7 +202,7 @@ These settings will apply to all test cases run against these models.
 
 To configure OpenAI and Replicate (Llama) providers, be sure to set the following environment variables:
 
-```bash
+```sh
 OPENAI_API_KEY=sk-abc123
 REPLICATE_API_TOKEN=abc123
 ```

site/docs/guides/dbrx-benchmark.md

@@ -24,7 +24,7 @@ The end result will be a custom benchmark that looks similar to this:
 
 Create a new directory for your comparison project and initialize it with `promptfoo init`.
 
-```bash
+```sh
 npx promptfoo@latest init dbrx-benchmark
 ```
 
@@ -53,7 +53,7 @@ providers:
 
 Set your API keys as environment variables:
 
-```bash
+```sh
 export OPENROUTER_API_KEY=your_openrouter_api_key
 export OPENAI_API_KEY=your_openai_api_key
 ```
@@ -179,15 +179,15 @@ Many types of assertions are supported, both deterministic and LLM-graded. See [
 
 With everything configured, run the evaluation using the `promptfoo` CLI:
 
-```bash
+```
 npx promptfoo@latest eval
 ```
 
 This command will execute each test case against each configured model and record the results.
 
 To visualize the results, use the `promptfoo` viewer:
 
-```bash
+```sh
 npx promptfoo@latest view
 ```
 
@@ -201,7 +201,7 @@ Clicking into a specific output will show details on the assertions:
 
 You can also output the results to a file in various formats, such as JSON, YAML, or CSV:
 
-```bash
+```
 npx promptfoo@latest eval -o results.csv
 ```
 

site/docs/guides/evaluate-llm-temperature.md

@@ -24,7 +24,7 @@ By running a temperature eval, you can make data-driven decisions that balance t
 
 Before setting up an evaluation to compare the performance of your LLM at different temperatures, you'll need to initialize a configuration file. Run the following command to create a `promptfooconfig.yaml` file:
 
-```bash
+```sh
 npx promptfoo@latest init
 ```
 
@@ -67,7 +67,7 @@ The `tests` section includes our test cases that will be run against both temper
 
 To run the evaluation, use the following command:
 
-```bash
+```
 npx promptfoo@latest eval
 ```
 
@@ -118,13 +118,13 @@ It's worth spending a few minutes to set up these automated checks. They help st
 
 After the evaluation is complete, you can use the web viewer to review the outputs and compare the performance at different temperatures:
 
-```bash
+```sh
 npx promptfoo@latest view
 ```
 
 ## Evaluating randomness
 
-LLMs are inherently nondeterminstic, which means their outputs will vary with each call at nonzero temperatures (and sometimes even at zero temperature). OpenAI introduced the `seed` variable to improve reproducibility of outputs, and other providers will probably follow suit.
+LLMs are inherently nondeterministic, which means their outputs will vary with each call at nonzero temperatures (and sometimes even at zero temperature). OpenAI introduced the `seed` variable to improve reproducibility of outputs, and other providers will probably follow suit.
 
 Set a constant seed in the provider config:
 
@@ -146,7 +146,7 @@ providers:
 
 The `eval` command also has a parameter, `repeat`, which runs each test multiple times:
 
-```bash
+```
 promptfoo eval --repeat 3
 ```