gh-hiroshi75-ccplugins-langgraph-master-plugin/skills/langgraph-master/06_llm_model_ids_gemini_advanced.md

Gemini Advanced Features

Advanced configuration and multimodal features for Google Gemini models.

Context Window and Output Limits

Model	Context Window	Max Output Tokens
Gemini 3 Pro	-	64K
Gemini 2.5 Pro	1M (2M planned)	-
Gemini 2.5 Flash	1M	-
Gemini 2.0 Flash	1M	-

Tier-based Limits:

• Gemini Advanced / Vertex AI: 1M tokens
• Free tier: ~32K tokens

Parameter Configuration

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    temperature=0.7,          # Creativity (0.0-1.0)
    top_p=0.9,               # Diversity
    top_k=40,                # Sampling
    max_tokens=8192,         # Max output
)

Multimodal Features

Image Input

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")

message = HumanMessage(
    content=[
        {"type": "text", "text": "What is in this image?"},
        {"type": "image_url", "image_url": "https://example.com/image.jpg"}
    ]
)

response = llm.invoke([message])

Image Generation (Gemini 3.x)

llm = ChatGoogleGenerativeAI(model="google/gemini-3-pro-image-preview")
response = llm.invoke("Generate a beautiful sunset landscape")

Streaming

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    streaming=True
)

for chunk in llm.stream("Question"):
    print(chunk.content, end="", flush=True)

Safety Settings

from langchain_google_genai import (
    ChatGoogleGenerativeAI,
    HarmBlockThreshold,
    HarmCategory
)

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    safety_settings={
        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
    }
)

Retrieving Model List

import google.generativeai as genai
import os

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

for model in genai.list_models():
    if 'generateContent' in model.supported_generation_methods:
        print(f"{model.name}: {model.input_token_limit} tokens")

Error Handling

from google.api_core import exceptions

try:
    response = llm.invoke("Question")
except exceptions.ResourceExhausted:
    print("Rate limit reached")
except exceptions.InvalidArgument as e:
    print(f"Invalid argument: {e}")

Reference Links

• Gemini API Models
• Vertex AI