# Define a prompt template
template = """Respond to the following query based on the context.
Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦
Exclusive Candle Box - $80 
Monthly Candle Box - $45 (NEW!)
Scent of The Month Box - $28 (NEW!)
Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉
Query: {query}
Result: """
prompt = PromptTemplate(template=template, input_variables=["query"])
# With "guarding" or controlling the output of the LLM. See the 
# Prediction Guard docs (https://docs.predictionguard.com) to learn how to 
# control the output with integer, float, boolean, JSON, and other types and
# structures.
pgllm = PredictionGuard(model="MPT-7B-Instruct", 
                        output={
                                "type": "categorical",
                                "categories": [
                                    "product announcement", 
                                    "apology", 
                                    "relational"
                                    ]
                                })
pgllm(prompt.format(query="What kind of post is this?"))
Basic LLM Chaining with the Prediction Guard:#
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import PredictionGuard
# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows
# you to access all the latest open access models (see https://docs.predictionguard.com)
os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"
# Your Prediction Guard API key. Get one at predictionguard.com
rtdocs_stable/api.python.langchain.com/en/stable/integrations/predictionguard.html