嵌入

from google import genai  client = genai.Client()  result = client.models.embed_content(         model="gemini-embedding-001",         contents="What is the meaning of life?")  print(result.embeddings) 

import { GoogleGenAI } from "@google/genai";  async function main() {      const ai = new GoogleGenAI({});      const response = await ai.models.embedContent({         model: 'gemini-embedding-001',         contents: 'What is the meaning of life?',     });      console.log(response.embeddings); }  main(); 

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-001",         contents,         nil,     )     if err != nil {         log.Fatal(err)     }      embeddings, err := json.MarshalIndent(result.Embeddings, "", "  ")     if err != nil {         log.Fatal(err)     }     fmt.Println(string(embeddings)) } 

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -d '{"model": "models/gemini-embedding-001",      "content": {"parts":[{"text": "What is the meaning of life?"}]}     }' 

from google import genai  client = genai.Client()  result = client.models.embed_content(         model="gemini-embedding-001",         contents= [             "What is the meaning of life?",             "What is the purpose of existence?",             "How do I bake a cake?"         ])  for embedding in result.embeddings:     print(embedding) 

import { GoogleGenAI } from "@google/genai";  async function main() {      const ai = new GoogleGenAI({});      const response = await ai.models.embedContent({         model: 'gemini-embedding-001',         contents: [             'What is the meaning of life?',             'What is the purpose of existence?',             'How do I bake a cake?'         ],     });      console.log(response.embeddings); }  main(); 

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.NewContentFromText("How does photosynthesis work?"),         genai.NewContentFromText("Tell me about the history of the internet."),     }     result, err := client.Models.EmbedContent(ctx,         "gemini-embedding-001",         contents,         nil,     )     if err != nil {         log.Fatal(err)     }      embeddings, err := json.MarshalIndent(result.Embeddings, "", "  ")     if err != nil {         log.Fatal(err)     }     fmt.Println(string(embeddings)) } 

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -d '{"model": "models/gemini-embedding-001",      "content": [         {"parts": [{"text": "What is the meaning of life?"}]},         {"parts": [{"text": "What is the purpose of existence?"}]},         {"parts": [{"text": "How do I bake a cake?"}]}         ]     }' 

from google import genai from google.genai import types import numpy as np from sklearn.metrics.pairwise import cosine_similarity  client = genai.Client()  texts = [     "What is the meaning of life?",     "What is the purpose of existence?",     "How do I bake a cake?"]  result = [     np.array(e.values) for e in client.models.embed_content(         model="gemini-embedding-001",         contents=texts,         config=types.EmbedContentConfig(task_type="SEMANTIC_SIMILARITY")).embeddings ]  # Calculate cosine similarity. Higher scores = greater semantic similarity.  embeddings_matrix = np.array(result) similarity_matrix = cosine_similarity(embeddings_matrix)  for i, text1 in enumerate(texts):     for j in range(i + 1, len(texts)):         text2 = texts[j]         similarity = similarity_matrix[i, j]         print(f"Similarity between '{text1}' and '{text2}': {similarity:.4f}") 

import { GoogleGenAI } from "@google/genai"; import * as cosineSimilarity from "compute-cosine-similarity";  async function main() {     const ai = new GoogleGenAI({});      const texts = [         "What is the meaning of life?",         "What is the purpose of existence?",         "How do I bake a cake?",     ];      const response = await ai.models.embedContent({         model: 'gemini-embedding-001',         contents: texts,         taskType: 'SEMANTIC_SIMILARITY'     });      const embeddings = response.embeddings.map(e => e.values);      for (let i = 0; i < texts.length; i++) {         for (let j = i + 1; j < texts.length; j++) {             const text1 = texts[i];             const text2 = texts[j];             const similarity = cosineSimilarity(embeddings[i], embeddings[j]);             console.log(`Similarity between '${text1}' and '${text2}': ${similarity.toFixed(4)}`);         }     } }  main(); 

