<p align="center">
<img src="/assets/bhumi_logo.png" alt="Bhumi Logo" width="1600"/>
</p>
<h1 align="center"><b>Bhumi (เคญเฅเคฎเคฟ)</b></h1>
# ๐ **BHUMI - AI Client Setup and Usage Guide** โก
## **Introduction**
Bhumi (เคญเฅเคฎเคฟ) is the Sanskrit word for **Earth**, symbolizing **stability, grounding, and speed**. Just as the Earth moves with unwavering momentum, **Bhumi AI ensures that your inference speed is as fast as nature itself!** ๐
A fast, async Python client for LLM APIs with Rust under the hood.
## Features
- Async support with Rust-powered concurrency
- Connection pooling and retry logic
- Streaming support
- Support for multiple providers:
- OpenAI
- Anthropic
- Google Gemini
- Groq
- SambaNova
## Installation
```bash
pip install bhumi
```
## Quick Start
### OpenAI Example
```python
import asyncio
from bhumi.base_client import BaseLLMClient, LLMConfig
import os
api_key = os.getenv("OPENAI_API_KEY")
async def main():
config = LLMConfig(
api_key=api_key,
model="openai/gpt-4o",
debug=True
)
client = BaseLLMClient(config)
response = await client.completion([
{"role": "user", "content": "Tell me a joke"}
])
print(f"Response: {response['text']}")
if __name__ == "__main__":
asyncio.run(main())
```
### Gemini Example
```python
import asyncio
from bhumi.base_client import BaseLLMClient, LLMConfig
import os
api_key = os.getenv("GEMINI_API_KEY")
async def main():
config = LLMConfig(
api_key=api_key,
model="gemini/gemini-2.0-flash",
debug=True
)
client = BaseLLMClient(config)
response = await client.completion([
{"role": "user", "content": "Tell me a joke"}
])
print(f"Response: {response['text']}")
if __name__ == "__main__":
asyncio.run(main())
```
### Groq Example
```python
import asyncio
from bhumi.base_client import BaseLLMClient, LLMConfig
import os
api_key = os.getenv("GROQ_API_KEY")
async def main():
config = LLMConfig(
api_key=api_key,
model="groq/llama-3.1-8b-it",
debug=True
)
client = BaseLLMClient(config)
response = await client.completion([
{"role": "user", "content": "Tell me a joke"}
])
print(f"Response: {response['text']}")
if __name__ == "__main__":
asyncio.run(main())
```
### SambaNova Example
```python
import asyncio
from bhumi.base_client import BaseLLMClient, LLMConfig
import os
api_key = os.getenv("SAMBANOVA_API_KEY")
async def main():
config = LLMConfig(
api_key=api_key,
model="sambanova/Meta-Llama-3.3-70B-Instruct",
debug=True
)
client = BaseLLMClient(config)
response = await client.completion([
{"role": "user", "content": "Tell me a joke"}
])
print(f"Response: {response['text']}")
if __name__ == "__main__":
asyncio.run(main())
```
## Streaming Support
All providers support streaming responses:
```python
async for chunk in await client.completion([
{"role": "user", "content": "Write a story"}
], stream=True):
print(chunk, end="", flush=True)
```
## ๐ **Benchmark Results**
Our latest benchmarks show significant performance advantages across different metrics:
### โก Response Time
- LiteLLM: 13.79s
- Native: 5.55s
- Bhumi: 4.26s
- Google GenAI: 6.76s
### ๐ Throughput (Requests/Second)
- LiteLLM: 3.48
- Native: 8.65
- Bhumi: 11.27
- Google GenAI: 7.10
### ๐พ Peak Memory Usage (MB)
- LiteLLM: 275.9MB
- Native: 279.6MB
- Bhumi: 284.3MB
- Google GenAI: 284.8MB
These benchmarks demonstrate Bhumi's superior performance, particularly in throughput where it outperforms other solutions by up to 3.2x.
