> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# SambanovaEmbeddings integration

> Integrate with the SambanovaEmbeddings embedding model using LangChain Python.

This will help you get started with SambaNova embedding models using LangChain. For detailed documentation on `SambaNovaEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).

**[SambaNova](https://sambanova.ai/)'s** [SambaCloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models

## Overview

### Integration details

|                          Provider                          |                                 Package                                |
| :--------------------------------------------------------: | :--------------------------------------------------------------------: |
| [SambaNova](/oss/python/integrations/providers/sambanova/) | [`langchain-sambanova`](/oss/python/integrations/providers/sambanova/) |

## Setup

To access `SambaNovaEmbeddings` models you will need to create a [SambaCloud](http://cloud.sambanova.ai?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup) account, get an API key, install the `langchain_sambanova` integration package.

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install langchain-sambanova
```

### Credentials

Get an API Key from [cloud.sambanova.ai](http://cloud.sambanova.ai/apis?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup).Once you've done this set the SAMBANOVA\_API\_KEY environment variable:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import getpass
import os

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

To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-sambanova
```

## Instantiation

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_sambanova import SambaNovaEmbeddings

embeddings = SambaNovaEmbeddings(
    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](/oss/python/langchain/rag).

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`.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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
```

## 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`:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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`:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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 [SambaNova Developer Guide](https://docs.sambanova.ai/cloud/docs/get-started/overview).

***

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