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community[patch]: langchain_community.vectorstores.azuresearch Raise LangChainException instead of bare Exception (#23935)

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Raise `LangChainException` instead of `Exception`. This alleviates the
need for library users to use bare try/except to handle exceptions
raised by `AzureSearch`.

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This commit is contained in:
Marc Gibbons 2024-07-26 11:59:06 -04:00 committed by GitHub
parent 3d16dcd88d
commit cc451effd1
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1 changed files with 6 additions and 5 deletions
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11
libs/community/langchain_community/vectorstores/azuresearch.py
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@ -29,6 +29,7 @@ from langchain_core.callbacks import (
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.exceptions import LangChainException
from langchain_core.pydantic_v1 import root_validator
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import get_from_env
@ -463,7 +464,7 @@ class AzureSearch(VectorStore):
response = self.client.upload_documents(documents=data)
# Check if all documents were successfully uploaded
if not all(r.succeeded for r in response):
raise Exception(response)
raise LangChainException(response)
# Reset data
data = []
@ -477,7 +478,7 @@ class AzureSearch(VectorStore):
if all(r.succeeded for r in response):
return ids
else:
raise Exception(response)
raise LangChainException(response)
async def aadd_embeddings(
self,
@ -521,7 +522,7 @@ class AzureSearch(VectorStore):
response = await async_client.upload_documents(documents=data)
# Check if all documents were successfully uploaded
if not all(r.succeeded for r in response):
raise Exception(response)
raise LangChainException(response)
# Reset data
data = []
@ -536,7 +537,7 @@ class AzureSearch(VectorStore):
if all(r.succeeded for r in response):
return ids
else:
raise Exception(response)
raise LangChainException(response)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
"""Delete by vector ID.
@ -1495,7 +1496,7 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
"""Azure Search instance used to find similar documents."""
search_type: str = "hybrid"
"""Type of search to perform. Options are "similarity", "hybrid",
"semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold",
"semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold",
or "semantic_hybrid_score_threshold"."""
k: int = 4
"""Number of documents to return."""