prebuilt.essay_grading¶

Essay Grading - TODO: Add brief description.

TODO: Add detailed description of module functionality

Examples

Basic usage:

from haive.essay_grading import module_function

# TODO: Add example

Classes¶

`BaseLLMConfig`	Placeholder for base LLM configuration.
`FactCheckResult`	dict() -> new empty dictionary
`JournamlsimReviewAgent`	Agent for reviewing the grammar and bias of an article.
`State`	dict() -> new empty dictionary
`SystemAction`	dict() -> new empty dictionary

Functions¶

`categorize_user_input`(state)	This node handles the categorization of the user input to identify the intended actions.
`chunk_large_text`(text)	Split large text into manageable chunks.
`fact_check_article`(article_text[, chunks])	Fact-check the given text by identifying factual inaccuracies, misleading information, unsupported claims, or vague language.
`fact_checking_node`(state)	Fact Checking Node.
`get_or_create_chunks`(state)	This function gets the article text from the state and splits it into manageable chunks.
`get_user_actions`(input_text)	Identify the user's intended actions based on their input.
`grammar_and_bias_review_node`(state)	Grammar And Bias Review Node.
`grammary_and_bias_analysis_article`(article_text[, chunks])	Review the given article text for grammar, spelling, punctuation, and bias.
`quote_extraction_article`(article_text[, chunks])	Extract direct quotes from the given article text.
`quote_extraction_node`(state)	Quote Extraction Node.
`route`(state)	Route.
`search_and_summarize`(keyword)	Search for keyword and summarize results.
`summarization_node`(state)	This node generates a summary of the article text.
`summarize_article`(article_text, chunks)	Summarize the article.
`tone_analysis_article`(article_text[, chunks])	Analyze the tones of the given article text.
`tone_analysis_node`(state)	Tone Analysis Node.

Package Contents¶

class prebuilt.essay_grading.BaseLLMConfig¶: Placeholder for base LLM configuration.

class prebuilt.essay_grading.FactCheckResult¶

Bases: TypedDict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

Initialize self. See help(type(self)) for accurate signature.

class prebuilt.essay_grading.JournamlsimReviewAgent(llm)¶

Bases: haive.agents.base.agent.Agent

Agent for reviewing the grammar and bias of an article.

Init .

Parameters:: llm (BaseLLMConfig) – [TODO: Add description]

setup_workflow()¶: Setup Workflow.

class prebuilt.essay_grading.State¶

Bases: TypedDict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

Initialize self. See help(type(self)) for accurate signature.

class prebuilt.essay_grading.SystemAction¶

Bases: TypedDict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

Initialize self. See help(type(self)) for accurate signature.

prebuilt.essay_grading.categorize_user_input(state)¶

This node handles the categorization of the user input to identify the intended actions.

Parameters:: state (State)
Return type:: State

prebuilt.essay_grading.chunk_large_text(text)¶

Split large text into manageable chunks.

Parameters:: text (str)
Return type:: List[str]

prebuilt.essay_grading.fact_check_article(article_text, chunks=None)¶

Fact-check the given text by identifying factual inaccuracies, misleading information, unsupported claims, or vague language.

Parameters:: article_text (str)

prebuilt.essay_grading.fact_checking_node(state)¶

Fact Checking Node.

Parameters:: state (State) – [TODO: Add description]
Returns:: Add return description]
Return type:: [TODO

prebuilt.essay_grading.get_or_create_chunks(state)¶

This function gets the article text from the state and splits it into manageable chunks. The chunks are stored in the state to avoid recomputing them multiple times.

Parameters:: state (State)

prebuilt.essay_grading.get_user_actions(input_text)¶

Identify the user’s intended actions based on their input.

Parameters:: input_text (str)
Return type:: List[str]

prebuilt.essay_grading.grammar_and_bias_review_node(state)¶

Grammar And Bias Review Node.

Parameters:: state (State) – [TODO: Add description]
Returns:: Add return description]
Return type:: [TODO

prebuilt.essay_grading.grammary_and_bias_analysis_article(article_text, chunks=None)¶

Review the given article text for grammar, spelling, punctuation, and bias.

Parameters:: article_text (str)

prebuilt.essay_grading.quote_extraction_article(article_text, chunks=None)¶

Extract direct quotes from the given article text.

Parameters:: article_text (str)

prebuilt.essay_grading.quote_extraction_node(state)¶

Quote Extraction Node.

Parameters:: state (State) – [TODO: Add description]
Returns:: Add return description]
Return type:: [TODO

prebuilt.essay_grading.route(state)¶

Route.

Parameters:: state (State) – [TODO: Add description]
Returns:: Add return description]
Return type:: [TODO

prebuilt.essay_grading.search_and_summarize(keyword)¶

Search for keyword and summarize results.

Parameters:: keyword (str)
Return type:: str

prebuilt.essay_grading.summarization_node(state)¶: This node generates a summary of the article text.

prebuilt.essay_grading.summarize_article(article_text, chunks)¶

Summarize the article.

Parameters:

article_text (str)
chunks (List[str])

Return type:

str

prebuilt.essay_grading.tone_analysis_article(article_text, chunks=None)¶

Analyze the tones of the given article text.

Parameters:: article_text (str)

prebuilt.essay_grading.tone_analysis_node(state)¶

Tone Analysis Node.

Parameters:: state (State) – [TODO: Add description]
Returns:: Add return description]
Return type:: [TODO