🚀 googlemodel-samrat-v1

Intelligent Gemini model discovery, multi-key rotation, automatic model fallback, and LangChain integration for Python.

googlemodel-samrat-v1 is a Python library designed to make working with the Google Gemini ecosystem more resilient and convenient.

It provides utilities for selecting the latest available Gemini models, rotating between multiple API keys, falling back between models when errors occur, and integrating Gemini models into LangChain-based applications.

The library is particularly useful for applications that need to handle API quota limits, temporary server failures, model availability changes, and multiple Gemini models without manually implementing complex fallback logic.

---

✨ Features

🔑 Automatic API Key Rotation

Use multiple Gemini API keys and automatically move to another key when the current key encounters quota or rate-limit errors.

API Key 1
    ↓
429 / Quota Error
    ↓
API Key 2
    ↓
429 / Quota Error
    ↓
API Key 3
    ↓
Continue Request

This helps applications remain operational when an individual API key reaches its available quota.

---

🤖 Smart Model Fallback

The library maintains model lists ordered from latest to oldest.

When a model becomes unavailable or encounters a model-specific error, the system can move through the configured model list instead of immediately terminating the request.

Latest Model
     ↓
Model Error
     ↓
Next Model
     ↓
Model Error
     ↓
Older Stable Model
     ↓
Successful Response

---

🔄 Intelligent Error Classification

The library is designed to distinguish between different types of failures.

Error Type	Typical Response
429 / Quota	Rotate API key
Model unavailable / deprecated	Rotate model
502 / 503	Rotate key and/or model
Successful request	Continue normally

This provides a more resilient request strategy than relying on a single API key and model.

---

🦜 LangChain Integration

Designed to work with the LangChain Gemini ecosystem and provide a convenient interface for conversational applications.

Example:

from googlemodel_samrat import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI()

response = llm.invoke("What is the capital of Nepal?")

print(response.content)

---

📦 Installation

Install the published package from PyPI:

pip install googlemodel-samrat-v1

Note: googlemodel-samrat-v1 is the PyPI distribution name. The Python import namespace is googlemodel_samrat.

After installation:

import googlemodel_samrat

---

🧩 Model Categories

googlemodel-samrat-v1 organizes Gemini-related models into 12 categories.

Each category provides a constant containing its model list and, where applicable, a helper function for retrieving the highest-priority model.

#	Category	Helper	Constant	Purpose
1	💬 Chat	chatmodel()	CHAT_MODELS	Conversational and multimodal LLMs
2	📝 Text	—	TEXT_MODELS	Text-focused and legacy models
3	🎙️ Audio	audiomodel()	AUDIO_MODELS	Speech, transcription, and audio
4	🖼️ Image	imagemodel()	IMAGE_MODELS	Image generation and visual models
5	🎬 Video	videomodel()	VIDEO_MODELS	Video generation and processing
6	🔢 Embedding	embeddingmodel()	EMBEDDING_MODELS	Vector embeddings
7	🎵 Music	musicmodel()	MUSIC_MODELS	Music generation
8	🤖 Robotics	roboticsmodel()	ROBOTICS_MODELS	Robotics and embodied reasoning
9	🖥️ Computer Use	computerusemodel()	COMPUTER_USE_MODELS	UI and computer interaction
10	🔬 Research	researchmodel()	RESEARCH_MODELS	Research and long-form analysis
11	🧠 Agent	agentmodel()	AGENT_MODELS	Agents and autonomous workflows
12	🦙 Gemma	gemmamodel()	GEMMA_MODELS	Open-weight Gemma models

---

📋 Model Registry

1. 💬 Chat Models

Constant: CHAT_MODELS

Primary conversational and multimodal models.

gemini-3.8-flash
gemini-3.5-flash
gemini-3.1-pro-preview
gemini-3-flash-preview
gemini-2.5-pro
gemini-2.5-flash
gemini-2.5-flash-lite

Get the highest-priority model:

from googlemodel_samrat import chatmodel

model = chatmodel()

print(model)

---

2. 📝 Text Models

Constant: TEXT_MODELS

Text-centric and legacy text endpoints.

