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
| 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 Vectores | 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")