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chore(lint): cleanup repeated cause exception in logging.exception replaced by helpful message (#10425)

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This commit is contained in:
Bowen Liang
2024-11-15 15:41:40 +08:00
committed by GitHub
parent db1d2aaff5
commit 51db59622c
47 changed files with 83 additions and 72 deletions
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2
api/core/rag/datasource/vdb/couchbase/couchbase_vector.py
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@@ -242,7 +242,7 @@ class CouchbaseVector(BaseVector):
try:
self._cluster.query(query, named_parameters={"doc_ids": ids}).execute()
except Exception as e:
logger.exception(e)
logger.exception(f"Failed to delete documents, ids: {ids}")
def delete_by_document_id(self, document_id: str):
query = f"""

8
api/core/rag/datasource/vdb/lindorm/lindorm_vector.py
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@@ -79,7 +79,7 @@ class LindormVectorStore(BaseVector):
existing_docs = self._client.mget(index=self._collection_name, body={"ids": batch_ids}, _source=False)
return {doc["_id"] for doc in existing_docs["docs"] if doc["found"]}
except Exception as e:
logger.exception(f"Error fetching batch {batch_ids}: {e}")
logger.exception(f"Error fetching batch {batch_ids}")
return set()
@retry(stop=stop_after_attempt(3), wait=wait_fixed(60))
@@ -96,7 +96,7 @@ class LindormVectorStore(BaseVector):
)
return {doc["_id"] for doc in existing_docs["docs"] if doc["found"]}
except Exception as e:
logger.exception(f"Error fetching batch {batch_ids}: {e}")
logger.exception(f"Error fetching batch ids: {batch_ids}")
return set()
if ids is None:
@@ -177,7 +177,7 @@ class LindormVectorStore(BaseVector):
else:
logger.warning(f"Index '{self._collection_name}' does not exist. No deletion performed.")
except Exception as e:
logger.exception(f"Error occurred while deleting the index: {e}")
logger.exception(f"Error occurred while deleting the index: {self._collection_name}")
raise e
def text_exists(self, id: str) -> bool:
@@ -201,7 +201,7 @@ class LindormVectorStore(BaseVector):
try:
response = self._client.search(index=self._collection_name, body=query)
except Exception as e:
logger.exception(f"Error executing search: {e}")
logger.exception(f"Error executing vector search, query: {query}")
raise
docs_and_scores = []

2
api/core/rag/datasource/vdb/myscale/myscale_vector.py
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@@ -142,7 +142,7 @@ class MyScaleVector(BaseVector):
for r in self._client.query(sql).named_results()
]
except Exception as e:
logging.exception(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
logging.exception(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m") # noqa:TRY401
return []
def delete(self) -> None:

2
api/core/rag/datasource/vdb/opensearch/opensearch_vector.py
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@@ -158,7 +158,7 @@ class OpenSearchVector(BaseVector):
try:
response = self._client.search(index=self._collection_name.lower(), body=query)
except Exception as e:
logger.exception(f"Error executing search: {e}")
logger.exception(f"Error executing vector search, query: {query}")
raise
docs = []

8
api/core/rag/embedding/cached_embedding.py
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@@ -69,7 +69,7 @@ class CacheEmbedding(Embeddings):
except IntegrityError:
db.session.rollback()
except Exception as e:
logging.exception("Failed transform embedding: %s", e)
logging.exception("Failed transform embedding")
cache_embeddings = []
try:
for i, embedding in zip(embedding_queue_indices, embedding_queue_embeddings):
@@ -89,7 +89,7 @@ class CacheEmbedding(Embeddings):
db.session.rollback()
except Exception as ex:
db.session.rollback()
logger.exception("Failed to embed documents: %s", ex)
logger.exception("Failed to embed documents: %s")
raise ex
return text_embeddings
@@ -112,7 +112,7 @@ class CacheEmbedding(Embeddings):
embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
except Exception as ex:
if dify_config.DEBUG:
logging.exception(f"Failed to embed query text: {ex}")
logging.exception(f"Failed to embed query text '{text[:10]}...({len(text)} chars)'")
raise ex
try:
@@ -126,7 +126,7 @@ class CacheEmbedding(Embeddings):
redis_client.setex(embedding_cache_key, 600, encoded_str)
except Exception as ex:
if dify_config.DEBUG:
logging.exception("Failed to add embedding to redis %s", ex)
logging.exception(f"Failed to add embedding to redis for the text '{text[:10]}...({len(text)} chars)'")
raise ex
return embedding_results

2
api/core/rag/extractor/word_extractor.py
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@@ -229,7 +229,7 @@ class WordExtractor(BaseExtractor):
for i in url_pattern.findall(x.text):
hyperlinks_url = str(i)
except Exception as e:
logger.exception(e)
logger.exception("Failed to parse HYPERLINK xml")
def parse_paragraph(paragraph):
paragraph_content = []

2
api/core/rag/index_processor/processor/qa_index_processor.py
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@@ -159,7 +159,7 @@ class QAIndexProcessor(BaseIndexProcessor):
qa_documents.append(qa_document)
format_documents.extend(qa_documents)
except Exception as e:
logging.exception(e)
logging.exception("Failed to format qa document")
all_qa_documents.extend(format_documents)