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from llama_index import ServiceContext, LLMPredictor, LangchainEmbedding
from type import Optional
from core.lifecycle import Lifecycle
from langchain.manager import BaseLangChainManager
class ServiceContextManager(Lifecycle):
service_context: Optional[ServiceContext]
def __init__(self, manager: BaseLangChainManager) -> None:
super().__init__()
self.manager = manager
self.service_context = None
def get_service_context(self) -> ServiceContext:
if self.lifecycle_state.is_started():
raise KeyError(
"incorrect lifecycle state: {}".format(self.lifecycle_state.phase)
)
if self.service_context is None:
raise ValueError(
"service context is not ready, check for lifecycle statement"
)
return self.service_context
def do_init(self) -> None:
# define embedding
embedding = LangchainEmbedding(self.manager.get_embedding())
# define LLM
llm_predictor = LLMPredictor(llm=self.manager.get_llm())
# configure service context
self.service_context = ServiceContext.from_defaults(
llm_predictor=llm_predictor, embed_model=embedding
)
def do_start(self) -> None:
pass
def do_stop(self) -> None:
pass
def do_dispose(self) -> None:
pass
class StorageContextManager(Lifecycle):
def __init__(self, dataset_path: Optional[str] = "./dataset") -> None:
super().__init__()
self.dataset_path = dataset_path
def do_init(self) -> None:
pass
def do_start(self) -> None:
pass
def do_stop(self) -> None:
pass
def do_dispose(self) -> None:
pass
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