WWW::MistralAI

In brief

This Raku package provides access to the machine learning service MistralAI, [MAI1]. For more details of the MistralAI's API usage see the documentation, [MAI2].

Remark: To use the MistralAI API one has to register and obtain authorization key.

Remark: This Raku package is much "less ambitious" than the official Python package, [MAIp1], developed by MistralAI's team. Gradually, over time, I expect to add features to the Raku package that correspond to features of [MAIp1].

This package is very similar to the packages "WWW::OpenAI", [AAp1], and "WWW::PaLM", [AAp2].

Installation

Package installations from both sources use zef installer (which should be bundled with the "standard" Rakudo installation file.)

To install the package from Zef ecosystem use the shell command:

zef install WWW::MistralAI

To install the package from the GitHub repository use the shell command:

zef install https://github.com/antononcube/Raku-WWW-MistralAI.git

Usage examples

Remark: When the authorization key, auth-key, is specified to be Whatever then the functions mistralai-* attempt to use the env variable MISTRAL_API_KEY.

Universal "front-end"

The package has an universal "front-end" function mistral-playground for the different functionalities provided by MistralAI.

Here is a simple call for a "chat completion":

use WWW::MistralAI;
mistralai-playground('Where is Roger Rabbit?', max-tokens => 64);

# [{finish_reason => length, index => 0, message => {content => Roger Rabbit is a fictional character from animation, specifically from the "Who Framed Roger Rabbit" film produced by Disney and Amblin Entertainment and released in 1988. He is a cartoon character who lives in the fictional Toon Town, which is a part of the larger city, role => assistant}}]

Another one using Bulgarian:

mistralai-playground('Колко групи могат да се намерят в този облак от точки.', max-tokens => 64);

# [{finish_reason => length, index => 0, message => {content => To answer your question, I would need more information. The provided phrase "този облак от точки" (this cloud of points) is not specific enough to determine the number of groups that can be found within it. The term "group" is also not clearly defined. Are we looking for clusters of points, role => assistant}}]

Remark: The function mistralai-completion can be used instead in the examples above.

Models

The current MistralAI models can be found with the function mistralai-models:

mistralai-models

# ((Any))

Code generation

There are two types of completions : text and chat. Let us illustrate the differences of their usage by Raku code generation. Here is a text completion:

mistralai-completion(
        'generate Raku code for making a loop over a list',
        max-tokens => 120,
        format => 'values');

# Here's an example of making a loop over a list in Raku:
# 
# ```raku
# my @list = (1, 2, 3, 4, 5); # define a list
# 
# for @list -> $element { # use a for loop to iterate over each element in the list
#     say $element; # print out the element
# }
# ```
# 
# You can also use other types of loops like `map`, `grep`, or `zip`, which may be more appropriate depending on your specific use case. Here

Here is a chat completion:

mistralai-completion(
        'generate Raku code for making a loop over a list',
        max-tokens => 120,
        format => 'values');

# Here is an example of making a loop over a list in Raku:
# 
# ```raku
# my @numbers = (1, 2, 3, 4, 5); # define a list of numbers
# 
# for my $number (@numbers) { # start a for loop over each element in the list
#     say "$number squared is: ", $number ** 2; # perform an operation on the element and print the result
# }
# ```
# 
# In this example, we first define a list of numbers `@numbers`.

Embeddings

Embeddings can be obtained with the function mistralai-embeddings. Here is an example of finding the embedding vectors for each of the elements of an array of strings:

my @queries = [
    'make a classifier with the method RandomForeset over the data dfTitanic',
    'show precision and accuracy',
    'plot True Positive Rate vs Positive Predictive Value',
    'what is a good meat and potatoes recipe'
];

my $embs = mistralai-embeddings(@queries, format => 'values', method => 'tiny');
$embs.elems;

# 4

Here we show:

• That the result is an array of four vectors each with length 1536

• The distributions of the values of each vector

use Data::Reshapers;
use Data::Summarizers;

say "\$embs.elems : { $embs.elems }";
say "\$embs>>.elems : { $embs>>.elems }";
records-summary($embs.kv.Hash.&transpose);

# $embs.elems : 4
# $embs>>.elems : 1024 1024 1024 1024
# +----------------------------------+----------------------------------+-----------------------------------+-----------------------------------+
# | 3                                | 1                                | 2                                 | 0                                 |
# +----------------------------------+----------------------------------+-----------------------------------+-----------------------------------+
# | Min    => -0.088867              | Min    => -0.091675              | Min    => -0.126343               | Min    => -0.140381               |
# | 1st-Qu => -0.022064              | 1st-Qu => -0.021881              | 1st-Qu => -0.021805               | 1st-Qu => -0.019684               |
# | Mean   => -0.0014019259251654148 | Mean   => -0.0014658444561064243 | Mean   => -0.00016893696738407016 | Mean   => 0.0001275218091905117   |
# | Median => -0.0015511512756347656 | Median => -0.0017815             | Median => 0.0005223751068115234   | Median => -0.00012609362602233888 |
# | 3rd-Qu => 0.019516               | 3rd-Qu => 0.019943               | 3rd-Qu => 0.021469                | 3rd-Qu => 0.021347                |
# | Max    => 0.10968                | Max    => 0.111755               | Max    => 0.097534                | Max    => 0.088867                |
# +----------------------------------+----------------------------------+-----------------------------------+-----------------------------------+

