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Embeddings | Gemini API | Google AI for Developers Skip to main content / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Introducing Batch Mode, with higher rate limits and a 50% token discount. Learn more Home Gemini API Models Send feedback Embeddings Note: Introducing our first Gemini embedding model, available now to developers as gemini-embedding-exp-03-07 in the API. The Gemini API supports several embedding models that generate embeddings for words, phrases, code, and sentences. The resulting embeddings can then be used for tasks such as semantic search, text classification, and clustering, among many others. What are embeddings? Embeddings are numerical representations of text (or other media formats) that capture relationships between inputs. Text embeddings work by converting text into arrays of floating point numbers, called vectors . These vectors are designed to capture the meaning of the text. The length of the embedding array is called the vector's dimensionality . A passage of text might be represented by a vector containing hundreds of dimensions. Embeddings capture semantic meaning and context, which results in text with similar meanings having "closer" embeddings. For example, the sentence "I took my dog to the vet" and "I took my cat to the vet" would have embeddings that are close to each other in the vector space. You can use embeddings to compare different texts and understand how they relate. For example, if the embeddings of the text "cat" and "dog" are close together you can infer that these words are similar in meaning, context, or both. This enables a variety of common AI use cases . Before you begin Before calling the Gemini API, ensure you have your SDK of choice installed, and a Gemini API key configured and ready to use. Generate embeddings Use the embedContent method to generate text embeddings: Python from google import genai client = genai . Client () result = client . models . embed_content ( model = "gemini-embedding-exp-03-07" , contents = "What is the meaning of life?" ) print ( result . embeddings ) JavaScript import { GoogleGenAI } from "@google/genai" ; async function main () { const ai = new GoogleGenAI ({}); const response = await ai . models . embedContent ({ model : 'gemini-embedding-exp-03-07' , contents : 'What is the meaning of life?' , }); console . log ( response . embeddings ); } main (); Go package main import ( "context" "encoding/json" "fmt" "log" "google.golang.org/genai" ) func main () { ctx := context . Background () client , err := genai . NewClient ( ctx , nil ) if err != nil { log . Fatal ( err ) } contents := [] * genai . Content { genai . NewContentFromText ( "What is the meaning of life?" , genai . RoleUser ), } result , err := client . Models . EmbedContent ( ctx , "gemini-embedding-exp-03-07" , contents , nil , ) if err != nil { log . Fatal ( err ) } embeddings , err := json . MarshalIndent ( result . Embeddings , "" , " " ) if err != nil { log . Fatal ( err ) } fmt . Println ( string ( embeddings )) } REST curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-exp-03-07:embedContent" \ -H "x-goog-api-key: $GEMINI_API_KEY " \ -H 'Content-Type: application/json' \ -d '{"model": "models/gemini-embedding-exp-03-07", "content": { "parts":[{ "text": "What is the meaning of life?"}]} }' You can also generate embeddings for multiple chunks at once by passing them in as a list of strings. Task types When building Retrieval Augmented Generation (RAG) systems, a common design is to use text embeddings to perform a similarity search. In some cases this can lead to degraded quality, because questions and their answers are not semantically similar. For example, a question like "Why is the sky blue?" and its answer "The scattering of sunlight causes the blue color," have distinctly different meanings as statements, which means that a RAG system won't automatically recognize their relation. Task types enable you to generate optimized embeddings for specific tasks, saving you time and cost and improving performance. Python from google import genai from google.genai import types client = genai . Client () result = client . models . embed_content ( model = "gemini-embedding-exp-03-07" , contents = "What is the meaning of life?" , config = types . EmbedContentConfig ( task_type = "SEMANTIC_SIMILARITY" ) ) print ( result . embeddings ) JavaScript import { GoogleGenAI } from "@google/genai" ; async function main () { const ai = new GoogleGenAI ({}); const response = await ai . models . embedContent ({ model : 'gemini-embedding-exp-03-07' , contents : 'What is the meaning of life?' , config : { taskType : "SEMANTIC_SIMILARITY" , } }); console . log ( response . embeddings ); } main (); REST curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-exp-03-07:embedContent" \ -H "x-goog-api-key: $GEMINI_API_KEY " \ -H 'Content-Type: application/json' \ -d '{"model": "models/gemini-embedding-exp-03-07", "content": { "parts":[{ "text": "What is the meaning of life?"}]}, "taskType": "SEMANTIC_SIMILARITY" }' Supported task types Task type Description 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 . Use cases Text embeddings are used in a variety of common AI use cases, such as: Information retrieval: You can use embeddings to retrieve semantically similar text given a piece of input text. Document search tutorial task Clustering: Comparing groups of embeddings can help identify hidden trends. Embedding clustering tutorial bubble_chart Vector database: As you take different embedding use cases to production, it is common to store embeddings in a vector database. Vector database tutorial bolt Classification: You can train a model using embeddings to classify documents into categories. Classification tutorial token Embedding models The Gemini API offers three models that generate text embeddings: gemini-embedding-exp-03-07 text-embedding-004 embedding-001 What's next Check out the embeddings quickstart notebook . Send feedback Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates. Last updated 2025-07-08 UTC. |
