.ipynb
.pdf
Prediction Guard
 Contents 
Prediction Guard
Control the output structure/ type of LLMs
Chaining
Prediction Guard#
Prediction Guard gives a quick and easy access to state-of-the-art open and closed access LLMs, without  needing to spend days and weeks figuring out all of the implementation details, managing a bunch of different API specs, and setting up the infrastructure for model deployments.
! pip install predictionguard langchain
import os
import predictionguard as pg
from langchain.llms import PredictionGuard
from langchain import PromptTemplate, LLMChain
# 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
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
pgllm("Tell me a joke")
Control the output structure/ type of LLMs#
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"])
rtdocs_stable/api.python.langchain.com/en/stable/modules/models/llms/integrations/predictionguard.html