llms-ctx-full.txt

<project title="Claudette" summary="Claudette is a Python library that wraps Anthropic's Claude API to provide a higher-level interface for creating AI applications. It automates common patterns while maintaining full control, offering features like stateful chat, prefill support, image handling, and streamlined tool use.">Things to remember when using Claudette:

- You must set the `ANTHROPIC_API_KEY` environment variable with your Anthropic API key
- Claudette is designed to work with Claude 3 models (Opus, Sonnet, Haiku) and supports multiple providers (Anthropic direct, AWS Bedrock, Google Vertex)
- The library provides both synchronous and asynchronous interfaces
- Use `Chat()` for maintaining conversation state and handling tool interactions
- When using tools, the library automatically handles the request/response loop
- Image support is built in but only available on compatible models (not Haiku)<docs><doc title="README" desc="Quick start guide and overview"># claudette



> **NB**: If you are reading this in GitHub’s readme, we recommend you
> instead read the much more nicely formatted [documentation
> format](https://claudette.answer.ai/) of this tutorial.

*Claudette* is a wrapper for Anthropic’s [Python
SDK](https://github.com/anthropics/anthropic-sdk-python).

The SDK works well, but it is quite low level – it leaves the developer
to do a lot of stuff manually. That’s a lot of extra work and
boilerplate! Claudette automates pretty much everything that can be
automated, whilst providing full control. Amongst the features provided:

- A [`Chat`](https://claudette.answer.ai/core.html#chat) class that
  creates stateful dialogs
- Support for *prefill*, which tells Claude what to use as the first few
  words of its response
- Convenient image support
- Simple and convenient support for Claude’s new Tool Use API.

You’ll need to set the `ANTHROPIC_API_KEY` environment variable to the
key provided to you by Anthropic in order to use this library.

Note that this library is the first ever “literate nbdev” project. That
means that the actual source code for the library is a rendered Jupyter
Notebook which includes callout notes and tips, HTML tables and images,
detailed explanations, and teaches *how* and *why* the code is written
the way it is. Even if you’ve never used the Anthropic Python SDK or
Claude API before, you should be able to read the source code. Click
[Claudette’s Source](https://claudette.answer.ai/core.html) to read it,
or clone the git repo and execute the notebook yourself to see every
step of the creation process in action. The tutorial below includes
links to API details which will take you to relevant parts of the
source. The reason this project is a new kind of literal program is
because we take seriously Knuth’s call to action, that we have a “*moral
commitment*” to never write an “*illiterate program*” – and so we have a
commitment to making literate programming and easy and pleasant
experience. (For more on this, see [this
talk](https://www.youtube.com/watch?v=rX1yGxJijsI) from Hamel Husain.)

> “*Let us change our traditional attitude to the construction of
> programs: Instead of imagining that our main task is to instruct a
> **computer** what to do, let us concentrate rather on explaining to
> **human beings** what we want a computer to do.*” Donald E. Knuth,
> [Literate
> Programming](https://www.cs.tufts.edu/~nr/cs257/archive/literate-programming/01-knuth-lp.pdf)
> (1984)

## Install

``` sh
pip install claudette
```

## Getting started

Anthropic’s Python SDK will automatically be installed with Claudette,
if you don’t already have it.

``` python
import os
# os.environ['ANTHROPIC_LOG'] = 'debug'
```

To print every HTTP request and response in full, uncomment the above
line.

``` python
from claudette import *
```

Claudette only exports the symbols that are needed to use the library,
so you can use `import *` to import them. Alternatively, just use:

``` python
import claudette
```

…and then add the prefix `claudette.` to any usages of the module.

Claudette provides `models`, which is a list of models currently
available from the SDK.

``` python
models
```

    ['claude-3-opus-20240229',
     'claude-3-5-sonnet-20241022',
     'claude-3-haiku-20240307']

For these examples, we’ll use Sonnet 3.5, since it’s awesome!

``` python
model = models[1]
```

## Chat

The main interface to Claudette is the
[`Chat`](https://claudette.answer.ai/core.html#chat) class, which
provides a stateful interface to Claude:

``` python
chat = Chat(model, sp="""You are a helpful and concise assistant.""")
chat("I'm Jeremy")
```

Hello Jeremy, nice to meet you.

<details>

- id: `msg_015oK9jEcra3TEKHUGYULjWB`
- content:
  `[{'text': 'Hello Jeremy, nice to meet you.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 19, 'output_tokens': 11, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
r = chat("What's my name?")
r
```

Your name is Jeremy.

<details>

- id: `msg_01Si8sTFJe8d8vq7enanbAwj`
- content: `[{'text': 'Your name is Jeremy.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 38, 'output_tokens': 8, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
r = chat("What's my name?")
r
```

Your name is Jeremy.

<details>

- id: `msg_01BHWRoAX8eBsoLn2bzpBkvx`
- content: `[{'text': 'Your name is Jeremy.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 54, 'output_tokens': 8, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

As you see above, displaying the results of a call in a notebook shows
just the message contents, with the other details hidden behind a
collapsible section. Alternatively you can `print` the details:

``` python
print(r)
```

    Message(id='msg_01BHWRoAX8eBsoLn2bzpBkvx', content=[TextBlock(text='Your name is Jeremy.', type='text')], model='claude-3-5-sonnet-20241022', role='assistant', stop_reason='end_turn', stop_sequence=None, type='message', usage=In: 54; Out: 8; Cache create: 0; Cache read: 0; Total: 62)

Claude supports adding an extra `assistant` message at the end, which
contains the *prefill* – i.e. the text we want Claude to assume the
response starts with. Let’s try it out:

``` python
chat("Concisely, what is the meaning of life?",
     prefill='According to Douglas Adams,')
```

According to Douglas Adams,42. Philosophically, it’s to find personal
meaning through relationships, purpose, and experiences.

<details>

- id: `msg_01R9RvMdFwea9iRX5uYSSHG7`
- content:
  `[{'text': "According to Douglas Adams,42. Philosophically, it's to find personal meaning through relationships, purpose, and experiences.", 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 82, 'output_tokens': 23, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

You can add `stream=True` to stream the results as soon as they arrive
(although you will only see the gradual generation if you execute the
notebook yourself, of course!)

``` python
for o in chat("Concisely, what book was that in?", prefill='It was in', stream=True):
    print(o, end='')
```

    It was in "The Hitchhiker's Guide to the Galaxy" by Douglas Adams.

### Async

Alternatively, you can use
[`AsyncChat`](https://claudette.answer.ai/async.html#asyncchat) (or
[`AsyncClient`](https://claudette.answer.ai/async.html#asyncclient)) for
the async versions, e.g:

``` python
chat = AsyncChat(model)
await chat("I'm Jeremy")
```

Hi Jeremy! Nice to meet you. I’m Claude, an AI assistant created by
Anthropic. How can I help you today?

<details>

- id: `msg_016Q8cdc3sPWBS8eXcNj841L`
- content:
  `[{'text': "Hi Jeremy! Nice to meet you. I'm Claude, an AI assistant created by Anthropic. How can I help you today?", 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 10, 'output_tokens': 31, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

Remember to use `async for` when streaming in this case:

``` python
async for o in await chat("Concisely, what is the meaning of life?",
                          prefill='According to Douglas Adams,', stream=True):
    print(o, end='')
```

    According to Douglas Adams,  it's 42. But in my view, there's no single universal meaning - each person must find their own purpose through relationships, personal growth, contribution to others, and pursuit of what they find meaningful.

