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# Prediction Guard

This page covers how to use the Prediction Guard ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.

## Installation and Setup

- Install the Python SDK with `pip install predictionguard`

- Get an Prediction Guard access token (as described here) 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 your Prediction Guard “proxy” as an argument when initializing the LLM:

    pgllm = PredictionGuard(name="your-text-gen-proxy")

Alternatively, you can use Prediction Guard’s default proxy for SOTA LLMs:

    pgllm = PredictionGuard(name="default-text-gen")

You can also provide your access token directly as an argument:

    pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")

## Example usage

Basic usage of the LLM wrapper:

    from langchain.llms import PredictionGuard

    pgllm = PredictionGuard(name="default-text-gen")
    pgllm("Tell me a joke")

Basic LLM Chaining with the Prediction Guard wrapper:

    from langchain import PromptTemplate, LLMChain
    from langchain.llms import PredictionGuard

    template = """Question: {question}

    Answer: Let's think step by step."""
    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)

    question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

    llm_chain.predict(question=question)