# Weaviate#

One can use Weaviate as the document store for DocumentArray. It is useful when one wants to have faster Document retrieval on embeddings, i.e. .match(), .find().

Tip

This feature requires weaviate-client. You can install it via pip install "docarray[weaviate]".

Here is a video tutorial that guides you to build a simple image search using Weaviate and Docarray.

## Usage#

### Start Weaviate service#

To use Weaviate as the storage backend, it is required to have the Weaviate service started. Create docker-compose.yml as follows:

---
version: '3.4'
services:
weaviate:
command:
- --host
- 0.0.0.0
- --port
- '8080'
- --scheme
- http
image: semitechnologies/weaviate:1.13.2
ports:
- "8080:8080"
restart: on-failure:0
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'none'
ENABLE_MODULES: ''
CLUSTER_HOSTNAME: 'node1'
...


Then

docker compose up


### Create DocumentArray with Weaviate backend#

Assuming service is started using the default configuration (i.e. server address is http://localhost:8080), one can instantiate a DocumentArray with Weaviate storage as such:

from docarray import DocumentArray

da = DocumentArray(storage='weaviate')


The usage would be the same as the ordinary DocumentArray.

To access a DocumentArray formerly persisted, one can specify the name, the host, the port and the protocol to connect to the server. name is required in this case but other connection parameters are optional. If they are not provided, then it will connect to the Weaviate service bound to http://localhost:8080.

Note, that the name parameter in config needs to be capitalized.

from docarray import DocumentArray

da = DocumentArray(
storage='weaviate', config={'name': 'Persisted', 'host': 'localhost', 'port': 1234}
)

da.summary()


Other functions behave the same as in-memory DocumentArray.

## Config#

The following configs can be set:

Name Description Default
host Hostname of the Weaviate server 'localhost'
port port of the Weaviate server 8080
protocol protocol to be used. Can be 'http' or 'https' 'http'
name Weaviate class name; the class name of Weaviate object to presesent this DocumentArray None
serialize_config Serialization config of each Document None
distance The distance metric used to compute the distance between vectors. Must be either cosine or l2-squared. None, defaults to the default value in Weaviate*
ef The size of the dynamic list for the nearest neighbors (used during the search). The higher ef is chosen, the more accurate, but also slower a search becomes. None, defaults to the default value in Weaviate*
ef_construction The size of the dynamic list for the nearest neighbors (used during the construction). Controls index search speed/build speed tradeoff. None, defaults to the default value in Weaviate*
timeout_config Set the timeout configuration for all requests to the Weaviate server. None, defaults to the default value in Weaviate*
max_connections The maximum number of connections per element in all layers. None, defaults to the default value in Weaviate*
dynamic_ef_min If using dynamic ef (set to -1), this value acts as a lower boundary. Even if the limit is small enough to suggest a lower value, ef will never drop below this value. This helps in keeping search accuracy high even when setting very low limits, such as 1, 2, or 3. None, defaults to the default value in Weaviate*
dynamic_ef_max If using dynamic ef (set to -1), this value acts as an upper boundary. Even if the limit is large enough to suggest a lower value, ef will be capped at this value. This helps to keep search speed reasonable when retrieving massive search result sets, e.g. 500+. None, defaults to the default value in Weaviate*
dynamic_ef_factor If using dynamic ef (set to -1), this value controls how ef is determined based on the given limit. E.g. with a factor of 8, ef will be set to 8*limit as long as this value is between the lower and upper boundary. It will be capped on either end, otherwise. None, defaults to the default value in Weaviate*
vector_cache_max_objects For optimal search and import performance all previously imported vectors need to be held in memory. However, Weaviate also allows for limiting the number of vectors in memory. By default, when creating a new class, this limit is set to 2M objects. A disk lookup for a vector is orders of magnitudes slower than memory lookup, so the cache should be used sparingly. None, defaults to the default value in Weaviate*
flat_search_cutoff Absolute number of objects configured as the threshold for a flat-search cutoff. If a filter on a filtered vector search matches fewer than the specified elements, the HNSW index is bypassed entirely and a flat (brute-force) search is performed instead. This can speed up queries with very restrictive filters considerably. Optional, defaults to 40000. Set to 0 to turn off flat-search cutoff entirely. None, defaults to the default value in Weaviate*
cleanup_interval_seconds How often the async process runs that “repairs” the HNSW graph after deletes and updates. (Prior to the repair/cleanup process, deleted objects are simply marked as deleted, but still a fully connected member of the HNSW graph. After the repair has run, the edges are reassigned and the datapoints deleted for good). Typically this value does not need to be adjusted, but if deletes or updates are very frequent it might make sense to adjust the value up or down. (Higher value means it runs less frequently, but cleans up more in a single batch. Lower value means it runs more frequently, but might not be as efficient with each run). None, defaults to the default value in Weaviate*
skip There are situations where it doesn’t make sense to vectorize a class. For example if the class is just meant as glue between two other class (consisting only of references) or if the class contains mostly duplicate elements (Note that importing duplicate vectors into HNSW is very expensive as the algorithm uses a check whether a candidate’s distance is higher than the worst candidate’s distance for an early exit condition. With (mostly) identical vectors, this early exit condition is never met leading to an exhaustive search on each import or query). In this case, you can skip indexing a vector all-together. To do so, set "skip" to "true". skip defaults to false; if not set to true, classes will be indexed normally. This setting is immutable after class initialization. None, defaults to the default value in Weaviate*

