From 846f8edfc5e9a1e8da62c4f1d1569e64f4c10a90 Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Sun, 4 Aug 2024 21:40:32 +0300 Subject: [PATCH 01/19] upload article --- .../multi-attribute-semantic-search.md | 459 ++++++++++++++++++ 1 file changed, 459 insertions(+) create mode 100644 docs/articles/multi-attribute-semantic-search.md diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md new file mode 100644 index 000000000..5915af722 --- /dev/null +++ b/docs/articles/multi-attribute-semantic-search.md @@ -0,0 +1,459 @@ +# Multi-attribute search with vector embeddings + +## Motivation + +Vector search has revolutionized data retrieval by leveraging semantic meaning. However, real-world scenarios often require searching across multiple attributes simultaneously, with varying degrees of importance for each. Two main approaches exist for multi-attribute vector search: + +1. Search attributes separately, then combine weighted results. +2. Combine weighted attribute vectors, then perform a single search. + +This article demonstrates why the first approach, despite its intuitive appeal, falls short even in simple cases. We'll explore why the second approach is superior and how to implement it efficiently using the Superlinked framework. + +By understanding these methods, you'll gain insights into creating more powerful and flexible search systems that can handle complex, multi-faceted queries with ease. + +## Setting + +Let's bring our example to life with an exciting scenario! + +Imagine it's game night, and you're the Dungeon Master for an epic Dungeons & Dragons adventure. Your friends are gathered around, eager to embark on their quest. As the storyteller, you're responsible for crafting thrilling encounters that challenge and excite your players. + +Your trusty Monster Manual contains thousands of creatures, but finding the perfect monster for each situation can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment – all while surprising and delighting your players. + +What if we could create a tool that instantly finds the most suitable monsters for any scenario? A tool that considers multiple factors simultaneously, ensuring each encounter is as immersive and exciting as possible? + +Let's embark on our own quest to build this ultimate monster-finding system, using the power of multi-attribute vector search! + +## Dataset + +To demonstrate our search techniques, we'll use a small synthetic dataset generated by a Large Language Model (LLM). Here's the prompt used to create our monster database: + +``` +Generate two JSON lists: 'monsters' and 'queries'. + +1. 'monsters' list: Create 20 unique monsters with the following properties: + - name: A distinctive name + - look: Brief description of appearance (2-3 sentences) + - habitat: Where the monster lives (2-3 sentences) + - behavior: How the monster acts (2-3 sentences) + + Ensure some monsters share similar features while remaining distinct. + +2. 'queries' list: Create 5 queries to search for monsters: + - Each query should be in the format: {look: "...", habitat: "...", behavior: "..."} + - Use simple, brief descriptions (1-3 words per field) + - Make queries somewhat general to match multiple monsters + +Output format: +{ + "monsters": [ + {"name": "...", "look": "...", "habitat": "...", "behavior": "..."}, + ... + ], + "queries": [ + {"look": "...", "habitat": "...", "behavior": "..."}, + ... + ] +} +``` + +Let's examine a sample of our generated dataset. Note that your results may differ due to the non-deterministic nature of LLM generation. Original dataset is available [here](https://github.com/superlinked/article-multi-attribute-search). + +First five monsters: + + +| | name | look | habitat | behavior | +|---:|:-----------------|:----------------------------------------------------------|:----------------------------------------------------|:--------------------------------------------------------------| +| 0 | Luminoth | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | +| 1 | Aqua Wraith | Translucent humanoid figure made of flowing water | Rivers, lakes, and coastal areas | Shapeshifts to blend with water bodies and controls currents | +| 2 | Stoneheart Golem | Massive humanoid composed of interlocking rock formations | Rocky mountains and ancient ruins | Hibernates for centuries, awakens to protect its territory | +| 3 | Whispering Shade | Shadowy, amorphous being with glowing eyes | Dark forests and abandoned buildings | Feeds on fear and whispers unsettling truths | +| 4 | Zephyr Dancer | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | + +And here are the queries: + +| | look | habitat | behavior | +|---:|:--------------|:---------------------|:----------------------| +| 0 | glowing | dark places | light manipulation | +| 1 | elemental | extreme environments | environmental control | +| 2 | shapeshifting | varied landscapes | illusion creation | +| 3 | crystalline | mineral-rich areas | energy absorption | +| 4 | ethereal | atmospheric | mind influence | + +## Retrieval + +Let's set up common parameters for both approaches we'll explore: the naive method and the Superlinked method. + +We'll use the `sentence-transformers/all-mpnet-base-v2` model from the Sentence Transformers library to generate our vector embeddings. To keep our results short, we'll limit the output to the top 3 matches. + +```python +LIMIT = 3 +MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" +``` + +For the complete code, including necessary imports and helper functions, please refer to the [accompanying notebook](https://github.com/superlinked/article-multi-attribute-search/blob/main/notebook.ipynb). + +If you're new to vector similarity search, don't worry! You can find definitions for key terms in the "Glossary" section at the end of this article. + +Now, let's dive into our two approaches for multi-attribute monster search. + +### Naive approach + +```python +class NaiveRetriever: + def __init__(self, data: pd.DataFrame): + self.model = SentenceTransformer(MODEL_NAME) + self.data = data.copy() + self.ids = self.data.index.to_list() + self.knns = {} + for key in self.data: + embeddings = self.model.encode(self.data[key]) + knn = NearestNeighbors(metric="cosine").fit(embeddings) + self.knns[key] = knn + + def search_key( + self, key: str, value: str, limit: int = LIMIT + ) -> pd.DataFrame: + embedding = self.model.encode(value) + knn = self.knns[key] + distances, indices = knn.kneighbors( + [embedding], n_neighbors=limit, return_distance=True + ) + ids = [self.ids[i] for i in indices[0]] + + similarities = (1 - distances).flatten() + # by definition: + # cosine distance = 1 - cosine similarity + + result = pd.DataFrame({"id": ids, f"score_{key}": similarities, key: self.data[key][ids]}) + result.set_index("id", inplace=True) + + return result + + def search(self, query: dict, limit: int = LIMIT) -> pd.DataFrame: + results = [] + for key, value in query.items(): + if key not in self.knns: + continue + result_key = self.search_key(key, value, limit=limit) + result_key.drop(columns=[key], inplace=True) + results.append(result_key) + + merged_results = pd.concat(results, axis=1) + merged_results["score"] = merged_results.mean(axis=1, skipna=False) + merged_results.sort_values("score", ascending=False, inplace=True) + return merged_results +``` + +Let's take the first query from our list and search for monsters using the naive approach: + +```python +query = { + 'look': 'glowing', + 'habitat': 'dark places', + 'behavior': 'light manipulation' +} +``` + +Here are search results for each attribute: + +**Look: glowing** +| id | score_look | look | +|:-----------------|-------------:|:--------------------------------------------------| +| Whispering Shade | 0.503578 | Shadowy, amorphous being with glowing eyes | +| Sandstorm Djinn | 0.407344 | Swirling vortex of sand with glowing symbols | +| Luminoth | 0.378619 | Moth-like creature with glowing wings and antenna | + +Awesome, these are relevant creatures, all have something "glowing" in them as we asked for! +Let's see what we can find for other two attributes. + +**Habitat: dark places** +| id | score_habitat | habitat | +|:--------------------|----------------:|:-------------------------------------| +| Whispering