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change milvus links from 2.0.0 to 2.0.x (#2496)

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* change milvus links from 2.0.0 to 2.0.x

* Update Documentation & Code Style

* fix two broken links

* Update Documentation & Code Style

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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Julian Risch 2022-05-04 18:30:50 +02:00 committed by GitHub
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2 changed files with 18 additions and 18 deletions
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18
docs/_src/api/api/document_store.md
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@ -2888,10 +2888,10 @@ Therefore, delete_documents() and update_embeddings() won't work yet.
Differences to 1.x:
Besides big architectural changes that impact performance and reliability 2.0 supports the filtering by scalar data types.
For Haystack users this means you can now run a query using vector similarity and filter for some meta data at the same time!
(See https://milvus.io/docs/v2.0.0/comparison.md for more details)
(See https://milvus.io/docs/v2.0.x/comparison.md for more details)
Usage:
1. Start a Milvus service via docker (see https://milvus.io/docs/v2.0.0/install_standalone-docker.md)
1. Start a Milvus service via docker (see https://milvus.io/docs/v2.0.x/install_standalone-docker.md)
2. Run pip install farm-haystack[milvus]
3. Init a MilvusDocumentStore() in Haystack
@ -2920,10 +2920,10 @@ def __init__(sql_url: str = "sqlite:///", host: str = "localhost", port: str = "
- `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/v2.0.0/data_manage.md.
Milvus metadata. For more details see https://milvus.io/docs/v1.1.0/data_manage.md.
- `milvus_url`: Milvus server connection URL for storing and processing vectors.
Protocol, host and port will automatically be inferred from the URL.
See https://milvus.io/docs/v2.0.0/install_milvus.md for instructions to start a Milvus instance.
See https://milvus.io/docs/v2.0.x/install_standalone-docker.md for instructions to start a Milvus instance.
- `connection_pool`: Connection pool type to connect with Milvus server. Default: "SingletonThread".
- `index`: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
- `vector_dim`: Deprecated. Use embedding_dim instead.
@ -2933,24 +2933,24 @@ When the size of newly inserted vectors reaches the specified volume, Milvus pac
Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
(From https://milvus.io/docs/v2.0.0/performance_faq.md)
(From https://milvus.io/docs/v2.0.x/performance_faq.md)
- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
However, you can normalize your embeddings and use `dot_product` to get the same results.
See https://milvus.io/docs/v2.0.0/metric.md.
See https://milvus.io/docs/v2.0.x/metric.md.
- `index_type`: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
Some popular options:
- FLAT (default): Exact method, slow
- IVF_FLAT, inverted file based heuristic, fast
- HSNW: Graph based, fast
- ANNOY: Tree based, fast
See: https://milvus.io/docs/v2.0.0/index.md
See: https://milvus.io/docs/v2.0.x/index.md
- `index_param`: Configuration parameters for the chose index_type needed at indexing time.
For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
See https://milvus.io/docs/v2.0.0/index.md
See https://milvus.io/docs/v2.0.x/index.md
- `search_param`: Configuration parameters for the chose index_type needed at query time
For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
See https://milvus.io/docs/v2.0.0/index.md
See https://milvus.io/docs/v2.0.x/index.md
- `return_embedding`: To return document embedding.
- `embedding_field`: Name of field containing an embedding vector.
- `progress_bar`: Whether to show a tqdm progress bar or not.

18
haystack/document_stores/milvus2.py
View File

@ -35,10 +35,10 @@ class Milvus2DocumentStore(SQLDocumentStore):
Differences to 1.x:
Besides big architectural changes that impact performance and reliability 2.0 supports the filtering by scalar data types.
For Haystack users this means you can now run a query using vector similarity and filter for some meta data at the same time!
(See https://milvus.io/docs/v2.0.0/comparison.md for more details)
(See https://milvus.io/docs/v2.0.x/comparison.md for more details)
Usage:
1. Start a Milvus service via docker (see https://milvus.io/docs/v2.0.0/install_standalone-docker.md)
1. Start a Milvus service via docker (see https://milvus.io/docs/v2.0.x/install_standalone-docker.md)
2. Run pip install farm-haystack[milvus]
3. Init a MilvusDocumentStore() in Haystack
@ -83,10 +83,10 @@ class Milvus2DocumentStore(SQLDocumentStore):
"""
: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/v2.0.0/data_manage.md.
Milvus metadata. For more details see https://milvus.io/docs/v1.1.0/data_manage.md.
:param milvus_url: Milvus server connection URL for storing and processing vectors.
Protocol, host and port will automatically be inferred from the URL.
See https://milvus.io/docs/v2.0.0/install_milvus.md for instructions to start a Milvus instance.
See https://milvus.io/docs/v2.0.x/install_standalone-docker.md for instructions to start a Milvus instance.
:param connection_pool: Connection pool type to connect with Milvus server. Default: "SingletonThread".
:param index: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
:param vector_dim: Deprecated. Use embedding_dim instead.
@ -96,24 +96,24 @@ class Milvus2DocumentStore(SQLDocumentStore):
Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
(From https://milvus.io/docs/v2.0.0/performance_faq.md)
(From https://milvus.io/docs/v2.0.x/performance_faq.md)
:param similarity: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
However, you can normalize your embeddings and use `dot_product` to get the same results.
See https://milvus.io/docs/v2.0.0/metric.md.
See https://milvus.io/docs/v2.0.x/metric.md.
:param index_type: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
Some popular options:
- FLAT (default): Exact method, slow
- IVF_FLAT, inverted file based heuristic, fast
- HSNW: Graph based, fast
- ANNOY: Tree based, fast
See: https://milvus.io/docs/v2.0.0/index.md
See: https://milvus.io/docs/v2.0.x/index.md
:param index_param: Configuration parameters for the chose index_type needed at indexing time.
For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
See https://milvus.io/docs/v2.0.0/index.md
See https://milvus.io/docs/v2.0.x/index.md
:param search_param: Configuration parameters for the chose index_type needed at query time
For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
See https://milvus.io/docs/v2.0.0/index.md
See https://milvus.io/docs/v2.0.x/index.md
:param return_embedding: To return document embedding.
:param embedding_field: Name of field containing an embedding vector.
:param progress_bar: Whether to show a tqdm progress bar or not.