Created
March 21, 2023 19:51
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An example LLM workflow using mr_graph
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| import openai | |
| import asyncio | |
| import sys | |
| import uvloop | |
| from mr_graph.graph import Graph | |
| # mr_graph >= 0.2.5 | |
| default_model_name = 'gpt-3.5-turbo' | |
| async def get_answer(user_question: str, temp=0): | |
| """get an answer from openai | |
| Args: | |
| user_question (str): question from the user | |
| Returns | |
| ------- | |
| completion : str | |
| LLM completion | |
| """ | |
| messages=[{"role": "user", "content": user_question}] | |
| completion = await openai.ChatCompletion.acreate(model=default_model_name, messages=messages, temperature=temp) | |
| return completion['choices'][0]['message']['content'] | |
| def format_answer(user_question:str, completion:str): | |
| """parse the answer | |
| Args: | |
| user_question (str): user question sent to the LLM. might be needed to determine formatting. | |
| completion (str): LLM completion. | |
| Returns | |
| ------- | |
| answer : dict[str, str] | |
| LLM completion | |
| """ | |
| answer = completion.strip(' \n').removeprefix('As an AI language model, I cannot provide a specific answer to this question').strip() | |
| return answer | |
| async def get_structured_answer(user_question: str): | |
| """get answer + structure it | |
| Args: | |
| user_question (str): user question sent to the LLM | |
| Returns | |
| ------- | |
| answer : dict[str, str] | |
| LLM completion | |
| """ | |
| llm = Graph(nodes=[get_answer, format_answer]) | |
| q = llm.input(name='user_question') | |
| o1 = llm.get_answer(q) | |
| llm.outputs = llm.format_answer(q, o1) | |
| a = await llm(user_question=user_question) | |
| return a.answer | |
| async def summarize_answers(answers: list[str], temp=0): | |
| """summarize answers | |
| Args: | |
| answers (str): answers sent to the LLM for summary | |
| Returns | |
| ------- | |
| summary : dict[str, str] | |
| LLM completion | |
| """ | |
| nl = "\n" | |
| prompt = f""" | |
| summarize the following text. | |
| {nl.join(answers)} | |
| """ | |
| messages=[{"role": "user", "content": prompt}] | |
| completion = await openai.ChatCompletion.acreate(model=default_model_name, messages=messages, temperature=temp) | |
| return completion['choices'][0]['message']['content'] | |
| async def get_summarized_q_and_a(questions: list[str]): | |
| """ask a bunch of questions, get answers, summarize them. | |
| Args: | |
| questions (list[str]): user questions sent to the LLM | |
| Returns | |
| ------- | |
| summary : dict[str, str] | |
| LLM completion | |
| """ | |
| llm = Graph(nodes=[get_structured_answer, summarize_answers]) | |
| answers = llm.aggregator(name="answers") | |
| for question in questions: | |
| sa = llm.get_structured_answer(user_question=question) | |
| answers += sa.answer | |
| llm.outputs = llm.summarize_answers(answers=answers) | |
| v = await llm(answers) | |
| return v.summary | |
| async def main(): | |
| questions = [ | |
| 'who is abraham lincoln?', | |
| 'what did abraham lincoln do?', | |
| 'when was abraham lincoln alive?', | |
| 'where did abraham lincoln live?', | |
| 'why is abraham lincoln famous?' | |
| ] | |
| r = await get_summarized_q_and_a(questions) | |
| print(r) | |
| if __name__ == '__main__': | |
| if sys.version_info >= (3, 11): | |
| with asyncio.Runner(loop_factory=uvloop.new_event_loop) as runner: | |
| runner.run(main()) | |
| else: | |
| uvloop.install() | |
| asyncio.run(main()) |
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Returns:
Abraham Lincoln was the 16th President of the United States, serving from 1861 until his assassination in 1865. He is known for leading the country through its Civil War, preserving the Union, and ending slavery. He issued the Emancipation Proclamation, delivered the Gettysburg Address, signed the Homestead Act, established the first income tax and Department of Agriculture, and promoted the idea of a transcontinental railroad. He is widely regarded as one of the greatest American presidents and was assassinated by John Wilkes Booth in 1865.