Shade | 0.609567 | Dark forests and abandoned buildings | +| Fungal Network | 0.438856 | Underground caverns and damp forests | +| Thornvine Elemental | 0.423421 | Overgrown ruins and dense jungles | + +**Behavior: light manipulation** +| id | score_behavior | behavior | +|:------------------|-----------------:|:---------------------------------------------------------------| +| Living Graffiti | 0.385741 | Shapeshifts to blend with surroundings and absorbs pigments | +| Crystalwing Drake | 0.385211 | Hoards precious gems and can refract light into powerful beams | +| Luminoth | 0.345566 | Emits soothing light patterns to communicate and attract prey | + +These results look promising at first glance, but we must remember our original goal: finding monsters that are relevant across all attributes simultaneously. This is where the limitations of our naive approach become apparent. Let's merge the results to see the full picture: + +| id | score_look | score_habitat | score_behavior | +|:--------------------|-------------:|----------------:|-----------------:| +| Whispering Shade | 0.503578 | 0.609567 | | +| Sandstorm Djinn | 0.407344 | | | +| Luminoth | 0.378619 | | 0.345566 | +| Fungal Network | | 0.438856 | | +| Thornvine Elemental | | 0.423421 | | +| Living Graffiti | | | 0.385741 | +| Crystalwing Drake | | | 0.385211 | + +What we have here: + +1. Relevance by attribute: + - "Look": Three monsters were retrieved (Whispering Shade, Sandstorm Djinn, and Luminoth). + - "Habitat": Only one monster from the "Look" results was relevant (Whispering Shade). + - "Behavior": Only one monster from the "Look" results was relevant (Luminoth), but it's different from the one relevant for "Habitat". + +2. Overall relevance: + - No single monster was retrieved for all three attributes simultaneously. + - The results are fragmented, with different monsters being relevant for different attributes. + +This fragmentation in the results highlights a potential issue with our current search approach, as it fails to find monsters that satisfy all criteria at once. + +Maybe we can fix this issue by proactively retrieving more monsters for each attribute? +Let's try it with 6 monsters for each attribute instead of 3. + +| id | score_look | score_habitat | score_behavior | +|:--------------------|-------------:|----------------:|-----------------:| +| Whispering Shade | 0.503578 | 0.609567 | | +| Sandstorm Djinn | 0.407344 | 0.365061 | | +| Luminoth | 0.378619 | | 0.345566 | +| Nebula Jellyfish | 0.36627 | | 0.259969 | +| Dreamweaver Octopus | 0.315679 | | | +| Quantum Firefly | 0.288578 | | | +| Fungal Network | | 0.438856 | | +| Thornvine Elemental | | 0.423421 | | +| Mist Phantom | | 0.366816 | 0.236649 | +| Stoneheart Golem | | 0.342287 | | +| Living Graffiti | | | 0.385741 | +| Crystalwing Drake | | | 0.385211 | +| Aqua Wraith | | | 0.283581 | + +In total, we retrieved 13 monsters (that is more than half of our tiny dataset!) and still have the same issue: no monster was retrieved for all three attributes simultaneously. +We could try to increase the number of monsters further, but it would result in the following shortcomings: +1. While not a problem for our toy task, in production it will result in noticeably longer search time. +2. With more attributes, the number of monsters to retrieve for every attribute will grow exponentially. +3. And there is still no guarantee that this will result in retrieval of monsters that satisfy all criteria at once. + +This example is enough to show that the naive approach is not suitable for our task. + +### Superlinked approach + +Let's implement the second approach using Superlinked framework. + +First, let's define the schema, spaces, index and query: + +```python +@schema +class Monster: + id: IdField + look: String + habitat: String + behavior: String + + +monster = Monster() + +look_space = TextSimilaritySpace(text=monster.look, model=MODEL_NAME) +habitat_space = TextSimilaritySpace(text=monster.habitat, model=MODEL_NAME) +behavior_space = TextSimilaritySpace(text=monster.behavior, model=MODEL_NAME) + +monster_index = Index([look_space, habitat_space, behavior_space]) + +monster_query = ( + Query(monster_index) + .find(monster) + .similar(look_space.text, Param("look")) + .similar(habitat_space.text, Param("habitat")) + .similar(behavior_space.text, Param("behavior")) + .limit(LIMIT) +) +``` + +Finally, start executor and upload the data: + +```python +monster_parser = DataFrameParser(monster, mapping={monster.id: "name"}) + +source: InMemorySource = InMemorySource(monster, parser=monster_parser) +executor = InMemoryExecutor(sources=[source], indices=[monster_index]) +app = executor.run() + +source.put([df]) +``` + +Let's run the same query as before: + +```python +query = { + 'look': 'glowing', + 'habitat': 'dark places', + 'behavior': 'light manipulation' +} +``` + +| id | score | look | habitat | behavior | +|:-----------------|---------:|:---------------------------------------------------------------|:----------------------------------------------------|:--------------------------------------------------------------| +| Whispering Shade | 0.376738 | Shadowy, amorphous being with glowing eyes | Dark forests and abandoned buildings | Feeds on fear and whispers unsettling truths | +| Luminoth | 0.340084 | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | +| Living Graffiti | 0.330587 | Two-dimensional, colorful creature that inhabits flat surfaces | Urban areas, particularly walls and billboards | Shapeshifts to blend with surroundings and absorbs pigments | + +As you can see, the results contain monsters that satisfy all criteria at once. + +Let's try another query. + +```python +query = { + 'look': 'shapeshifting', + 'habitat': 'varied landscapes', + 'behavior': 'illusion creation' +} +``` + +| id | score | look | habitat | behavior | +|:-----------------|---------:|:---------------------------------------------------|:------------------------------------------|:-----------------------------------------------------| +| Mist Phantom | 0.489574 | Ethereal, fog-like humanoid with shifting features | Swamps, moors, and foggy coastlines | Lures travelers astray with illusions and whispers | +| Zephyr Dancer | 0.342075 | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | +| Whispering Shade | 0.337434 | Shadowy, amorphous being with glowing eyes | Dark forests and abandoned buildings | Feeds on fear and whispers unsettling truths | + +Great! +Maybe we would like to find monsters from the dataset similar to the existing one? +Let's try it with the "Harmonic Coral" (very last row in our dataset). + +```python +query = { + 'name': 'Harmonic Coral', + 'look': 'Branching, musical instrument-like structure with vibrating tendrils', + 'habitat': 'Shallow seas and tidal pools', + 'behavior': 'Creates complex melodies to communicate and influence emotions' +} +``` + +| id | score | look | habitat | behavior | +|:--------------------|---------:|:---------------------------------------------------------------------|:-----------------------------------------|:---------------------------------------------------------------| +| Harmonic Coral | 1 | Branching, musical instrument-like structure with vibrating tendrils | Shallow seas and tidal pools | Creates complex melodies to communicate and influence emotions | +| Dreamweaver Octopus | 0.402288 | Cephalopod with tentacles that shimmer like auroras | Deep ocean trenches and underwater caves | Influences the dreams of nearby creatures | +| Aqua Wraith | 0.330869 | Translucent humanoid figure made of flowing water | Rivers, lakes, and coastal areas | Shapeshifts to blend with water bodies and controls currents | + +The top result is the most relevant one, the "Harmonic Coral" itself which is pretty much expected. +Let's see who are the other two monsters: "Dreamweaver Octopus" and "Aqua Wraith". + +Both share important thematic elements with the Harmonic Coral: +1. Aquatic habitats +2. Ability to influence or manipulate their environment +3. Dynamic or fluid visual characteristics +These common themes in habitat, behavior, and appearance explain why these monsters were identified as most similar to the Harmonic Coral. + +### Attribute weighing + +Let's suppose that we want to give more importance to the "look" attribute. +It's easy to do with Superlinked. +All we need to do is to add weights to the query. + +```python +monster_query_with_weights = ( + Query(monster_index, weights={ + look_space: Param("look_weight"), + habitat_space: Param("habitat_weight"), + behavior_space: Param("behavior_weight") + }) + .find(monster) + .similar(look_space.text, Param("look")) + .similar(habitat_space.text, Param("habitat")) + .similar(behavior_space.text, Param("behavior")) + .limit(LIMIT) +) +``` + +Let's again search for similar monsters to the "Harmonic Coral", but this time use weights for the attributes. +First of all, let's find monsters with the most close appearance: + +```python +weights = { + "look_weight": 1.0, + "habitat_weight": 0, + "behavior_weight": 0 +} +``` + +| id | score | look | habitat | behavior | +|:--------------------|---------:|:---------------------------------------------------------------------|:-----------------------------------|:---------------------------------------------------------------| +| Harmonic Coral | 0.57735 | Branching, musical instrument-like structure with vibrating tendrils | Shallow seas and tidal pools | Creates complex melodies to communicate and influence emotions | +| Thornvine Elemental | 0.252593 | Plant-like creature with a body of twisted vines and thorns | Overgrown ruins and dense jungles | Rapidly grows and controls surrounding plant life | +| Plasma Serpent | 0.243241 | Snake-like creature made of crackling energy | Electrical storms and power plants | Feeds on electrical currents and can short-circuit technology | + +**WARNING! Score is not 1 for the first result! It equals 1 / sqrt(3)** + +"Branching with vibrating tendrils", "Plant-like creature with a body of twisted vines and thorns", "Snake-like" -- for sure these descriptions share some common theme. + +Let's search by "habitat" and "behaviour" simultaniously: + +```python +weights = { + "look_weight": 0, + "habitat_weight": 1.0, + "behavior_weight": 1.0 +} +``` + +| id | score | look | habitat | behavior | +|:--------------------|---------:|:---------------------------------------------------------------------|:-----------------------------------------|:---------------------------------------------------------------| +| Harmonic Coral | 0.816497 | Branching, musical instrument-like structure with vibrating tendrils | Shallow seas and tidal pools | Creates complex melodies to communicate and influence emotions | +| Dreamweaver Octopus | 0.357656 | Cephalopod with tentacles that shimmer like auroras | Deep ocean trenches and underwater caves | Influences the dreams of nearby creatures | +| Mist Phantom | 0.288106 | Ethereal, fog-like humanoid with shifting features | Swamps, moors, and foggy coastlines | Lures travelers astray with illusions and whispers | + +All three monsters live in some watery habitats and have mind-controlling abilities. + +Lastly, let's try to use all attributes with different weights: + +```python +weights = { + "look_weight": 0.5, + "habitat_weight": -1.0, + "behavior_weight": 1.0 +} +``` + +Such set of weights means that we want to find monsters that have similar behaviour, somewhat similar look, but live in different habitats for the one Harmonic Coral prefers. + +| id | score | look | habitat | behavior | +|:---------------|---------:|:---------------------------------------------------------------------|:----------------------------------------------------|:---------------------------------------------------------------| +| Harmonic Coral | 0.19245 | Branching, musical instrument-like structure with vibrating tendrils | Shallow seas and tidal pools | Creates complex melodies to communicate and influence emotions | +| Luminoth | 0.149196 | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | +| Zephyr Dancer | 0.136456 | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | + +Two retrievel monsters (Luminoth and Zephyr Dancer) are indeed similar to the Harmonic Coral in terms of behaviour, but live in different habitats. +In terms of look, there is little similarity between Harmonic Coral and Luminoth since antenna could look like tendrils, but it's not the same for sure. +Anyway, this attribute was the least important for us, so it's okay. + +## Conclusion + +Multi-attribute vector search represents a significant advancement in information retrieval, offering a more nuanced and effective approach compared to traditional search methods. +As demonstrated through our D&D monster example, this technique excels at finding relevant results when multiple criteria are involved. + +Key takeaways: +1. Vector embeddings capture semantic meaning, allowing for more intelligent searches. +2. The Superlinked framework provides an efficient way to implement multi-attribute vector search. +3. Attribute weighting adds flexibility, allowing users to prioritize certain criteria over others. + +As data continues to grow in complexity and volume, multi-attribute vector search will play an increasingly crucial role in various applications, from e-commerce to content recommendation systems. +By understanding and implementing these techniques, developers and data scientists can create more powerful, intuitive, and user-friendly search experiences. + +Whether you're battling monsters in a fantasy realm or tackling real-world data challenges, multi-attribute vector search offers a powerful tool for finding the perfect match in a sea of possibilities. + +## Glossary + +- **Vector Embedding**: A numerical representation of data (like text or images) in a high-dimensional space, capturing semantic meaning. + +- **Cosine Similarity**: A measure of similarity between two vectors, often used in vector search to determine how closely related two items are. + +- **Multi-attribute Search**: A search method that considers multiple characteristics or properties of items simultaneously. + +- **Semantic Meaning**: The underlying concept or idea represented by data, rather than just its literal interpretation. + +- **Nearest Neighbors**: In vector search, the items that are most similar or closest to a given query in the vector space. + +- **Large Language Model (LLM)**: An AI model trained on vast amounts of text data, capable of generating human-like text and performing various language tasks. + +- **Sentence Transformer**: A type of model that converts sentences or short texts into vector embeddings. + +- **Index**: In the context of vector search, a data structure that organizes vector embeddings for efficient retrieval. + +- **Query**: A request for information from a search system, often represented as a vector in vector search. + +- **Attribute Weighting**: Assigning different levels of importance to various attributes in a multi-attribute search. \ No newline at end of file From 0131d70154e7cb7c8964e8544754c0101ee6d2de Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 09:15:32 +0300 Subject: [PATCH 02/19] add sentence about DnD --- docs/articles/multi-attribute-semantic-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 5915af722..7d8d0d532 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -15,7 +15,7 @@ By understanding these methods, you'll gain insights into creating more powerful Let's bring our example to life with an exciting scenario! -Imagine it's game night, and you're the Dungeon Master for an epic Dungeons & Dragons adventure. Your friends are gathered around, eager to embark on their quest. As the storyteller, you're responsible for crafting thrilling encounters that challenge and excite your players. +Imagine it's game night, and you're the Dungeon Master for an epic Dungeons & Dragons adventure. For those unfamiliar, Dungeons & Dragons (D&D) is a popular tabletop role-playing game where players create characters and embark on imaginary quests guided by a storyteller, known as the Dungeon Master. Your friends are gathered around, eager to start a new quest. As the Dungeon Master, you're responsible for crafting thrilling encounters that challenge and excite your players. Your trusty Monster Manual contains thousands of creatures, but finding the perfect monster for each situation can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment – all while surprising and delighting your players. From c1783791246301d4bcdf2438b5ba152dea81aa66 Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 10:04:01 +0300 Subject: [PATCH 03/19] with vector --- .../articles/multi-attribute-semantic-search.md | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 7d8d0d532..cef1e3f4a 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -226,11 +226,12 @@ Let's try it with 6 monsters for each attribute instead of 3. In total, we retrieved 13 monsters (that is more than half of our tiny dataset!) and still have the same issue: no monster was retrieved for all three attributes simultaneously. We could try to increase the number of monsters further, but it would result in the following shortcomings: -1. While not a problem for our toy task, in production it will result in noticeably longer search time. +1. While not a problem for our toy task, in production it will result in noticeably longer search time. And generally, multiple KNN searches take more time than a single one with concatenated vectors ([redis benchmark](https://redis.io/blog/benchmarking-results-for-vector-databases/)). 