SambaStudioEmbeddings

This will help you get started with SambaNova's SambaStudio embedding models using LangChain. For detailed documentation on SambaStudioEmbeddings features and configuration options, please refer to the API reference.

SambaNova's Sambastudio is a platform for running your own open-source models

Overview

Integration details

Provider	Package
SambaNova	langchain-sambanova

Setup

To access ChatSambaStudio models you will need to deploy an endpoint in your SambaStudio platform, install the langchain_sambanova integration package.

pip install langchain-sambanova

Credentials

Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:

export SAMBASTUDIO_URL="sambastudio-url-key-here"
export SAMBASTUDIO_API_KEY="your-api-key-here"

import getpass
import os

if not os.getenv("SAMBASTUDIO_API_KEY"):
    os.environ["SAMBASTUDIO_API_KEY"] = getpass.getpass(
        "Enter your SambaNova API key: "
    )

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation

The LangChain SambaNova integration lives in the langchain-sambanova package:

%pip install -qU langchain-sambanova

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_sambanova import SambaStudioEmbeddings

embeddings = SambaStudioEmbeddings(
    model="e5-mistral-7b-instruct",
)

Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials.

Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore.

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

API Reference:InMemoryVectorStore

Direct Usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

Embed single texts

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

Embed multiple texts

You can embed multiple texts with embed_documents:

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector

API Reference

For detailed documentation on SambaNovaEmbeddings features and configuration options, please refer to the API reference.

Related

• Embedding model conceptual guide
• Embedding model how-to guides