| Name | llama-index-llms-konko JSON |
| Version |
0.2.2
JSON |
| download |
| home_page | None |
| Summary | llama-index llms konko integration |
| upload_time | 2024-10-08 22:29:10 |
| 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.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# LlamaIndex Llms Integration: Konko
## Installation
1. Install the required Python packages:
```bash
%pip install llama-index-llms-konko
!pip install llama-index
```
2. Set the API keys as environment variables:
```bash
export KONKO_API_KEY=<your-api-key>
export OPENAI_API_KEY=<your-api-key>
```
## Usage
### Import Required Libraries
```python
import os
from llama_index.llms.konko import Konko
from llama_index.core.llms import ChatMessage
```
### Chat with Konko Model
To chat with a Konko model:
```python
os.environ["KONKO_API_KEY"] = "<your-api-key>"
llm = Konko(model="meta-llama/llama-2-13b-chat")
messages = ChatMessage(role="user", content="Explain Big Bang Theory briefly")
resp = llm.chat([messages])
print(resp)
```
### Chat with OpenAI Model
To chat with an OpenAI model:
```python
os.environ["OPENAI_API_KEY"] = "<your-api-key>"
llm = Konko(model="gpt-3.5-turbo")
message = ChatMessage(role="user", content="Explain Big Bang Theory briefly")
resp = llm.chat([message])
print(resp)
```
### Streaming Responses
To stream a response for longer messages:
```python
message = ChatMessage(role="user", content="Tell me a story in 250 words")
resp = llm.stream_chat([message], max_tokens=1000)
for r in resp:
print(r.delta, end="")
```
### Complete with Prompt
To generate a completion based on a system prompt:
```python
llm = Konko(model="phind/phind-codellama-34b-v2", max_tokens=100)
text = """### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
..."""
resp = llm.stream_complete(text, max_tokens=1000)
for r in resp:
print(r.delta, end="")
```
### LLM Implementation example
https://docs.llamaindex.ai/en/stable/examples/llm/konko/
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"description": "# LlamaIndex Llms Integration: Konko\n\n## Installation\n\n1. Install the required Python packages:\n\n ```bash\n %pip install llama-index-llms-konko\n !pip install llama-index\n ```\n\n2. Set the API keys as environment variables:\n\n ```bash\n export KONKO_API_KEY=<your-api-key>\n export OPENAI_API_KEY=<your-api-key>\n ```\n\n## Usage\n\n### Import Required Libraries\n\n```python\nimport os\nfrom llama_index.llms.konko import Konko\nfrom llama_index.core.llms import ChatMessage\n```\n\n### Chat with Konko Model\n\nTo chat with a Konko model:\n\n```python\nos.environ[\"KONKO_API_KEY\"] = \"<your-api-key>\"\nllm = Konko(model=\"meta-llama/llama-2-13b-chat\")\nmessages = ChatMessage(role=\"user\", content=\"Explain Big Bang Theory briefly\")\n\nresp = llm.chat([messages])\nprint(resp)\n```\n\n### Chat with OpenAI Model\n\nTo chat with an OpenAI model:\n\n```python\nos.environ[\"OPENAI_API_KEY\"] = \"<your-api-key>\"\nllm = Konko(model=\"gpt-3.5-turbo\")\nmessage = ChatMessage(role=\"user\", content=\"Explain Big Bang Theory briefly\")\n\nresp = llm.chat([message])\nprint(resp)\n```\n\n### Streaming Responses\n\nTo stream a response for longer messages:\n\n```python\nmessage = ChatMessage(role=\"user\", content=\"Tell me a story in 250 words\")\nresp = llm.stream_chat([message], max_tokens=1000)\n\nfor r in resp:\n print(r.delta, end=\"\")\n```\n\n### Complete with Prompt\n\nTo generate a completion based on a system prompt:\n\n```python\nllm = Konko(model=\"phind/phind-codellama-34b-v2\", max_tokens=100)\ntext = \"\"\"### System Prompt\nYou are an intelligent programming assistant.\n\n### User Message\nImplement a linked list in C++\n\n### Assistant\n...\"\"\"\n\nresp = llm.stream_complete(text, max_tokens=1000)\nfor r in resp:\n print(r.delta, end=\"\")\n```\n\n### LLM Implementation example\n\nhttps://docs.llamaindex.ai/en/stable/examples/llm/konko/\n",
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