gemini-1.5-pro
gemini-1.5-flash
gemini-pro

---

3. 🎙️ Audio Models

Constant: AUDIO_MODELS

Models intended for real-time audio, transcription, and speech generation.

gemini-3.8-live
gemini-3.8-live-extended-thinking
gemini-3.5-transcribe
gemini-3.1-flash-live-preview
gemini-3.1-flash-tts-preview
gemini-2.5-flash-native-audio-preview-12-2025
gemini-2.5-flash-preview-tts
gemini-2.5-pro-preview-tts

Get the highest-priority audio model:

from googlemodel_samrat import audiomodel

print(audiomodel())

---

4. 🖼️ Image Models

Constant: IMAGE_MODELS

Visual generation models.

gemini-3.1-flash-image
gemini-3.1-flash-lite-image
gemini-3-pro-image

Get the highest-priority image model:

from googlemodel_samrat import imagemodel

print(imagemodel())

---

5. 🎬 Video Models

Constant: VIDEO_MODELS

Video generation and processing models.

veo-3.1-generate-preview
veo-3.1-lite-generate-preview

Get the highest-priority video model:

from googlemodel_samrat import videomodel

print(videomodel())

---

6. 🔢 Embedding Models

Constant: EMBEDDING_MODELS

Embedding models for semantic search, RAG systems, vector databases, and similarity applications.

gemini-embedding-2-preview
gemini-embedding-001

Get the highest-priority embedding model:

from googlemodel_samrat import embeddingmodel

print(embeddingmodel())

---

7. 🎵 Music Models

Constant: MUSIC_MODELS

Specialized music-generation models.

music-fx-001
lyria-preview

Get the highest-priority music model:

from googlemodel_samrat import musicmodel

print(musicmodel())

---

8. 🤖 Robotics Models

Constant: ROBOTICS_MODELS

Models designed for robotics and embodied reasoning applications.

gemini-robotics-er-2-preview
gemini-robotics-er-1.6-preview

Get the highest-priority robotics model:

from googlemodel_samrat import roboticsmodel

print(roboticsmodel())

---

9. 🖥️ Computer Use Models

Constant: COMPUTER_USE_MODELS

Models intended for UI navigation and computer interaction.

gemini-computer-use-preview
gemini-desktop-agent-001

Get the highest-priority computer-use model:

from googlemodel_samrat import computerusemodel

print(computerusemodel())

---

10. 🔬 Research Models

Constant: RESEARCH_MODELS

Models intended for deep analysis and research workflows.

gemini-3.1-pro-preview
gemini-deep-research-1.0

Get the highest-priority research model:

from googlemodel_samrat import researchmodel

print(researchmodel())

---

11. 🧠 Agent Models

Constant: AGENT_MODELS

Models intended for multi-step workflows and agent-based applications.

gemini-3.8-flash
gemini-agent-engine-001

Get the highest-priority agent model:

from googlemodel_samrat import agentmodel

print(agentmodel())

---

12. 🦙 Gemma Models

Constant: GEMMA_MODELS

Open-weight Gemma models for local deployment and customized applications.

gemma-4
gemma-3-27b
gemma-3-9b
gemma-2-2b

Get the highest-priority Gemma model:

from googlemodel_samrat import gemmamodel

print(gemmamodel())

---

🚀 Quick Start

Get the Latest Model From Every Category

You can import the model getters and dynamically select the highest-priority model for each modality.

from googlemodel_samrat import (
    chatmodel,
    audiomodel,
    imagemodel,
    videomodel,
    embeddingmodel,
    musicmodel,
    roboticsmodel,
    computerusemodel,
    researchmodel,
    agentmodel,
    gemmamodel,
)

print(f"Chat:          {chatmodel()}")
print(f"Audio:         {audiomodel()}")
print(f"Image:         {imagemodel()}")
print(f"Video:         {videomodel()}")
print(f"Embedding:     {embeddingmodel()}")
print(f"Music:         {musicmodel()}")
print(f"Robotics:      {roboticsmodel()}")
print(f"Computer Use:  {computerusemodel()}")
print(f"Research:      {researchmodel()}")
print(f"Agent:         {agentmodel()}")
print(f"Gemma:         {gemmamodel()}")

