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178 | class NotDiamondSelector(LLMSingleSelector):
def __init__(
self,
client: NotDiamond,
metric: Metric = None,
timeout: int = 10,
api_key: str = None,
*args,
**kwargs,
):
"""
Initialize a NotDiamondSelector. Users should instantiate and configure a NotDiamond client as needed before
creating this selector. The constructor will raise errors re: required client fields.
"""
# Not needed - we will route using our own client based on the query prompt
# Add @property for _llm here
_encap_selector = LLMSingleSelector.from_defaults()
self._llm = None
self._prompt = _encap_selector._prompt
if not getattr(client, "llm_configs", None):
raise ValueError(
"NotDiamond client must have llm_configs before creating a NotDiamondSelector."
)
if metric and not isinstance(metric, Metric):
raise ValueError(f"Invalid metric - needed type Metric but got {metric}")
self._metric = metric or Metric("accuracy")
self._client = client
self._llms = [
self._llm_config_to_client(llm_config)
for llm_config in self._client.llm_configs
]
self._timeout = timeout
super().__init__(_encap_selector._llm, _encap_selector._prompt, *args, **kwargs)
def _llm_config_to_client(self, llm_config: LLMConfig | str) -> LLM:
"""
For the selected LLMConfig dynamically create an LLM instance. NotDiamondSelector will
assign this to self._llm to help select the best index.
"""
if isinstance(llm_config, str):
llm_config = LLMConfig.from_string(llm_config)
provider, model = llm_config.provider, llm_config.model
output = None
if provider == "openai":
from llama_index.llms.openai import OpenAI
output = OpenAI(model=model, api_key=os.getenv("OPENAI_API_KEY"))
elif provider == "anthropic":
from llama_index.llms.anthropic import Anthropic
output = Anthropic(model=model, api_key=os.getenv("ANTHROPIC_API_KEY"))
elif provider == "cohere":
from llama_index.llms.cohere import Cohere
output = Cohere(model=model, api_key=os.getenv("COHERE_API_KEY"))
elif provider == "mistral":
from llama_index.llms.mistralai import MistralAI
output = MistralAI(model=model, api_key=os.getenv("MISTRALAI_API_KEY"))
elif provider == "togetherai":
from llama_index.llms.together import TogetherLLM
output = TogetherLLM(model=model, api_key=os.getenv("TOGETHERAI_API_KEY"))
else:
raise ValueError(f"Unsupported provider for NotDiamondSelector: {provider}")
return output
def _select(
self, choices: Sequence[ToolMetadata], query: QueryBundle, timeout: int = None
) -> SelectorResult:
"""
Call Not Diamond to select the best LLM for the given prompt, then have the LLM select the best tool.
"""
messages = [
{"role": "system", "content": self._format_prompt(choices, query)},
{"role": "user", "content": query.query_str},
]
session_id, best_llm = self._client.model_select(
messages=messages,
llm_configs=self._client.llm_configs,
metric=self._metric,
notdiamond_api_key=self._client.api_key,
max_model_depth=self._client.max_model_depth,
hash_content=self._client.hash_content,
tradeoff=self._client.tradeoff,
preference_id=self._client.preference_id,
tools=self._client.tools,
timeout=timeout or self._timeout,
)
self._llm = self._llm_config_to_client(best_llm)
return NotDiamondSelectorResult.from_selector_result(
super()._select(choices, query), session_id, best_llm
)
async def _aselect(
self, choices: Sequence[ToolMetadata], query: QueryBundle, timeout: int = None
) -> SelectorResult:
"""
Call Not Diamond asynchronously to select the best LLM for the given prompt, then have the LLM select the best tool.
"""
messages = [
{"role": "system", "content": self._format_prompt(choices, query)},
{"role": "user", "content": query.query_str},
]
session_id, best_llm = await self._client.amodel_select(
messages=messages,
llm_configs=self._client.llm_configs,
metric=self._metric,
notdiamond_api_key=self._client.api_key,
max_model_depth=self._client.max_model_depth,
hash_content=self._client.hash_content,
tradeoff=self._client.tradeoff,
preference_id=self._client.preference_id,
tools=self._client.tools,
timeout=timeout or self._timeout,
)
self._llm = self._llm_config_to_client(best_llm)
return NotDiamondSelectorResult.from_selector_result(
await super()._aselect(choices, query), session_id, best_llm
)
def _format_prompt(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> str:
"""
A system prompt for selection is created when instantiating the parent LLMSingleSelector class.
This method formats the prompt into a str so that it can be serialized for the NotDiamond API.
"""
context_list = _build_choices_text(choices)
return self._prompt.format(
num_choices=len(choices),
context_list=context_list,
query_str=query.query_str,
)
|