| Name | llama-index-llms-ai21 JSON |
| Version |
0.3.4
JSON |
| download |
| home_page | None |
| Summary | llama-index llms ai21 integration |
| upload_time | 2024-09-13 19:55:52 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
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# LlamaIndex LLMs Integration: AI21 Labs
## Installation
First, you need to install the package. You can do this using pip:
```bash
pip install llama-index-llms-ai21
```
## Usage
Here's a basic example of how to use the AI21 class to generate text completions and handle chat interactions.
## Initializing the AI21 Client
You need to initialize the AI21 client with the appropriate model and API key.
```python
from llama_index.llms.ai21 import AI21
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
```
### Chat Completions
```python
from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
messages = [ChatMessage(role="user", content="What is the meaning of life?")]
response = llm.chat(messages)
print(response.message.content)
```
### Chat Streaming
```python
from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
messages = [ChatMessage(role="user", content="What is the meaning of life?")]
for chunk in llm.stream_chat(messages):
print(chunk.message.content)
```
### Text Completion
```python
from llama_index.llms.ai21 import AI21
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
response = llm.complete(prompt="What is the meaning of life?")
print(response.text)
```
### Stream Text Completion
```python
from llama_index.llms.ai21 import AI21
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
response = llm.stream_complete(prompt="What is the meaning of life?")
for chunk in response:
print(response.text)
```
## Other Models Support
You could also use more model types. For example the `j2-ultra` and `j2-mid`
These models support `chat` and `complete` methods only.
### Chat
```python
from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage
api_key = "your_api_key"
llm = AI21(model="j2-chat", api_key=api_key)
messages = [ChatMessage(role="user", content="What is the meaning of life?")]
response = llm.chat(messages)
print(response.message.content)
```
### Complete
```python
from llama_index.llms.ai21 import AI21
api_key = "your_api_key"
llm = AI21(model="j2-ultra", api_key=api_key)
response = llm.complete(prompt="What is the meaning of life?")
print(response.text)
```
## Tokenizer
The type of the tokenizer is determined by the name of the model
```python
from llama_index.llms.ai21 import AI21
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
tokenizer = llm.tokenizer
tokens = tokenizer.encode("What is the meaning of life?")
print(tokens)
text = tokenizer.decode(tokens)
print(text)
```
## Async Support
You can also use the async functionalities
### async chat
```python
from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage
async def main():
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
messages = [
ChatMessage(role="user", content="What is the meaning of life?")
]
response = await llm.achat(messages)
print(response.message.content)
```
### async stream_chat
```python
from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage
async def main():
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
messages = [
ChatMessage(role="user", content="What is the meaning of life?")
]
response = await llm.astream_chat(messages)
async for chunk in response:
print(chunk.message.content)
```
## Tool Calling
```python
from llama_index.core.agent import FunctionCallingAgentWorker
from llama_index.llms.ai21 import AI21
from llama_index.core.tools import FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
def subtract(a: int, b: int) -> int:
"""Subtract two integers and returns the result integer"""
return a - b
def divide(a: int, b: int) -> float:
"""Divide two integers and returns the result float"""
return a - b
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)
subtract_tool = FunctionTool.from_defaults(fn=subtract)
divide_tool = FunctionTool.from_defaults(fn=divide)
api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
agent_worker = FunctionCallingAgentWorker.from_tools(
[multiply_tool, add_tool, subtract_tool, divide_tool],
llm=llm,
verbose=True,
allow_parallel_tool_calls=True,
)
agent = agent_worker.as_agent()
response = agent.chat(
"My friend Moses had 10 apples. He ate 5 apples in the morning. Then he found a box with 25 apples."
"He divided all his apples between his 5 friends. How many apples did each friend get?"
