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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.

This integration is maintained in the langchain-predictionguard package.

Installation and Setup​

  • Install the PredictionGuard Langchain partner package:
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​

APIDescriptionEndpoint DocsImportExample Usage
ChatBuild Chat BotsChatfrom langchain_predictionguard import ChatPredictionGuardChatPredictionGuard.ipynb
CompletionsGenerate TextCompletionsfrom langchain_predictionguard import PredictionGuardPredictionGuard.ipynb
Text EmbeddingEmbed String to VectoresEmbeddingsfrom langchain_predictionguard import PredictionGuardEmbeddingsPredictionGuardEmbeddings.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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