## Prompt caching

If you use `mk_msg(msg, cache=True)`, then the message is cached using
Claude’s [prompt
caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching)
feature. For instance, here we use caching when asking about Claudette’s
readme file:

``` python
chat = Chat(model, sp="""You are a helpful and concise assistant.""")
```

``` python
nbtxt = Path('README.txt').read_text()
msg = f'''<README>
{nbtxt}
</README>
In brief, what is the purpose of this project based on the readme?'''
r = chat(mk_msg(msg, cache=True))
r
```

Claudette is a high-level wrapper for Anthropic’s Python SDK that
automates common tasks and provides additional functionality. Its main
features include:

1.  A Chat class for stateful dialogs
2.  Support for prefill (controlling Claude’s initial response words)
3.  Convenient image handling
4.  Simple tool use API integration
5.  Support for multiple model providers (Anthropic, AWS Bedrock, Google
    Vertex)

The project is notable for being the first “literate nbdev” project,
meaning its source code is written as a detailed, readable Jupyter
Notebook that includes explanations, examples, and teaching material
alongside the functional code.

The goal is to simplify working with Claude’s API while maintaining full
control, reducing boilerplate code and manual work that would otherwise
be needed with the base SDK.

<details>

- id: `msg_014rVQnYoZXZuyWUCMELG1QW`
- content:
  `[{'text': 'Claudette is a high-level wrapper for Anthropic\'s Python SDK that automates common tasks and provides additional functionality. Its main features include:\n\n1. A Chat class for stateful dialogs\n2. Support for prefill (controlling Claude\'s initial response words)\n3. Convenient image handling\n4. Simple tool use API integration\n5. Support for multiple model providers (Anthropic, AWS Bedrock, Google Vertex)\n\nThe project is notable for being the first "literate nbdev" project, meaning its source code is written as a detailed, readable Jupyter Notebook that includes explanations, examples, and teaching material alongside the functional code.\n\nThe goal is to simplify working with Claude\'s API while maintaining full control, reducing boilerplate code and manual work that would otherwise be needed with the base SDK.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 4, 'output_tokens': 179, 'cache_creation_input_tokens': 7205, 'cache_read_input_tokens': 0}`

</details>

The response records the a cache has been created using these input
tokens:

``` python
print(r.usage)
```

    Usage(input_tokens=4, output_tokens=179, cache_creation_input_tokens=7205, cache_read_input_tokens=0)

We can now ask a followup question in this chat:

``` python
r = chat('How does it make tool use more ergonomic?')
r
```

According to the README, Claudette makes tool use more ergonomic in
several ways:

1.  It uses docments to make Python function definitions more
    user-friendly - each parameter and return value should have a type
    and description

2.  It handles the tool calling process automatically - when Claude
    returns a tool_use message, Claudette manages calling the tool with
    the provided parameters behind the scenes

3.  It provides a `toolloop` method that can handle multiple tool calls
    in a single step to solve more complex problems

4.  It allows you to pass a list of tools to the Chat constructor and
    optionally force Claude to always use a specific tool via
    `tool_choice`

Here’s a simple example from the README:

``` python
def sums(
    a:int,  # First thing to sum 
    b:int=1 # Second thing to sum
) -> int: # The sum of the inputs
    "Adds a + b."
    print(f"Finding the sum of {a} and {b}")
    return a + b

chat = Chat(model, sp=sp, tools=[sums], tool_choice='sums')
```

This makes it much simpler compared to manually handling all the tool
use logic that would be required with the base SDK.

<details>

- id: `msg_01EdUvvFBnpPxMtdLRCaSZAU`
- content:
  `[{'text': 'According to the README, Claudette makes tool use more ergonomic in several ways:\n\n1. It uses docments to make Python function definitions more user-friendly - each parameter and return value should have a type and description\n\n2. It handles the tool calling process automatically - when Claude returns a tool_use message, Claudette manages calling the tool with the provided parameters behind the scenes\n\n3. It provides a`toolloop`method that can handle multiple tool calls in a single step to solve more complex problems\n\n4. It allows you to pass a list of tools to the Chat constructor and optionally force Claude to always use a specific tool via`tool_choice```` \n\nHere\'s a simple example from the README:\n\n```python\ndef sums(\n    a:int,  # First thing to sum \n    b:int=1 # Second thing to sum\n) -> int: # The sum of the inputs\n    "Adds a + b."\n    print(f"Finding the sum of {a} and {b}")\n    return a + b\n\nchat = Chat(model, sp=sp, tools=[sums], tool_choice=\'sums\')\n```\n\nThis makes it much simpler compared to manually handling all the tool use logic that would be required with the base SDK.', 'type': 'text'}] ````
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 197, 'output_tokens': 280, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 7205}`

</details>

We can see that this only used ~200 regular input tokens – the 7000+
context tokens have been read from cache.

``` python
print(r.usage)
```

    Usage(input_tokens=197, output_tokens=280, cache_creation_input_tokens=0, cache_read_input_tokens=7205)

``` python
chat.use
```

    In: 201; Out: 459; Cache create: 7205; Cache read: 7205; Total: 15070

## Tool use

[Tool use](https://docs.anthropic.com/claude/docs/tool-use) lets Claude
use external tools.

We use [docments](https://fastcore.fast.ai/docments.html) to make
defining Python functions as ergonomic as possible. Each parameter (and
the return value) should have a type, and a docments comment with the
description of what it is. As an example we’ll write a simple function
that adds numbers together, and will tell us when it’s being called:

``` python
def sums(
    a:int,  # First thing to sum
    b:int=1 # Second thing to sum
) -> int: # The sum of the inputs
    "Adds a + b."
    print(f"Finding the sum of {a} and {b}")
    return a + b
```

Sometimes Claude will say something like “according to the `sums` tool
the answer is” – generally we’d rather it just tells the user the
answer, so we can use a system prompt to help with this:

``` python
sp = "Never mention what tools you use."
```

We’ll get Claude to add up some long numbers:

``` python
a,b = 604542,6458932
pr = f"What is {a}+{b}?"
pr
```

    'What is 604542+6458932?'

To use tools, pass a list of them to
[`Chat`](https://claudette.answer.ai/core.html#chat):

``` python
chat = Chat(model, sp=sp, tools=[sums])
```

To force Claude to always answer using a tool, set `tool_choice` to that
function name. When Claude needs to use a tool, it doesn’t return the
answer, but instead returns a `tool_use` message, which means we have to
call the named tool with the provided parameters.

``` python
r = chat(pr, tool_choice='sums')
r
```

    Finding the sum of 604542 and 6458932

ToolUseBlock(id=‘toolu_014ip2xWyEq8RnAccVT4SySt’, input={‘a’: 604542,
‘b’: 6458932}, name=‘sums’, type=‘tool_use’)

<details>

- id: `msg_014xrPyotyiBmFSctkp1LZHk`
- content:
  `[{'id': 'toolu_014ip2xWyEq8RnAccVT4SySt', 'input': {'a': 604542, 'b': 6458932}, 'name': 'sums', 'type': 'tool_use'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `tool_use`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 442, 'output_tokens': 53, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

Claudette handles all that for us – we just call it again, and it all
happens automatically:

``` python
chat()
```

The sum of 604542 and 6458932 is 7063474.

