Prediction guard integrations

Integrate with Prediction guard using LangChain Python.

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.

This integration is maintained in the langchain-predictionguard package.

Installation and setup

• Install the PredictionGuard LangChain partner package:

pip

pip install langchain-predictionguard

• Get a Prediction Guard API key (as described here) and set it as an environment variable (PREDICTIONGUARD_API_KEY)

Prediction guard LangChain integrations

API	Description	Endpoint Docs	Import	Example Usage
Chat	Build Chat Bots	Chat	from langchain_predictionguard import ChatPredictionGuard	ChatPredictionGuard.ipynb
Completions	Generate Text	Completions	from langchain_predictionguard import PredictionGuard	PredictionGuard.ipynb
Text Embedding	Embed String to Vectors	Embeddings	from langchain_predictionguard import PredictionGuardEmbeddings	PredictionGuardEmbeddings.ipynb

Getting started

Chat models

Prediction guard chat

See a usage example

from langchain_predictionguard import ChatPredictionGuard

Usage

# If predictionguard_api_key is not passed, default behavior is to use the `PREDICTIONGUARD_API_KEY` environment variable.
chat = ChatPredictionGuard(model="Hermes-3-Llama-3.1-8B")

chat.invoke("Tell me a joke")

Embedding models

Prediction guard embeddings

See a usage example

from langchain_predictionguard import PredictionGuardEmbeddings

Usage

# If predictionguard_api_key is not passed, default behavior is to use the `PREDICTIONGUARD_API_KEY` environment variable.
embeddings = PredictionGuardEmbeddings(model="bridgetower-large-itm-mlm-itc")

text = "This is an embedding example."
output = embeddings.embed_query(text)

LLMs

Prediction guard LLM

See a usage example

from langchain_predictionguard import PredictionGuard

Usage

# If predictionguard_api_key is not passed, default behavior is to use the `PREDICTIONGUARD_API_KEY` environment variable.
llm = PredictionGuard(model="Hermes-2-Pro-Llama-3-8B")

llm.invoke("Tell me a joke about bears")

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