620b2b03ca
Design the base interface of vector store, and apply it to the Chroma Vector Store (wrapped around llama_index's implementation). Provide the pipelines to populate and retrieve from vector store.
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
from abc import abstractmethod
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from typing import List, Type
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from langchain.embeddings.base import Embeddings as LCEmbeddings
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from theflow import Param
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from ..components import BaseComponent
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from ..documents.base import Document
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class BaseEmbeddings(BaseComponent):
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@abstractmethod
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def run_raw(self, text: str) -> List[float]:
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...
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@abstractmethod
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def run_batch_raw(self, text: List[str]) -> List[List[float]]:
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...
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@abstractmethod
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def run_document(self, text: Document) -> List[float]:
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...
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@abstractmethod
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def run_batch_document(self, text: List[Document]) -> List[List[float]]:
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...
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def is_document(self, text) -> bool:
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if isinstance(text, Document):
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return True
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elif isinstance(text, List) and isinstance(text[0], Document):
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return True
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return False
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def is_batch(self, text) -> bool:
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if isinstance(text, list):
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return True
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return False
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class LangchainEmbeddings(BaseEmbeddings):
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_lc_class: Type[LCEmbeddings]
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def __init__(self, **params):
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if self._lc_class is None:
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raise AttributeError(
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"Should set _lc_class attribute to the LLM class from Langchain "
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"if using LLM from Langchain"
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)
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self._kwargs: dict = {}
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for param in list(params.keys()):
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if param in self._lc_class.__fields__: # type: ignore
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self._kwargs[param] = params.pop(param)
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super().__init__(**params)
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def __setattr__(self, name, value):
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if name in self._lc_class.__fields__:
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setattr(self.agent, name, value)
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else:
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super().__setattr__(name, value)
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@Param.decorate(no_cache=True)
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def agent(self):
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return self._lc_class(**self._kwargs)
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def run_raw(self, text: str) -> List[float]:
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return self.agent.embed_query(text) # type: ignore
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def run_batch_raw(self, text: List[str]) -> List[List[float]]:
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return self.agent.embed_documents(text) # type: ignore
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def run_document(self, text: Document) -> List[float]:
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return self.agent.embed_query(text.text) # type: ignore
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def run_batch_document(self, text: List[Document]) -> List[List[float]]:
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return self.agent.embed_documents([each.text for each in text]) # type: ignore
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