*You can read more about the HNSW parameters and their default values here

## Minimum example#

The following example shows how to use DocArray with Weaviate Document Store in order to index and search text Documents.

First, let’s run the create the DocumentArray instance (make sure a Weaviate server is up and running):

from docarray import DocumentArray

da = DocumentArray(
storage="weaviate", config={"name": "Persisted", "host": "localhost", "port": 8080}
)


Then, we can index some Documents:

from docarray import Document

da.extend(
[
Document(text='Persist Documents with Weaviate.'),
Document(text='And enjoy fast nearest neighbor search.'),
Document(text='All while using DocArray API.'),
]
)


Now, we can generate embeddings inside the database using BERT model:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')

def collate_fn(da):
return tokenizer(da.texts, return_tensors='pt', truncation=True, padding=True)

da.embed(model, collate_fn=collate_fn)


Finally, we can query the database and print the results:

results = da.find(
DocumentArray([Document(text='How to persist Documents')]).embed(
model, collate_fn=collate_fn
),
limit=1,
)

print(results[0].texts)

Persist Documents with Weaviate.


## Filtering#

Search with .find can be restricted by user-defined filters. Such filters can be constructed following the guidelines in Weaviate’s Documentation.

### Example of .find with a filter only#

Consider you store Documents with a certain tag price into weaviate and you want to retrieve all Documents with price lower or equal to some max_price value.

You can index such Documents as follows:

from docarray import Document, DocumentArray
import numpy as np

n_dim = 3
da = DocumentArray(
storage='weaviate',
config={
'n_dim': n_dim,
'columns': [('price', 'float')],
},
)

with da:
da.extend([Document(id=f'r{i}', tags={'price': i}) for i in range(10)])

print('\nIndexed Prices:\n')
for price in da[:, 'tags__price']:
print(f'\t price={price}')


Then you can retrieve all documents whose price is lower than or equal to max_price by applying the following filter:

max_price = 3
n_limit = 4

filter = {'path': 'price', 'operator': 'LessThanEqual', 'valueNumber': max_price}
results = da.find(filter=filter)

print('\n Returned examples that verify filter "price at most 3":\n')
for price in results[:, 'tags__price']:
print(f'\t price={price}')


