SambaNova
SambaNova's Sambastudio is a platform for running your own open-source models
This example goes over how to use LangChain to interact with SambaNova embedding models
SambaStudio
SambaStudio allows you to train, run batch inference jobs, and deploy online inference endpoints to run open source models that you fine tuned yourself.
A SambaStudio environment is required to deploy a model. Get more information at sambanova.ai/products/enterprise-ai-platform-sambanova-suite
Register your environment variables:
import os
sambastudio_base_url = "<Your SambaStudio environment URL>"
sambastudio_project_id = "<Your SambaStudio project id>"
sambastudio_endpoint_id = "<Your SambaStudio endpoint id>"
sambastudio_api_key = "<Your SambaStudio endpoint API key>"
# Set the environment variables
os.environ["SAMBASTUDIO_EMBEDDINGS_BASE_URL"] = sambastudio_base_url
os.environ["SAMBASTUDIO_EMBEDDINGS_PROJECT_ID"] = sambastudio_project_id
os.environ["SAMBASTUDIO_EMBEDDINGS_ENDPOINT_ID"] = sambastudio_endpoint_id
os.environ["SAMBASTUDIO_EMBEDDINGS_API_KEY"] = sambastudio_api_key
Call SambaStudio hosted embeddings directly from LangChain!
from langchain_community.embeddings.sambanova import SambaStudioEmbeddings
embeddings = SambaStudioEmbeddings()
text = "Hello, this is a test"
result = embeddings.embed_query(text)
print(result)
texts = ["Hello, this is a test", "Hello, this is another test"]
results = embeddings.embed_documents(texts)
print(results)