prompeter 0.0.3

Prompeter defines a data model for structuring prompts for LLMs and loads yaml and json files following this model

Prompeter

Prompeter defines a model for structuring prompts for LLMs. This is done using pydantic

Prompeter can read prompt templates structured using this model from either json or yaml files. For json a schema with the definitions is available.

For now the prompt template only supports question-answer tasks (you're welcome to add summarization, named entity recog., info extraction or what ever)

The data model

A prompt template consist of a:

• Systemprompt: Basically a text string with possibility to define variables that can be substitutes when the prompt is created from the template.
• Optional section with few short examples.
• Optional context section for retrival argumentation.
• Question section
• Seperator specifying how the sections should be concatenated

and finally as a form of metadata a category entry that classifies the template (a list of keywords that specifies the category entry)

in yaml:

keywords:
system:  # Systemprompt
examples:  # Optional few-shot examples
context:  # Optional
question:
seperator:  # defaults to "\n\n"

Example prompt structured by the prompeter model

Given a prompt like:

You are {name}, a friendly assistant, that answers colleagues questions
about feet hygiene.

Examples of possible questions and how you are supposed to answer:
Example 1 of 2
--------------
Question 1 of 2: <question>
{name}: "<answer>"

Example 2 of 2
--------------
Question 2 of 2: <question>
{name}: "<answer>"
End of examples

Context
-------
1. piece of context:
<context>
ref: {url}

2. piece of context:
<context>
ref: {url}
-------

Take a step back - give it your best shot. If you succeed in delivering a perfect answer you'll get a reward of 500$.
Question: <question>

in the prompeter prompt format it will look like

keywords:
  - QnA  # Task
  - instruct  # Style
  - RAG  # Method used
  - fewshot  # Method used
  - en  # language
system:
  text: |-
    You are {name}, a friendly assistant, that answers colleagues questions
    about feet hygiene.
  meta_data_vars:
    - name  # name must now be provided as metadata and will be substituted when the prompt is constructed
examples:
  text_before: "Examples of possible questions and how you are supposed to answer:"
  # When now variables have to be specified and the default seperator is fitting, then instead of providing an object with a text-field the text can just be provided directly
  text_before_set:
    text: "Example {num} of {tot}\n--------------"
    numbered: true
    num_var: num  # here we indicate that num is used instead of the default variable {#}
    counted: true  # for the total count we use the default (being {tot})
  question_text:
    text_before:
      text: "Question {#} of {tot}:"
      numbered: true
      counted: true
      seperator: ' '
  answer_text:
    text_before:
      text: "{name}: \""
      meta_data_vars:
        - "name"
      seperator: ""
    text_after:
      text: "\""
      seperator: ""
  text_after: "End of examples"  
  inner_seperator: '\n'
  outer_seperator: "\n\n"
context:
  text_before: "Context\n-------"
  text_after: "-------"
  main_text:
    text_before:
      text: "{#}. piece of context:"
      numbered: true
    text_after:
      text: "ref: {url}"
      meta_data_vars:
        - "url"
question:
  text_before:
    text: "Take a step back - give it your best shot. If you succeed in delivering a perfect answer you'll get a reward of 500$.\nQuestion:"
    seperator: ' '

Admitted, the plain text version of the prompt might be easier read, but structuring the parts of the prompt template makes it possible to automatically scale the number of context snippets provided or the number of examples provided.

The prompt template class shown diagramtically

Below is the class diagram.

• It is indicated which classes are Pydantic BaseModel classes.
• Optional class members defaults to None, if nothing is indicated
• Internal methods have not been included
• Default arguments for methods have not been included
• Compared to the codebase some classes indicate to be children of BaseModel are actually grandchildren, with their parent class only implementing a class method adjusting how a json schema is created by pydantic.

