Bases: OpenAILike
Neutrino LLM.
Examples:
pip install llama-index-llms-neutrino
You can create an API key at: platform.neutrinoapp.com
import os
os.environ["NEUTRINO_API_KEY"] = "<your-neutrino-api-key>"
A router is a collection of LLMs that you can route queries to. You can create a router in the Neutrino dashboard or use the default router,
which includes all supported models.
You can treat a router as a LLM.
from llama_index.llms.neutrino import Neutrino
llm = Neutrino(
# api_key="<your-neutrino-api-key>",
# router="<your-router-id>" # (or 'default')
)
response = llm.complete("In short, a Neutrino is")
print(f"Optimal model: {response.raw['model']}")
print(response)
Source code in llama-index-integrations/llms/llama-index-llms-neutrino/llama_index/llms/neutrino/base.py
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95 | class Neutrino(OpenAILike):
"""Neutrino LLM.
Examples:
`pip install llama-index-llms-neutrino`
You can create an API key at: <a href="https://platform.neutrinoapp.com/">platform.neutrinoapp.com</a>
```python
import os
os.environ["NEUTRINO_API_KEY"] = "<your-neutrino-api-key>"
```
A router is a collection of LLMs that you can route queries to. You can create a router in the Neutrino <a href="https://platform.neutrinoapp.com/">dashboard</a> or use the default router,
which includes all supported models.
You can treat a router as a LLM.
```python
from llama_index.llms.neutrino import Neutrino
llm = Neutrino(
# api_key="<your-neutrino-api-key>",
# router="<your-router-id>" # (or 'default')
)
response = llm.complete("In short, a Neutrino is")
print(f"Optimal model: {response.raw['model']}")
print(response)
```
"""
model: str = Field(
description="The Neutrino router to use. See https://docs.neutrinoapp.com/router for details."
)
context_window: int = Field(
default=MAX_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model. Defaults to the largest supported model (Claude).",
gt=0,
)
is_chat_model: bool = Field(
default=True,
description=LLMMetadata.__fields__["is_chat_model"].field_info.description,
)
def __init__(
self,
model: Optional[str] = None,
router: str = DEFAULT_ROUTER,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 5,
api_base: Optional[str] = DEFAULT_API_BASE,
api_key: Optional[str] = None,
**kwargs: Any,
) -> None:
additional_kwargs = additional_kwargs or {}
api_base = get_from_param_or_env("api_base", api_base, "NEUTRINO_API_BASE")
api_key = get_from_param_or_env("api_key", api_key, "NEUTRINO_API_KEY")
model = model or router
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
api_base=api_base,
api_key=api_key,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
**kwargs,
)
@classmethod
def class_name(cls) -> str:
return "Neutrino_LLM"
|