Using Prediction Guard proxies in LangChain

LangChain (opens in a new tab) is one of the most popular AI projects, and for good reason! LangChain helps you "Build applications with LLMs through composability." LangChain doesn't host LLMs or provide a standardized API for controlling LLMs, which is addressed by Prediction Guard. Therefore, combining the two (Prediction Guard + LangChain) gives you a framework for developing controlled and compliant applications powered by language models.

Installation and Setup

Install the Python SDK with pip install predictionguard
Get an Prediction Guard access token (as described here (opens in a new tab)) and set it as an environment variable (PREDICTIONGUARD_TOKEN)

LLM Wrapper

There exists a Prediction Guard LLM wrapper, which you can access with

from langchain.llms import PredictionGuard

You 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 usage

Basic usage of the controlled or guarded LLM wrapper:

import os
 
import predictionguard as pg
from langchain.llms import PredictionGuard
from langchain import PromptTemplate, LLMChain
 
# Your Prediction Guard API key. Get one at predictionguard.com
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
 
# 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!)
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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 wrapper:

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
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
 
pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
 
template = """Question: {question}
 
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)
 
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
 
llm_chain.predict(question=question)

Guarded Chatbot Typed Document Q&A with Chroma