Here we find the corresponding dot products and (cross-)tabulate them:

use Data::Reshapers;
use Data::Summarizers;
my @ct = (^$embs.elems X ^$embs.elems).map({ %( i => $_[0], j => $_[1], dot => sum($embs[$_[0]] >>*<< $embs[$_[1]])) }).Array;

say to-pretty-table(cross-tabulate(@ct, 'i', 'j', 'dot'), field-names => (^$embs.elems)>>.Str);

# +---+----------+----------+----------+----------+
# |   |    0     |    1     |    2     |    3     |
# +---+----------+----------+----------+----------+
# | 0 | 1.000405 | 0.580262 | 0.736048 | 0.546892 |
# | 1 | 0.580262 | 1.000174 | 0.663682 | 0.534618 |
# | 2 | 0.736048 | 0.663682 | 1.000578 | 0.545032 |
# | 3 | 0.546892 | 0.534618 | 0.545032 | 0.999975 |
# +---+----------+----------+----------+----------+

Remark: Note that the fourth element (the cooking recipe request) is an outlier. (Judging by the table with dot products.)

Chat completions with engineered prompts

Here is a prompt for "emojification" (see the Wolfram Prompt Repository entry "Emojify"):

my $preEmojify = q:to/END/;
Rewrite the following text and convert some of it into emojis.
The emojis are all related to whatever is in the text.
Keep a lot of the text, but convert key words into emojis.
Do not modify the text except to add emoji.
Respond only with the modified text, do not include any summary or explanation.
Do not respond with only emoji, most of the text should remain as normal words.
END

# Rewrite the following text and convert some of it into emojis.
# The emojis are all related to whatever is in the text.
# Keep a lot of the text, but convert key words into emojis.
# Do not modify the text except to add emoji.
# Respond only with the modified text, do not include any summary or explanation.
# Do not respond with only emoji, most of the text should remain as normal words.

Here is an example of chat completion with emojification:

mistralai-chat-completion([ system => $preEmojify, user => 'Python sucks, Raku rocks, and Perl is annoying'], max-tokens => 200, format => 'values')

# 🐍 Python 🤤 sucks, 🌺 Raku 🚀 rocks, and 🐘 Perl 😓 is annoying

For more examples see the document "Chat-completion-examples".

Command Line Interface

Playground access

The package provides a Command Line Interface (CLI) script:

mistralai-playground --help

# Usage:
#   mistralai-playground [<words> ...] [--path=<Str>] [--mt|--max-tokens[=UInt]] [-m|--model=<Str>] [-r|--role=<Str>] [-t|--temperature[=Real]] [--response-format=<Str>] [-a|--auth-key=<Str>] [--timeout[=UInt]] [-f|--format=<Str>] [--method=<Str>] -- Command given as a sequence of words.
#   
#     --path=<Str>                Path, one of 'chat/completions', 'images/generations', 'images/edits', 'images/variations', 'moderations', 'audio/transcriptions', 'audio/translations', 'embeddings', or 'models'. [default: 'chat/completions']
#     --mt|--max-tokens[=UInt]    The maximum number of tokens to generate in the completion. [default: 100]
#     -m|--model=<Str>            Model. [default: 'Whatever']
#     -r|--role=<Str>             Role. [default: 'user']
#     -t|--temperature[=Real]     Temperature. [default: 0.7]
#     --response-format=<Str>     The format in which the response is returned. [default: 'url']
#     -a|--auth-key=<Str>         Authorization key (to use MistralAI API.) [default: 'Whatever']
#     --timeout[=UInt]            Timeout. [default: 10]
#     -f|--format=<Str>           Format of the result; one of "json", "hash", "values", or "Whatever". [default: 'Whatever']
#     --method=<Str>              Method for the HTTP POST query; one of "tiny" or "curl". [default: 'tiny']

Remark: When the authorization key argument "auth-key" is specified set to "Whatever" then mistralai-playground attempts to use the env variable MISTRAL_API_KEY.

Mermaid diagram

The following flowchart corresponds to the steps in the package function mistralai-playground:

References

Packages

[AAp1] Anton Antonov, WWW::OpenAI Raku package, (2023), GitHub/antononcube.

[AAp2] Anton Antonov, WWW::PaLM Raku package, (2023), GitHub/antononcube.

[MAI1] MistralAI, MistralAI platform.

[MAI2] MistralAI Platform documentation, MistralAI documentation.

[MAIp1] MistralAI, [https://github.com/mistralai/client-python), (2023), GitHub/mistralai.