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Vector search example #1778
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4cda418
Vector search example
miguelgrinberg 753649d
Merge branch 'main' into vector-example
pquentin 34432f2
addressed feedback
miguelgrinberg e7cb856
vectors example tests
miguelgrinberg 0bdfab3
skip vectors integration test for stacks < 8.11
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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. | ||
|
||
""" | ||
# Vector database example | ||
|
||
Requirements: | ||
|
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$ pip install nltk sentence_transformers tqdm elasticsearch-dsl[async] | ||
|
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To run the example: | ||
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$ python vectors.py "text to search" | ||
|
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The index will be created automatically if it does not exist. Add | ||
`--recreate-index` 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 vectors.py "work from home" | ||
$ python vectors.py "vacation time" | ||
$ python vectors.py "can I bring a bird to work?" | ||
|
||
When the index is created, the documents are split into short passages, and for | ||
each passage an embedding is generated using the open source | ||
"all-MiniLM-L6-v2" model. The documents that are returned as search results are | ||
those that have the highest scored passages. Add `--show-inner-hits` to the | ||
command to see individual passage results as well. | ||
""" | ||
|
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import argparse | ||
import asyncio | ||
import json | ||
import os | ||
from urllib.request import urlopen | ||
|
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import nltk | ||
from sentence_transformers import SentenceTransformer | ||
from tqdm import tqdm | ||
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from elasticsearch_dsl import ( | ||
AsyncDocument, | ||
Date, | ||
DenseVector, | ||
InnerDoc, | ||
Keyword, | ||
Nested, | ||
Text, | ||
async_connections, | ||
) | ||
|
||
DATASET_URL = "https://raw.githubusercontent.com/elastic/elasticsearch-labs/main/datasets/workplace-documents.json" | ||
MODEL_NAME = "all-MiniLM-L6-v2" | ||
|
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# initialize sentence tokenizer | ||
nltk.download("punkt", quiet=True) | ||
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class Passage(InnerDoc): | ||
content = Text() | ||
embedding = DenseVector() | ||
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class WorkplaceDoc(AsyncDocument): | ||
class Index: | ||
name = "workplace_documents" | ||
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name = Text() | ||
summary = Text() | ||
content = Text() | ||
created = Date() | ||
updated = Date() | ||
url = Keyword() | ||
category = Keyword() | ||
passages = Nested(Passage) | ||
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_model = None | ||
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@classmethod | ||
def get_embedding_model(cls): | ||
if cls._model is None: | ||
cls._model = SentenceTransformer(MODEL_NAME) | ||
return cls._model | ||
|
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def clean(self): | ||
# split the content into sentences | ||
passages = nltk.sent_tokenize(self.content) | ||
|
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# generate an embedding for each passage and save it as a nested document | ||
model = self.get_embedding_model() | ||
for passage in passages: | ||
self.passages.append( | ||
Passage(content=passage, embedding=list(model.encode(passage))) | ||
) | ||
|
||
|
||
async def create(): | ||
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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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async def search(query): | ||
model = WorkplaceDoc.get_embedding_model() | ||
return WorkplaceDoc.search().knn( | ||
field="passages.embedding", | ||
k=5, | ||
num_candidates=50, | ||
query_vector=list(model.encode(query)), | ||
inner_hits={"size": 2}, | ||
) | ||
|
||
|
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def parse_args(): | ||
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( | ||
"--show-inner-hits", | ||
action="store_true", | ||
help="Show results for individual passages", | ||
) | ||
parser.add_argument("query", action="store", help="The search query") | ||
return parser.parse_args() | ||
|
||
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async def main(): | ||
args = parse_args() | ||
|
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# initiate the default connection to elasticsearch | ||
async_connections.create_connection(hosts=[os.environ["ELASTICSEARCH_URL"]]) | ||
|
||
