| Name | llama-index-llms-google-genai JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | None |
| Summary | llama-index llms google genai integration |
| upload_time | 2025-07-16 09:49:22 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.9 |
| license | None |
| keywords |
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| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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|
# LlamaIndex Llms Integration: Google GenAI
## Installation
1. Install the required Python packages:
```bash
%pip install llama-index-llms-google-genai
```
2. Set the Google API key as an environment variable:
```bash
%env GOOGLE_API_KEY=your_api_key_here
```
## Usage
### Basic Content Generation
To generate a poem using the Gemini model, use the following code:
```python
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-2.0-flash")
resp = llm.complete("Write a poem about a magic backpack")
print(resp)
```
### Chat with Messages
To simulate a conversation, send a list of messages:
```python
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-2.0-flash")
resp = llm.chat(messages)
print(resp)
```
### Streaming Responses
To stream content responses in real-time:
```python
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-2.0-flash")
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:
```python
from llama_index.core.llms import ChatMessage
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="gemini-2.0-flash")
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:
```python
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="models/gemini-pro")
resp = llm.complete("Write a short, but joyous, ode to LlamaIndex")
print(resp)
```
### Asynchronous API
To use the asynchronous completion API:
```python
from llama_index.llms.google_genai import GoogleGenAI
llm = GoogleGenAI(model="models/gemini-pro")
resp = await llm.acomplete("Llamas are famous for ")
print(resp)
```
For asynchronous streaming of responses:
```python
resp = await llm.astream_complete("Llamas are famous for ")
async for chunk in resp:
print(chunk.text, end="")
```
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"description": "# LlamaIndex Llms Integration: Google GenAI\n\n## Installation\n\n1. Install the required Python packages:\n\n ```bash\n %pip install llama-index-llms-google-genai\n ```\n\n2. Set the Google API key as an environment variable:\n\n ```bash\n %env GOOGLE_API_KEY=your_api_key_here\n ```\n\n## Usage\n\n### Basic Content Generation\n\nTo generate a poem using the Gemini model, use the following code:\n\n```python\nfrom llama_index.llms.google_genai import GoogleGenAI\n\nllm = GoogleGenAI(model=\"gemini-2.0-flash\")\nresp = llm.complete(\"Write a poem about a magic backpack\")\nprint(resp)\n```\n\n### Chat with Messages\n\nTo simulate a conversation, send a list of messages:\n\n```python\nfrom llama_index.core.llms import ChatMessage\nfrom llama_index.llms.google_genai import GoogleGenAI\n\nmessages = [\n ChatMessage(role=\"user\", content=\"Hello friend!\"),\n ChatMessage(role=\"assistant\", content=\"Yarr what is shakin' matey?\"),\n ChatMessage(\n role=\"user\", content=\"Help me decide what to have for dinner.\"\n ),\n]\n\nllm = GoogleGenAI(model=\"gemini-2.0-flash\")\nresp = llm.chat(messages)\nprint(resp)\n```\n\n### Streaming Responses\n\nTo stream content responses in real-time:\n\n```python\nfrom llama_index.llms.google_genai import GoogleGenAI\n\nllm = GoogleGenAI(model=\"gemini-2.0-flash\")\nresp = llm.stream_complete(\n \"The story of Sourcrust, the bread creature, is really interesting. It all started when...\"\n)\nfor r in resp:\n print(r.text, end=\"\")\n```\n\nTo stream chat responses:\n\n```python\nfrom llama_index.core.llms import ChatMessage\nfrom llama_index.llms.google_genai import GoogleGenAI\n\nllm = GoogleGenAI(model=\"gemini-2.0-flash\")\nmessages = [\n ChatMessage(role=\"user\", content=\"Hello friend!\"),\n ChatMessage(role=\"assistant\", content=\"Yarr what is shakin' matey?\"),\n ChatMessage(\n role=\"user\", content=\"Help me decide what to have for dinner.\"\n ),\n]\nresp = llm.stream_chat(messages)\n```\n\n### Specific Model Usage\n\nTo use a specific model, you can configure it like this:\n\n```python\nfrom llama_index.llms.google_genai import GoogleGenAI\n\nllm = GoogleGenAI(model=\"models/gemini-pro\")\nresp = llm.complete(\"Write a short, but joyous, ode to LlamaIndex\")\nprint(resp)\n```\n\n### Asynchronous API\n\nTo use the asynchronous completion API:\n\n```python\nfrom llama_index.llms.google_genai import GoogleGenAI\n\nllm = GoogleGenAI(model=\"models/gemini-pro\")\nresp = await llm.acomplete(\"Llamas are famous for \")\nprint(resp)\n```\n\nFor asynchronous streaming of responses:\n\n```python\nresp = await llm.astream_complete(\"Llamas are famous for \")\nasync for chunk in resp:\n print(chunk.text, end=\"\")\n```\n",
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