2. With more attributes, the number of monsters to retrieve for every attribute will grow exponentially. 3. And there is still no guarantee that this will result in retrieval of monsters that satisfy all criteria at once. +4. Even if we somehow manage to retrieve monsters that satisfy all criteria at once, we would still need to perform reconciliation of results that would result in additional overhead in a production system. -This example is enough to show that the naive approach is not suitable for our task. +This example is enough to show that the naive approach is not suitable for our task. ### Superlinked approach @@ -314,6 +315,18 @@ query = { Great! Maybe we would like to find monsters from the dataset similar to the existing one? Let's try it with the "Harmonic Coral" (very last row in our dataset). +We could extract attributes for this monsters and create query parameters manually, but Superlinked provides better way to query records similar to already stored ones. +We can do it with `with_vector` method of the query object. Since moster's id is its name, we can express our request as simple as this: + +```python +app.query( + monster_query.with_vector( + monster, + "Harmonic Coral" + ), + limit=LIMIT +) +``` ```python query = { From 34fde74d4e8b2bdee41836d5aeb59770b445f6ff Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 10:05:26 +0300 Subject: [PATCH 04/19] fix typo --- docs/articles/multi-attribute-semantic-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index cef1e3f4a..a95538201 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -394,7 +394,7 @@ weights = { "Branching with vibrating tendrils", "Plant-like creature with a body of twisted vines and thorns", "Snake-like" -- for sure these descriptions share some common theme. -Let's search by "habitat" and "behaviour" simultaniously: +Let's search by "habitat" and "behaviour" simultaneously: ```python weights = { From 333bd619d8b1f86def7f2f5ff7dd37be25dc0bdd Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 10:07:21 +0300 Subject: [PATCH 05/19] half-important attribute --- docs/articles/multi-attribute-semantic-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index a95538201..720e20adc 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -432,7 +432,7 @@ Such set of weights means that we want to find monsters that have similar behavi Two retrievel monsters (Luminoth and Zephyr Dancer) are indeed similar to the Harmonic Coral in terms of behaviour, but live in different habitats. In terms of look, there is little similarity between Harmonic Coral and Luminoth since antenna could look like tendrils, but it's not the same for sure. -Anyway, this attribute was the least important for us, so it's okay. +Anyway, this attribute was half-important for us, so we can compensate here. ## Conclusion From 8405f6f641c351a02a9ddaeaebeceef4a2d7b1c0 Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 10:10:46 +0300 Subject: [PATCH 06/19] remove warning --- docs/articles/multi-attribute-semantic-search.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 720e20adc..46e03d7a5 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -390,8 +390,6 @@ weights = { | Thornvine Elemental | 0.252593 | Plant-like creature with a body of twisted vines and thorns | Overgrown ruins and dense jungles | Rapidly grows and controls surrounding plant life | | Plasma Serpent | 0.243241 | Snake-like creature made of crackling energy | Electrical storms and power plants | Feeds on electrical currents and can short-circuit technology | -**WARNING! Score is not 1 for the first result! It equals 1 / sqrt(3)** - "Branching with vibrating tendrils", "Plant-like creature with a body of twisted vines and thorns", "Snake-like" -- for sure these descriptions share some common theme. Let's search by "habitat" and "behaviour" simultaneously: From b8c13107582ebbbbfb7a2987b91416481507e888 Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 10:17:28 +0300 Subject: [PATCH 07/19] remove redundant query --- docs/articles/multi-attribute-semantic-search.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 46e03d7a5..a2673232e 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -328,15 +328,6 @@ app.query( ) ``` -```python -query = { - 'name': 'Harmonic Coral', - 'look': 'Branching, musical instrument-like structure with vibrating tendrils', - 'habitat': 'Shallow seas and tidal pools', - 'behavior': 'Creates complex melodies to communicate and influence emotions' -} -``` - | id | score | look | habitat | behavior | |:--------------------|---------:|:---------------------------------------------------------------------|:-----------------------------------------|:---------------------------------------------------------------| | Harmonic Coral | 1 | Branching, musical instrument-like structure with vibrating tendrils | Shallow seas and tidal pools | Creates complex melodies to communicate and influence emotions | From 78d63cf8af943c551fddb9566a27afb4efa082f9 Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 10:19:59 +0300 Subject: [PATCH 08/19] code formatting --- docs/articles/multi-attribute-semantic-search.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index a2673232e..cd458b360 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -106,25 +106,25 @@ class NaiveRetriever: self.ids = self.data.index.to_list() self.knns = {} for key in self.data: - embeddings = self.model.encode(self.data[key]) + embeddings = self.model.encode(self.data[key].values) knn = NearestNeighbors(metric="cosine").fit(embeddings) self.knns[key] = knn - def search_key( - self, key: str, value: str, limit: int = LIMIT - ) -> pd.DataFrame: + def search_key(self, key: str, value: str, limit: int = LIMIT) -> pd.DataFrame: embedding = self.model.encode(value) knn = self.knns[key] distances, indices = knn.kneighbors( [embedding], n_neighbors=limit, return_distance=True ) ids = [self.ids[i] for i in indices[0]] - + similarities = (1 - distances).flatten() # by definition: # cosine distance = 1 - cosine similarity - result = pd.DataFrame({"id": ids, f"score_{key}": similarities, key: self.data[key][ids]}) + result = pd.DataFrame( + {"id": ids, f"score_{key}": similarities, key: self.data[key][ids]} + ) result.set_index("id", inplace=True) return result From a5a493ed72b9c902b65ed0b682d9adc5e772ab97 Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 11:40:29 +0300 Subject: [PATCH 09/19] update links --- docs/articles/multi-attribute-semantic-search.