| Name | llama-index-llms-perplexity JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | llama-index llms perplexity integration |
| upload_time | 2024-10-08 22:36:18 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| coveralls test coverage |
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|
# LlamaIndex Llms Integration: Perplexity
## Installation
To install the required packages, run:
```bash
%pip install llama-index-llms-perplexity
!pip install llama-index
```
## Setup
### Import Libraries and Configure API Key
Import the necessary libraries and set your Perplexity API key:
```python
from llama_index.llms.perplexity import Perplexity
pplx_api_key = "your-perplexity-api-key" # Replace with your actual API key
```
### Initialize the Perplexity LLM
Create an instance of the Perplexity LLM with your API key and desired model settings:
```python
llm = Perplexity(
api_key=pplx_api_key, model="mistral-7b-instruct", temperature=0.5
)
```
## Chat Example
### Sending a Chat Message
You can send a chat message using the `chat` method. Here’s how to do that:
```python
from llama_index.core.llms import ChatMessage
messages_dict = [
{"role": "system", "content": "Be precise and concise."},
{"role": "user", "content": "Tell me 5 sentences about Perplexity."},
]
messages = [ChatMessage(**msg) for msg in messages_dict]
# Get response from the model
response = llm.chat(messages)
print(response)
```
### Async Chat
To send messages asynchronously, you can use the `achat` method:
```python
response = await llm.achat(messages)
print(response)
```
### Stream Chat
For streaming responses, you can use the `stream_chat` method:
```python
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
```
### Async Stream Chat
To stream responses asynchronously, use the `astream_chat` method:
```python
resp = await llm.astream_chat(messages)
async for delta in resp:
print(delta.delta, end="")
```
### LLM Implementation example
https://docs.llamaindex.ai/en/stable/examples/llm/perplexity/
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"description": "# LlamaIndex Llms Integration: Perplexity\n\n## Installation\n\nTo install the required packages, run:\n\n```bash\n%pip install llama-index-llms-perplexity\n!pip install llama-index\n```\n\n## Setup\n\n### Import Libraries and Configure API Key\n\nImport the necessary libraries and set your Perplexity API key:\n\n```python\nfrom llama_index.llms.perplexity import Perplexity\n\npplx_api_key = \"your-perplexity-api-key\" # Replace with your actual API key\n```\n\n### Initialize the Perplexity LLM\n\nCreate an instance of the Perplexity LLM with your API key and desired model settings:\n\n```python\nllm = Perplexity(\n api_key=pplx_api_key, model=\"mistral-7b-instruct\", temperature=0.5\n)\n```\n\n## Chat Example\n\n### Sending a Chat Message\n\nYou can send a chat message using the `chat` method. Here\u2019s how to do that:\n\n```python\nfrom llama_index.core.llms import ChatMessage\n\nmessages_dict = [\n {\"role\": \"system\", \"content\": \"Be precise and concise.\"},\n {\"role\": \"user\", \"content\": \"Tell me 5 sentences about Perplexity.\"},\n]\n\nmessages = [ChatMessage(**msg) for msg in messages_dict]\n\n# Get response from the model\nresponse = llm.chat(messages)\nprint(response)\n```\n\n### Async Chat\n\nTo send messages asynchronously, you can use the `achat` method:\n\n```python\nresponse = await llm.achat(messages)\nprint(response)\n```\n\n### Stream Chat\n\nFor streaming responses, you can use the `stream_chat` method:\n\n```python\nresp = llm.stream_chat(messages)\nfor r in resp:\n print(r.delta, end=\"\")\n```\n\n### Async Stream Chat\n\nTo stream responses asynchronously, use the `astream_chat` method:\n\n```python\nresp = await llm.astream_chat(messages)\nasync for delta in resp:\n print(delta.delta, end=\"\")\n```\n\n### LLM Implementation example\n\nhttps://docs.llamaindex.ai/en/stable/examples/llm/perplexity/\n",
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