LlamaIndex Llms Integration: Google GenAI
Installation
-
Install the required Python packages:
%pip install llama-index-llms-google-genai
-
Set the Google API key as an environment variable:
%env GOOGLE_API_KEY=your_api_key_here
Usage
Basic Content Generation
To generate a poem using the Gemini model, use the following code:
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-3-flash-preview")
resp = llm.complete("Write a poem about a magic backpack")
print(resp)
Chat with Messages
To simulate a conversation, send a list of messages:
from llama_index.core.llms import ChatMessage
from llama_index.llms.google_genai import GoogleGenAI
messages = [
ChatMessage(role="user", content="Hello friend!"),
ChatMessage(role="assistant", content="Yarr what is shakin' matey?"),
ChatMessage(
role="user", content="Help me decide what to have for dinner."
),
]
llm = GoogleGenAI(model="gemini-3-flash-preview")
resp = llm.chat(messages)
print(resp)
Streaming Responses
To stream content responses in real-time:
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-3-flash-preview")
resp = llm.stream_complete(
"The story of Sourcrust, the bread creature, is really interesting. It all started when..."
)
for r in resp:
print(r.text, end="")
To stream chat responses:
from llama_index.core.llms import ChatMessage
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-3-flash-preview")
messages = [
ChatMessage(role="user", content="Hello friend!"),
ChatMessage(role="assistant", content="Yarr what is shakin' matey?"),
ChatMessage(
role="user", content="Help me decide what to have for dinner."
),
]
resp = llm.stream_chat(messages)
Specific Model Usage
To use a specific model, you can configure it like this:
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-3-flash-preview")
resp = llm.complete("Write a short, but joyous, ode to LlamaIndex")
print(resp)
Asynchronous API
To use the asynchronous completion API:
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-3-flash-preview")
resp = await llm.acomplete("Llamas are famous for ")
print(resp)
For asynchronous streaming of responses:
resp = await llm.astream_complete("Llamas are famous for ")
async for chunk in resp:
print(chunk.text, end="")
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