> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# CohereEmbeddings integration

> Integrate with the CohereEmbeddings embedding model using LangChain Python.

This will help you get started with Cohere embedding models using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/python/langchain-cohere/embeddings/CohereEmbeddings).

## Overview

### Integration details

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

## Setup

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

### Credentials

Head to [cohere.com](https://cohere.com) to sign up to Cohere and generate an API key. Once you’ve done this set the COHERE\_API\_KEY environment variable:

```python theme={null}
import getpass
import os

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

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

```python theme={null}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain Cohere integration lives in the `langchain-cohere` package:

```python theme={null}
pip install -qU langchain-cohere
```

## Instantiation

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

```python theme={null}
from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(
    model="embed-english-v3.0",
)
```

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

```text theme={null}
[-0.022979736, -0.030212402, -0.08886719, -0.08569336, 0.007030487, -0.0010671616, -0.033813477, 0.0
```

### Embed multiple texts

You can embed multiple texts with `embed_documents`:

```python theme={null}
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={null}
[-0.028869629, -0.030410767, -0.099121094, -0.07116699, -0.012748718, -0.0059432983, -0.04360962, 0.
[-0.047332764, -0.049957275, -0.07458496, -0.034332275, -0.057922363, -0.0112838745, -0.06994629, 0.
```

***

## API reference

For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/python/langchain-cohere/embeddings/CohereEmbeddings).

***

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