classDiagram
  class PromptStyle {
    <<enumeration>>
    continuation
    instruct
    dialog
  }
  class PromptTask {
    <<enumeration>>
    question_answer
    summarization
  }
  class PromptMethod {
    <<enumeration>>
    zero_shot
    RAG
  }
  class Language {
    <<enumeration>>
    danish
    english
  }
  class Markup {
    <<enumeration>>
    unordered_list
    quote
    bold
    italics
  }
  class PromptCategory~BaseModel~ { 
    PromptStyle style
    PromptTask task
    Language language
    methods List[~]PromptMethod]
    %% get_enum_attr(data: Any) Any
  }
  class PromptTextSnippet~BaseModel~ {
    text : str
    numbered : bool
    num_var : str
    counted : bool
    count_var : str
    meta_data_vars : List[str]
    seperator : str = #quot;#bsol;n#quot;
    %% check_variables_in_text() 'PromptTextSnippet'
    get_text(number: int, total_count: int, **metadata) str
  }
  class PromptSection~BaseModel~ {
    prompt_text_markup : Optional[Markup]
    text_after : Optional[PromptTextSnippet | str]
    text_before : Optional[PromptTextSnippet | str]
    %% cast_str_to_prompt_text_snippets(data: Any) Any
    get_text(prompt_text: PromptInput | str, number: int, total_count: int,  **metadata) str
  }
  class RepeatablePromptSection { 
    main_text: PromptSection
    seperator: str = #quot;#bsol;n#quot;
    get_text(prompt_texts: List[PromptInput | dict | str], **metadata) str
  }
  class QnAFewShotPromptSection {
      text_before_set: Optional[PromptTextSnippet]
      text_after_set: Optional[PromptTextSnippet]
      question_text: PromptSection
      answer_text: PromptSection
      context_text: Optional[RepeatablePromptSection]
      inner_seperator: str = #quot;#bsol;n#bsol;n#quot;
      outer_seperator: str = #quot;#bsol;n#bsol;n---#bsol;n#bsol;n#quot;
      get_text(prompt_texts: List[QnAPromptInput | dict | str], **metadata) str
  }
  class PromptTemplate~BaseModel~ { 
    keywords : Optional[List[str]]
    category : Optional[PromptCategory]
    system : PromptTextSnippet
    examples : Optional[QnAFewShotPromptSection]
    context : Optional[RepeatablePromptSection]
    question :  PromptSection
    seperator : str
    %% cast_str_to_prompt_text_snippets(data: Any) Any
    construct_messages(example_texts: List[QnAPromptInput | dict | str], context_texts: List[PromptInput | dict | str], user_prompt: PromptInput | dict | str], **metadata) list[dict]
    construct_prompt(example_texts: List[QnAPromptInput | dict | str], context_texts: List[PromptInput | dict | str], user_prompt: PromptInput | dict | str], **metadata) str
    %% validate_category() 'PromptTemplate'
  }
  PromptStyle --* PromptCategory : style
  PromptTask --* PromptCategory : task
  PromptMethod --* PromptCategory : method
  Language --* PromptCategory : language
  Markup --* PromptSection
  PromptTextSnippet --* PromptSection : text_after, text_before
  PromptSection --|> RepeatablePromptSection
  PromptSection --|> QnAFewShotPromptSection
  PromptTextSnippet --* QnAFewShotPromptSection : text_after_set, text_before_set
  PromptSection --* QnAFewShotPromptSection : question_text, answer_text
  RepeatablePromptSection --* PromptTemplate : context
  RepeatablePromptSection --* QnAFewShotPromptSection : context_text
  QnAFewShotPromptSection --* PromptTemplate : examples
  PromptCategory --* PromptTemplate : category
  PromptSection --* PromptTemplate : question
  PromptTextSnippet --* PromptTemplate : system

In addition input classes for the methods are defined as:

classDiagram
  class PromptInput~BaseModel~ {
    text: str
    metadata: Optional[dict]
  }
  class QnAPromptInput~BaseModel~ {
    question: PromptInput | dict | str
    answer: PromptInput | dict | str
    context: Optional[List[PromptInput | dict | str]]
  }
  PromptInput --* QnAPromptInput : question, answer, context

Finally, Prompeter defines the method get_prompt_template, that given a path to prompt template structured in yaml or json, returns an instance of the PromptTemplate object

Install Prompeter

Download or clone the repo. Run

pip install -e .

Prompeter usage

from prompeter import get_prompt_template
prompt_template = get_prompt_template('prompts/example_template_instruct_QA_few_shot_RAG.yaml')

user_question = '<a question>'
with open('<path to few shot examples>') as fp:
  response_examples = json.load(fp)
retrieved_context = search_in_context_collection_DEFINE_YOURSELF(
   search_string=user_question
)

prompt = prompt_template.construct_prompt(
  example_texts=[{'question': qna_pair['question'], 'answer': qna_pair['answer']} for qna_pair in response_examples],
  context_texts=[{'text': piece_context['text'], 'metadata': {'url': piece_context['ref_url']}} for piece_context in retrieved_context],
  user_prompt=user_question,
  name='Eliza'  # Metadata according to the template
)

The prompt can then be passed to a LLM e.g. via a call to an API.

Generating the json schema

After prompeter have been installed the json schema for the prompt template model can be exported by:

python scripts/dump_prompt_template_json_schema.py > prompt_template.schema.json