| Name | llama-index-llms-gemini JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | llama-index llms gemini integration |
| upload_time | 2024-11-18 00:43:10 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
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# LlamaIndex Llms Integration: Gemini
## Installation
1. Install the required Python packages:
```bash
%pip install llama-index-llms-gemini
!pip install -q llama-index google-generativeai
```
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.gemini import Gemini
resp = Gemini().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.gemini import Gemini
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 = Gemini().chat(messages)
print(resp)
```
### Streaming Responses
To stream content responses in real-time:
```python
from llama_index.llms.gemini import Gemini
llm = Gemini()
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.llms.gemini import Gemini
from llama_index.core.llms import ChatMessage
llm = Gemini()
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)
```
### Using Other Models
To find suitable models available in the Gemini model site:
```python
import google.generativeai as genai
for m in genai.list_models():
if "generateContent" in m.supported_generation_methods:
print(m.name)
```
### Specific Model Usage
To use a specific model, you can configure it like this:
```python
from llama_index.llms.gemini import Gemini
llm = Gemini(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.gemini import Gemini
llm = Gemini()
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="")
```
### LLM Implementation example
https://docs.llamaindex.ai/en/stable/examples/llm/gemini/
Raw data
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"description": "# LlamaIndex Llms Integration: Gemini\n\n## Installation\n\n1. Install the required Python packages:\n\n ```bash\n %pip install llama-index-llms-gemini\n !pip install -q llama-index google-generativeai\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.gemini import Gemini\n\nresp = Gemini().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.gemini import Gemini\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]\nresp = Gemini().chat(messages)\nprint(resp)\n```\n\n### Streaming Responses\n\nTo stream content responses in real-time:\n\n```python\nfrom llama_index.llms.gemini import Gemini\n\nllm = Gemini()\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.llms.gemini import Gemini\nfrom llama_index.core.llms import ChatMessage\n\nllm = Gemini()\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### Using Other Models\n\nTo find suitable models available in the Gemini model site:\n\n```python\nimport google.generativeai as genai\n\nfor m in genai.list_models():\n if \"generateContent\" in m.supported_generation_methods:\n print(m.name)\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.gemini import Gemini\n\nllm = Gemini(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.gemini import Gemini\n\nllm = Gemini()\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\n### LLM Implementation example\n\nhttps://docs.llamaindex.ai/en/stable/examples/llm/gemini/\n",
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