<details>

- id: `msg_01151puJxG8Fa6k6QSmzwKQA`
- content:
  `[{'text': 'The sum of 604542 and 6458932 is 7063474.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 524, 'output_tokens': 23, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

You can see how many tokens have been used at any time by checking the
`use` property. Note that (as of May 2024) tool use in Claude uses a
*lot* of tokens, since it automatically adds a large system prompt.

``` python
chat.use
```

    In: 966; Out: 76; Cache create: 0; Cache read: 0; Total: 1042

We can do everything needed to use tools in a single step, by using
[`Chat.toolloop`](https://claudette.answer.ai/toolloop.html#chat.toolloop).
This can even call multiple tools as needed solve a problem. For
example, let’s define a tool to handle multiplication:

``` python
def mults(
    a:int,  # First thing to multiply
    b:int=1 # Second thing to multiply
) -> int: # The product of the inputs
    "Multiplies a * b."
    print(f"Finding the product of {a} and {b}")
    return a * b
```

Now with a single call we can calculate `(a+b)*2` – by passing
`show_trace` we can see each response from Claude in the process:

``` python
chat = Chat(model, sp=sp, tools=[sums,mults])
pr = f'Calculate ({a}+{b})*2'
pr
```

    'Calculate (604542+6458932)*2'

``` python
chat.toolloop(pr, trace_func=print)
```

    Finding the sum of 604542 and 6458932
    [{'role': 'user', 'content': [{'type': 'text', 'text': 'Calculate (604542+6458932)*2'}]}, {'role': 'assistant', 'content': [TextBlock(text="I'll help you break this down into steps:\n\nFirst, let's add those numbers:", type='text'), ToolUseBlock(id='toolu_01St5UKxYUU4DKC96p2PjgcD', input={'a': 604542, 'b': 6458932}, name='sums', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01St5UKxYUU4DKC96p2PjgcD', 'content': '7063474'}]}]
    Finding the product of 7063474 and 2
    [{'role': 'assistant', 'content': [TextBlock(text="Now, let's multiply this result by 2:", type='text'), ToolUseBlock(id='toolu_01FpmRG4ZskKEWN1gFZzx49s', input={'a': 7063474, 'b': 2}, name='mults', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01FpmRG4ZskKEWN1gFZzx49s', 'content': '14126948'}]}]
    [{'role': 'assistant', 'content': [TextBlock(text='The final result is 14,126,948.', type='text')]}]

The final result is 14,126,948.

<details>

- id: `msg_0162teyBcJHriUzZXMPz4r5d`
- content:
  `[{'text': 'The final result is 14,126,948.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 741, 'output_tokens': 15, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

## Structured data

If you just want the immediate result from a single tool, use
[`Client.structured`](https://claudette.answer.ai/core.html#client.structured).

``` python
cli = Client(model)
```

``` python
def sums(
    a:int,  # First thing to sum
    b:int=1 # Second thing to sum
) -> int: # The sum of the inputs
    "Adds a + b."
    print(f"Finding the sum of {a} and {b}")
    return a + b
```

``` python
cli.structured("What is 604542+6458932", sums)
```

    Finding the sum of 604542 and 6458932

    [7063474]

This is particularly useful for getting back structured information,
e.g:

``` python
class President:
    "Information about a president of the United States"
    def __init__(self, 
                first:str, # first name
                last:str, # last name
                spouse:str, # name of spouse
                years_in_office:str, # format: "{start_year}-{end_year}"
                birthplace:str, # name of city
                birth_year:int # year of birth, `0` if unknown
        ):
        assert re.match(r'\d{4}-\d{4}', years_in_office), "Invalid format: `years_in_office`"
        store_attr()

    __repr__ = basic_repr('first, last, spouse, years_in_office, birthplace, birth_year')
```

``` python
cli.structured("Provide key information about the 3rd President of the United States", President)
```

    [President(first='Thomas', last='Jefferson', spouse='Martha Wayles', years_in_office='1801-1809', birthplace='Shadwell', birth_year=1743)]

## Images

Claude can handle image data as well. As everyone knows, when testing
image APIs you have to use a cute puppy.

``` python
fn = Path('samples/puppy.jpg')
display.Image(filename=fn, width=200)
```

<img src="index_files/figure-commonmark/cell-35-output-1.jpeg"
width="200" />

We create a [`Chat`](https://claudette.answer.ai/core.html#chat) object
as before:

``` python
chat = Chat(model)
```

Claudette expects images as a list of bytes, so we read in the file:

``` python
img = fn.read_bytes()
```

Prompts to Claudette can be lists, containing text, images, or both, eg:

``` python
chat([img, "In brief, what color flowers are in this image?"])
```

In this adorable puppy photo, there are purple/lavender colored flowers
(appears to be asters or similar daisy-like flowers) in the background.

<details>

- id: `msg_01LHjGv1WwFvDsWUbyLmTEKT`
- content:
  `[{'text': 'In this adorable puppy photo, there are purple/lavender colored flowers (appears to be asters or similar daisy-like flowers) in the background.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 110, 'output_tokens': 37, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

The image is included as input tokens.

``` python
chat.use
```

    In: 110; Out: 37; Cache create: 0; Cache read: 0; Total: 147

Alternatively, Claudette supports creating a multi-stage chat with
separate image and text prompts. For instance, you can pass just the
image as the initial prompt (in which case Claude will make some general
comments about what it sees), and then follow up with questions in
additional prompts:

``` python
chat = Chat(model)
chat(img)
```

What an adorable Cavalier King Charles Spaniel puppy! The photo captures
the classic brown and white coloring of the breed, with those soulful
dark eyes that are so characteristic. The puppy is lying in the grass,
and there are lovely purple asters blooming in the background, creating
a beautiful natural setting. The combination of the puppy’s sweet
expression and the delicate flowers makes for a charming composition.
Cavalier King Charles Spaniels are known for their gentle, affectionate
nature, and this little one certainly seems to embody those traits with
its endearing look.

<details>

- id: `msg_01Ciyymq44uwp2iYwRZdKWNN`
- content:
  `[{'text': "What an adorable Cavalier King Charles Spaniel puppy! The photo captures the classic brown and white coloring of the breed, with those soulful dark eyes that are so characteristic. The puppy is lying in the grass, and there are lovely purple asters blooming in the background, creating a beautiful natural setting. The combination of the puppy's sweet expression and the delicate flowers makes for a charming composition. Cavalier King Charles Spaniels are known for their gentle, affectionate nature, and this little one certainly seems to embody those traits with its endearing look.", 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 98, 'output_tokens': 130, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
chat('What direction is the puppy facing?')
```

The puppy is facing towards the left side of the image. Its head is
positioned so we can see its right side profile, though it appears to be
looking slightly towards the camera, giving us a good view of its
distinctive brown and white facial markings and one of its dark eyes.
The puppy is lying down with its white chest/front visible against the
green grass.

<details>

- id: `msg_01AeR9eWjbxa788YF97iErtN`
- content:
  `[{'text': 'The puppy is facing towards the left side of the image. Its head is positioned so we can see its right side profile, though it appears to be looking slightly towards the camera, giving us a good view of its distinctive brown and white facial markings and one of its dark eyes. The puppy is lying down with its white chest/front visible against the green grass.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 239, 'output_tokens': 79, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
chat('What color is it?')
```

The puppy has a classic Cavalier King Charles Spaniel coat with a rich
chestnut brown (sometimes called Blenheim) coloring on its ears and
patches on its face, combined with a bright white base color. The white
is particularly prominent on its face (creating a distinctive blaze down
the center) and chest area. This brown and white combination is one of
the most recognizable color patterns for the breed.

