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community: Fix Milvus got multiple values for keyword argument 'timeout' (#19232)
- **Description:** Fix Milvus got multiple values for keyword argument 'timeout' - **Issue:** fix #18580 - @baskaryan @eyurtsev PTAL
This commit is contained in:
Guangdong Liu
GitHub
parent
95904fe443
commit
c3310c5e7f
@ -465,6 +465,7 @@ class Milvus(VectorStore):
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from pymilvus import Collection, utility
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from pymilvus.client.types import LoadState
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timeout = self.timeout or timeout
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if (
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isinstance(self.col, Collection)
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and self._get_index() is not None
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@ -481,7 +482,7 @@ class Milvus(VectorStore):
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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batch_size: int = 1000,
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*,
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ids: Optional[List[str]] = None,
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@ -502,7 +503,7 @@ class Milvus(VectorStore):
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metadatas (Optional[List[dict]]): Metadata dicts attached to each of
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the texts. Defaults to None.
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should be less than 65535 bytes. Required and work when auto_id is False.
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timeout (Optional[int]): Timeout for each batch insert. Defaults
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timeout (Optional[float]): Timeout for each batch insert. Defaults
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to None.
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batch_size (int, optional): Batch size to use for insertion.
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Defaults to 1000.
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@ -590,6 +591,7 @@ class Milvus(VectorStore):
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# Insert into the collection.
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try:
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res: Collection
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timeout = self.timeout or timeout
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res = self.col.insert(insert_list, timeout=timeout, **kwargs)
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pks.extend(res.primary_keys)
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except MilvusException as e:
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@ -606,7 +608,7 @@ class Milvus(VectorStore):
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k: int = 4,
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param: Optional[dict] = None,
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expr: Optional[str] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform a similarity search against the query string.
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@ -627,6 +629,7 @@ class Milvus(VectorStore):
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if self.col is None:
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logger.debug("No existing collection to search.")
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return []
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timeout = self.timeout or timeout
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res = self.similarity_search_with_score(
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query=query, k=k, param=param, expr=expr, timeout=timeout, **kwargs
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)
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@ -638,7 +641,7 @@ class Milvus(VectorStore):
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k: int = 4,
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param: Optional[dict] = None,
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expr: Optional[str] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform a similarity search against the query string.
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@ -659,6 +662,7 @@ class Milvus(VectorStore):
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if self.col is None:
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logger.debug("No existing collection to search.")
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return []
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timeout = self.timeout or timeout
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res = self.similarity_search_with_score_by_vector(
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embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
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)
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@ -670,7 +674,7 @@ class Milvus(VectorStore):
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k: int = 4,
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param: Optional[dict] = None,
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expr: Optional[str] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Perform a search on a query string and return results with score.
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@ -685,7 +689,7 @@ class Milvus(VectorStore):
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param (dict): The search params for the specified index.
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Defaults to None.
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expr (str, optional): Filtering expression. Defaults to None.
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timeout (int, optional): How long to wait before timeout error.
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timeout (float, optional): How long to wait before timeout error.
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Defaults to None.
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kwargs: Collection.search() keyword arguments.
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@ -698,7 +702,7 @@ class Milvus(VectorStore):
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# Embed the query text.
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embedding = self.embedding_func.embed_query(query)
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timeout = self.timeout or timeout
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res = self.similarity_search_with_score_by_vector(
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embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
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)
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@ -710,7 +714,7 @@ class Milvus(VectorStore):
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k: int = 4,
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param: Optional[dict] = None,
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expr: Optional[str] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Perform a search on a query string and return results with score.
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@ -725,7 +729,7 @@ class Milvus(VectorStore):
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param (dict): The search params for the specified index.
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Defaults to None.
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expr (str, optional): Filtering expression. Defaults to None.
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timeout (int, optional): How long to wait before timeout error.
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timeout (float, optional): How long to wait before timeout error.
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Defaults to None.
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kwargs: Collection.search() keyword arguments.
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@ -742,7 +746,7 @@ class Milvus(VectorStore):
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# Determine result metadata fields with PK.
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output_fields = self.fields[:]
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output_fields.remove(self._vector_field)
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timeout = self.timeout or timeout
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# Perform the search.
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res = self.col.search(
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data=[embedding],
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@ -772,7 +776,7 @@ class Milvus(VectorStore):
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lambda_mult: float = 0.5,
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param: Optional[dict] = None,
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expr: Optional[str] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform a search and return results that are reordered by MMR.
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@ -789,7 +793,7 @@ class Milvus(VectorStore):
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param (dict, optional): The search params for the specified index.
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Defaults to None.
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expr (str, optional): Filtering expression. Defaults to None.
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timeout (int, optional): How long to wait before timeout error.
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timeout (float, optional): How long to wait before timeout error.
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Defaults to None.
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kwargs: Collection.search() keyword arguments.
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@ -802,7 +806,7 @@ class Milvus(VectorStore):
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return []
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embedding = self.embedding_func.embed_query(query)
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timeout = self.timeout or timeout
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return self.max_marginal_relevance_search_by_vector(
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embedding=embedding,
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k=k,
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@ -822,7 +826,7 @@ class Milvus(VectorStore):
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lambda_mult: float = 0.5,
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param: Optional[dict] = None,
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expr: Optional[str] = None,
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timeout: Optional[int] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform a search and return results that are reordered by MMR.
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@ -839,7 +843,7 @@ class Milvus(VectorStore):
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param (dict, optional): The search params for the specified index.
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Defaults to None.
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expr (str, optional): Filtering expression. Defaults to None.
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timeout (int, optional): How long to wait before timeout error.
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timeout (float, optional): How long to wait before timeout error.
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Defaults to None.
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kwargs: Collection.search() keyword arguments.
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@ -856,7 +860,7 @@ class Milvus(VectorStore):
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# Determine result metadata fields.
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output_fields = self.fields[:]
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output_fields.remove(self._vector_field)
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timeout = self.timeout or timeout
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# Perform the search.
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res = self.col.search(
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data=[embedding],
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