)
```
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"description": "# LlamaIndex LLMs Integration: AI21 Labs\n\n## Installation\n\nFirst, you need to install the package. You can do this using pip:\n\n```bash\npip install llama-index-llms-ai21\n```\n\n## Usage\n\nHere's a basic example of how to use the AI21 class to generate text completions and handle chat interactions.\n\n## Initializing the AI21 Client\n\nYou need to initialize the AI21 client with the appropriate model and API key.\n\n```python\nfrom llama_index.llms.ai21 import AI21\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n```\n\n### Chat Completions\n\n```python\nfrom llama_index.llms.ai21 import AI21\nfrom llama_index.core.base.llms.types import ChatMessage\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\nmessages = [ChatMessage(role=\"user\", content=\"What is the meaning of life?\")]\nresponse = llm.chat(messages)\nprint(response.message.content)\n```\n\n### Chat Streaming\n\n```python\nfrom llama_index.llms.ai21 import AI21\nfrom llama_index.core.base.llms.types import ChatMessage\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\nmessages = [ChatMessage(role=\"user\", content=\"What is the meaning of life?\")]\n\nfor chunk in llm.stream_chat(messages):\n print(chunk.message.content)\n```\n\n### Text Completion\n\n```python\nfrom llama_index.llms.ai21 import AI21\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\nresponse = llm.complete(prompt=\"What is the meaning of life?\")\nprint(response.text)\n```\n\n### Stream Text Completion\n\n```python\nfrom llama_index.llms.ai21 import AI21\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\nresponse = llm.stream_complete(prompt=\"What is the meaning of life?\")\n\nfor chunk in response:\n print(response.text)\n```\n\n## Other Models Support\n\nYou could also use more model types. For example the `j2-ultra` and `j2-mid`\n\nThese models support `chat` and `complete` methods only.\n\n### Chat\n\n```python\nfrom llama_index.llms.ai21 import AI21\nfrom llama_index.core.base.llms.types import ChatMessage\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"j2-chat\", api_key=api_key)\n\nmessages = [ChatMessage(role=\"user\", content=\"What is the meaning of life?\")]\nresponse = llm.chat(messages)\nprint(response.message.content)\n```\n\n### Complete\n\n```python\nfrom llama_index.llms.ai21 import AI21\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"j2-ultra\", api_key=api_key)\n\nresponse = llm.complete(prompt=\"What is the meaning of life?\")\nprint(response.text)\n```\n\n## Tokenizer\n\nThe type of the tokenizer is determined by the name of the model\n\n```python\nfrom llama_index.llms.ai21 import AI21\n\napi_key = \"your_api_key\"\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\ntokenizer = llm.tokenizer\n\ntokens = tokenizer.encode(\"What is the meaning of life?\")\nprint(tokens)\n\ntext = tokenizer.decode(tokens)\nprint(text)\n```\n\n## Async Support\n\nYou can also use the async functionalities\n\n### async chat\n\n```python\nfrom llama_index.llms.ai21 import AI21\nfrom llama_index.core.base.llms.types import ChatMessage\n\n\nasync def main():\n api_key = \"your_api_key\"\n llm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\n messages = [\n ChatMessage(role=\"user\", content=\"What is the meaning of life?\")\n ]\n response = await llm.achat(messages)\n print(response.message.content)\n```\n\n### async stream_chat\n\n```python\nfrom llama_index.llms.ai21 import AI21\nfrom llama_index.core.base.llms.types import ChatMessage\n\n\nasync def main():\n api_key = \"your_api_key\"\n llm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\n messages = [\n ChatMessage(role=\"user\", content=\"What is the meaning of life?\")\n ]\n response = await llm.astream_chat(messages)\n\n async for chunk in response:\n print(chunk.message.content)\n```\n\n## Tool Calling\n\n```python\nfrom llama_index.core.agent import FunctionCallingAgentWorker\nfrom llama_index.llms.ai21 import AI21\nfrom llama_index.core.tools import FunctionTool\n\n\ndef multiply(a: int, b: int) -> int:\n \"\"\"Multiply two integers and returns the result integer\"\"\"\n return a * b\n\n\ndef subtract(a: int, b: int) -> int:\n \"\"\"Subtract two integers and returns the result integer\"\"\"\n return a - b\n\n\ndef divide(a: int, b: int) -> float:\n \"\"\"Divide two integers and returns the result float\"\"\"\n return a - b\n\n\ndef add(a: int, b: int) -> int:\n \"\"\"Add two integers and returns the result integer\"\"\"\n return a + b\n\n\nmultiply_tool = FunctionTool.from_defaults(fn=multiply)\nadd_tool = FunctionTool.from_defaults(fn=add)\nsubtract_tool = FunctionTool.from_defaults(fn=subtract)\ndivide_tool = FunctionTool.from_defaults(fn=divide)\n\napi_key = \"your_api_key\"\n\nllm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n\nagent_worker = FunctionCallingAgentWorker.from_tools(\n [multiply_tool, add_tool, subtract_tool, divide_tool],\n llm=llm,\n verbose=True,\n allow_parallel_tool_calls=True,\n)\nagent = agent_worker.as_agent()\n\nresponse = agent.chat(\n \"My friend Moses had 10 apples. He ate 5 apples in the morning. Then he found a box with 25 apples.\"\n \"He divided all his apples between his 5 friends. How many apples did each friend get?\"\n)\n```\n",
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