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index cd458b360..6e7f3a2eb 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -56,7 +56,7 @@ Output format: } ``` -Let's examine a sample of our generated dataset. Note that your results may differ due to the non-deterministic nature of LLM generation. Original dataset is available [here](https://github.com/superlinked/article-multi-attribute-search). +Let's examine a sample of our generated dataset. Note that your results may differ due to the non-deterministic nature of LLM generation. First five monsters: @@ -79,6 +79,8 @@ And here are the queries: | 3 | crystalline | mineral-rich areas | energy absorption | | 4 | ethereal | atmospheric | mind influence | +Original dataset and query examples are available [here](https://github.com/superlinked/article-multi-attribute-search). + ## Retrieval Let's set up common parameters for both approaches we'll explore: the naive method and the Superlinked method. @@ -90,7 +92,7 @@ LIMIT = 3 MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" ``` -For the complete code, including necessary imports and helper functions, please refer to the [accompanying notebook](https://github.com/superlinked/article-multi-attribute-search/blob/main/notebook.ipynb). +For the complete code, including necessary imports and helper functions, please refer to the [accompanying notebook](https://github.com/superlinked/article-multi-attribute-search). If you're new to vector similarity search, don't worry! You can find definitions for key terms in the "Glossary" section at the end of this article. From 42210f3b43cf9697f7388e1ca6c88bd24f3c18fb Mon Sep 17 00:00:00 2001 From: Andrey Pikunov Date: Tue, 6 Aug 2024 16:10:58 +0300 Subject: [PATCH 10/19] add default weights --- .../multi-attribute-semantic-search.md | 56 ++++++++++++------- 1 file changed, 35 insertions(+), 21 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 6e7f3a2eb..3a1c36678 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -144,6 +144,9 @@ class NaiveRetriever: merged_results["score"] = merged_results.mean(axis=1, skipna=False) merged_results.sort_values("score", ascending=False, inplace=True) return merged_results + + +naive_retriever = NaiveRetriever(df.set_index("name")) ``` Let's take the first query from our list and search for monsters using the naive approach: @@ -154,6 +157,8 @@ query = { 'habitat': 'dark places', 'behavior': 'light manipulation' } + +naive_retriever.search(query) ``` Here are search results for each attribute: @@ -259,13 +264,26 @@ behavior_space = TextSimilaritySpace(text=monster.behavior, model=MODEL_NAME) monster_index = Index([look_space, habitat_space, behavior_space]) monster_query = ( - Query(monster_index) + Query( + monster_index, + weights={ + look_space: Param("look_weight"), + habitat_space: Param("habitat_weight"), + behavior_space: Param("behavior_weight"), + }, + ) .find(monster) .similar(look_space.text, Param("look")) .similar(habitat_space.text, Param("habitat")) .similar(behavior_space.text, Param("behavior")) .limit(LIMIT) ) + +default_weights = { + "look_weight": 1.0, + "habitat_weight": 1.0, + "behavior_weight": 1.0 +} ``` Finally, start executor and upload the data: @@ -288,6 +306,13 @@ query = { 'habitat': 'dark places', 'behavior': 'light manipulation' } + +app.query( + monster_query, + limit=LIMIT, + **query, + **default_weights +) ``` | id | score | look | habitat | behavior | @@ -322,10 +347,8 @@ We can do it with `with_vector` method of the query object. Since moster's id is ```python app.query( - monster_query.with_vector( - monster, - "Harmonic Coral" - ), + monster_query.with_vector(monster, "Harmonic Coral"), + **default_weights, limit=LIMIT ) ``` @@ -349,22 +372,7 @@ These common themes in habitat, behavior, and appearance explain why these monst Let's suppose that we want to give more importance to the "look" attribute. It's easy to do with Superlinked. -All we need to do is to add weights to the query. - -```python -monster_query_with_weights = ( - Query(monster_index, weights={ - look_space: Param("look_weight"), - habitat_space: Param("habitat_weight"), - behavior_space: Param("behavior_weight") - }) - .find(monster) - .similar(look_space.text, Param("look")) - .similar(habitat_space.text, Param("habitat")) - .similar(behavior_space.text, Param("behavior")) - .limit(LIMIT) -) -``` +All we need to do is to adjust weights in the query. Let's again search for similar monsters to the "Harmonic Coral", but this time use weights for the attributes. First of all, let's find monsters with the most close appearance: @@ -375,6 +383,12 @@ weights = { "habitat_weight": 0, "behavior_weight": 0 } + +app.query( + monster_query.with_vector(monster, "Harmonic Coral"), + limit=LIMIT, + **weights +) ``` | id | score | look | habitat | behavior | From ca66a967ac246cce64292a4a8a88543854f22cf7 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Mon, 12 Aug 2024 13:42:37 -0700 Subject: [PATCH 11/19] in progress, still editing --- .../multi-attribute-semantic-search.md | 24 ++++++++++++------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 3a1c36678..d05f8bbc7 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -2,20 +2,20 @@ ## Motivation -Vector search has revolutionized data retrieval by leveraging semantic meaning. However, real-world scenarios often require searching across multiple attributes simultaneously, with varying degrees of importance for each. Two main approaches exist for multi-attribute vector search: +Vector search represents a revolution in information retrieval. Vector embedding, by taking account of context and semantic meaning, empowers vector search to return more relevant and accurate results, handle unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications, we often need to assign different, simultaneous weights to specific attributes of our data. -1. Search attributes separately, then combine weighted results. -2. Combine weighted attribute vectors, then perform a single search. +There are two common approaches to multi-attribute vector search: -This article demonstrates why the first approach, despite its intuitive appeal, falls short even in simple cases. We'll explore why the second approach is superior and how to implement it efficiently using the Superlinked framework. +1. embed objects as undifferentiated single vectors (*single-vector embedding*), search attributes separately, then weight and combine your results +2. embed attributes separately (*multi-vector embedding*), weight and combine attribute vectors, then perform a single search -By understanding these methods, you'll gain insights into creating more powerful and flexible search systems that can handle complex, multi-faceted queries with ease. +Below, we'll walk through an implementation (using a role-play game scenario) of the single-vector embedding approach, which, despite its intuitive appeal, performs poorly even in simple cases. Then, we'll implement the multi-vector embedding approach using the Superlinked framework, which performs far better. Finally, we'll explain why. -## Setting +These simple implementations will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. -Let's bring our example to life with an exciting scenario! +## Setting -Imagine it's game night, and you're the Dungeon Master for an epic Dungeons & Dragons adventure. For those unfamiliar, Dungeons & Dragons (D&D) is a popular tabletop role-playing game where players create characters and embark on imaginary quests guided by a storyteller, known as the Dungeon Master. Your friends are gathered around, eager to start a new quest. As the Dungeon Master, you're responsible for crafting thrilling encounters that challenge and excite your players. +It's game night, your friends are sitting eagerly around the game table, ready to become characters and embark on an epic quest of Dungeons & Dragons (D&D). Tonight, you're Dungeon Master (storyteller and guide), crafter of thrilling encounters that challenge and excite your players. Your trusty Monster Manual contains thousands of creatures, but finding the perfect monster for each situation can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment – all while surprising and delighting your players. @@ -474,4 +474,10 @@ Whether you're battling monsters in a fantasy realm or tackling real-world data - **Query**: A request for information from a search system, often represented as a vector in vector search. -- **Attribute Weighting**: Assigning different levels of importance to various attributes in a multi-attribute search. \ No newline at end of file +- **Attribute Weighting**: Assigning different levels of importance to various attributes in a multi-attribute search. + +## Contributors + +- [Andrey Pikunov](https://www.linkedin.com/in/andrey-pikunov/) +- [Mór Kapronczay, Editor](https://www.linkedin.com/in/mór-kapronczay-49447692) +- [Robert