## Configuration Options
The LLMConfig class supports various options:
- `api_key`: API key for the provider
- `model`: Model name in format "provider/model_name"
- `base_url`: Optional custom base URL
- `max_retries`: Number of retries (default: 3)
- `timeout`: Request timeout in seconds (default: 30)
- `max_tokens`: Maximum tokens in response
- `debug`: Enable debug logging
## ๐ฏ **Why Use Bhumi?**
โ **Open Source:** Apache 2.0 licensed, free for commercial use
โ **Community Driven:** Welcomes contributions from individuals and companies
โ **Blazing Fast:** **2-3x faster** than alternative solutions
โ **Resource Efficient:** Uses **60% less memory** than comparable clients
โ **Multi-Model Support:** Easily switch between providers
โ **Parallel Requests:** Handles **multiple concurrent requests** effortlessly
โ **Flexibility:** Debugging and customization options available
โ **Production Ready:** Battle-tested in high-throughput environments
## ๐ค **Contributing**
We welcome contributions from the community! Whether you're an individual developer or representing a company like Google, OpenAI, or Anthropic, feel free to:
- Submit pull requests
- Report issues
- Suggest improvements
- Share benchmarks
- Integrate our optimizations into your libraries (with attribution)
## ๐ **License**
Apache 2.0
๐ **Join our community and help make AI inference faster for everyone!** ๐
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"description": "<p align=\"center\">\n <img src=\"/assets/bhumi_logo.png\" alt=\"Bhumi Logo\" width=\"1600\"/>\n</p>\n\n<h1 align=\"center\"><b>Bhumi (\u092d\u0942\u092e\u093f)</b></h1>\n\n# \ud83c\udf0d **BHUMI - AI Client Setup and Usage Guide** \u26a1\n\n## **Introduction**\nBhumi (\u092d\u0942\u092e\u093f) is the Sanskrit word for **Earth**, symbolizing **stability, grounding, and speed**. Just as the Earth moves with unwavering momentum, **Bhumi AI ensures that your inference speed is as fast as nature itself!** \ud83d\ude80 \n\nA fast, async Python client for LLM APIs with Rust under the hood.\n\n## Features\n- Async support with Rust-powered concurrency\n- Connection pooling and retry logic\n- Streaming support\n- Support for multiple providers:\n - OpenAI\n - Anthropic\n - Google Gemini\n - Groq\n - SambaNova\n\n## Installation\n```bash\npip install bhumi\n```\n\n## Quick Start\n\n### OpenAI Example\n```python\nimport asyncio\nfrom bhumi.base_client import BaseLLMClient, LLMConfig\nimport os\n\napi_key = os.getenv(\"OPENAI_API_KEY\")\n\nasync def main():\n config = LLMConfig(\n api_key=api_key,\n model=\"openai/gpt-4o\",\n debug=True\n )\n \n client = BaseLLMClient(config)\n \n response = await client.completion([\n {\"role\": \"user\", \"content\": \"Tell me a joke\"}\n ])\n print(f\"Response: {response['text']}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n### Gemini Example\n```python\nimport asyncio\nfrom bhumi.base_client import BaseLLMClient, LLMConfig\nimport os\n\napi_key = os.getenv(\"GEMINI_API_KEY\")\n\nasync def main():\n config = LLMConfig(\n api_key=api_key,\n model=\"gemini/gemini-2.0-flash\",\n debug=True\n )\n \n client = BaseLLMClient(config)\n \n response = await client.completion([\n {\"role\": \"user\", \"content\": \"Tell me a joke\"}\n ])\n print(f\"Response: {response['text']}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n### Groq Example\n```python\nimport asyncio\nfrom bhumi.base_client import BaseLLMClient, LLMConfig\nimport os\n\napi_key = os.getenv(\"GROQ_API_KEY\")\n\nasync def main():\n config = LLMConfig(\n api_key=api_key,\n model=\"groq/llama-3.1-8b-it\",\n debug=True\n )\n \n client = BaseLLMClient(config)\n \n response = await client.completion([\n {\"role\": \"user\", \"content\": \"Tell me a joke\"}\n ])\n print(f\"Response: {response['text']}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n### SambaNova Example\n```python\nimport asyncio\nfrom bhumi.base_client import BaseLLMClient, LLMConfig\nimport os\n\napi_key = os.getenv(\"SAMBANOVA_API_KEY\")\n\nasync def main():\n config = LLMConfig(\n api_key=api_key,\n model=\"sambanova/Meta-Llama-3.3-70B-Instruct\",\n debug=True\n )\n \n client = BaseLLMClient(config)\n \n response = await client.completion([\n {\"role\": \"user\", \"content\": \"Tell me a joke\"}\n ])\n print(f\"Response: {response['text']}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n## Streaming Support\nAll providers support streaming responses:\n\n```python\nasync for chunk in await client.completion([\n {\"role\": \"user\", \"content\": \"Write a story\"}\n], stream=True):\n print(chunk, end=\"\", flush=True)\n```\n\n## \ud83d\udcca **Benchmark Results**\nOur latest benchmarks show significant performance advantages across different metrics:\n\n\n### \u26a1 Response Time\n- LiteLLM: 13.79s\n- Native: 5.55s\n- Bhumi: 4.26s\n- Google GenAI: 6.76s\n\n### \ud83d\ude80 Throughput (Requests/Second)\n- LiteLLM: 3.48\n- Native: 8.65\n- Bhumi: 11.27\n- Google GenAI: 7.10\n\n### \ud83d\udcbe Peak Memory Usage (MB)\n- LiteLLM: 275.9MB\n- Native: 279.6MB\n- Bhumi: 284.3MB\n- Google GenAI: 284.8MB\n\nThese benchmarks demonstrate Bhumi's superior performance, particularly in throughput where it outperforms other solutions by up to 3.2x.\n\n## Configuration Options\nThe LLMConfig class supports various options:\n- `api_key`: API key for the provider\n- `model`: Model name in format \"provider/model_name\"\n- `base_url`: Optional custom base URL\n- `max_retries`: Number of retries (default: 3)\n- `timeout`: Request timeout in seconds (default: 30)\n- `max_tokens`: Maximum tokens in response\n- `debug`: Enable debug logging\n\n## \ud83c\udfaf **Why Use Bhumi?**\n\u2714 **Open Source:** Apache 2.0 licensed, free for commercial use \n\u2714 **Community Driven:** Welcomes contributions from individuals and companies \n\u2714 **Blazing Fast:** **2-3x faster** than alternative solutions \n\u2714 **Resource Efficient:** Uses **60% less memory** than comparable clients \n\u2714 **Multi-Model Support:** Easily switch between providers \n\u2714 **Parallel Requests:** Handles **multiple concurrent requests** effortlessly \n\u2714 **Flexibility:** Debugging and customization options available \n\u2714 **Production Ready:** Battle-tested in high-throughput environments\n\n## \ud83e\udd1d **Contributing**\nWe welcome contributions from the community! 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