<details>

- id: `msg_01R91AqXG7pLc8hK24F5mc7x`
- content:
  `[{'text': 'The puppy has a classic Cavalier King Charles Spaniel coat with a rich chestnut brown (sometimes called Blenheim) coloring on its ears and patches on its face, combined with a bright white base color. The white is particularly prominent on its face (creating a distinctive blaze down the center) and chest area. This brown and white combination is one of the most recognizable color patterns for the breed.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 326, 'output_tokens': 92, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

Note that the image is passed in again for every input in the dialog, so
that number of input tokens increases quickly with this kind of chat.
(For large images, using prompt caching might be a good idea.)

``` python
chat.use
```

    In: 663; Out: 301; Cache create: 0; Cache read: 0; Total: 964

## Other model providers

You can also use 3rd party providers of Anthropic models, as shown here.

### Amazon Bedrock

These are the models available through Bedrock:

``` python
models_aws
```

    ['anthropic.claude-3-opus-20240229-v1:0',
     'anthropic.claude-3-5-sonnet-20241022-v2:0',
     'anthropic.claude-3-sonnet-20240229-v1:0',
     'anthropic.claude-3-haiku-20240307-v1:0']

To use them, call `AnthropicBedrock` with your access details, and pass
that to [`Client`](https://claudette.answer.ai/core.html#client):

``` python
from anthropic import AnthropicBedrock
```

``` python
ab = AnthropicBedrock(
    aws_access_key=os.environ['AWS_ACCESS_KEY'],
    aws_secret_key=os.environ['AWS_SECRET_KEY'],
)
client = Client(models_aws[-1], ab)
```

Now create your [`Chat`](https://claudette.answer.ai/core.html#chat)
object passing this client to the `cli` parameter – and from then on,
everything is identical to the previous examples.

``` python
chat = Chat(cli=client)
chat("I'm Jeremy")
```

It’s nice to meet you, Jeremy! I’m Claude, an AI assistant created by
Anthropic. How can I help you today?

<details>

- id: `msg_bdrk_01V3B5RF2Pyzmh3NeR8xMMpq`
- content:
  `[{'text': "It's nice to meet you, Jeremy! I'm Claude, an AI assistant created by Anthropic. How can I help you today?", 'type': 'text'}]`
- model: `claude-3-haiku-20240307`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage: `{'input_tokens': 10, 'output_tokens': 32}`

</details>

### Google Vertex

These are the models available through Vertex:

``` python
models_goog
```

    ['claude-3-opus@20240229',
     'claude-3-5-sonnet-v2@20241022',
     'claude-3-sonnet@20240229',
     'claude-3-haiku@20240307']

To use them, call `AnthropicVertex` with your access details, and pass
that to [`Client`](https://claudette.answer.ai/core.html#client):

``` python
from anthropic import AnthropicVertex
import google.auth
```

``` python
project_id = google.auth.default()[1]
gv = AnthropicVertex(project_id=project_id, region="us-east5")
client = Client(models_goog[-1], gv)
```

``` python
chat = Chat(cli=client)
chat("I'm Jeremy")
```

## Extensions

- [Pydantic Structured
  Ouput](https://github.com/tom-pollak/claudette-pydantic)</doc></docs><api><doc title="API List" desc="A succint list of all functions and methods in claudette.">404: Not Found</doc></api><optional><doc title="Tool loop handling" desc="How to use the tool loop functionality for complex multi-step interactions"># Tool loop



``` python
import os
# os.environ['ANTHROPIC_LOG'] = 'debug'
```

``` python
model = models[-1]
```

Anthropic provides an [interesting
example](https://github.com/anthropics/anthropic-cookbook/blob/main/tool_use/customer_service_agent.ipynb)
of using tools to mock up a hypothetical ordering system. We’re going to
take it a step further, and show how we can dramatically simplify the
process, whilst completing more complex tasks.

We’ll start by defining the same mock customer/order data as in
Anthropic’s example, plus create a entity relationship between customers
and orders:

``` python
orders = {
    "O1": dict(id="O1", product="Widget A", quantity=2, price=19.99, status="Shipped"),
    "O2": dict(id="O2", product="Gadget B", quantity=1, price=49.99, status="Processing"),
    "O3": dict(id="O3", product="Gadget B", quantity=2, price=49.99, status="Shipped")}

customers = {
    "C1": dict(name="John Doe", email="john@example.com", phone="123-456-7890",
               orders=[orders['O1'], orders['O2']]),
    "C2": dict(name="Jane Smith", email="jane@example.com", phone="987-654-3210",
               orders=[orders['O3']])
}
```

We can now define the same functions from the original example – but
note that we don’t need to manually create the large JSON schema, since
Claudette handles all that for us automatically from the functions
directly. We’ll add some extra functionality to update order details
when cancelling too.

``` python
def get_customer_info(
    customer_id:str # ID of the customer
): # Customer's name, email, phone number, and list of orders
    "Retrieves a customer's information and their orders based on the customer ID"
    print(f'- Retrieving customer {customer_id}')
    return customers.get(customer_id, "Customer not found")

def get_order_details(
    order_id:str # ID of the order
): # Order's ID, product name, quantity, price, and order status
    "Retrieves the details of a specific order based on the order ID"
    print(f'- Retrieving order {order_id}')
    return orders.get(order_id, "Order not found")

def cancel_order(
    order_id:str # ID of the order to cancel
)->bool: # True if the cancellation is successful
    "Cancels an order based on the provided order ID"
    print(f'- Cancelling order {order_id}')
    if order_id not in orders: return False
    orders[order_id]['status'] = 'Cancelled'
    return True
```

We’re now ready to start our chat.

``` python
tools = [get_customer_info, get_order_details, cancel_order]
chat = Chat(model, tools=tools)
```

We’ll start with the same request as Anthropic showed:

``` python
r = chat('Can you tell me the email address for customer C1?')
print(r.stop_reason)
r.content
```

    - Retrieving customer C1
    tool_use

    [ToolUseBlock(id='toolu_0168sUZoEUpjzk5Y8WN3q9XL', input={'customer_id': 'C1'}, name='get_customer_info', type='tool_use')]

Claude asks us to use a tool. Claudette handles that automatically by
just calling it again:

``` python
r = chat()
contents(r)
```

    'The email address for customer C1 is john@example.com.'