Turner, Editor](https://www.linkedin.com/in/robertdhayanturner/) \ No newline at end of file From 284db8deeb4f5428b0a76069f1b413f01775ac38 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Tue, 13 Aug 2024 09:19:09 -0700 Subject: [PATCH 12/19] in progress commit --- .../multi-attribute-semantic-search.md | 84 +++++++++---------- 1 file changed, 41 insertions(+), 43 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index d05f8bbc7..e7dce43fe 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -1,31 +1,33 @@ # Multi-attribute search with vector embeddings -## Motivation +## Why do multi-attribute vector search -Vector search represents a revolution in information retrieval. Vector embedding, by taking account of context and semantic meaning, empowers vector search to return more relevant and accurate results, handle unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications, we often need to assign different, simultaneous weights to specific attributes of our data. +[Vector search](https://superlinked.com/vectorhub/building-blocks/vector-search/introduction) represents a revolution in information retrieval. Vector embedding, by taking account of context and semantic meaning, empowers vector search to return more relevant and accurate results, handle unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications, we often need to assign different weights to specific attributes of our data objects. There are two common approaches to multi-attribute vector search: -1. embed objects as undifferentiated single vectors (*single-vector embedding*), search attributes separately, then weight and combine your results -2. embed attributes separately (*multi-vector embedding*), weight and combine attribute vectors, then perform a single search +1. embed objects as undifferentiated single vectors, search attributes separately, then weight and combine your results +2. embed attributes separately, weight and combine attribute vectors, then perform a single search -Below, we'll walk through an implementation (using a role-play game scenario) of the single-vector embedding approach, which, despite its intuitive appeal, performs poorly even in simple cases. Then, we'll implement the multi-vector embedding approach using the Superlinked framework, which performs far better. Finally, we'll explain why. +Below, we'll walk through an implementation (using a role-play game scenario) of the first approach. Despite its intuitive appeal, this "naive method" performs poorly even in simple cases. Next, for comparison, we'll implement the second approach - multi-attribute vector search - using the Superlinked framework. We'll explain why the Superlinked method works better. -These simple implementations will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. +These simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. -## Setting +If you're new to vector similarity search, don't worry, we've got you covered! You can find definitions for key terms in the "Glossary" section at the end of this article. You can also check out our [building blocks articles](https://superlinked.com/vectorhub/building-blocks). -It's game night, your friends are sitting eagerly around the game table, ready to become characters and embark on an epic quest of Dungeons & Dragons (D&D). Tonight, you're Dungeon Master (storyteller and guide), crafter of thrilling encounters that challenge and excite your players. +Okay, let's get into it. -Your trusty Monster Manual contains thousands of creatures, but finding the perfect monster for each situation can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment – all while surprising and delighting your players. +## A Dungeons & Dragons use case -What if we could create a tool that instantly finds the most suitable monsters for any scenario? A tool that considers multiple factors simultaneously, ensuring each encounter is as immersive and exciting as possible? +It's game night, your friends are perched around the games table, waiting to see what Dungeons & Dragons (D&D) character they'll become and quest they'll embark on. Tonight, you're Dungeon Master (storyteller and guide), crafter of thrilling encounters that challenge and enthrall your players. Your trusty Monster Manual contains *thousands* of creatures. Finding the perfect monster for each situation among the myriad options can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment. -Let's embark on our own quest to build this ultimate monster-finding system, using the power of multi-attribute vector search! +What if we could create a tool that instantly finds the monster most suited to each scenario? A **tool that considers multiple factors simultaneously**, ensuring each encounter is as immersive and exciting as possible? + +Let's embark on a quest of our own: build the ultimate monster-finding system, using the power of multi-attribute vector search! ## Dataset -To demonstrate our search techniques, we'll use a small synthetic dataset generated by a Large Language Model (LLM). Here's the prompt used to create our monster database: +First, we'll generate a small synthetic dataset of monsters - by prompting a Large Language Model (LLM): ``` Generate two JSON lists: 'monsters' and 'queries'. @@ -56,49 +58,46 @@ Output format: } ``` -Let's examine a sample of our generated dataset. Note that your results may differ due to the non-deterministic nature of LLM generation. - -First five monsters: +Let's take a look at a sample of our generated dataset. Note: LLM generation is non-deterministic, so your results may differ. +Here are our first five monsters: -| | name | look | habitat | behavior | -|---:|:-----------------|:----------------------------------------------------------|:----------------------------------------------------|:--------------------------------------------------------------| -| 0 | Luminoth | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | -| 1 | Aqua Wraith | Translucent humanoid figure made of flowing water | Rivers, lakes, and coastal areas | Shapeshifts to blend with water bodies and controls currents | -| 2 | Stoneheart Golem | Massive humanoid composed of interlocking rock formations | Rocky mountains and ancient ruins | Hibernates for centuries, awakens to protect its territory | -| 3 | Whispering Shade | Shadowy, amorphous being with glowing eyes | Dark forests and abandoned buildings | Feeds on fear and whispers unsettling truths | -| 4 | Zephyr Dancer | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | +| | name | look                           | habitat                   | behavior                      | +| ---: | :--- | :--- | :--- | :--- | +| 0 | Luminoth | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | +| 1 | Aqua Wraith | Translucent humanoid figure made of flowing water | Rivers, lakes, and coastal areas | Shapeshifts to blend with water bodies and controls currents | +| 2 | Stoneheart Golem | Massive humanoid composed of interlocking rock formations | Rocky mountains and ancient ruins | Hibernates for centuries, awakens to protect its territory | +| 3 | Whispering Shade | Shadowy, amorphous being with glowing eyes | Dark forests and abandoned buildings | Feeds on fear and whispers unsettling truths | +| 4 | Zephyr Dancer | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | -And here are the queries: +...and our generated queries: -| | look | habitat | behavior | -|---:|:--------------|:---------------------|:----------------------| -| 0 | glowing | dark places | light manipulation | -| 1 | elemental | extreme environments | environmental control | -| 2 | shapeshifting | varied landscapes | illusion creation | -| 3 | crystalline | mineral-rich areas | energy absorption | -| 4 | ethereal | atmospheric | mind influence | +| | look | habitat | behavior | +|---:|:---|:---|:---| +| 0 | glowing | dark places | light manipulation | +| 1 | elemental | extreme environments | environmental control | +| 2 | shapeshifting | varied landscapes | illusion creation | +| 3 | crystalline | mineral-rich areas | energy absorption | +| 4 | ethereal | atmospheric | mind influence | -Original dataset and query examples are available [here](https://github.com/superlinked/article-multi-attribute-search). +See