| Name | llama-index-llms-openllm JSON |
| Version |
0.4.1
JSON |
| download |
| home_page | None |
| Summary | llama-index llms openllm integration |
| upload_time | 2024-12-11 01:53:29 |
| maintainer | None |
| docs_url | None |
| author | Aaron Pham |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# LlamaIndex LLM Integration: OpenLLM
## Installation
To install the required packages, run:
```bash
%pip install llama-index-llms-openllm
!pip install llama-index
```
## Setup
### Initialize OpenLLM
First, import the necessary libraries and set up your `OpenLLM` instance. Replace `my-model`, `https://hostname.com/v1`, and `na` with your model name, API base URL, and API key, respectively:
```python
import os
from typing import List, Optional
from llama_index.llms.openllm import OpenLLM
from llama_index.core.llms import ChatMessage
llm = OpenLLM(
model="my-model", api_base="https://hostname.com/v1", api_key="na"
)
```
## Generate Completions
To generate a completion, use the `complete` method:
```python
completion_response = llm.complete("To infinity, and")
print(completion_response)
```
### Stream Completions
You can also stream completions using the `stream_complete` method:
```python
async for it in llm.stream_complete(
"The meaning of time is", max_new_tokens=128
):
print(it, end="", flush=True)
```
## Chat Functionality
OpenLLM supports chat APIs, allowing you to handle conversation-like interactions. Here’s how to use it:
### Synchronous Chat
You can perform a synchronous chat by constructing a list of `ChatMessage` instances:
```python
from llama_index.core.llms import ChatMessage
chat_messages = [
ChatMessage(role="system", content="You are acting as Ernest Hemmingway."),
ChatMessage(role="user", content="Hi there!"),
ChatMessage(role="assistant", content="Yes?"),
ChatMessage(role="user", content="What is the meaning of life?"),
]
for it in llm.chat(chat_messages):
print(it.message.content, flush=True, end="")
```
### Asynchronous Chat
To perform an asynchronous chat, use the `astream_chat` method:
```python
async for it in llm.astream_chat(chat_messages):
print(it.message.content, flush=True, end="")
```
### LLM Implementation example
https://docs.llamaindex.ai/en/stable/examples/llm/openllm/
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"description": "# LlamaIndex LLM Integration: OpenLLM\n\n## Installation\n\nTo install the required packages, run:\n\n```bash\n%pip install llama-index-llms-openllm\n!pip install llama-index\n```\n\n## Setup\n\n### Initialize OpenLLM\n\nFirst, import the necessary libraries and set up your `OpenLLM` instance. Replace `my-model`, `https://hostname.com/v1`, and `na` with your model name, API base URL, and API key, respectively:\n\n```python\nimport os\nfrom typing import List, Optional\nfrom llama_index.llms.openllm import OpenLLM\nfrom llama_index.core.llms import ChatMessage\n\nllm = OpenLLM(\n model=\"my-model\", api_base=\"https://hostname.com/v1\", api_key=\"na\"\n)\n```\n\n## Generate Completions\n\nTo generate a completion, use the `complete` method:\n\n```python\ncompletion_response = llm.complete(\"To infinity, and\")\nprint(completion_response)\n```\n\n### Stream Completions\n\nYou can also stream completions using the `stream_complete` method:\n\n```python\nasync for it in llm.stream_complete(\n \"The meaning of time is\", max_new_tokens=128\n):\n print(it, end=\"\", flush=True)\n```\n\n## Chat Functionality\n\nOpenLLM supports chat APIs, allowing you to handle conversation-like interactions. Here\u2019s how to use it:\n\n### Synchronous Chat\n\nYou can perform a synchronous chat by constructing a list of `ChatMessage` instances:\n\n```python\nfrom llama_index.core.llms import ChatMessage\n\nchat_messages = [\n ChatMessage(role=\"system\", content=\"You are acting as Ernest Hemmingway.\"),\n ChatMessage(role=\"user\", content=\"Hi there!\"),\n ChatMessage(role=\"assistant\", content=\"Yes?\"),\n ChatMessage(role=\"user\", content=\"What is the meaning of life?\"),\n]\n\nfor it in llm.chat(chat_messages):\n print(it.message.content, flush=True, end=\"\")\n```\n\n### Asynchronous Chat\n\nTo perform an asynchronous chat, use the `astream_chat` method:\n\n```python\nasync for it in llm.astream_chat(chat_messages):\n print(it.message.content, flush=True, end=\"\")\n```\n\n### LLM Implementation example\n\nhttps://docs.llamaindex.ai/en/stable/examples/llm/openllm/\n",
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