llm/docs/usage.md

(usage)=
# Usage

The command to run a prompt is `llm prompt 'your prompt'`. This is the default command, so you can use `llm 'your prompt'` as a shortcut.

(usage-executing-prompts)=
## Executing a prompt

These examples use the default OpenAI `gpt-4o-mini` model, which requires you to first {ref}`set an OpenAI API key <api-keys>`.

You can {ref}`install LLM plugins <installing-plugins>` to use models from other providers, including openly licensed models you can run directly on your own computer.

To run a prompt, streaming tokens as they come in:
```bash
llm 'Ten names for cheesecakes'
```
To disable streaming and only return the response once it has completed:
```bash
llm 'Ten names for cheesecakes' --no-stream
```
To switch from ChatGPT 4o-mini (the default) to GPT-4o:
```bash
llm 'Ten names for cheesecakes' -m gpt-4o
```
You can use `-m 4o` as an even shorter shortcut.

Pass `--model <model name>` to use a different model. Run `llm models` to see a list of available models.

You can also send a prompt to standard input, for example:
```bash
echo 'Ten names for cheesecakes' | llm
```
If you send text to standard input and provide arguments, the resulting prompt will consist of the piped content followed by the arguments:
```bash
cat myscript.py | llm 'explain this code'
```
Will run a prompt of:
```
<contents of myscript.py> explain this code
```
For models that support them, {ref}`system prompts <usage-system-prompts>` are a better tool for this kind of prompting.

Some models support options. You can pass these using `-o/--option name value` - for example, to set the temperature to 1.5 run this:

```bash
llm 'Ten names for cheesecakes' -o temperature 1.5
```

(usage-extract-fenced-code)=
### Extracting fenced code blocks

If you are using an LLM to generate code it can be useful to retrieve just the code it produces without any of the surrounding explanatory text.

The `-x/--extract` option will scan the response for the first instance of a Markdown fenced code block - something that looks like this:

````
```python
def my_function():
    # ...
```
````
It will extract and returns just the content of that block, excluding the fenced coded delimiters. If there are no fenced code blocks it will return the full response.

Use `--xl/--extract-last` to return the last fenced code block instead of the first.

The entire response including explanatory text is still logged to the database, and can be viewed using `llm logs -c`.

(usage-attachments)=
### Attachments

Some models are multi-modal, which means they can accept input in more than just text. GPT-4o and GPT-4o mini can accept images, and models such as Google Gemini 1.5 can accept audio and video as well.

LLM calls these **attachments**. You can pass attachments using the `-a` option like this:

```bash
llm "describe this image" -a https://static.simonwillison.net/static/2024/pelicans.jpg
```
Attachments can be passed using URLs or file paths, and you can attach more than one attachment to a single prompt:
```bash
llm "extract text" -a image1.jpg -a image2.jpg
```
You can also pipe an attachment to LLM by using `-` as the filename:
```bash
cat image.jpg | llm "describe this image" -a -
```
LLM will attempt to automatically detect the content type of the image. If this doesn't work you can instead use the `--attachment-type` option (`--at` for short) which takes the URL/path plus an explicit content type:
```bash
cat myfile | llm "describe this image" --at - image/jpeg
```

(usage-system-prompts)=
### System prompts

You can use `-s/--system '...'` to set a system prompt.
```bash
llm 'SQL to calculate total sales by month' \
  --system 'You are an exaggerated sentient cheesecake that knows SQL and talks about cheesecake a lot'
```
This is useful for piping content to standard input, for example:
```bash
curl -s 'https://simonwillison.net/2023/May/15/per-interpreter-gils/' | \
  llm -s 'Suggest topics for this post as a JSON array'
```
Or to generate a description of changes made to a Git repository since the last commit:
```bash
git diff | llm -s 'Describe these changes'
```
Different models support system prompts in different ways.

The OpenAI models are particularly good at using system prompts as instructions for how they should process additional input sent as part of the regular prompt.

Other models might use system prompts change the default voice and attitude of the model.

System prompts can be saved as {ref}`templates <prompt-templates>` to create reusable tools. For example, you can create a template called `pytest` like this:

```bash
llm -s 'write pytest tests for this code' --save pytest
```
And then use the new template like this:
```bash
cat llm/utils.py | llm -t pytest
```
See {ref}`prompt templates <prompt-templates>` for more.

(usage-schemas)=
### Schemas

Some models include the ability to return JSON that matches a provided [JSON schema](https://json-schema.org/). Models from OpenAI, Anthropic and Google Gemini all include this capability.

Take a look at the {ref}`schemas documentation <schemas>` for a detailed guide to using this feature.

