google-ai-studioGoogle AI Studio and the Gemini API give programmatic access to Gemini models for multimodal (text, image, video, audio, PDF) generation, long context, structured JSON output, function calling and Google Search grounding. Use when asked to "call Gemini from Python or Node", "get a Gemini API key", "analyze a PDF or image with Gemini", "return JSON from Gemi…
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Google AI Studio is the web playground and API-key console for the Gemini API. In code you use the Google Gen AI SDK: google-genai for Python and @google/genai for JavaScript/TypeScript. The older google-generativeai (Python) and @google/generative-ai (JS) packages are deprecated since 30 November 2025 and no longer maintained, so do not start new code on them. Code written as genai.configure(...) / genai.GenerativeModel(...) belongs to the old SDK and must be ported (see Migration).
The API supports multimodal input, long context, streaming, structured output, function calling, Google Search grounding, file uploads and embeddings. Google also ships a newer Interactions API (client.interactions.create); client.models.generate_content remains the stable, widely documented entry point and is used below.
pip install google-genai # Python
npm install @google/genai # Node.js
export GEMINI_API_KEY=your-key-from-aistudio # GOOGLE_API_KEY also works
Create the key at https://aistudio.google.com/apikey. genai.Client() with no arguments reads the environment variable. If both GEMINI_API_KEY and GOOGLE_API_KEY are set, the SDK warns and prefers GOOGLE_API_KEY. Keep keys out of source control and never ship one in browser code.
Model IDs and limits change every few months and old generations are retired or restricted to existing users. Before hard-coding an ID, read https://ai.google.dev/gemini-api/docs/models. The SDK README uses the alias gemini-flash-latest (always the newest Flash model), which is a safe default for prototypes; pin an exact stable ID in production so a model update cannot change behaviour. Gemini 1.5 and 2.0 IDs found in older tutorials are no longer the recommended choice.
| Need | Pick |
|---|---|
| Fast, cheap, high volume | A current Flash or Flash-Lite model |
| Hardest reasoning and coding | The current Pro model (often listed as preview) |
| Text embeddings | gemini-embedding-001 (text only) or gemini-embedding-2 (multimodal) |
from google import genai
from google.genai import types
client = genai.Client() # reads GEMINI_API_KEY
response = client.models.generate_content(
model="gemini-flash-latest",
contents="Explain neural networks in one paragraph.",
config=types.GenerateContentConfig(
system_instruction="You are a Python expert. Always show working code.",
temperature=0.3,
),
)
print(response.text)
chat = client.chats.create(model="gemini-flash-latest")
print(chat.send_message("How do I read a CSV with pandas?").text)
print(chat.send_message("Now filter rows where age > 30.").text)
for chunk in client.models.generate_content_stream(
model="gemini-flash-latest",
contents="Write a 200-word story about a lighthouse keeper.",
):
print(chunk.text, end="", flush=True)
Small inputs go inline as types.Part.from_bytes; larger ones go through the File API.
import pathlib
image = types.Part.from_bytes(
data=pathlib.Path("dashboard.png").read_bytes(), mime_type="image/png"
)
r = client.models.generate_content(
model="gemini-flash-latest",
contents=["List all visible text and the three largest numbers.", image],
)
print(r.text)
# File API: required when the whole request would exceed 100 MB (PDFs: 50 MB)
report = client.files.upload(file="annual-report-2025.pdf")
r = client.models.generate_content(
model="gemini-flash-latest",
contents=["Summarize the key financial metrics.", report],
)
print(r.text)
Uploaded files are stored for 48 hours (up to 2 GB per file, 20 GB per project) and can be deleted earlier with client.files.delete(name=report.name).
