> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-lginte-1765488813-6406a61.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Google Generative AI Embeddings (AI Studio & Gemini API)
Connect to Google's generative AI embeddings service using the `GoogleGenerativeAIEmbeddings` class, found in the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) package.
This will help you get started with Google's Generative AI embedding models (like Gemini) using LangChain. For detailed documentation on `GoogleGenerativeAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_genai/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html).
## Overview
### Integration details
## Setup
To access Google Generative AI 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
To use Google Generative AI models, you must have an API key. You can create one in Google AI Studio. See the [Google documentation](https://ai.google.dev/gemini-api/docs/api-key) for instructions.
Once you have a key, set it as an environment variable `GOOGLE_API_KEY`:
```python theme={null}
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](https://docs.langchain.com/langsmith/home) API key:
```python theme={null}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```
## Installation
```python theme={null}
pip install -qU langchain-google-genai
```
## Usage
```python theme={null}
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001")
vector = embeddings.embed_query("hello, world!")
vector[:5]
```
```output theme={null}
[-0.024917153641581535,
0.012005362659692764,
-0.003886754624545574,
-0.05774897709488869,
0.0020742062479257584]
```
## Batch
You can also embed multiple strings at once for a processing speedup:
```python theme={null}
vectors = embeddings.embed_documents(
[
"Today is Monday",
"Today is Tuesday",
"Today is April Fools day",
]
)
len(vectors), len(vectors[0])
```
```output theme={null}
(3, 3072)
```
## 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
```
```output theme={null}
'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.
```python theme={null}
pip install -qU matplotlib scikit-learn
```
```python theme={null}
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from sklearn.metrics.pairwise import cosine_similarity
query_embeddings = GoogleGenerativeAIEmbeddings(
model="models/gemini-embedding-001", task_type="RETRIEVAL_QUERY"
)
doc_embeddings = GoogleGenerativeAIEmbeddings(
model="models/gemini-embedding-001", 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 is 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("---")
```
```output theme={null}
Document 1
Cosine similarity with query: 0.7892893360164779
---
Document 2
Cosine similarity with query: 0.5438283285204146
---
```
***
## API reference
For detailed documentation on `GoogleGenerativeAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_genai/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html).
## Additional Configuration
You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:
* `client_options`: [Client Options](https://googleapis.dev/python/google-api-core/latest/client_options.html#module-google.api_core.client_options) to pass to the Google API Client, such as a custom `client_options["api_endpoint"]`
* `transport`: The transport method to use, such as `rest`, `grpc`, or `grpc_asyncio`.
***
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