llama-index-llms-digitalocean-gradientai

LlamaIndex integration for DigitalOcean Gradient AI with full support for function/tool calling.

Installation

pip install llama-index-llms-digitalocean-gradientai

This package uses the official gradient SDK (PyPI package: gradient) under the hood; it is installed automatically as a dependency.

Usage

Basic Usage

from llama_index.llms.digitalocean.gradientai import GradientAI

llm = GradientAI(
    model="openai-gpt-oss-120b",
    model_access_key="your-api-key",
)

response = llm.complete("What is DigitalOcean Gradient AI Platform?")
print(response.text)

Chat Interface

from llama_index.core.llms import ChatMessage
from llama_index.llms.digitalocean.gradientai import GradientAI

llm = GradientAI(
    model="openai-gpt-oss-120b",
    model_access_key="your-api-key",
)

messages = [
    ChatMessage(role="system", content="You are a helpful assistant."),
    ChatMessage(role="user", content="What is the capital of France?")
]

response = llm.chat(messages)
print(response.message.content)

Streaming

from llama_index.llms.digitalocean.gradientai import GradientAI

llm = GradientAI(
    model="openai-gpt-oss-120b",
    model_access_key="your-api-key",
)

# Streaming completion
for chunk in llm.stream_complete("Tell me a story about AI:"):
    print(chunk.delta, end="", flush=True)

Async Usage

import asyncio
from llama_index.llms.digitalocean.gradientai import GradientAI

async def main():
    llm = GradientAI(
        model="openai-gpt-oss-120b",
        model_access_key="your-api-key",
    )
    response = await llm.acomplete("What is Gradient?")
    print(response.text)

asyncio.run(main())

Function/Tool Calling

This integration supports OpenAI-compatible function calling, enabling the LLM to invoke tools based on user queries.

Using chat_with_tools

from llama_index.llms.digitalocean.gradientai import GradientAI
from llama_index.core.tools import FunctionTool

# Define tools
def add(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

def multiply(a: int, b: int) -> int:
    """Multiply two numbers together."""
    return a * b

# Create tool instances
add_tool = FunctionTool.from_defaults(fn=add)
multiply_tool = FunctionTool.from_defaults(fn=multiply)
tools = [add_tool, multiply_tool]

# Initialize LLM
llm = GradientAI(
    model="openai-gpt-oss-120b",
    model_access_key="your-api-key",
)

# Chat with tools
response = llm.chat_with_tools(
    tools=tools,
    user_msg="What is 5 multiplied by 8?",
)
print(response.message)

# Extract tool calls from response
tool_calls = llm.get_tool_calls_from_response(
    response,
    error_on_no_tool_call=False
)
for tool_call in tool_calls:
    print(f"Tool: {tool_call.tool_name}, Args: {tool_call.tool_kwargs}")

Using predict_and_call

For automatic tool execution and result handling:

from llama_index.llms.digitalocean.gradientai import GradientAI
from llama_index.core.tools import FunctionTool

def add(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

add_tool = FunctionTool.from_defaults(fn=add)

llm = GradientAI(
    model="openai-gpt-oss-120b",
    model_access_key="your-api-key",
)

# Automatically calls the tool and returns the result
response = llm.predict_and_call(
    tools=[add_tool],
    user_msg="What is 10 plus 15?",
)
print(response)  # Output: 25

Async Function Calling

import asyncio
from llama_index.llms.digitalocean.gradientai import GradientAI
from llama_index.core.tools import FunctionTool

def multiply(a: int, b: int) -> int:
    """Multiply two numbers together."""
    return a * b

multiply_tool = FunctionTool.from_defaults(fn=multiply)

async def main():
    llm = GradientAI(
        model="openai-gpt-oss-120b",
        model_access_key="your-api-key",
    )

    response = await llm.achat_with_tools(
        tools=[multiply_tool],
        user_msg="What is 7 times 9?",
    )
    print(response.message)

asyncio.run(main())

RAG (Retrieval-Augmented Generation)

Use GradientAI with LlamaIndex's RAG capabilities to query your own documents.

Setup: Create a data/ folder and add your text files (.txt, .pdf, .docx, etc.):

your_project/
├── data/
│   ├── document1.txt
│   ├── document2.pdf
│   └── ...
└── rag_example.py

Example:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.digitalocean.gradientai import GradientAI
from llama_index.embeddings.fastembed import FastEmbedEmbedding

# Initialize the DigitalOcean Gradient AI LLM
llm = GradientAI(
    model="openai-gpt-oss-120b",
    model_access_key="your-api-key",
)

# Use FastEmbed embeddings (lightweight, runs locally, no API key needed)
embed_model = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")

# Configure LlamaIndex settings
Settings.llm = llm
Settings.embed_model = embed_model

# Load documents and build index
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)

# Query index
query_engine = index.as_query_engine()
response = query_engine.query("Your question about the documents?")

print(response)

Note: Install the FastEmbed embeddings package: pip install fastembed

SimpleDirectoryReader supports many file types including .txt, .pdf, .docx, .csv, .md, and more.

License

MIT