---

🧠 Using Chat Models

The model getter can be used directly with ChatGoogleGenerativeAI.

import os

from dotenv import load_dotenv
from googlemodel_samrat import chatmodel
from langchain_google_genai import ChatGoogleGenerativeAI

load_dotenv()

api_key = os.getenv("GEMINI_API_KEY")

llm = ChatGoogleGenerativeAI(
    model=chatmodel(),
    api_key=api_key,
)

response = llm.invoke(
    "What is the capital of Nepal?"
)

print(response.content)

---

🔢 Using Embeddings

The same approach can be used for Gemini embeddings.

import os

from dotenv import load_dotenv
from googlemodel_samrat import embeddingmodel
from langchain_google_genai import GoogleGenerativeAIEmbeddings

load_dotenv()

api_key = os.getenv("GEMINI_API_KEY")

embedding_model = GoogleGenerativeAIEmbeddings(
    model=embeddingmodel(),
    api_key=api_key,
)

embedding = embedding_model.embed_query(
    "My name is Samrat."
)

print(embedding[:5])

---

🔐 Environment Variables

For applications that use a single API key, store your key in an environment variable rather than hard-coding it.

Create a .env file:

GEMINI_API_KEY=your_api_key_here

Then load it:

from dotenv import load_dotenv

load_dotenv()

⚠️ Security

Never commit API keys to GitHub or publish them inside source code.

Add .env to .gitignore:

.env

If an API key is accidentally exposed, revoke it and create a replacement key.

---

🔄 Multi-Key Rotation

Applications that use multiple Gemini API keys can configure a key pool.

from googlemodel_samrat import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(
    api_keys=[
        "YOUR_PRIMARY_API_KEY",
        "YOUR_BACKUP_API_KEY",
    ],
    temperature=0.8,
    max_output_tokens=500,
)

response = llm.invoke(
    "Write a creative science-fiction story opening."
)

print(response.content)

The library can rotate between the configured keys when supported failures occur.

Security: Never publish real API keys in README files, GitHub repositories, screenshots, or package source code.

---

💬 Multi-Turn Conversations

Because the package is designed around the LangChain ecosystem, it can be used with LangChain message objects.

from googlemodel_samrat import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage, AIMessage

llm = ChatGoogleGenerativeAI(
    api_keys=[
        "YOUR_API_KEY_1",
        "YOUR_API_KEY_2",
    ]
)

conversation_history = [
    HumanMessage(
        content="Hi, I'm learning Python."
    ),
    AIMessage(
        content="That's awesome! How can I help you with Python today?"
    ),
    HumanMessage(
        content="Can you write a quick Hello World program?"
    ),
]

response = llm.generate_messages(
    conversation_history
)

print(response)

---

📊 Rotation & Failover Statistics

The rotation system can expose statistics about the current key/model state.

from googlemodel_samrat import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(
    api_keys=[
        "YOUR_API_KEY_1",
        "YOUR_API_KEY_2",
    ]
)

llm.invoke("Test query")

stats = llm.get_rotation_stats()

print(stats)

Example structure:

{
    "total_keys": 2,
    "total_models": 15,
    "failed_keys": 0,
    "failed_models": 0,
    "available_combinations": 30,
    "current_key_index": 0,
    "current_model_index": 0,
}

---

🏗️ How the Rotation System Works

The core idea is to treat API keys and models as a pool of available request combinations.

For example:

Key 1 × Model 1
Key 1 × Model 2
Key 1 × Model 3
       ↓
Key 2 × Model 1
Key 2 × Model 2
Key 2 × Model 3
       ↓
Key 3 × Model 1
Key 3 × Model 2
Key 3 × Model 3

When a request fails because of a supported quota, model, or server issue, the library can move to another available combination.

This allows applications to continue operating without manually implementing every fallback path.