You can pass JSON schemas directly to the `--schema` option:

```bash
llm --schema '{
  "type": "object",
  "properties": {
    "dogs": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "bio": {
            "type": "string"
          }
        }
      }
    }
  }
}' -m gpt-4o-mini 'invent two dogs'
```

Or use LLM's custom {ref}`concise schema syntax <schemas-dsl>` like this:
```bash
llm --schema 'name,bio' 'invent a dog'
```
Two use the same concise schema for multiple items use `--schema-multi`:
```bash
llm --schema-multi 'name,bio' 'invent two dogs'
```
You can also save the JSON schema to a file and reference the filename using `--schema`:

```bash
llm --schema dogs.schema.json 'invent two dogs'
```

Or save your schema {ref}`to a template <prompt-templates>` like this:

```bash
llm --schema dogs.schema.json --save dogs
# Then to use it:
llm -t dogs 'invent two dogs'
```

Be warned that different models may support different dialects of the JSON schema specification.

See {ref}`schemas-logs` for tips on using the `llm logs --schema X` command to access JSON objects you have previously logged using this option.

(usage-conversation)=
### Continuing a conversation

By default, the tool will start a new conversation each time you run it.

You can opt to continue the previous conversation by passing the `-c/--continue` option:
```bash
llm 'More names' -c
```
This will re-send the prompts and responses for the previous conversation as part of the call to the language model. Note that this can add up quickly in terms of tokens, especially if you are using expensive models.

`--continue` will automatically use the same model as the conversation that you are continuing, even if you omit the `-m/--model` option.

To continue a conversation that is not the most recent one, use the `--cid/--conversation <id>` option:
```bash
llm 'More names' --cid 01h53zma5txeby33t1kbe3xk8q
```
You can find these conversation IDs using the `llm logs` command.

### Tips for using LLM with Bash or Zsh

To learn more about your computer's operating system based on the output of `uname -a`, run this:
```bash
llm "Tell me about my operating system: $(uname -a)"
```
This pattern of using `$(command)` inside a double quoted string is a useful way to quickly assemble prompts.

(usage-completion-prompts)=
### Completion prompts

Some models are completion models - rather than being tuned to respond to chat style prompts, they are designed to complete a sentence or paragraph.

An example of this is the `gpt-3.5-turbo-instruct` OpenAI model.

You can prompt that model the same way as the chat models, but be aware that the prompt format that works best is likely to differ.

```bash
llm -m gpt-3.5-turbo-instruct 'Reasons to tame a wild beaver:'
```

(usage-chat)=

## Starting an interactive chat

The `llm chat` command starts an ongoing interactive chat with a model.

This is particularly useful for models that run on your own machine, since it saves them from having to be loaded into memory each time a new prompt is added to a conversation.

Run `llm chat`, optionally with a `-m model_id`, to start a chat conversation:

```bash
llm chat -m chatgpt
```
Each chat starts a new conversation. A record of each conversation can be accessed through {ref}`the logs <logging-conversation>`.

You can pass `-c` to start a conversation as a continuation of your most recent prompt. This will automatically use the most recently used model:

```bash
llm chat -c
```

For models that support them, you can pass options using `-o/--option`:
```bash
llm chat -m gpt-4 -o temperature 0.5
```

You can pass a system prompt to be used for your chat conversation:

```bash
llm chat -m gpt-4 -s 'You are a sentient cheesecake'
```
You can also pass {ref}`a template <prompt-templates>` - useful for creating chat personas that you wish to return to.

Here's how to create a template for your GPT-4 powered cheesecake:
```bash
llm --system 'You are a sentient cheesecake' -m gpt-4 --save cheesecake
```
Now you can start a new chat with your cheesecake any time you like using this:
```bash
llm chat -t cheesecake
```
```
Chatting with gpt-4
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> who are you?
I am a sentient cheesecake, meaning I am an artificial
intelligence embodied in a dessert form, specifically a
cheesecake. However, I don't consume or prepare foods
like humans do, I communicate, learn and help answer
your queries.
```

Type `quit` or `exit` followed by `<enter>` to end a chat session.

Sometimes you may want to paste multiple lines of text into a chat at once - for example when debugging an error message.

To do that, type `!multi` to start a multi-line input. Type or paste your text, then type `!end` and hit `<enter>` to finish.

If your pasted text might itself contain a `!end` line, you can set a custom delimiter using `!multi abc` followed by `!end abc` at the end:

```
Chatting with gpt-4
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> !multi custom-end
 Explain this error:

   File "/opt/homebrew/Caskroom/miniconda/base/lib/python3.10/urllib/request.py", line 1391, in https_open
    return self.do_open(http.client.HTTPSConnection, req,
  File "/opt/homebrew/Caskroom/miniconda/base/lib/python3.10/urllib/request.py", line 1351, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 8] nodename nor servname provided, or not known>

 !end custom-end
```

## Listing available models

The `llm models` command lists every model that can be used with LLM, along with their aliases. This includes models that have been installed using {ref}`plugins <plugins>`.

```bash
llm models
```
Example output:
```
OpenAI Chat: gpt-4o (aliases: 4o)
OpenAI Chat: gpt-4o-mini (aliases: 4o-mini)
OpenAI Chat: o1-preview
OpenAI Chat: o1-mini
GeminiPro: gemini-1.5-pro-002
GeminiPro: gemini-1.5-flash-002
...
```

