text: It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.Installation and Setupâ€‹Install the Python SDK with pip install predictionguardGet an Prediction Guard access token (as described here) and set it as an environment variable (PREDICTIONGUARD_TOKEN)LLM Wrapperâ€‹There exists a Prediction Guard LLM wrapper, which you can access with from langchain.llms import PredictionGuardYou can provide the name of the Prediction Guard model as an argument when initializing the LLM:pgllm = PredictionGuard(model="MPT-7B-Instruct")You can also provide your access token directly as an argument:pgllm = PredictionGuard(model="MPT-7B-Instruct", token="<your access token>")Finally, you can provide an "output" argument that is used to structure/ control the output of the LLM:pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"})Example usageâ€‹Basic usage of the controlled or guarded LLM wrapper:import osimport predictionguard as pgfrom langchain.llms import PredictionGuardfrom langchain import PromptTemplate, LLMChain# Your Prediction Guard API key. Get one at predictionguard.comos.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"# Define a prompt templatetemplate = """Respond to the following query based on the context.Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! ğŸ�‰ We have officially added TWO new candle subscription box options! ğŸ“¦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! ğŸ‘† BONUS: Save 50%