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Added support for the
semantic_text
field and semantic
query type
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# Licensed to Elasticsearch B.V. under one or more contributor | ||
# license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright | ||
# ownership. Elasticsearch B.V. licenses this file to you under | ||
# the Apache License, Version 2.0 (the "License"); you may | ||
# not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
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""" | ||
# Semantic Text example | ||
Requirements: | ||
$ pip install "elasticsearch-dsl[async]" tqdm | ||
Before running this example, an ELSER inference endpoint must be created in the | ||
Elasticsearch cluster. This can be done manually from Kibana, or with the | ||
following curl command from a terminal: | ||
curl -X PUT \ | ||
"$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-endpoint" \ | ||
-H "Content-Type: application/json" \ | ||
-d '{"service":"elser","service_settings":{"num_allocations":1,"num_threads":1}}' | ||
To run the example: | ||
$ python semantic_text.py "text to search" | ||
The index will be created automatically if it does not exist. Add | ||
`--recreate-index` to the command to regenerate it. | ||
The example dataset includes a selection of workplace documents. The | ||
following are good example queries to try out with this dataset: | ||
$ python semantic_text.py "work from home" | ||
$ python semantic_text.py "vacation time" | ||
$ python semantic_text.py "can I bring a bird to work?" | ||
When the index is created, the inference service will split the documents into | ||
short passages, and for each passage a sparse embedding will be generated using | ||
Elastic's ELSER v2 model. | ||
""" | ||
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import argparse | ||
import asyncio | ||
import json | ||
import os | ||
from datetime import datetime | ||
from typing import Any, Optional | ||
from urllib.request import urlopen | ||
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from tqdm import tqdm | ||
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import elasticsearch_dsl as dsl | ||
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DATASET_URL = "https://raw.githubusercontent.com/elastic/elasticsearch-labs/main/datasets/workplace-documents.json" | ||
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class WorkplaceDoc(dsl.AsyncDocument): | ||
class Index: | ||
name = "workplace_documents_semantic" | ||
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name: str | ||
summary: str | ||
content: Any = dsl.mapped_field( | ||
dsl.field.SemanticText(inference_id="my-elser-endpoint") | ||
) | ||
created: datetime | ||
updated: Optional[datetime] | ||
url: str = dsl.mapped_field(dsl.Keyword()) | ||
category: str = dsl.mapped_field(dsl.Keyword()) | ||
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async def create() -> None: | ||
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# create the index | ||
await WorkplaceDoc._index.delete(ignore_unavailable=True) | ||
await WorkplaceDoc.init() | ||
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# download the data | ||
dataset = json.loads(urlopen(DATASET_URL).read()) | ||
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# import the dataset | ||
for data in tqdm(dataset, desc="Indexing documents..."): | ||
doc = WorkplaceDoc( | ||
name=data["name"], | ||
summary=data["summary"], | ||
content=data["content"], | ||
created=data.get("created_on"), | ||
updated=data.get("updated_at"), | ||
url=data["url"], | ||
category=data["category"], | ||
) | ||
await doc.save() | ||
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# refresh the index | ||
await WorkplaceDoc._index.refresh() | ||
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async def search(query: str) -> dsl.AsyncSearch[WorkplaceDoc]: | ||
return WorkplaceDoc.search()[:5].query( | ||
"semantic", | ||
field=WorkplaceDoc.content, | ||
query=query, | ||
) | ||
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def parse_args() -> argparse.Namespace: | ||
parser = argparse.ArgumentParser(description="Vector database with Elasticsearch") | ||
parser.add_argument( | ||
"--recreate-index", action="store_true", help="Recreate and populate the index" | ||
) | ||
parser.add_argument("query", action="store", help="The search query") | ||
return parser.parse_args() | ||
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async def main() -> None: | ||
args = parse_args() | ||
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# initiate the default connection to elasticsearch | ||
dsl.async_connections.create_connection(hosts=[os.environ["ELASTICSEARCH_URL"]]) | ||
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if args.recreate_index or not await WorkplaceDoc._index.exists(): | ||
await create() | ||
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results = await search(args.query) | ||
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async for hit in results: | ||
print( | ||
f"Document: {hit.name} [Category: {hit.category}] [Score: {hit.meta.score}]" | ||
) | ||
print(f"Content: {hit.content.text}") | ||
print("--------------------\n") | ||
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# close the connection | ||