Add one or more `-q term` options to search for models matching all of those search terms:
```bash
llm models -q gpt-4o
llm models -q 4o -q mini
```
Use one or more `-m` options to indicate specific models, either by their model ID or one of their aliases:
```bash
llm models -m gpt-4o -m gemini-1.5-pro-002
```
Add `--options` to also see documentation for the options supported by each model:
```bash
llm models --options
```
Output:
<!-- [[[cog
from click.testing import CliRunner
from llm.cli import cli
result = CliRunner().invoke(cli, ["models", "list", "--options"])
cog.out("```\n{}\n```".format(result.output))
]]] -->
```
OpenAI Chat: gpt-4o (aliases: 4o)
  Options:
    temperature: float
      What sampling temperature to use, between 0 and 2. Higher values like
      0.8 will make the output more random, while lower values like 0.2 will
      make it more focused and deterministic.
    max_tokens: int
      Maximum number of tokens to generate.
    top_p: float
      An alternative to sampling with temperature, called nucleus sampling,
      where the model considers the results of the tokens with top_p
      probability mass. So 0.1 means only the tokens comprising the top 10%
      probability mass are considered. Recommended to use top_p or
      temperature but not both.
    frequency_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on their existing frequency in the text so far, decreasing the model's
      likelihood to repeat the same line verbatim.
    presence_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on whether they appear in the text so far, increasing the model's
      likelihood to talk about new topics.
    stop: str
      A string where the API will stop generating further tokens.
    logit_bias: dict, str
      Modify the likelihood of specified tokens appearing in the completion.
      Pass a JSON string like '{"1712":-100, "892":-100, "1489":-100}'
    seed: int
      Integer seed to attempt to sample deterministically
    json_object: boolean
      Output a valid JSON object {...}. Prompt must mention JSON.
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - streaming
  - schemas
  - async
OpenAI Chat: chatgpt-4o-latest (aliases: chatgpt-4o)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4o-mini (aliases: 4o-mini)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - streaming
  - schemas
  - async
OpenAI Chat: gpt-4o-audio-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    audio/mpeg, audio/wav
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4o-audio-preview-2024-12-17
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    audio/mpeg, audio/wav
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4o-audio-preview-2024-10-01
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    audio/mpeg, audio/wav
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4o-mini-audio-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    audio/mpeg, audio/wav
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4o-mini-audio-preview-2024-12-17
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    audio/mpeg, audio/wav
  Features:
  - streaming
  - async
OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4 (aliases: 4, gpt4)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4-32k (aliases: 4-32k)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4-1106-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4-0125-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4-turbo-2024-04-09
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4-turbo (aliases: gpt-4-turbo-preview, 4-turbo, 4t)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: gpt-4.5-preview-2025-02-27
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - streaming
  - schemas
  - async
OpenAI Chat: gpt-4.5-preview (aliases: gpt-4.5)
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - streaming
  - schemas
  - async
OpenAI Chat: o1
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
    reasoning_effort: str
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - schemas
  - async
OpenAI Chat: o1-2024-12-17
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
    reasoning_effort: str
  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp
  Features:
  - schemas
  - async
OpenAI Chat: o1-preview
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: o1-mini
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
  Features:
  - streaming
  - async
OpenAI Chat: o3-mini
  Options:
    temperature: float
    max_tokens: int
    top_p: float
    frequency_penalty: float
    presence_penalty: float
    stop: str
    logit_bias: dict, str
    seed: int
    json_object: boolean
    reasoning_effort: str
  Features:
  - streaming
  - schemas
  - async
OpenAI Completion: gpt-3.5-turbo-instruct (aliases: 3.5-instruct, chatgpt-instruct)
  Options:
    temperature: float
      What sampling temperature to use, between 0 and 2. Higher values like
      0.8 will make the output more random, while lower values like 0.2 will
      make it more focused and deterministic.
    max_tokens: int
      Maximum number of tokens to generate.
    top_p: float
      An alternative to sampling with temperature, called nucleus sampling,
      where the model considers the results of the tokens with top_p
      probability mass. So 0.1 means only the tokens comprising the top 10%
      probability mass are considered. Recommended to use top_p or
      temperature but not both.
    frequency_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on their existing frequency in the text so far, decreasing the model's
      likelihood to repeat the same line verbatim.
    presence_penalty: float
      Number between -2.0 and 2.0. Positive values penalize new tokens based
      on whether they appear in the text so far, increasing the model's
      likelihood to talk about new topics.
    stop: str
      A string where the API will stop generating further tokens.
    logit_bias: dict, str
      Modify the likelihood of specified tokens appearing in the completion.
      Pass a JSON string like '{"1712":-100, "892":-100, "1489":-100}'
    seed: int
      Integer seed to attempt to sample deterministically
    logprobs: int
      Include the log probabilities of most likely N per token
  Features:
  - streaming

```
<!-- [[[end]]] -->

When running a prompt you can pass the full model name or any of the aliases to the `-m/--model` option:
```bash
llm -m 4o \
  'As many names for cheesecakes as you can think of, with detailed descriptions'
```