Prompt Engineering

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$ pip install predictionguard langchain

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import os
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import json
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from langchain.prompts import PromptTemplate, FewShotPromptTemplate
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import numpy as np
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from predictionguard import PredictionGuard
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# Set your Prediction Guard token as an environmental variable.
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os.environ["PREDICTIONGUARD_API_KEY"] = "<api key>"
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client = PredictionGuard()

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template = """### Instruction:
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Read the context below and respond with an answer to the question. If the question cannot be answered based on the context alone or the context does not explicitly say the answer to the question, write "Sorry I had trouble answering this question, based on the information I found."
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### Input:
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Context: {context}
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Question: {question}
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### Response:
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"""
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prompt = PromptTemplate(
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    input_variables=["context", "question"],
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    template=template,
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)
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context = "Domino's gift cards are great for any person and any occasion. There are a number of different options to choose from. Each comes with a personalized card carrier and is delivered via US Mail."
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question = "How are gift cards delivered?"
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myprompt = prompt.format(context=context, question=question)
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print(myprompt)

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# Create a string formatter for sentiment analysis demonstrations.
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demo_formatter_template = """
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Text: {text}
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Sentiment: {sentiment}
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"""
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# Define a prompt template for the demonstrations.
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demo_prompt = PromptTemplate(
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    input_variables=["text", "sentiment"],
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    template=demo_formatter_template,
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)
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# Each row here includes:
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# 1. an example text input (that we want to analyze for sentiment)
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# 2. an example sentiment output (NEU, NEG, POS)
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few_examples = [
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    ['The flight was exceptional.', 'POS'],
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    ['That pilot is adorable.', 'POS'],
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    ['This was an awful seat.', 'NEG'],
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    ['This pilot was brilliant.', 'POS'],
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    ['I saw the aircraft.', 'NEU'],
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    ['That food was exceptional.', 'POS'],
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    ['That was a private aircraft.', 'NEU'],
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    ['This is an unhappy pilot.', 'NEG'],
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    ['The staff is rough.', 'NEG'],
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    ['This staff is Australian.', 'NEU']
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]
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examples = []
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for ex in few_examples:
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  examples.append({
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      "text": ex[0],
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      "sentiment": ex[1]
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  })
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few_shot_prompt = FewShotPromptTemplate(
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    # This is the demonstration data we want to insert into the prompt.
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    examples=examples,
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    example_prompt=demo_prompt,
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    example_separator="",
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    # This is the boilerplate portion of the prompt corresponding to
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    # the prompt task instructions.
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    prefix="Classify the sentiment of the text. Use the label NEU for neutral sentiment, NEG for negative sentiment, and POS for positive sentiment.\n",
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    # The suffix of the prompt is where we will put the output indicator
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    # and define where the "on-the-fly" user input would go.
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    suffix="\nText: {input}\nSentiment:",
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    input_variables=["input"],
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)
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myprompt = few_shot_prompt.format(input="The flight is boring.")
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print(myprompt)

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template1 = """### Instruction:
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Read the context below and respond with an answer to the question. If the question cannot be answered based on the context alone or the context does not explicitly say the answer to the question, write "Sorry I had trouble answering this question, based on the information I found."
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### Input:
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Context: {context}
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Question: {question}
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### Response:
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"""
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prompt1 = PromptTemplate(
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	input_variables=["context", "question"],
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	template=template1,
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)
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template2 = """### Instruction:
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Answer the question below based on the given context. If the answer is unclear, output: "Sorry I had trouble answering this question, based on the information I found."
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### Input:
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Context: {context}
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Question: {question}
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### Response:
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"""
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prompt2 = PromptTemplate(
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	input_variables=["context", "question"],
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	template=template2,
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)
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context = "Domino's gift cards are great for any person and any occasion. There are a number of different options to choose from. Each comes with a personalized card carrier and is delivered via US Mail."
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question = "How are gift cards delivered?"
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completions = client.completions.create(
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    	model="Nous-Hermes-Llama2-13B",
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    	prompt=[
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        	prompt1.format(context=context, question=question),
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        	prompt2.format(context=context, question=question)
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    	],
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    	temperature=0.5
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	)
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for i in [0,1]:
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  print("Answer", str(i+1) + ": ", completions['choices'][i]['text'].strip())

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client.completions.create(model="neural-chat-7b-v3-3",
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    prompt="""### Instruction:
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Respond with a sentiment label for the input text below. Use the label NEU for neutral sentiment, NEG for negative sentiment, and POS for positive sentiment.
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### Input:
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This workshop is spectacular. I love it! So wonderful.
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### Response:
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""",
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    output={
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        "consistency": True
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    }
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)

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template = """### Instruction:
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Read the context below and respond with an answer to the question.
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### Input:
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Context: {context}
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Question: {question}
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### Response:
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"""
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prompt = PromptTemplate(
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	input_variables=["context", "question"],
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	template=template,
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)
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context = "California is a state in the Western United States. With over 38.9 million residents across a total area of approximately 163,696 square miles (423,970 km2), it is the most populous U.S. state, the third-largest U.S. state by area, and the most populated subnational entity in North America. California borders Oregon to the north, Nevada and Arizona to the east, and the Mexican state of Baja California to the south; it has a coastline along the Pacific Ocean to the west. "
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result = client.completions.create(
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    model="Nous-Hermes-Llama2-13B",
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    prompt=prompt.format(
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        context=context,
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        question="What is California?"
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    )
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)
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fact_score = client.factuality.check(
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    reference=context,
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    text=result['choices'][0]['text']
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)
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print("COMPLETION:", result['choices'][0]['text'])
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print("FACT SCORE:", fact_score['checks'][0]['score'])

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result = client.completions.create(
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    model="Nous-Hermes-Llama2-13B",
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    prompt=prompt.format(
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        context=context,
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        question="Make up something completely fictitious about California. Contradict a fact in the given context."
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    )
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)
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fact_score = client.factuality.check(
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    reference=context,
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    text=result['choices'][0]['text']
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)
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print("COMPLETION:", result['choices'][0]['text'])
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print("FACT SCORE:", fact_score['checks'][0]['score'])

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result = client.completions.create(
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    model="Nous-Hermes-Llama2-13B",
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    prompt=prompt.format(
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        context=context,
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        question="Respond with a really offensive tweet about California and use many curse words. Make it really bad and offensive. Really bad."
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    ),
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    output={
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        "toxicity": True
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    }
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)
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print(json.dumps(
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    result,
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    sort_keys=True,
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    indent=4,
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    separators=(',', ': ')
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))

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ValueError: Could not make prediction. failed toxicity check