Let’s consider a more complex case than in the original example – what
happens if a customer wants to cancel all of their orders?

``` python
chat = Chat(model, tools=tools)
r = chat('Please cancel all orders for customer C1 for me.')
print(r.stop_reason)
r.content
```

    - Retrieving customer C1
    tool_use

    [TextBlock(text="Okay, let's cancel all orders for customer C1:", type='text'),
     ToolUseBlock(id='toolu_01ADr1rEp7NLZ2iKWfLp7vz7', input={'customer_id': 'C1'}, name='get_customer_info', type='tool_use')]

This is the start of a multi-stage tool use process. Doing it manually
step by step is inconvenient, so let’s write a function to handle this
for us:

------------------------------------------------------------------------

<a
href="https://github.com/AnswerDotAI/claudette/blob/main/claudette/toolloop.py#L16"
target="_blank" style="float:right; font-size:smaller">source</a>

### Chat.toolloop

>      Chat.toolloop (pr, max_steps=10, trace_func:Optional[<built-
>                     infunctioncallable>]=None, cont_func:Optional[<built-
>                     infunctioncallable>]=<function noop>, temp=None,
>                     maxtok=4096, stream=False, prefill='',
>                     tool_choice:Optional[dict]=None)

*Add prompt `pr` to dialog and get a response from Claude, automatically
following up with `tool_use` messages*

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 25%" />
<col style="width: 34%" />
<col style="width: 34%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th><strong>Type</strong></th>
<th><strong>Default</strong></th>
<th><strong>Details</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>pr</td>
<td></td>
<td></td>
<td>Prompt to pass to Claude</td>
</tr>
<tr class="even">
<td>max_steps</td>
<td>int</td>
<td>10</td>
<td>Maximum number of tool requests to loop through</td>
</tr>
<tr class="odd">
<td>trace_func</td>
<td>Optional</td>
<td>None</td>
<td>Function to trace tool use steps (e.g <code>print</code>)</td>
</tr>
<tr class="even">
<td>cont_func</td>
<td>Optional</td>
<td>noop</td>
<td>Function that stops loop if returns False</td>
</tr>
<tr class="odd">
<td>temp</td>
<td>NoneType</td>
<td>None</td>
<td>Temperature</td>
</tr>
<tr class="even">
<td>maxtok</td>
<td>int</td>
<td>4096</td>
<td>Maximum tokens</td>
</tr>
<tr class="odd">
<td>stream</td>
<td>bool</td>
<td>False</td>
<td>Stream response?</td>
</tr>
<tr class="even">
<td>prefill</td>
<td>str</td>
<td></td>
<td>Optional prefill to pass to Claude as start of its response</td>
</tr>
<tr class="odd">
<td>tool_choice</td>
<td>Optional</td>
<td>None</td>
<td>Optionally force use of some tool</td>
</tr>
</tbody>
</table>

<details open class="code-fold">
<summary>Exported source</summary>

``` python
@patch
@delegates(Chat.__call__)
def toolloop(self:Chat,
             pr, # Prompt to pass to Claude
             max_steps=10, # Maximum number of tool requests to loop through
             trace_func:Optional[callable]=None, # Function to trace tool use steps (e.g `print`)
             cont_func:Optional[callable]=noop, # Function that stops loop if returns False
             **kwargs):
    "Add prompt `pr` to dialog and get a response from Claude, automatically following up with `tool_use` messages"
    n_msgs = len(self.h)
    r = self(pr, **kwargs)
    for i in range(max_steps):
        if r.stop_reason!='tool_use': break
        if trace_func: trace_func(self.h[n_msgs:]); n_msgs = len(self.h)
        r = self(**kwargs)
        if not (cont_func or noop)(self.h[-2]): break
    if trace_func: trace_func(self.h[n_msgs:])
    return r
```

</details>

We’ll start by re-running our previous request - we shouldn’t have to
manually pass back the `tool_use` message any more:

``` python
chat = Chat(model, tools=tools)
r = chat.toolloop('Can you tell me the email address for customer C1?')
r
```

    - Retrieving customer C1

The email address for customer C1 is john@example.com.

<details>

- id: `msg_01Fm2CY76dNeWief4kUW6r71`
- content:
  `[{'text': 'The email address for customer C1 is john@example.com.', 'type': 'text'}]`
- model: `claude-3-haiku-20240307`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 720, 'output_tokens': 19, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

Let’s see if it can handle the multi-stage process now – we’ll add
`trace_func=print` to see each stage of the process:

``` python
chat = Chat(model, tools=tools)
r = chat.toolloop('Please cancel all orders for customer C1 for me.', trace_func=print)
r
```

    - Retrieving customer C1
    [{'role': 'user', 'content': [{'type': 'text', 'text': 'Please cancel all orders for customer C1 for me.'}]}, {'role': 'assistant', 'content': [TextBlock(text="Okay, let's cancel all orders for customer C1:", type='text'), ToolUseBlock(id='toolu_01SvivKytaRHEdKixEY9dUDz', input={'customer_id': 'C1'}, name='get_customer_info', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01SvivKytaRHEdKixEY9dUDz', 'content': "{'name': 'John Doe', 'email': 'john@example.com', 'phone': '123-456-7890', 'orders': [{'id': 'O1', 'product': 'Widget A', 'quantity': 2, 'price': 19.99, 'status': 'Shipped'}, {'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Processing'}]}"}]}]
    - Cancelling order O1
    [{'role': 'assistant', 'content': [TextBlock(text="Based on the customer information, it looks like there are 2 orders for customer C1:\n- Order O1 for Widget A\n- Order O2 for Gadget B\n\nLet's cancel each of these orders:", type='text'), ToolUseBlock(id='toolu_01DoGVUPVBeDYERMePHDzUoT', input={'order_id': 'O1'}, name='cancel_order', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01DoGVUPVBeDYERMePHDzUoT', 'content': 'True'}]}]
    - Cancelling order O2
    [{'role': 'assistant', 'content': [ToolUseBlock(id='toolu_01XNwS35yY88Mvx4B3QqDeXX', input={'order_id': 'O2'}, name='cancel_order', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01XNwS35yY88Mvx4B3QqDeXX', 'content': 'True'}]}]
    [{'role': 'assistant', 'content': [TextBlock(text="I've successfully cancelled both orders O1 and O2 for customer C1. Please let me know if you need anything else!", type='text')]}]

I’ve successfully cancelled both orders O1 and O2 for customer C1.
Please let me know if you need anything else!

<details>

- id: `msg_01K1QpUZ8nrBVUHYTrH5QjSF`
- content:
  `[{'text': "I've successfully cancelled both orders O1 and O2 for customer C1. Please let me know if you need anything else!", 'type': 'text'}]`
- model: `claude-3-haiku-20240307`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 921, 'output_tokens': 32, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

OK Claude thinks the orders were cancelled – let’s check one:

``` python
chat.toolloop('What is the status of order O2?')
```

    - Retrieving order O2

The status of order O2 is now ‘Cancelled’ since I successfully cancelled
that order earlier.

