claudette

Claudette is Claude’s friend

NB: If you are reading this in GitHub’s readme, we recommend you instead read the much more nicely formatted documentation format of this tutorial.

Claudette is a wrapper for Anthropic’s Python SDK.

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:

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 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 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 (1984)

Install

pip install claudette

Getting started

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

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

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

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:

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.

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!

model = models[1]

Chat

The main interface to Claudette is the Chat class, which provides a stateful interface to Claude:

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

Hello Jeremy, nice to meet you.

  • 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}
r = chat("What's my name?")
r

Your name is Jeremy.

  • 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}
r = chat("What's my name?")
r

Your name is Jeremy.

  • 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}

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:

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:

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.

  • 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}

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!)

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 (or AsyncClient) for the async versions, e.g:

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?

  • 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}

Remember to use async for when streaming in this case:

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 feature. For instance, here we use caching when asking about Claudette’s readme file:

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

  • 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}

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

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:

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:

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.

  • 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 atoolloopmethod 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 viatool_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}

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

print(r.usage)
Usage(input_tokens=197, output_tokens=280, cache_creation_input_tokens=0, cache_read_input_tokens=7205)
chat.use
In: 201; Out: 459; Cache create: 7205; Cache read: 7205; Total: 15070

Tool use

Tool use lets Claude use external tools.

We use docments 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:

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:

sp = "Never mention what tools you use."

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

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:

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.

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’)

  • 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}

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

chat()

The sum of 604542 and 6458932 is 7063474.

  • 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}

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.

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. This can even call multiple tools as needed solve a problem. For example, let’s define a tool to handle multiplication:

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:

chat = Chat(model, sp=sp, tools=[sums,mults])
pr = f'Calculate ({a}+{b})*2'
pr
'Calculate (604542+6458932)*2'
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.

  • 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}

Structured data

If you just want the immediate result from a single tool, use Client.structured.

cli = Client(model)
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
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:

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')
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.

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

We create a Chat object as before:

chat = Chat(model)

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

img = fn.read_bytes()

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

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.

  • 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}

The image is included as input tokens.

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:

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.

  • 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}
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.

  • 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}
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.

  • 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}

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.)

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:

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:

from anthropic import AnthropicBedrock
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 object passing this client to the cli parameter – and from then on, everything is identical to the previous examples.

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?

  • 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}

Google Vertex

These are the models available through Vertex:

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:

from anthropic import AnthropicVertex
import google.auth
project_id = google.auth.default()[1]
gv = AnthropicVertex(project_id=project_id, region="us-east5")
client = Client(models_goog[-1], gv)
chat = Chat(cli=client)
chat("I'm Jeremy")

Extensions