langchain/docs/ecosystem/predictionguard.md

# 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](https://docs.predictionguard.com/)) and set it as an environment variable (`PREDICTIONGUARD_TOKEN`)

## LLM Wrapper

There exists a Prediction Guard LLM wrapper, which you can access with 
```python
from langchain.llms import PredictionGuard
```

You can provide the name of your Prediction Guard "proxy" as an argument when initializing the LLM:
```python
pgllm = PredictionGuard(name="your-text-gen-proxy")
```

Alternatively, you can use Prediction Guard's default proxy for SOTA LLMs:
```python
pgllm = PredictionGuard(name="default-text-gen")
```

You can also provide your access token directly as an argument:
```python
pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
```

## Example usage

Basic usage of the LLM wrapper:
```python
from langchain.llms import PredictionGuard

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

Basic LLM Chaining with the Prediction Guard wrapper:
```python
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)
```