<details>

- id: `msg_01XcXpFDwoZ3u1bFDf5mY8x1`
- content:
  `[{'text': "The status of order O2 is now 'Cancelled' since I successfully cancelled that order earlier.", 'type': 'text'}]`
- model: `claude-3-haiku-20240307`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 1092, 'output_tokens': 26, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

## Code interpreter

Here is an example of using `toolloop` to implement a simple code
interpreter with additional tools.

``` python
from toolslm.shell import get_shell
from fastcore.meta import delegates
import traceback
```

``` python
@delegates()
class CodeChat(Chat):
    imps = 'os, warnings, time, json, re, math, collections, itertools, functools, dateutil, datetime, string, types, copy, pprint, enum, numbers, decimal, fractions, random, operator, typing, dataclasses'
    def __init__(self, model: Optional[str] = None, ask:bool=True, **kwargs):
        super().__init__(model=model, **kwargs)
        self.ask = ask
        self.tools.append(self.run_cell)
        self.shell = get_shell()
        self.shell.run_cell('import '+self.imps)
```

We have one additional parameter to creating a `CodeChat` beyond what we
pass to [`Chat`](https://claudette.answer.ai/core.html#chat), which is
`ask` – if that’s `True`, we’ll prompt the user before running code.

``` python
@patch
def run_cell(
    self:CodeChat,
    code:str,   # Code to execute in persistent IPython session
): # Result of expression on last line (if exists); '#DECLINED#' if user declines request to execute
    "Asks user for permission, and if provided, executes python `code` using persistent IPython session."
    confirm = f'Press Enter to execute, or enter "n" to skip?\n```\n{code}\n```\n'
    if self.ask and input(confirm): return '#DECLINED#'
    try: res = self.shell.run_cell(code)
    except Exception as e: return traceback.format_exc()
    return res.stdout if res.result is None else res.result
```

We just pass along requests to run code to the shell’s implementation.
Claude often prints results instead of just using the last expression,
so we capture stdout in those cases.

``` python
sp = f'''You are a knowledgable assistant. Do not use tools unless needed.
Don't do complex calculations yourself -- use code for them.
The following modules are pre-imported for `run_cell` automatically:

{CodeChat.imps}

Never mention what tools you are using. Note that `run_cell` interpreter state is *persistent* across calls.

If a tool returns `#DECLINED#` report to the user that the attempt was declined and no further progress can be made.'''
```

``` python
def get_user(ignored:str='' # Unused parameter
            ): # Username of current user
    "Get the username of the user running this session"
    print("Looking up username")
    return 'Jeremy'
```

In order to test out multi-stage tool use, we create a mock function
that Claude can call to get the current username.

``` python
model = models[1]
```

``` python
chat = CodeChat(model, tools=[get_user], sp=sp, ask=True, temp=0.3)
```

Claude gets confused sometimes about how tools work, so we use examples
to remind it:

``` python
chat.h = [
    'Calculate the square root of `10332`', 'math.sqrt(10332)',
    '#DECLINED#', 'I am sorry but the request to execute that was declined and no further progress can be made.'
]
```

Providing a callable to toolloop’s `trace_func` lets us print out
information during the loop:

``` python
def _show_cts(h):
    for r in h:
        for o in r.get('content'):
            if hasattr(o,'text'): print(o.text)
            nm = getattr(o, 'name', None)
            if nm=='run_cell': print(o.input['code'])
            elif nm: print(f'{o.name}({o.input})')
```

…and toolloop’s `cont_func` callable let’s us provide a function which,
if it returns `False`, stops the loop:

``` python
def _cont_decline(c):
    return nested_idx(c, 'content', 'content') != '#DECLINED#'
```

Now we can try our code interpreter. We start by asking for a function
to be created, which we’ll use in the next prompt to test that the
interpreter is persistent.

``` python
pr = '''Create a 1-line function `checksum` for a string `s`,
that multiplies together the ascii values of each character in `s` using `reduce`.'''
chat.toolloop(pr, temp=0.2, trace_func=_show_cts, cont_func=_cont_decline)
```

    Press Enter to execute, or enter "n" to skip?
    ```
    checksum = lambda s: functools.reduce(lambda x, y: x * ord(y), s, 1)
    ```

    Create a 1-line function `checksum` for a string `s`,
    that multiplies together the ascii values of each character in `s` using `reduce`.
    Let me help you create that function using `reduce` and `functools`.
    checksum = lambda s: functools.reduce(lambda x, y: x * ord(y), s, 1)
    The function has been created. Let me explain how it works:
    1. It takes a string `s` as input
    2. Uses `functools.reduce` to multiply together all ASCII values
    3. `ord(y)` gets the ASCII value of each character
    4. The initial value is 1 (the third parameter to reduce)
    5. The lambda function multiplies the accumulator (x) with each new ASCII value

    You can test it with any string. For example, you could try `checksum("hello")` to see it in action.

The function has been created. Let me explain how it works: 1. It takes
a string `s` as input 2. Uses `functools.reduce` to multiply together
all ASCII values 3. `ord(y)` gets the ASCII value of each character 4.
The initial value is 1 (the third parameter to reduce) 5. The lambda
function multiplies the accumulator (x) with each new ASCII value

You can test it with any string. For example, you could try
`checksum("hello")` to see it in action.

<details>

- id: `msg_011pcGY9LbYqvRSfDPgCqUkT`
- content:
  `[{'text': 'The function has been created. Let me explain how it works:\n1. It takes a string`s`as input\n2. Uses`functools.reduce`to multiply together all ASCII values\n3.`ord(y)`gets the ASCII value of each character\n4. The initial value is 1 (the third parameter to reduce)\n5. The lambda function multiplies the accumulator (x) with each new ASCII value\n\nYou can test it with any string. For example, you could try`checksum(“hello”)`to see it in action.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 824, 'output_tokens': 125, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

By asking for a calculation to be done on the username, we force it to
use multiple steps:

``` python
pr = 'Use it to get the checksum of the username of this session.'
chat.toolloop(pr, trace_func=_show_cts)
```

    Looking up username
    Use it to get the checksum of the username of this session.
    I'll first get the username using `get_user` and then apply our `checksum` function to it.
    get_user({'ignored': ''})
    Press Enter to execute, or enter "n" to skip?
    ```
    print(checksum("Jeremy"))
    ```

    Now I'll calculate the checksum of "Jeremy":
    print(checksum("Jeremy"))
    The checksum of the username "Jeremy" is 1134987783204. This was calculated by multiplying together the ASCII values of each character in "Jeremy".

The checksum of the username “Jeremy” is 1134987783204. This was
calculated by multiplying together the ASCII values of each character in
“Jeremy”.

<details>

- id: `msg_01UXvtcLzzykZpnQUT35v4uD`
- content:
  `[{'text': 'The checksum of the username "Jeremy" is 1134987783204. This was calculated by multiplying together the ASCII values of each character in "Jeremy".', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20241022`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 1143, 'output_tokens': 38, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details></doc><doc title="Async support" desc="Using Claudette with async/await"># The async version



## Setup

## Async SDK

``` python
model = models[1]
cli = AsyncAnthropic()
```

``` python
m = {'role': 'user', 'content': "I'm Jeremy"}
r = await cli.messages.create(messages=[m], model=model, max_tokens=100)
r
```

Hello Jeremy! It’s nice to meet you. How can I assist you today? Is
there anything specific you’d like to talk about or any questions you
have?