This would print

 Returned examples that satisfy condition "price at most 3":

price=0
price=1
price=2
price=3


### Example of .find with query vector and filter#

Consider Documents with embeddings [0,0,0] up to  [9,9,9] where the document with embedding [i,i,i] has as tag price with value i. We can create such example with the following code:

from docarray import Document, DocumentArray
import numpy as np

n_dim = 3

da = DocumentArray(
storage='weaviate',
config={'n_dim': n_dim, 'columns': [('price', 'int')], 'distance': 'l2-squared'},
)

with da:
da.extend(
[
Document(id=f'r{i}', embedding=i * np.ones(n_dim), tags={'price': i})
for i in range(10)
]
)

print('\nIndexed Prices:\n')
for embedding, price in zip(da.embeddings, da[:, 'tags__price']):
print(f'\tembedding={embedding},\t price={price}')


Consider we want the nearest vectors to the embedding [8. 8. 8.], with the restriction that prices must follow a filter. As an example, let’s consider that retrieved documents must have price value lower or equal than max_price. We can encode this information in weaviate using filter = {'path': ['price'], 'operator': 'LowerThanEqual', 'valueInt': max_price}.

Then the search with the proposed filter can be implemented and used with the following code:

max_price = 7
n_limit = 4

np_query = np.ones(n_dim) * 8
print(f'\nQuery vector: \t{np_query}')

filter = {'path': ['price'], 'operator': 'LessThanEqual', 'valueInt': max_price}
results = da.find(np_query, filter=filter, limit=n_limit)

print('\nEmbeddings Nearest Neighbours with "price" at most 7:\n')
for embedding, price in zip(results.embeddings, results[:, 'tags__price']):
print(f'\tembedding={embedding},\t price={price}')


This would print:

Embeddings Nearest Neighbours with "price" at most 7:

embedding=[7. 7. 7.],	 price=7
embedding=[6. 6. 6.],	 price=6
embedding=[5. 5. 5.],	 price=5
embedding=[4. 4. 4.],	 price=4


## Sorting#

Note: Support for sorting was added to the weaviate-client v3.5.0. You can upgrade with the following command: pip install --upgrade weaviate-client

You can sort results by any primitive property, typically a text, string, number, or int property. When a query has a natural order (e.g. because of a near vector search), adding a sort operator will override the order.

Further documentation here.

### Example of .find with vector and sort#

Consider Documents with the column ‘price’ and on the return you want to sort these documents by highest price to lowest price. You can create an example with the following code:

from docarray import Document, DocumentArray
import numpy as np

n_dim = 3

# make connection and set columns
da = DocumentArray(
storage='weaviate',
config={
'n_dim': n_dim,
'columns': [('price', 'float')],
'distance': 'l2-squared',
"name": "Persisted",
"host": "localhost",
"port": 8080
},
)

# load the dummy data
with da:
da.extend([Document(text=f'word{i}', id=f'r{i}', embedding=i * np.ones(n_dim), tags={'price': i}) for i in range(10)])

np_query = np.ones(n_dim) * 8
sort = sort = [{"path": ["price"], "order": "desc"}]  # or "asc"
results = da.find(np_query, sort=sort)

print('\n Returned examples that verify results are in order from highest price to lowest:\n')
for embedding, price in zip(results.embeddings, results[:, 'tags__price']):
print(f'\tembedding={embedding},\t price={price}')



This would print:

Returned examples that verify results are in order from highest price to lowest:

embedding=[9. 9. 9.],	 price=9
embedding=[8. 8. 8.],	 price=8
embedding=[7. 7. 7.],	 price=7
embedding=[6. 6. 6.],	 price=6
embedding=[5. 5. 5.],	 price=5
embedding=[4. 4. 4.],	 price=4
embedding=[3. 3. 3.],	 price=3
embedding=[2. 2. 2.],	 price=2
embedding=[1. 1. 1.],	 price=1
embedding=[0. 0. 0.],	 price=0


For ascending the results would be as expected:

    embedding=[0. 0. 0.],    price=0
embedding=[1. 1. 1.],    price=1
embedding=[2. 2. 2.],    price=2
embedding=[3. 3. 3.],    price=3
embedding=[4. 4. 4.],    price=4
embedding=[5. 5. 5.],    price=5
embedding=[6. 6. 6.],    price=6
embedding=[7. 7. 7.],    price=7
embedding=[8. 8. 8.],    price=8
embedding=[9. 9. 9.],    price=9