if args.recreate_index or not await WorkplaceDoc._index.exists(): | ||
await create() | ||
|
||
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"Summary: {hit.summary}") | ||
if args.show_inner_hits: | ||
for passage in hit.meta.inner_hits.passages: | ||
print(f" - [Score: {passage.meta.score}] {passage.content!r}") | ||
print("") | ||
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# close the connection | ||
await async_connections.get_connection().close() | ||
|
||
|
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if __name__ == "__main__": | ||
asyncio.run(main()) |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,185 @@ | ||
# 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. | ||
|
||
""" | ||
# Vector database example | ||
|
||
Requirements: | ||
|
||
$ pip install nltk sentence_transformers tqdm elasticsearch-dsl | ||
|
||
To run the example: | ||
|
||
$ python vectors.py "text to search" | ||
|
||
The index will be created automatically if it does not exist. Add | ||
`--recreate-index` 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 vectors.py "work from home" | ||
$ python vectors.py "vacation time" | ||
$ python vectors.py "can I bring a bird to work?" | ||
|
||
When the index is created, the documents are split into short passages, and for | ||
each passage an embedding is generated using the open source | ||
"all-MiniLM-L6-v2" model. The documents that are returned as search results are | ||
those that have the highest scored passages. Add `--show-inner-hits` to the | ||
command to see individual passage results as well. | ||
""" | ||
|
||
import argparse | ||
import json | ||
import os | ||
from urllib.request import urlopen | ||
|
||
import nltk | ||
from sentence_transformers import SentenceTransformer | ||
from tqdm import tqdm | ||
|
||
from elasticsearch_dsl import ( | ||
Date, | ||
DenseVector, | ||
Document, | ||
InnerDoc, | ||
Keyword, | ||
Nested, | ||
Text, | ||
connections, | ||
) | ||
|
||
DATASET_URL = "https://raw.githubusercontent.com/elastic/elasticsearch-labs/main/datasets/workplace-documents.json" | ||
MODEL_NAME = "all-MiniLM-L6-v2" | ||
|
||
# initialize sentence tokenizer | ||
nltk.download("punkt", quiet=True) | ||
|
||
|
||
class Passage(InnerDoc): | ||
content = Text() | ||
embedding = DenseVector() | ||
|
||
|
||
class WorkplaceDoc(Document): | ||
class Index: | ||
name = "workplace_documents" | ||
|
||
name = Text() | ||
summary = Text() | ||
content = Text() | ||
created = Date() | ||
updated = Date() | ||
url = Keyword() | ||
category = Keyword() | ||
passages = Nested(Passage) | ||
|
||
_model = None | ||
|
||
@classmethod | ||
def get_embedding_model(cls): | ||
if cls._model is None: | ||
cls._model = SentenceTransformer(MODEL_NAME) | ||
return cls._model | ||
|
||
def clean(self): | ||
# split the content into sentences | ||
passages = nltk.sent_tokenize(self.content) | ||
|
||
# generate an embedding for each passage and save it as a nested document | ||
model = self.get_embedding_model() | ||
for passage in passages: | ||
self.passages.append( | ||
Passage(content=passage, embedding=list(model.encode(passage))) | ||
) | ||
|
||
|
||
def create(): | ||
|
||
# create the index | ||
WorkplaceDoc._index.delete(ignore_unavailable=True) | ||
WorkplaceDoc.init() | ||
|
||
# download the data | ||
dataset = json.loads(urlopen(DATASET_URL).read()) | ||
|
||
# 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() | ||
|
||
|
||
def search(query): | ||
model = WorkplaceDoc.get_embedding_model() | ||
return WorkplaceDoc.search().knn( | ||
field="passages.embedding", | ||
k=5, | ||
num_candidates=50, | ||
query_vector=list(model.encode(query)), | ||
inner_hits={"size": 2}, | ||
) | ||
|
||
|
||
def parse_args(): | ||
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( | ||
"--show-inner-hits", | ||
action="store_true", | ||
help="Show results for individual passages", | ||
) | ||
parser.add_argument("query", action="store", help="The search query") | ||
return parser.parse_args() | ||
|
||
|
||
def main(): | ||
args = parse_args() | ||
|
||
# initiate the default connection to elasticsearch | ||
connections.create_connection(hosts=[os.environ["ELASTICSEARCH_URL"]]) | ||
|
||
if args.recreate_index or not WorkplaceDoc._index.exists(): | ||
create() | ||
|
||
results = search(args.query) | ||
|
||
for hit in results: | ||
print( | ||
f"Document: {hit.name} [Category: {hit.category}] [Score: {hit.meta.score}]" | ||
) | ||
print(f"Summary: {hit.summary}") | ||
if args.show_inner_hits: | ||
for passage in hit.meta.inner_hits.passages: | ||
print(f" - [Score: {passage.meta.score}] {passage.content!r}") | ||
print("") | ||
|
||
# close the connection | ||
connections.get_connection().close() | ||
|
||
|
||
if __name__ == "__main__": | ||
main() |
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