class LangchainEmbedding(BaseEmbedding):
"""外部嵌入（来自Langchain）。
Args:
langchain_embedding（langchain.embeddings.Embeddings）：Langchain嵌入类。"""
_langchain_embedding: "LCEmbeddings" = PrivateAttr()
_async_not_implemented_warned: bool = PrivateAttr(default=False)
def __init__(
self,
langchain_embeddings: "LCEmbeddings",
model_name: Optional[str] = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
callback_manager: Optional[CallbackManager] = None,
):
# attempt to get a useful model name
if model_name is not None:
model_name = model_name
elif hasattr(langchain_embeddings, "model_name"):
model_name = langchain_embeddings.model_name
elif hasattr(langchain_embeddings, "model"):
model_name = langchain_embeddings.model
else:
model_name = type(langchain_embeddings).__name__
self._langchain_embedding = langchain_embeddings
super().__init__(
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
model_name=model_name,
)
@classmethod
def class_name(cls) -> str:
return "LangchainEmbedding"
def _async_not_implemented_warn_once(self) -> None:
if not self._async_not_implemented_warned:
print("Async embedding not available, falling back to sync method.")
self._async_not_implemented_warned = True
def _get_query_embedding(self, query: str) -> List[float]:
"""获取查询嵌入。"""
return self._langchain_embedding.embed_query(query)
async def _aget_query_embedding(self, query: str) -> List[float]:
try:
return await self._langchain_embedding.aembed_query(query)
except NotImplementedError:
# Warn the user that sync is being used
self._async_not_implemented_warn_once()
return self._get_query_embedding(query)
async def _aget_text_embedding(self, text: str) -> List[float]:
try:
embeds = await self._langchain_embedding.aembed_documents([text])
return embeds[0]
except NotImplementedError:
# Warn the user that sync is being used
self._async_not_implemented_warn_once()
return self._get_text_embedding(text)
def _get_text_embedding(self, text: str) -> List[float]:
"""获取文本嵌入。"""
return self._langchain_embedding.embed_documents([text])[0]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""获取文本嵌入。"""
return self._langchain_embedding.embed_documents(texts)