package main  import (     "context"     "fmt"     "log"     "math"      "google.golang.org/genai" )  // cosineSimilarity calculates the similarity between two vectors. func cosineSimilarity(a, b []float32) (float64, error) {     if len(a) != len(b) {         return 0, fmt.Errorf("vectors must have the same length")     }      var dotProduct, aMagnitude, bMagnitude float64     for i := 0; i < len(a); i++ {         dotProduct += float64(a[i] * b[i])         aMagnitude += float64(a[i] * a[i])         bMagnitude += float64(b[i] * b[i])     }      if aMagnitude == 0 || bMagnitude == 0 {         return 0, nil     }      return dotProduct / (math.Sqrt(aMagnitude) * math.Sqrt(bMagnitude)), nil }  func main() {     ctx := context.Background()     client, _ := genai.NewClient(ctx, nil)     defer client.Close()      texts := []string{         "What is the meaning of life?",         "What is the purpose of existence?",         "How do I bake a cake?",     }      var contents []*genai.Content     for _, text := range texts {         contents = append(contents, genai.NewContentFromText(text, genai.RoleUser))     }      result, _ := client.Models.EmbedContent(ctx,         "gemini-embedding-001",         contents,         &genai.EmbedContentRequest{TaskType: genai.TaskTypeSemanticSimilarity},     )      embeddings := result.Embeddings      for i := 0; i < len(texts); i++ {         for j := i + 1; j < len(texts); j++ {             similarity, _ := cosineSimilarity(embeddings[i].Values, embeddings[j].Values)             fmt.Printf("Similarity between '%s' and '%s': %.4f\n", texts[i], texts[j], similarity)         }     } } 

curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -d '{     "contents": [         {"parts": [{"text": "What is the meaning of life?"}]},         {"parts": [{"text": "What is the purpose of existence?"}]},         {"parts": [{"text": "How do I bake a cake?"}]}     ],     "embedding_config": {         "task_type": "SEMANTIC_SIMILARITY"     } }' 

from google import genai from google.genai import types  client = genai.Client()  result = client.models.embed_content(     model="gemini-embedding-001",     contents="What is the meaning of life?",     config=types.EmbedContentConfig(output_dimensionality=768) )  [embedding_obj] = result.embeddings embedding_length = len(embedding_obj.values)  print(f"Length of embedding: {embedding_length}") 

import { GoogleGenAI } from "@google/genai";  async function main() {     const ai = new GoogleGenAI({});      const response = await ai.models.embedContent({         model: 'gemini-embedding-001',         content: 'What is the meaning of life?',         outputDimensionality: 768,     });      const embeddingLength = response.embedding.values.length;     console.log(`Length of embedding: ${embeddingLength}`); }  main(); 

package main  import (     "context"     "fmt"     "log"      "google.golang.org/genai" )  func main() {     ctx := context.Background()     // The client uses Application Default Credentials.     // Authenticate with 'gcloud auth application-default login'.     client, err := genai.NewClient(ctx, nil)     if err != nil {         log.Fatal(err)     }     defer client.Close()      contents := []*genai.Content{         genai.NewContentFromText("What is the meaning of life?", genai.RoleUser),     }      result, err := client.Models.EmbedContent(ctx,         "gemini-embedding-001",         contents,         &genai.EmbedContentRequest{OutputDimensionality: 768},     )     if err != nil {         log.Fatal(err)     }      embedding := result.Embeddings[0]     embeddingLength := len(embedding.Values)     fmt.Printf("Length of embedding: %d\n", embeddingLength) } 

curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \ -H "x-goog-api-key: YOUR_GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -d '{     "contents": [         {"parts": [{"text": "What is the meaning of life?"}]}     ],     "embedding_config": {         "output_dimensionality": 768     } }' 

import numpy as np from numpy.linalg import norm  embedding_values_np = np.array(embedding_obj.values) normed_embedding = embedding_values_np / np.linalg.norm(embedding_values_np)  print(f"Normed embedding length: {len(normed_embedding)}") print(f"Norm of normed embedding: {np.linalg.norm(normed_embedding):.6f}") # Should be very close to 1