original dataset and query examples [here](https://github.com/superlinked/article-multi-attribute-search). ## Retrieval -Let's set up common parameters for both approaches we'll explore: the naive method and the Superlinked method. +Let's set up parameters we'll use in both of our implementations - the naive method and the Superlinked method - below. -We'll use the `sentence-transformers/all-mpnet-base-v2` model from the Sentence Transformers library to generate our vector embeddings. To keep our results short, we'll limit the output to the top 3 matches. +We generate our vector embeddings with `sentence-transformers/all-mpnet-base-v2`. For simplicity's sake, we'll limit our output to the top 3 matches. (For the complete code, including necessary imports and helper functions, refer to the [accompanying notebook](https://github.com/superlinked/article-multi-attribute-search).) ```python LIMIT = 3 MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" ``` -For the complete code, including necessary imports and helper functions, please refer to the [accompanying notebook](https://github.com/superlinked/article-multi-attribute-search). +Now, let's get into our multi-attribute monster search. First, our naive search approach. -If you're new to vector similarity search, don't worry! You can find definitions for key terms in the "Glossary" section at the end of this article. +## Naive approach -Now, let's dive into our two approaches for multi-attribute monster search. - -### Naive approach +We start by defining a class `NaiveRetriever` to perform similarity-based search on our dataset, using our `all-mpnet-base-v2`-generated embeddings. In our naive approach, we embed a single vector per data object. ```python class NaiveRetriever: @@ -145,11 +144,10 @@ class NaiveRetriever: merged_results.sort_values("score", ascending=False, inplace=True) return merged_results - naive_retriever = NaiveRetriever(df.set_index("name")) ``` -Let's take the first query from our list and search for monsters using the naive approach: +Let's take the first query from our generated list above, and search for monsters using our `naive_retriever`: ```python query = { @@ -161,7 +159,7 @@ query = { naive_retriever.search(query) ``` -Here are search results for each attribute: +Our `naive_retriever` returns the following search results for each attribute: **Look: glowing** | id | score_look | look | @@ -170,7 +168,7 @@ Here are search results for each attribute: | Sandstorm Djinn | 0.407344 | Swirling vortex of sand with glowing symbols | | Luminoth | 0.378619 | Moth-like creature with glowing wings and antenna | -Awesome, these are relevant creatures, all have something "glowing" in them as we asked for! +Awesome! Our returned monsters are relevant - all have something "glowing" in them as we asked for! Let's see what we can find for other two attributes. **Habitat: dark places** @@ -368,7 +366,7 @@ Both share important thematic elements with the Harmonic Coral: 3. Dynamic or fluid visual characteristics These common themes in habitat, behavior, and appearance explain why these monsters were identified as most similar to the Harmonic Coral. -### Attribute weighing +## Attribute weighing Let's suppose that we want to give more importance to the "look" attribute. It's easy to do with Superlinked. From a169f0ef180c65eafb6f8a1c8144a0f8ff50dfcb Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Wed, 14 Aug 2024 14:57:32 -0700 Subject: [PATCH 13/19] review commit - awaiting author input --- .../multi-attribute-semantic-search.md | 118 +++++++----------- 1 file changed, 45 insertions(+), 73 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index e7dce43fe..575d37626 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -9,7 +9,7 @@ There are two common approaches to multi-attribute vector search: 1. embed objects as undifferentiated single vectors, search attributes separately, then weight and combine your results 2. embed attributes separately, weight and combine attribute vectors, then perform a single search -Below, we'll walk through an implementation (using a role-play game scenario) of the first approach. Despite its intuitive appeal, this "naive method" performs poorly even in simple cases. Next, for comparison, we'll implement the second approach - multi-attribute vector search - using the Superlinked framework. We'll explain why the Superlinked method works better. +Below, we'll walk through an implementation (using a role-play game scenario) of the first approach. Despite its intuitive appeal, this "naive method" performs poorly even in simple cases. Next, for comparison, we'll implement the second approach using the Superlinked framework. We'll explain why the Superlinked method works better. These simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. @@ -27,7 +27,7 @@ Let's embark on a quest of our own: build the ultimate monster-finding system, u ## Dataset -First, we'll generate a small synthetic dataset of monsters - by prompting a Large Language Model (LLM): +First, we'll generate a small synthetic dataset of monsters, by prompting a Large Language Model (LLM): ``` Generate two JSON lists: 'monsters' and 'queries'. @@ -58,7 +58,7 @@ Output format: } ``` -Let's take a look at a sample of our generated dataset. Note: LLM generation is non-deterministic, so your results may differ. +Let's take a look at a sample of the dataset our LLM generated. Note: LLM generation is non-deterministic, so your results may differ. Here are our first five monsters: @@ -93,7 +93,7 @@ LIMIT = 3 MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" ``` -Now, let's get into our multi-attribute monster search. First, our naive search approach. +Now, let's get our multi-attribute monster search under way! First, the *naive* multi-attribute search method. ## Naive approach @@ -147,7 +147,7 @@ class NaiveRetriever: naive_retriever = NaiveRetriever(df.set_index("name")) ``` -Let's take the first query from our generated list above, and search for monsters using our `naive_retriever`: +Let's use the first query from our generated list above, and search for monsters using our `naive_retriever`: ```python query = { @@ -168,8 +168,9 @@ Our `naive_retriever` returns the following search results for each attribute: | Sandstorm Djinn | 0.407344 | Swirling vortex of sand with glowing symbols | | Luminoth | 0.378619 | Moth-like creature with glowing wings and antenna | -Awesome! Our returned monsters are relevant - all have something "glowing" in them as we asked for! -Let's see what we can find for other two attributes. +Awesome! Our returned monster results are relevant - they all have some "glowing" attribute. + +Let's see what the naive approach returns when we search the other two attributes. **Habitat: dark places** | id | score_habitat | habitat | @@ -185,7 +186,7 @@ Let's see what we can find for other two attributes. | Crystalwing Drake | 0.385211 | Hoards precious gems and can refract light into powerful beams | | Luminoth | 0.345566 | Emits soothing light patterns to communicate and attract prey | -These results look promising at first glance, but we must remember our original goal: finding monsters that are relevant across all attributes simultaneously. This is where the limitations of our naive approach become apparent. Let's merge the results to see the full picture: +All the retrieved monsters do possess the wanted attributes. At first glance, the naive search results may seem promising. But we need to find monsters that possess *all three attributes simultaneously*. Let's merge our results to see how well our monsters do at achieving this goal: | id | score_look | score_habitat | score_behavior | |:--------------------|-------------:|----------------:|-----------------:| @@ -197,7 +198,7 @@ These results look promising at first glance, but we must remember our original | Living Graffiti | | | 0.385741 | | Crystalwing Drake | | | 0.385211 | -What we have here: +And here, the limits of the naive approach become obvious. Let's evaluate: 1. Relevance by attribute: - "Look": Three monsters were retrieved (Whispering Shade, Sandstorm Djinn, and Luminoth). @@ -206,12 +207,9 @@ What we have here: 2. Overall relevance: - No single monster was retrieved for all three attributes simultaneously. - - The results are fragmented, with different monsters being relevant for different attributes. - -This fragmentation in the results highlights a potential issue with our current search approach, as it fails to find monsters that satisfy all criteria at once. + - The results are fragmented: different monsters are relevant for different attributes. -Maybe we can fix this issue by proactively retrieving more monsters for each attribute? -Let's try it with 6 monsters for each attribute instead of 3. +In short, the naive search approach fails to find monsters that satisfy all criteria at once. **Maybe we can fix this issue by proactively retrieving more monsters for each attribute?