LLMVec

LLMVec(backend='', model='', _cache='_mall_cache', **kwargs)

Class that adds ability to use an LLM to run batch predictions

from chatlas import ChatOllama
from mall import LLMVec

chat = ChatOllama(model = "llama3.2")

llm = LLMVec(chat)

Methods

Name	Description
classify	Classify text into specific categories.
custom	Provide the full prompt that the LLM will process.
extract	Pull a specific label from the text.
sentiment	Use an LLM to run a sentiment analysis
summarize	Summarize the text down to a specific number of words.
translate	Translate text into another language.
verify	Check to see if something is true about the text.

LLMVec.classify(x, labels='', additional='')

Classify text into specific categories.

Name	Type	Description	Default
x	list	A list of texts	required
labels	list	A list or a DICT object that defines the categories to classify the text as. It will return one of the provided labels.	`''`
additional	str	Inserts this text into the prompt sent to the LLM	`''`

llm.classify(['this is important!', 'there is no rush'], ['urgent', 'not urgent'])

['urgent', None]

LLMVec.custom(x, prompt='', valid_resps='')

Provide the full prompt that the LLM will process.

Name	Type	Description	Default
x	list	A list of texts	required
prompt	str	The prompt to send to the LLM along with the `col`	`''`

LLMVec.extract(x, labels='', additional='')

Pull a specific label from the text.

Name	Type	Description	Default
x	list	A list of texts	required
labels	list	A list or a DICT object that defines tells the LLM what to look for and return	`''`
additional	str	Inserts this text into the prompt sent to the LLM	`''`

llm.extract(['bob smith, 123 3rd street'], labels=['name', 'address'])

['| bob smith | 123 3rd street |']

LLMVec.sentiment(x, options=['positive', 'negative', 'neutral'], additional='')

Use an LLM to run a sentiment analysis

Name	Type	Description	Default
x	list	A list of texts	required
options	list or dict	A list of the sentiment options to use, or a named DICT object	`['positive', 'negative', 'neutral']`
additional	str	Inserts this text into the prompt sent to the LLM	`''`

llm.sentiment(['I am happy', 'I am sad'])

['positive', 'negative']

LLMVec.summarize(x, max_words=10, additional='')

Summarize the text down to a specific number of words.

Name	Type	Description	Default
x	list	A list of texts	required
max_words	int	Maximum number of words to use for the summary	`10`
additional	str	Inserts this text into the prompt sent to the LLM	`''`

llm.summarize(['This has been the best TV Ive ever used. Great screen, and sound.'], max_words = 5)

['this tv has exceeded expectations']

LLMVec.translate(x, language='', additional='')

Translate text into another language.

Name	Type	Description	Default
x	list	A list of texts	required
language	str	The target language to translate to. For example ‘French’.	`''`
additional	str	Inserts this text into the prompt sent to the LLM	`''`

llm.translate(['This has been the best TV Ive ever used. Great screen, and sound.'], language = 'spanish')

['Esto ha sido la mejor televisión que he tenido, gran pantalla y sonido.']

LLMVec.verify(x, what='', yes_no=[1, 0], additional='')

Check to see if something is true about the text.

Name	Type	Description	Default
x	list	A list of texts	required
what	str	The statement or question that needs to be verified against the provided text	`''`
yes_no	list	A positional list of size 2, which contains the values to return if true and false. The first position will be used as the ‘true’ value, and the second as the ‘false’ value	`[1, 0]`
additional	str	Inserts this text into the prompt sent to the LLM	`''`