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Deprecate Milvus1DocumentStore (#2495)

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* Add warning message

* Update doc string

* Update Documentation & Code Style

* Change DeprecationWarning to FutureWarning

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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bogdankostic 2022-05-04 15:09:57 +02:00 committed by GitHub
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3 changed files with 19 additions and 6 deletions
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10
docs/_src/api/api/document_store.md
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@ -2569,6 +2569,10 @@ Usage:
def __init__(sql_url: str = "sqlite:///", milvus_url: str = "tcp://localhost:19530", connection_pool: str = "SingletonThread", index: str = "document", vector_dim: int = None, embedding_dim: int = 768, index_file_size: int = 1024, similarity: str = "dot_product", index_type: IndexType = IndexType.FLAT, index_param: Optional[Dict[str, Any]] = None, search_param: Optional[Dict[str, Any]] = None, return_embedding: bool = False, embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = "overwrite", isolation_level: str = None)
```
**WARNING:** Milvus1DocumentStore is deprecated and will be removed in a future version. Please switch to Milvus2
or consider using another DocumentStore.
**Arguments**:
- `sql_url`: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
@ -4436,9 +4440,9 @@ deployment, Postgres is recommended.
- `embedding_dim`: The embedding vector size.
- `return_embedding`: Whether to return document embeddings.
- `index`: Name of index in document store to use.
- `similarity`: The similarity function used to compare document vectors. `"dot_product"` is the default
since it is more performant with DPR embeddings. `"cosine"` is recommended if you are using a
Sentence-Transformer model.
- `similarity`: The similarity function used to compare document vectors. `"cosine"` is the default
and is recommended if you are using a Sentence-Transformer model. `"dot_product"` is more performant
with DPR embeddings.
In both cases, the returned values in Document.score are normalized to be in range [0,1]:
- For `"dot_product"`: `expit(np.asarray(raw_score / 100))`
- For `"cosine"`: `(raw_score + 1) / 2`

6
docs/_src/tutorials/tutorials/16.md
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@ -26,10 +26,10 @@ This tutorial will show you how to integrate a classification model into your pr
# Install the latest master of Haystack
!pip install --upgrade pip
!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab, ocr]
!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]
!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
!tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
# Install pygraphviz
!apt install libgraphviz-dev

9
haystack/document_stores/milvus1.py
View File

@ -62,6 +62,9 @@ class Milvus1DocumentStore(SQLDocumentStore):
isolation_level: str = None,
):
"""
**WARNING:** Milvus1DocumentStore is deprecated and will be removed in a future version. Please switch to Milvus2
or consider using another DocumentStore.
:param sql_url: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
deployment, Postgres is recommended. If using MySQL then same server can also be used for
Milvus metadata. For more details see https://milvus.io/docs/v1.0.0/data_manage.md.
@ -105,6 +108,12 @@ class Milvus1DocumentStore(SQLDocumentStore):
exists.
:param isolation_level: see SQLAlchemy's `isolation_level` parameter for `create_engine()` (https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.isolation_level)
"""
deprecation_message = (
"Milvus1DocumentStore is deprecated and will be removed in a future version. "
"Please consider switching to Milvus2 or to another DocumentStore."
)
warnings.warn(message=deprecation_message, category=FutureWarning, stacklevel=3)
super().__init__(
url=sql_url, index=index, duplicate_documents=duplicate_documents, isolation_level=isolation_level
)