## 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[​](#installation-and-setup "Direct link to Installation and Setup")

*   Install the Python SDK with `pip install predictionguard`
*   Get a Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as an environment variable (`PREDICTIONGUARD_TOKEN`)

## LLM Wrapper[​](#llm-wrapper "Direct link to LLM Wrapper")

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

```
from langchain_community.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[​](#example-usage "Direct link to Example usage")

Basic usage of the controlled or guarded LLM wrapper:

```
import osimport predictionguard as pgfrom langchain_community.llms import PredictionGuardfrom langchain_core.prompts import PromptTemplatefrom langchain.chains import 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! 🎉 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 ALL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉Query: {query}Result: """prompt = PromptTemplate.from_template(template)# 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 osfrom langchain_core.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain_community.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.comos.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"pgllm = PredictionGuard(model="OpenAI-gpt-3.5-turbo-instruct")template = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate.from_template(template)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)
```