Pass a Pydantic model (or a JSON Schema via response_json_schema) and set the JSON MIME type. response.parsed returns the model instance.
from pydantic import BaseModel
class Company(BaseModel):
name: str
founded: int
country: str
r = client.models.generate_content(
model="gemini-flash-latest",
contents="List 3 major AI companies with founding year and country.",
config=types.GenerateContentConfig(
response_mime_type="application/json",
response_schema=list[Company],
),
)
for company in r.parsed:
print(company.name, company.founded, company.country)
Passing a plain Python function with type hints and a docstring enables automatic function calling: the SDK calls it and returns the final text.
def get_order_status(order_id: str) -> dict:
"""Look up the shipping status of an order by its ID."""
return {"order_id": order_id, "status": "shipped", "eta": "2026-10-09"}
r = client.models.generate_content(
model="gemini-flash-latest",
contents="Where is order A-10432?",
config=types.GenerateContentConfig(tools=[get_order_status]),
)
print(r.text)
The SDK warns that automatic function calling behaviour will change in SDK 3.0; if you rely on it, pin google-genai<3.
r = client.models.generate_content(
model="gemini-flash-latest",
contents="Who won the most recent Formula 1 race?",
config=types.GenerateContentConfig(
tools=[types.Tool(google_search=types.GoogleSearch())]
),
)
print(r.text)
meta = r.candidates[0].grounding_metadata
if meta:
print(meta.web_search_queries)
for chunk in meta.grounding_chunks or []:
print(chunk.web.title, chunk.web.uri)
If you display grounded answers to end users, the terms require showing the search suggestions in meta.search_entry_point. Grounding is billed separately from tokens; check the pricing page.
Flash and Pro models accept up to roughly a million tokens. Count before you send, then pass the text as one contents string.
import pathlib
code = "\n\n".join(
f"# File: {p}\n{p.read_text()}" for p in pathlib.Path("src").rglob("*.py")
)
n = client.models.count_tokens(model="gemini-flash-latest", contents=code)
print(n.total_tokens)
r = client.models.generate_content(
model="gemini-flash-latest",
contents=["Find security vulnerabilities in this codebase:", code],
)
print(r.text)
r = client.models.embed_content(
model="gemini-embedding-001",
contents=["Machine learning transforms industries.", "Hello world"],
config=types.EmbedContentConfig(
task_type="RETRIEVAL_DOCUMENT", output_dimensionality=768
),
)
vectors = [e.values for e in r.embeddings]
print(len(vectors), len(vectors[0])) # 2 768
The default dimension is 3072 (adjustable from 128 to 3072). Dimensions below 3072 must be normalized by you for gemini-embedding-001. task_type values include RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, SEMANTIC_SIMILARITY and CLASSIFICATION; gemini-embedding-2 instead takes the task as an instruction in the prompt text and returns one aggregated embedding for several inputs.
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const response = await ai.models.generateContent({
model: "gemini-flash-latest",
contents: "Explain event loops in two sentences.",
});
console.log(response.text);
| Old | New |
|---|---|
pip install google-generativeai | pip install google-genai |
import google.generativeai as genai | from google import genai and from google.genai import types |
genai.configure(api_key=...) | client = genai.Client(api_key=...) |
genai.GenerativeModel(name).generate_content(x) | client.models.generate_content(model=name, contents=x) |
generation_config={...}, tools=, system_instruction= on the model | all go in config=types.GenerateContentConfig(...) |
model.start_chat() | client.chats.create(model=...) |
genai.upload_file(path=...) | client.files.upload(file=...) |
genai.embed_content(...) | client.models.embed_content(...) |
User: "Pull vendor, total and due date out of invoice-8841.pdf as JSON."
class Invoice(BaseModel):
vendor: str
total: float
due_date: str
pdf = client.files.upload(file="invoice-8841.pdf")
r = client.models.generate_content(
model="gemini-flash-latest",
contents=["Extract the invoice fields.", pdf],
config=types.GenerateContentConfig(
response_mime_type="application/json", response_schema=Invoice
),
)
print(r.parsed)
Result: vendor='Northwind Traders' total=1842.5 due_date='2026-10-31'.
User: "This script uses google.generativeai and fails with a deprecation warning, fix it."
Replace the import, build a Client, move generation_config into types.GenerateContentConfig, and call client.models.generate_content(model=..., contents=...). Run the script once and confirm the response text prints.
google-genai, not google-generativeai; tutorials showing genai.configure are outdated.gemini-flash-latest move without notice.response.candidates[0].finish_reason and response.prompt_feedback: safety filters can return empty text.types.ThinkingConfig in the config.