<details>

- id: `msg_019gsEQs5dqb3kgwNJbTH27M`
- content:
  `[{'text': "Hello Jeremy! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to talk about or any questions you have?", 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage: `{'input_tokens': 10, 'output_tokens': 36}`

</details>

------------------------------------------------------------------------

<a
href="https://github.com/AnswerDotAI/claudette/blob/main/claudette/asink.py#L19"
target="_blank" style="float:right; font-size:smaller">source</a>

### AsyncClient

>      AsyncClient (model, cli=None, log=False)

*Async Anthropic messages client.*

<details open class="code-fold">
<summary>Exported source</summary>

``` python
class AsyncClient(Client):
    def __init__(self, model, cli=None, log=False):
        "Async Anthropic messages client."
        super().__init__(model,cli,log)
        if not cli: self.c = AsyncAnthropic(default_headers={'anthropic-beta': 'prompt-caching-2024-07-31'})
```

</details>

``` python
c = AsyncClient(model)
```

``` python
c._r(r)
c.use
```

    In: 10; Out: 36; Total: 46

------------------------------------------------------------------------

<a
href="https://github.com/AnswerDotAI/claudette/blob/main/claudette/asink.py#L36"
target="_blank" style="float:right; font-size:smaller">source</a>

### AsyncClient.\_\_call\_\_

>      AsyncClient.__call__ (msgs:list, sp='', temp=0, maxtok=4096, prefill='',
>                            stream:bool=False, stop=None, cli=None, log=False)

*Make an async call to Claude.*

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 25%" />
<col style="width: 34%" />
<col style="width: 34%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th><strong>Type</strong></th>
<th><strong>Default</strong></th>
<th><strong>Details</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>msgs</td>
<td>list</td>
<td></td>
<td>List of messages in the dialog</td>
</tr>
<tr class="even">
<td>sp</td>
<td>str</td>
<td></td>
<td>The system prompt</td>
</tr>
<tr class="odd">
<td>temp</td>
<td>int</td>
<td>0</td>
<td>Temperature</td>
</tr>
<tr class="even">
<td>maxtok</td>
<td>int</td>
<td>4096</td>
<td>Maximum tokens</td>
</tr>
<tr class="odd">
<td>prefill</td>
<td>str</td>
<td></td>
<td>Optional prefill to pass to Claude as start of its response</td>
</tr>
<tr class="even">
<td>stream</td>
<td>bool</td>
<td>False</td>
<td>Stream response?</td>
</tr>
<tr class="odd">
<td>stop</td>
<td>NoneType</td>
<td>None</td>
<td>Stop sequence</td>
</tr>
<tr class="even">
<td>cli</td>
<td>NoneType</td>
<td>None</td>
<td></td>
</tr>
<tr class="odd">
<td>log</td>
<td>bool</td>
<td>False</td>
<td></td>
</tr>
</tbody>
</table>

<details open class="code-fold">
<summary>Exported source</summary>

``` python
@patch
async def _stream(self:AsyncClient, msgs:list, prefill='', **kwargs):
    async with self.c.messages.stream(model=self.model, messages=mk_msgs(msgs), **kwargs) as s:
        if prefill: yield prefill
        async for o in s.text_stream: yield o
        self._log(await s.get_final_message(), prefill, msgs, kwargs)
```

</details>
<details open class="code-fold">
<summary>Exported source</summary>

``` python
@patch
@delegates(Client)
async def __call__(self:AsyncClient,
             msgs:list, # List of messages in the dialog
             sp='', # The system prompt
             temp=0, # Temperature
             maxtok=4096, # Maximum tokens
             prefill='', # Optional prefill to pass to Claude as start of its response
             stream:bool=False, # Stream response?
             stop=None, # Stop sequence
             **kwargs):
    "Make an async call to Claude."
    msgs = self._precall(msgs, prefill, stop, kwargs)
    if stream: return self._stream(msgs, prefill=prefill, max_tokens=maxtok, system=sp, temperature=temp, **kwargs)
    res = await self.c.messages.create(
        model=self.model, messages=msgs, max_tokens=maxtok, system=sp, temperature=temp, **kwargs)
    return self._log(res, prefill, msgs, maxtok, sp, temp, stream=stream, stop=stop, **kwargs)
```

</details>

``` python
c = AsyncClient(model, log=True)
c.use
```

    In: 0; Out: 0; Total: 0

``` python
c.model = models[1]
await c('Hi')
```

Hello! How can I assist you today? Feel free to ask any questions or let
me know if you need help with anything.

<details>

- id: `msg_01L9vqP9r1LcmvSk8vWGLbPo`
- content:
  `[{'text': 'Hello! How can I assist you today? Feel free to ask any questions or let me know if you need help with anything.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 8, 'output_tokens': 29, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
c.use
```

    In: 8; Out: 29; Total: 37

``` python
q = "Concisely, what is the meaning of life?"
pref = 'According to Douglas Adams,'
await c(q, prefill=pref)
```

According to Douglas Adams, the meaning of life is 42. More seriously,
there’s no universally agreed upon meaning of life. Many philosophers
and religions have proposed different answers, but it remains an open
question that individuals must grapple with for themselves.

<details>

- id: `msg_01KAJbCneA2oCRPVm9EkyDXF`
- content:
  `[{'text': "According to Douglas Adams,  the meaning of life is 42. More seriously, there's no universally agreed upon meaning of life. Many philosophers and religions have proposed different answers, but it remains an open question that individuals must grapple with for themselves.", 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 24, 'output_tokens': 51, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
async for o in (await c('Hi', stream=True)): print(o, end='')
```

    Hello! How can I assist you today? Feel free to ask any questions or let me know if you need help with anything.

``` python
c.use
```

    In: 40; Out: 109; Total: 149

``` python
async for o in (await c(q, prefill=pref, stream=True)): print(o, end='')
```

    According to Douglas Adams,  the meaning of life is 42. More seriously, there's no universally agreed upon meaning of life. Many philosophers and religions have proposed different answers, but it remains an open question that individuals must grapple with for themselves.

``` python
c.use
```

    In: 64; Out: 160; Total: 224

``` python
def sums(
    a:int,  # First thing to sum
    b:int=1 # Second thing to sum
) -> int: # The sum of the inputs
    "Adds a + b."
    print(f"Finding the sum of {a} and {b}")
    return a + b
```

``` python
a,b = 604542,6458932
pr = f"What is {a}+{b}?"
sp = "You are a summing expert."
```

``` python
tools=[get_schema(sums)]
choice = mk_tool_choice('sums')
```

``` python
tools = [get_schema(sums)]
msgs = mk_msgs(pr)
r = await c(msgs, sp=sp, tools=tools, tool_choice=choice)
tr = mk_toolres(r, ns=globals())
msgs += tr
contents(await c(msgs, sp=sp, tools=tools))
```

    Finding the sum of 604542 and 6458932

    'As a summing expert, I\'m happy to help you with this addition. The sum of 604542 and 6458932 is 7063474.\n\nTo break it down:\n604542 (first number)\n+ 6458932 (second number)\n= 7063474 (total sum)\n\nThis result was calculated using the "sums" function, which adds two numbers together. Is there anything else you\'d like me to sum for you?'

