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

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

Overview​

Integration details​

Setup​

To access SambaStudio models you will need todeploy an endpoint in your SambaStudio platform, install thelangchain_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

ifnot os.getenv("SAMBASTUDIO_URL"):
    os.environ["SAMBASTUDIO_URL"]= getpass.getpass(
"Enter your SambaStudio endpoint URL: "
)

ifnot os.getenv("SAMBASTUDIO_API_KEY"):
    os.environ["SAMBASTUDIO_API_KEY"]= getpass.getpass(
"Enter your SambaStudio API key: "
)

If you want to get automated tracing of your model calls you can also set yourLangSmith 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 thelangchain-sambanova package:

%pip install-qU langchain-sambanova

Instantiation​

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

from langchain_sambanovaimport 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 ourRAG tutorials.

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

# Create a vector store with a sample text
from langchain_core.vectorstoresimport 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

Direct Usage​

Under the hood, the vectorstore and retriever implementations are callingembeddings.embed_documents(...) andembeddings.embed_query(...) to create embeddings for the text(s) used infrom_texts and retrievalinvoke 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 withembed_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 withembed_documents:

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

API Reference​

For detailed documentation onSambaStudio features and configuration options, please refer to theAPI reference.

Related​

• Embedding modelconceptual guide
• Embedding modelhow-to guides