await dsl.async_connections.get_connection().close() | ||
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if __name__ == "__main__": | ||
asyncio.run(main()) |
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# Licensed to Elasticsearch B.V. under one or more contributor | ||
# license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright | ||
# ownership. Elasticsearch B.V. licenses this file to you under | ||
# the Apache License, Version 2.0 (the "License"); you may | ||
# not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
|
||
|
||
""" | ||
# Semantic Text example | ||
Requirements: | ||
$ pip install "elasticsearch-dsl" tqdm | ||
Before running this example, an ELSER inference endpoint must be created in the | ||
Elasticsearch cluster. This can be done manually from Kibana, or with the | ||
following curl command from a terminal: | ||
curl -X PUT \ | ||
"$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-endpoint" \ | ||
-H "Content-Type: application/json" \ | ||
-d '{"service":"elser","service_settings":{"num_allocations":1,"num_threads":1}}' | ||
To run the example: | ||
$ python semantic_text.py "text to search" | ||
The index will be created automatically if it does not exist. Add | ||
`--recreate-index` to the command to regenerate it. | ||
The example dataset includes a selection of workplace documents. The | ||
following are good example queries to try out with this dataset: | ||
$ python semantic_text.py "work from home" | ||
$ python semantic_text.py "vacation time" | ||
$ python semantic_text.py "can I bring a bird to work?" | ||
When the index is created, the inference service will split the documents into | ||
short passages, and for each passage a sparse embedding will be generated using | ||
Elastic's ELSER v2 model. | ||
""" | ||
|
||
import argparse | ||
import json | ||
import os | ||
from datetime import datetime | ||
from typing import Any, Optional | ||
from urllib.request import urlopen | ||
|
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from tqdm import tqdm | ||
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import elasticsearch_dsl as dsl | ||
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DATASET_URL = "https://raw.githubusercontent.com/elastic/elasticsearch-labs/main/datasets/workplace-documents.json" | ||
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class WorkplaceDoc(dsl.Document): | ||
class Index: | ||
name = "workplace_documents_semantic" | ||
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name: str | ||
summary: str | ||
content: Any = dsl.mapped_field( | ||
dsl.field.SemanticText(inference_id="my-elser-endpoint") | ||
) | ||
created: datetime | ||
updated: Optional[datetime] | ||
url: str = dsl.mapped_field(dsl.Keyword()) | ||
category: str = dsl.mapped_field(dsl.Keyword()) | ||
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def create() -> None: | ||
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# create the index | ||
WorkplaceDoc._index.delete(ignore_unavailable=True) | ||
WorkplaceDoc.init() | ||
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# download the data | ||
dataset = json.loads(urlopen(DATASET_URL).read()) | ||
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# import the dataset | ||
for data in tqdm(dataset, desc="Indexing documents..."): | ||
doc = WorkplaceDoc( | ||
name=data["name"], | ||
summary=data["summary"], | ||
content=data["content"], | ||
created=data.get("created_on"), | ||
updated=data.get("updated_at"), | ||
url=data["url"], | ||
category=data["category"], | ||
) | ||
doc.save() | ||
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# refresh the index | ||
WorkplaceDoc._index.refresh() | ||
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def search(query: str) -> dsl.Search[WorkplaceDoc]: | ||
return WorkplaceDoc.search()[:5].query( | ||
"semantic", | ||
field=WorkplaceDoc.content, | ||
query=query, | ||
) | ||
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def parse_args() -> argparse.Namespace: | ||
parser = argparse.ArgumentParser(description="Vector database with Elasticsearch") | ||
parser.add_argument( | ||
"--recreate-index", action="store_true", help="Recreate and populate the index" | ||
) | ||
parser.add_argument("query", action="store", help="The search query") | ||
return parser.parse_args() | ||
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def main() -> None: | ||
args = parse_args() | ||
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# initiate the default connection to elasticsearch | ||
dsl.connections.create_connection(hosts=[os.environ["ELASTICSEARCH_URL"]]) | ||
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if args.recreate_index or not WorkplaceDoc._index.exists(): | ||
create() | ||
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results = search(args.query) | ||
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for hit in results: | ||
print( | ||
f"Document: {hit.name} [Category: {hit.category}] [Score: {hit.meta.score}]" | ||
) | ||
print(f"Content: {hit.content.text}") | ||
print("--------------------\n") | ||
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# close the connection | ||
dsl.connections.get_connection().close() | ||
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if __name__ == "__main__": | ||
main() |