## Set minimum certainty on query results#

The DocArray/Weaviate find class uses the NearVector search argument since Weaviate is only being used in this combination to store vectors generated by DocArray. Sometimes you want to set the certainty at a certain level to limit the return results. You can do this with the query_params argument in the find() method.

query_params is a Dictionary element that combines itself with the request body. To set you must pass the value as a Dict (query_params={"key": "value}) within the find() function

If you are familiar with Weaviates GraphQL structure then you can see where the query_params goes:

{
Get{
Persisted(
nearVector: {
vector: [-0.36840257,0.13973749,-0.28994447, ...]
# query_params goes here. Example: "certainty": 0.9
}
){
name
_additional {
certainty
}
}
}
}


With that said, theoretically, anything other than Certainty that can be used in this query position can be used with query_params as well.

Further documentation Here

Using the above minimum example you can add the minimum certainty score.

from docarray import DocumentArray, Document
from transformers import AutoModel, AutoTokenizer

# make connection
da = DocumentArray(
storage="weaviate", config={"name": "Persisted", "host": "localhost", "port": 8080}
)

# load the dummy data
da.extend(
[
Document(text='Persist Documents with Weaviate.'),
Document(text='And enjoy fast nearest neighbor search.'),
Document(text='All while using DocArray API.'),
]
)

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')

def collate_fn(da):
return tokenizer(da.texts, return_tensors='pt', truncation=True, padding=True)

da.embed(model, collate_fn=collate_fn)

results = da.find(
DocumentArray([Document(text='Persist Documents with Weaviate.')]).embed(
model,
collate_fn=collate_fn,
),
query_params={"certainty": 0.9},
)

print("Only results that have a 'weaviate_certainty' of higher than 0.9 should show:")
for res in results:
print(f"\t text={res[:, 'text']}")
print(f"\t scores={res[:, 'scores']}")


This should return something similar to:

Only results that have a 'weaviate_certainty' of higher than 0.9 should show:
text=['Persist Documents with Weaviate.']
scores=[{'weaviate_certainty': {'value': 1.0000001}, 'cosine_similarity': {'value': 1.0000002000000001}}]


## Include additional properties in the return#

GraphQL additional properties can be used on data objects in Get{} Queries to get additional information about the returned data objects. Which additional properties are available depends on the modules that are attached to Weaviate. The fields id, certainty, featureProjection and classification are available from Weaviate Core. On nested GraphQL fields (references to other data classes), only the id can be returned. Explanation on specific additional properties can be found on the module pages, see for example text2vec-contextionary.

Further documentation here

In order to include additional properties on the request you can use the additional parameter of the find() function. These will be included as Tags on the response.

Assume you want to know when the document was inserted and last updated in the DB. You can run the following:

from docarray import DocumentArray, Document
from transformers import AutoModel, AutoTokenizer

# make connection
da = DocumentArray(
storage="weaviate", config={"name": "Persisted", "host": "localhost", "port": 8080}
)

# load some dummy data
da.extend(
[
Document(text='Persist Documents with Weaviate.'),
Document(text='And enjoy fast nearest neighbor search.'),
Document(text='All while using DocArray API.'),
]
)

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')

def collate_fn(da):
return tokenizer(da.texts, return_tensors='pt', truncation=True, padding=True)

da.embed(model, collate_fn=collate_fn)

# set additional params and do a lookup
additional = ['creationTimeUnix', 'lastUpdateTimeUnix']
results = da.find(
DocumentArray([Document(text='How to persist Documents')]).embed(
model,
collate_fn=collate_fn,
),
limit=1,
additional=additional,
)

print('\n See when the Document was created and updated:\n')
for res in results:
print(f"\t creationTimeUnix={res[:, 'tags__creationTimeUnix']}")
print(f"\t lastUpdateTimeUnix={res[:, 'tags__lastUpdateTimeUnix']}")



This should return:

 See when the Document was created and updated:

creationTimeUnix=['1656020204563']
lastUpdateTimeUnix=['1656020204563']