---

🧱 Architecture

                    ┌──────────────────────┐
                    │   Application/User   │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │ googlemodel-samrat   │
                    └──────────┬───────────┘
                               │
                ┌──────────────┼──────────────┐
                ▼              ▼              ▼
          ┌──────────┐  ┌────────────┐  ┌─────────────┐
          │ API Keys │  │ Model Pool │  │ Error Logic │
          └────┬─────┘  └─────┬──────┘  └──────┬──────┘
               │              │                │
               └──────────────┼────────────────┘
                              ▼
                    ┌──────────────────┐
                    │ Gemini / LangChain│
                    └──────────────────┘

---

🛠️ Intended Use Cases

googlemodel-samrat-v1 can be useful for:

• 🤖 AI chatbots
• 💬 Conversational applications
• 📚 RAG applications
• 🔎 Semantic search systems
• 🧠 AI agents
• 🖥️ Computer-use experiments
• 🎙️ Voice applications
• 🖼️ Image-generation workflows
• 🎬 Video-generation workflows
• 🧪 AI experimentation
• 🎓 Academic and student projects
• 🏗️ Prototypes requiring model fallback
• 🔄 Applications using multiple Gemini API keys

---

⚠️ Important Notes

API Quotas

API key rotation does not remove Google's API quotas or usage policies. It only provides application-level handling for multiple configured keys.

Model Availability

Google may introduce, rename, replace, deprecate, or remove models.

The model lists included in this package should therefore be treated as a snapshot/configuration rather than a guarantee that every listed endpoint will remain available indefinitely.

Preview Models

Models containing identifiers such as:

-preview

may change or become unavailable as their lifecycle progresses.

API Compatibility

Not every model supports every Gemini API capability. A model listed in a category should not automatically be assumed to support every LangChain operation.

---

📋 Requirements

The package is designed to work with the Google Gemini and LangChain ecosystem.

Typical dependencies include:

langchain-google-genai
google-genai
google-api-core
langchain-core
python-dotenv

Install or update the relevant dependencies with:

pip install -U googlemodel-samrat-v1

---

🧪 Development

Clone the repository:

git clone YOUR_REPOSITORY_URL
cd googlemodel-samrat

Create a virtual environment:

python -m venv .venv

Activate it on macOS/Linux:

source .venv/bin/activate

Activate it on Windows:

.venv\Scripts\activate

Install the project:

pip install -e .

---

📦 Publishing

Build the package:

python -m build

This produces:

dist/
├── googlemodel_samrat_v1-<version>.tar.gz
└── googlemodel_samrat_v1-<version>-py3-none-any.whl

Upload to PyPI using your preferred publishing workflow.

Never place PyPI API tokens directly inside shell history, README files, source code, or public repositories.

---

🗺️ Roadmap

Potential future improvements include:

• Automatic model-list synchronization
• Automatic Gemini API model discovery
• Persistent key health tracking
• Configurable retry policies
• Async API support
• Streaming support
• Better telemetry and diagnostics
• Model capability detection
• Automatic deprecated-model removal
• Configuration through .env
• CLI utilities
• Expanded test coverage
• Documentation website

---

🤝 Contributing

Contributions, issues, and suggestions are welcome.

A typical contribution workflow:

git checkout -b feature/my-feature

Make your changes, test them, and submit a pull request.

When reporting an issue, include:

• Python version
• Package version
• Operating system
• Model being used
• Relevant error message
• Minimal reproducible example

Never include API keys or other credentials in an issue report.

---

📄 License

Add your project's license here.

Example:

MIT License

If your project uses a different license, replace the above with the appropriate license information.

---

👨‍💻 Author

Samrat Dhakal

Python • Generative AI • Gemini • LangChain • RAG

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⭐ Support the Project

If you find googlemodel-samrat-v1 useful:

• ⭐ Star the repository
• 🐛 Report bugs
• 💡 Suggest improvements
• 🤝 Contribute improvements
• 📦 Share the package with other developers

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🚀 Quick Reference

Install

pip install googlemodel-samrat-v1

Get the latest chat model

from googlemodel_samrat import chatmodel

print(chatmodel())

Get the latest embedding model

from googlemodel_samrat import embeddingmodel

print(embeddingmodel())

Use with LangChain

from googlemodel_samrat import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI()

response = llm.invoke("Hello, Gemini!")

print(response.content)

---

googlemodel-samrat-v1 — making Gemini model selection and fallback simpler for Python developers.