> ## 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.

# GoogleGenerativeAIEmbeddings integration

> Integrate with Google Gemini API embedding models using LangChain Python.

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

## Overview

<Note>
  `gemini-embedding-2-preview` natively supports text, image, video, audio, and PDF inputs via the Google GenAI SDK's `embed_content()` API. However, the LangChain `Embeddings` interface (`embed_query` / `embed_documents`) currently only accepts text inputs. Multimodal embedding support in LangChain is planned for a future release. For multimodal use cases today, use the [Google GenAI SDK](https://ai.google.dev/gemini-api/docs) directly.
</Note>

### Integration details

## Setup

To access Google Gemini embedding models you'll need to create a Google Cloud project, enable the Generative Language API, get an API key, and install the `langchain-google-genai` integration package.

### Credentials

Head to [Google AI Studio](https://aistudio.google.com/apikey) to sign up and generate an API key. See the [Gemini API keys documentation](https://ai.google.dev/gemini-api/docs/api-key) for more details. Once you've done this set the `GOOGLE_API_KEY` environment variable:

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

if not os.getenv("GOOGLE_API_KEY"):
    os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google 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 Google Generative AI integration lives in the `langchain-google-genai` package:

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

## Instantiation

Now we can instantiate our model object and generate embeddings:

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

embeddings = GoogleGenerativeAIEmbeddings(model="gemini-embedding-2-preview")
vector = embeddings.embed_query("hello, world!")
vector[:5]
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[-0.024917153641581535,
 0.012005362659692764,
 -0.003886754624545574,
 -0.05774897709488869,
 0.0020742062479257584]
```

### Reduced dimensionality

`gemini-embedding-2-preview` supports flexible output dimensions via Matryoshka Representation Learning (MRL). You can reduce dimensionality to optimize storage and latency:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-2-preview",
    output_dimensionality=768,  # Suggested: 768, 1536, or 3072 (default)
)
vector = embeddings.embed_query("hello, world!")
len(vector)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
768
```

## Batch

You can also embed multiple strings at once for a processing speedup:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
vectors = embeddings.embed_documents(
    [
        "Today is Monday",
        "Today is Tuesday",
        "Today is April Fools day",
    ]
)
len(vectors), len(vectors[0])
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
(3, 768)
```

## 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
```

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

## Task type

`GoogleGenerativeAIEmbeddings` optionally support a `task_type`, which currently must be one of:

* `SEMANTIC_SIMILARITY`: Used to generate embeddings that are optimized to assess text similarity.
* `CLASSIFICATION`: Used to generate embeddings that are optimized to classify texts according to preset labels.
* `CLUSTERING`: Used to generate embeddings that are optimized to cluster texts based on their similarities.
* `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`, `QUESTION_ANSWERING`, and `FACT_VERIFICATION`: Used to generate embeddings that are optimized for document search or information retrieval.
* `CODE_RETRIEVAL_QUERY`: Used to retrieve a code block based on a natural language query, such as sort an array or reverse a linked list. Embeddings of the code blocks are computed using `RETRIEVAL_DOCUMENT`.

By default, we use `RETRIEVAL_DOCUMENT` in the `embed_documents` method and `RETRIEVAL_QUERY` in the `embed_query` method. If you provide a task type, we will use that for all methods.

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU matplotlib scikit-learn
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from sklearn.metrics.pairwise import cosine_similarity

query_embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-2-preview", task_type="RETRIEVAL_QUERY"
)
doc_embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-2-preview", task_type="RETRIEVAL_DOCUMENT"
)

q_embed = query_embeddings.embed_query("What is the capital of France?")
d_embed = doc_embeddings.embed_documents(
    ["The capital of France is Paris.", "Philipp likes to eat pizza."]
)

for i, d in enumerate(d_embed):
    print(f"Document {i + 1}:")
    print(f"Cosine similarity with query: {cosine_similarity([q_embed], [d])[0][0]}")
    print("---")
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Document 1:
Cosine similarity with query: 0.7892893360164779
---
Document 2:
Cosine similarity with query: 0.5438283285204146
---
```

## Additional configuration

You can pass the following parameters to `GoogleGenerativeAIEmbeddings` to customize the SDK's behavior:

* `base_url`: Custom base URL for the API client (e.g., a custom endpoint)
* `output_dimensionality`: Reduce the dimensionality of returned embeddings (e.g., `output_dimensionality=256`)
* `request_options`: Request options dict (e.g., `{"timeout": 10}`)
* `additional_headers`: Additional HTTP headers to include in API requests
* `client_args`: Additional arguments to pass to the underlying HTTP client

***

## API reference

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

***

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