Embed a Single Chunk

Call the Gemini embedding API and return the vector for one piece of text

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The embedding API call

Use client.models.embed_content() to convert text into a vector. Pass the model name, the text, and a config object that sets the task type.

Argument	Value
`model`	`"gemini-embedding-001"`
`contents`	The text string to embed
`config`	`types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")`

The function returns a response object. Access the vector with result.embeddings[0].values.

from google.genai import types

result = client.models.embed_content(
    model="gemini-embedding-001",
    contents=text,
    config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")
)
return result.embeddings[0].values

Instructions

Complete the embed_text function. The starter code provides the signature.

Create a variable named result. Assign it client.models.embed_content(model="gemini-embedding-001", contents=text, config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")).
Return result.embeddings[0].values.

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import os
import pypdf
from dotenv import load_dotenv
from google import genai
from google.genai import types

def extract_text(pdf_path):
    reader = pypdf.PdfReader(pdf_path)
    pages = [page.extract_text() for page in reader.pages]
    return "\n".join(pages)

def chunk_text(text, chunk_size=500, overlap=100):
    chunks = []
    for i in range(0, len(text), chunk_size - overlap):
        chunks.append(text[i : i + chunk_size])
    return chunks

def preview_chunks(chunks):
    print(f"Total chunks: {len(chunks)}")
    print(f"First chunk:\n{chunks[0]}")

def create_client():
    load_dotenv()
    api_key = os.getenv("GEMINI_API_KEY")
    client = genai.Client(api_key=api_key)
    return client

def embed_text(client, text):
    # Step 1: Call client.models.embed_content()
    # Step 2: Return result.embeddings[0].values

Embed a Single Chunk

The embedding API call

Instructions

Interactive Code Editor