prompt_with_sources.py

""""This example demonstrates:
      prompt_with_sources - powerful abstraction to integrate various knowledge sources into a prompt
"""


import os
from llmware.prompts import Prompt
from llmware.setup import Setup
from llmware.models import PromptCatalog
from llmware.library import Library
from llmware.retrieval import Query
from llmware.configs import LLMWareConfig


def prompt_with_sources(model_name, library_name):

    print(f"Example - prompt_with_sources - attaching several different knowledge sources to a Prompt directly.")

    library = Library().create_new_library(library_name)

    sample_files_path = Setup().load_sample_files(over_write=False)

    ingestion_folder_path = os.path.join(sample_files_path, "Agreements")
    parsing_output = library.add_files(ingestion_folder_path)

    local_file = "Apollo EXECUTIVE EMPLOYMENT AGREEMENT.pdf"

    prompter = Prompt().load_model(model_name)

    #   Use #1 - add_source_document - parses the document in memory, filters the text chunks by query, and then
    #   creates a 'source' context to be passed to the model

    print(f"\n#1 - add a source document file directly into a prompt")

    sources2 = prompter.add_source_document(ingestion_folder_path, local_file, query="base salary")

    prompt = "What is the base salary amount?"
    prompt_instruction="default_with_context"
    response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
    print (f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
    prompter.clear_source_materials()

    #   Use #2 - add_source_wikipedia - gets a source document from Wikipedia on Barack Obama,
    #   and creates source context

    print(f"\n#2 - add a wikipedia article by api call by topic into a prompt")

    prompt = "Was Barack Obama the Prime Minister of Canada?"
    wiki_topic = "Barack Obama"
    prompt_instruction = "yes_no"
    sources3 = prompter.add_source_wikipedia(wiki_topic, article_count=1)
    response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
    print (f"- Context: {wiki_topic}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
    prompter.clear_source_materials()

    #   Use #3 - add_source_query_results - produces the same results as the first case, but runs a query on the library
    #   and then adds the query results to the prompt which are concatenated into a source context

    print(f"\n#3 - run a query on a library and then pass the query results into a prompt")

    query_results = Query(library).text_query("base salary")
    prompt = "What is the annual rate of the base salary?"
    sources4 = prompter.add_source_query_results(query_results)
    response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
    print(f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
    prompter.clear_source_materials()

    return 0


if __name__ == "__main__":

    LLMWareConfig().set_active_db("sqlite")

    #   this model is a placeholder which will run on local laptop - swap out for higher accuracy, larger models
    model_name = "llmware/bling-1b-0.1"

    library_name = "lib_prompt_with_sources_1"

    prompt_with_sources(model_name,library_name)