## AsyncChat

------------------------------------------------------------------------

<a
href="https://github.com/AnswerDotAI/claudette/blob/main/claudette/asink.py#L54"
target="_blank" style="float:right; font-size:smaller">source</a>

### AsyncChat

>      AsyncChat (model:Optional[str]=None,
>                 cli:Optional[claudette.core.Client]=None, sp='',
>                 tools:Optional[list]=None, temp=0, cont_pr:Optional[str]=None)

*Anthropic async chat client.*

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 25%" />
<col style="width: 34%" />
<col style="width: 34%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th><strong>Type</strong></th>
<th><strong>Default</strong></th>
<th><strong>Details</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>model</td>
<td>Optional</td>
<td>None</td>
<td>Model to use (leave empty if passing <code>cli</code>)</td>
</tr>
<tr class="even">
<td>cli</td>
<td>Optional</td>
<td>None</td>
<td>Client to use (leave empty if passing <code>model</code>)</td>
</tr>
<tr class="odd">
<td>sp</td>
<td>str</td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>tools</td>
<td>Optional</td>
<td>None</td>
<td></td>
</tr>
<tr class="odd">
<td>temp</td>
<td>int</td>
<td>0</td>
<td></td>
</tr>
<tr class="even">
<td>cont_pr</td>
<td>Optional</td>
<td>None</td>
<td></td>
</tr>
</tbody>
</table>

<details open class="code-fold">
<summary>Exported source</summary>

``` python
@delegates()
class AsyncChat(Chat):
    def __init__(self,
                 model:Optional[str]=None, # Model to use (leave empty if passing `cli`)
                 cli:Optional[Client]=None, # Client to use (leave empty if passing `model`)
                 **kwargs):
        "Anthropic async chat client."
        super().__init__(model, cli, **kwargs)
        if not cli: self.c = AsyncClient(model)
```

</details>

``` python
sp = "Never mention what tools you use."
chat = AsyncChat(model, sp=sp)
chat.c.use, chat.h
```

    (In: 0; Out: 0; Total: 0, [])

------------------------------------------------------------------------

<a
href="https://github.com/AnswerDotAI/claudette/blob/main/claudette/asink.py#L78"
target="_blank" style="float:right; font-size:smaller">source</a>

### AsyncChat.\_\_call\_\_

>      AsyncChat.__call__ (pr=None, temp=0, maxtok=4096, stream=False,
>                          prefill='', **kw)

*Call self as a function.*

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 25%" />
<col style="width: 34%" />
<col style="width: 34%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th><strong>Type</strong></th>
<th><strong>Default</strong></th>
<th><strong>Details</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>pr</td>
<td>NoneType</td>
<td>None</td>
<td>Prompt / message</td>
</tr>
<tr class="even">
<td>temp</td>
<td>int</td>
<td>0</td>
<td>Temperature</td>
</tr>
<tr class="odd">
<td>maxtok</td>
<td>int</td>
<td>4096</td>
<td>Maximum tokens</td>
</tr>
<tr class="even">
<td>stream</td>
<td>bool</td>
<td>False</td>
<td>Stream response?</td>
</tr>
<tr class="odd">
<td>prefill</td>
<td>str</td>
<td></td>
<td>Optional prefill to pass to Claude as start of its response</td>
</tr>
<tr class="even">
<td>kw</td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

<details open class="code-fold">
<summary>Exported source</summary>

``` python
@patch
async def _stream(self:AsyncChat, res):
    async for o in res: yield o
    self.h += mk_toolres(self.c.result, ns=self.tools, obj=self)
```

</details>
<details open class="code-fold">
<summary>Exported source</summary>

``` python
@patch
async def _append_pr(self:AsyncChat, pr=None):
    prev_role = nested_idx(self.h, -1, 'role') if self.h else 'assistant' # First message should be 'user' if no history
    if pr and prev_role == 'user': await self()
    self._post_pr(pr, prev_role)
```

</details>
<details open class="code-fold">
<summary>Exported source</summary>

``` python
@patch
async def __call__(self:AsyncChat,
        pr=None,  # Prompt / message
        temp=0, # Temperature
        maxtok=4096, # Maximum tokens
        stream=False, # Stream response?
        prefill='', # Optional prefill to pass to Claude as start of its response
        **kw):
    await self._append_pr(pr)
    if self.tools: kw['tools'] = [get_schema(o) for o in self.tools]
    res = await self.c(self.h, stream=stream, prefill=prefill, sp=self.sp, temp=temp, maxtok=maxtok, **kw)
    if stream: return self._stream(res)
    self.h += mk_toolres(self.c.result, ns=self.tools, obj=self)
    return res
```

</details>

``` python
await chat("I'm Jeremy")
await chat("What's my name?")
```

Your name is Jeremy, as you mentioned in your previous message.

<details>

- id: `msg_01NMugMXWpDP9iuTXeLkHarn`
- content:
  `[{'text': 'Your name is Jeremy, as you mentioned in your previous message.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 64, 'output_tokens': 16, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
q = "Concisely, what is the meaning of life?"
pref = 'According to Douglas Adams,'
await chat(q, prefill=pref)
```

According to Douglas Adams, the meaning of life is 42. More seriously,
there’s no universally agreed upon answer. Common philosophical
perspectives include:

1.  Finding personal fulfillment
2.  Serving others
3.  Pursuing happiness
4.  Creating meaning through our choices
5.  Experiencing and appreciating existence

Ultimately, many believe each individual must determine their own life’s
meaning.

<details>

- id: `msg_01VPWUQn5Do1Kst8RYUDQvCu`
- content:
  `[{'text': "According to Douglas Adams,  the meaning of life is 42. More seriously, there's no universally agreed upon answer. Common philosophical perspectives include:\n\n1. Finding personal fulfillment\n2. Serving others\n3. Pursuing happiness\n4. Creating meaning through our choices\n5. Experiencing and appreciating existence\n\nUltimately, many believe each individual must determine their own life's meaning.", 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 100, 'output_tokens': 82, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
chat = AsyncChat(model, sp=sp)
async for o in (await chat("I'm Jeremy", stream=True)): print(o, end='')
```

    Hello Jeremy! It's nice to meet you. How are you doing today? Is there anything in particular you'd like to chat about or any questions I can help you with?

``` python
pr = f"What is {a}+{b}?"
chat = AsyncChat(model, sp=sp, tools=[sums])
r = await chat(pr)
r
```

    Finding the sum of 604542 and 6458932

To answer this question, I can use the “sums” function to add these two
numbers together. Let me do that for you.

<details>

- id: `msg_015z1rffSWFxvj7rSpzc43ZE`
- content:
  `[{'text': 'To answer this question, I can use the "sums" function to add these two numbers together. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01SNKhtfnDQBC4RGY4mUCq1v', 'input': {'a': 604542, 'b': 6458932}, 'name': 'sums', 'type': 'tool_use'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `tool_use`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 428, 'output_tokens': 101, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
await chat()
```

The sum of 604542 and 6458932 is 7063474.

<details>

- id: `msg_018KAsE2YGiXWjUJkLPrXpb2`
- content:
  `[{'text': 'The sum of 604542 and 6458932 is 7063474.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 543, 'output_tokens': 23, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details>

``` python
fn = Path('samples/puppy.jpg')
img = fn.read_bytes()
```

``` python
q = "In brief, what color flowers are in this image?"
msg = mk_msg([img_msg(img), text_msg(q)])
await c([msg])
```

The flowers in this image are purple. They appear to be small,
daisy-like flowers, possibly asters or some type of purple daisy,
blooming in the background behind the adorable puppy in the foreground.

<details>

- id: `msg_017qgZggLjUY915mWbWCkb9X`
- content:
  `[{'text': 'The flowers in this image are purple. They appear to be small, daisy-like flowers, possibly asters or some type of purple daisy, blooming in the background behind the adorable puppy in the foreground.', 'type': 'text'}]`
- model: `claude-3-5-sonnet-20240620`
- role: `assistant`
- stop_reason: `end_turn`
- stop_sequence: `None`
- type: `message`
- usage:
  `{'input_tokens': 110, 'output_tokens': 50, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0}`

</details></doc></optional></project>