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[features] bring your own vectors (#381)
* updated * update mock * update mock * change sum to mean. * change sum to mean. * updated * updated * add query type check logic * add maximum number of vectors limit * add tests * updated * catch mainline * catch mainline * catch mainline * catch mainline * add test for vectors * add test for vectors * add test for vectors
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,31 @@ | ||
| context_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "type": "object", | ||
| "properties": { | ||
| "tensor": { | ||
| "type": "array", | ||
| "minItems":1, | ||
| "maxItems" : 64, | ||
| "items": | ||
| { | ||
| "type": "object", | ||
| "properties": { | ||
| "vector": { | ||
| "type": "array", | ||
| "items": {"type": "number"} | ||
| }, | ||
| "weight": { | ||
| "type": "number" | ||
| } | ||
| }, | ||
| "required": [ | ||
| "vector", | ||
| "weight" | ||
| ] | ||
| }, | ||
| } | ||
| }, | ||
| "required": [ | ||
| "tensor" | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,111 @@ | ||
| import unittest.mock | ||
| import pprint | ||
|
|
||
| import torch | ||
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| import marqo.tensor_search.backend | ||
| from marqo.errors import IndexNotFoundError, InvalidArgError | ||
| from marqo.tensor_search import tensor_search | ||
| from marqo.tensor_search.enums import TensorField, IndexSettingsField, SearchMethod | ||
| from tests.marqo_test import MarqoTestCase | ||
| from unittest.mock import patch | ||
| import numpy as np | ||
|
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|
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| class TestMultimodalTensorCombination(MarqoTestCase): | ||
|
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| def setUp(self): | ||
| self.index_name_1 = "my-test-index-1" | ||
| self.endpoint = self.authorized_url | ||
|
|
||
| try: | ||
| tensor_search.delete_index(config=self.config, index_name=self.index_name_1) | ||
| except IndexNotFoundError as e: | ||
| pass | ||
|
|
||
| tensor_search.create_vector_index( | ||
| index_name=self.index_name_1, config=self.config, index_settings={ | ||
| IndexSettingsField.index_defaults: { | ||
| IndexSettingsField.model: "ViT-B/32", | ||
| IndexSettingsField.treat_urls_and_pointers_as_images: True, | ||
| IndexSettingsField.normalize_embeddings: True | ||
| } | ||
| }) | ||
| tensor_search.add_documents(config=self.config, index_name=self.index_name_1, docs=[ | ||
| { | ||
| "Title": "Horse rider", | ||
| "text_field": "A rider is riding a horse jumping over the barrier.", | ||
| "_id": "1" | ||
| }], auto_refresh=True) | ||
|
|
||
| def tearDown(self) -> None: | ||
| try: | ||
| tensor_search.delete_index(config=self.config, index_name=self.index_name_1) | ||
| except: | ||
| pass | ||
|
|
||
| def test_search(self): | ||
| query = { | ||
| "A rider is riding a horse jumping over the barrier": 1, | ||
| } | ||
| res = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query, context= | ||
| {"tensor": [{"vector": [1, ] * 512, "weight": 2}, {"vector": [2, ] * 512, "weight": -1}], }) | ||
|
|
||
| def test_search_with_incorrect_tensor_dimension(self): | ||
| query = { | ||
| "A rider is riding a horse jumping over the barrier": 1, | ||
| } | ||
| try: | ||
| res = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query, context= | ||
| {"tensor": [{"vector": [1, ] * 3, "weight": 0}, {"vector": [2, ] * 512, "weight": 0}], }) | ||
| raise AssertionError | ||
| except InvalidArgError as e: | ||
| assert "This causes the error when we do `numpy.mean()` over" in e.message | ||
|
|
||
| def test_search_with_incorrect_query_format(self): | ||
| query = "A rider is riding a horse jumping over the barrier" | ||
| try: | ||
| res = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query, context= | ||
| {"tensor": [{"vector": [1, ] * 512, "weight": 0}, {"vector": [2, ] * 512, "weight": 0}], }) | ||
| raise AssertionError | ||
| except InvalidArgError as e: | ||
| assert "This is not supported as the context only works when the query is a dictionary." in e.message | ||
|
|
||
| def test_search_score(self): | ||
| query = { | ||
| "A rider is riding a horse jumping over the barrier": 1, | ||
| } | ||
|
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| res_1 = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query) | ||
| res_2 = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query, context= | ||
| {"tensor": [{"vector": [1, ] * 512, "weight": 0}, {"vector": [2, ] * 512, "weight": 0}], }) | ||
| res_3 = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query, context= | ||
| {"tensor": [{"vector": [1, ] * 512, "weight": -1}, {"vector": [1, ] * 512, "weight": 1}], }) | ||
|
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| assert res_1["hits"][0]["_score"] == res_2["hits"][0]["_score"] | ||
| assert res_1["hits"][0]["_score"] == res_3["hits"][0]["_score"] | ||
|
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| def test_search_vectors(self): | ||
| with patch("numpy.mean", wraps = np.mean) as mock_mean: | ||
| query = { | ||
| "A rider is riding a horse jumping over the barrier": 1, | ||
| } | ||
| res_1 = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query) | ||
|
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| weight_1, weight_2, weight_3 = 2.5, 3.4, -1.334 | ||
| vector_2 = [-1,] * 512 | ||
| vector_3 = [1.3,] * 512 | ||
| query = { | ||
| "A rider is riding a horse jumping over the barrier": weight_1, | ||
| } | ||
|
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| res_2 = tensor_search.search(config=self.config, index_name=self.index_name_1, text=query, context= | ||
| {"tensor": [{"vector": vector_2, "weight": weight_2}, {"vector": vector_3, "weight": weight_3}], }) | ||
|
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| args_list = [args[0] for args in mock_mean.call_args_list] | ||
| vectorised_string = args_list[0][0][0] | ||
| weighted_vectors = args_list[1][0] | ||
|
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| assert np.allclose(vectorised_string * weight_1, weighted_vectors[0], atol=1e-9) | ||
| assert np.allclose(np.array(vector_2) * weight_2, weighted_vectors[1], atol=1e-9) | ||
| assert np.allclose(np.array(vector_3) * weight_3, weighted_vectors[2], atol=1e-9) |
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