> ## 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.

# BasetenEmbeddings integration

> Integrate with the BasetenEmbeddings embedding model using LangChain Python.

This will help you get started with Baseten embedding models using LangChain.

## Overview

Baseten provides inference designed for production applications. Built on the Baseten Inference Stack, these APIs deliver enterprise-grade performance and reliability for leading open-source or custom models: [https://www.baseten.co/library/](https://www.baseten.co/library/).

### Integration details

<ItemTable category="embeddings" item="Baseten" />

## Setup

To access Baseten embedding models you'll need to create a Baseten account, get an API key, and install the `langchain-baseten` integration package.

**Baseten embeddings are only available as dedicated models.** You must deploy an embedding model from the [Baseten model library](https://www.baseten.co/library/tag/embedding) before using this integration.

The embeddings functionality uses Baseten's [Performance Client](https://www.baseten.co/blog/introducing-baseten-embeddings-inference-bei/) for optimized performance, which is automatically included as a dependency.

### Credentials

Head to [https://app.baseten.co](https://app.baseten.co) to sign up to Baseten and generate an API key. Once you've done this set the BASETEN\_API\_KEY environment variable:

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

if not os.getenv("BASETEN_API_KEY"):
    os.environ["BASETEN_API_KEY"] = getpass.getpass("Enter your Baseten 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 Baseten integration lives in the `langchain-baseten` package:

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

## Instantiation

Now we can instantiate our embeddings object using your deployed model's URL:

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

embeddings = BasetenEmbeddings(
    model_url="https://model-<id>.api.baseten.co/environments/production/sync",  # Your model URL
    api_key="your-api-key",  # Or set BASETEN_API_KEY env var
)
```

## 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/deepagents/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
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
'LangChain is the framework for building context-aware reasoning applications'
```

## 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
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[0.013201533816754818, 0.02222288027405739, -0.036066457629203796, 0.027374643832445145, -0.01692997
```

### 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
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[0.013201533816754818, 0.02222288027405739, -0.036066457629203796, 0.027374643832445145, -0.01692997
[0.018247194588184357, 0.007369577884674072, -0.005529594141989946, 0.022589316591620445, -0.0699259
```

***

***

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