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.
# 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")
# 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)
# 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")