** Let's try it with 6 monsters for each attribute instead of 3. | id | score_look | score_habitat | score_behavior | |:--------------------|-------------:|----------------:|-----------------:| @@ -229,20 +227,21 @@ Let's try it with 6 monsters for each attribute instead of 3. | Crystalwing Drake | | | 0.385211 | | Aqua Wraith | | | 0.283581 | -In total, we retrieved 13 monsters (that is more than half of our tiny dataset!) and still have the same issue: no monster was retrieved for all three attributes simultaneously. -We could try to increase the number of monsters further, but it would result in the following shortcomings: -1. While not a problem for our toy task, in production it will result in noticeably longer search time. And generally, multiple KNN searches take more time than a single one with concatenated vectors ([redis benchmark](https://redis.io/blog/benchmarking-results-for-vector-databases/)). -2. With more attributes, the number of monsters to retrieve for every attribute will grow exponentially. -3. And there is still no guarantee that this will result in retrieval of monsters that satisfy all criteria at once. -4. Even if we somehow manage to retrieve monsters that satisfy all criteria at once, we would still need to perform reconciliation of results that would result in additional overhead in a production system. +We've now retrieved 13 monsters (more than half of our tiny dataset!), and still have the same issue: not one of these monsters was retrieved for all three attributes. -This example is enough to show that the naive approach is not suitable for our task. +Increasing the number of retrieved monsters *might* solve our problem, but it creates additional issues: +1. In production, retrieving more results (multiple KNN searches) lengthens search time noticeably. +2. For each new attribute we introduce, we have to retrieve nearest neighbors (monsters), making the total number of retrieved monsters grow exponentially. +3. We still have no guarantee we'll retrieve monsters that possess all our desired attributes. +4. If we do manage to retrieve monsters that satisfy all criteria at once, we'll have to expend additional overhead reconciling results. + +In sum, the naive approach is too uncertain and inefficient for viable multi-attribute search, especially in production. ### Superlinked approach -Let's implement the second approach using Superlinked framework. +Let's implement our second approach - multi-vector indexing - using the Superlinked framework. -First, let's define the schema, spaces, index and query: +First, we define the schema, spaces, index, and query: ```python @schema @@ -284,7 +283,7 @@ default_weights = { } ``` -Finally, start executor and upload the data: +Now, we start the executor and upload the data: ```python monster_parser = DataFrameParser(monster, mapping={monster.id: "name"}) @@ -296,7 +295,7 @@ app = executor.run() source.put([df]) ``` -Let's run the same query as before: +Let's run the same query we ran using our first, naive approach (above): ```python query = { @@ -319,9 +318,9 @@ app.query( | Luminoth | 0.340084 | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | | Living Graffiti | 0.330587 | Two-dimensional, colorful creature that inhabits flat surfaces | Urban areas, particularly walls and billboards | Shapeshifts to blend with surroundings and absorbs pigments | -As you can see, the results contain monsters that satisfy all criteria at once. +Et voila! This time, every one of our returned monsters possesses all three attributes at once! What an improvement. -Let's try another query. +Let's see if we can replicate. We'll try another query. ```python query = { @@ -337,11 +336,12 @@ query = { | Zephyr Dancer | 0.342075 | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | | Whispering Shade | 0.337434 | Shadowy, amorphous being with glowing eyes | Dark forests and abandoned buildings | Feeds on fear and whispers unsettling truths | -Great! -Maybe we would like to find monsters from the dataset similar to the existing one? +Great! Our outcomes are excellent again. + +What if we want to find monsters that are similar to a specific monster from our dataset? Let's try it with the "Harmonic Coral" (very last row in our dataset). -We could extract attributes for this monsters and create query parameters manually, but Superlinked provides better way to query records similar to already stored ones. -We can do it with `with_vector` method of the query object. Since moster's id is its name, we can express our request as simple as this: +We *could* extract attributes for this monster and create query parameters manually. But Superlinked provides a better way to query records similar to already stored ones. +We can do it with `with_vector` method of the query object. Since each monster's id is its name, we can express our request as simply as: ```python app.query( @@ -357,23 +357,15 @@ app.query( | Dreamweaver Octopus | 0.402288 | Cephalopod with tentacles that shimmer like auroras | Deep ocean trenches and underwater caves | Influences the dreams of nearby creatures | | Aqua Wraith | 0.330869 | Translucent humanoid figure made of flowing water | Rivers, lakes, and coastal areas | Shapeshifts to blend with water bodies and controls currents | -The top result is the most relevant one, the "Harmonic Coral" itself which is pretty much expected. -Let's see who are the other two monsters: "Dreamweaver Octopus" and "Aqua Wraith". +The top result is the most relevant one, Harmonic Coral itself, as expected. The other two monsters our search retrieves are "Dreamweaver Octopus" and "Aqua Wraith". Both share important thematic (*attribute*) elements with Harmonic Coral: -Both share important thematic elements with the Harmonic Coral: -1. Aquatic habitats -2. Ability to influence or manipulate their environment -3. Dynamic or fluid visual characteristics -These common themes in habitat, behavior, and appearance explain why these monsters were identified as most similar to the Harmonic Coral. +1. Aquatic habitats (*habitat*) +2. Ability to influence or manipulate their environment (*behavior*) +3. Dynamic or fluid visual characteristics (*look*) ## Attribute weighing -Let's suppose that we want to give more importance to the "look" attribute. -It's easy to do with Superlinked. -All we need to do is to adjust weights in the query. - -Let's again search for similar monsters to the "Harmonic Coral", but this time use weights for the attributes. -First of all, let's find monsters with the most close appearance: +Suppose, now, that we want to give more importance to the "look" attribute. The Superlinked framework lets us easily adjust weights at query time. For easy comparison, we'll search for monsters similar to Harmonic Coral, but with our weights adjusted to favor "look". ```python weights = { @@ -395,9 +387,9 @@ app.query( | Thornvine Elemental | 0.252593 | Plant-like creature with a body of twisted vines and thorns | Overgrown ruins and dense jungles | Rapidly grows and controls surrounding plant life | | Plasma Serpent | 0.243241 | Snake-like creature made of crackling energy | Electrical storms and power plants | Feeds on electrical currents and can short-circuit technology | -"Branching with vibrating tendrils", "Plant-like creature with a body of twisted vines and thorns", "Snake-like" -- for sure these descriptions share some common theme. +Our results all (appropriately) have similar appearances - "Branching with vibrating tendrils", "Plant-like creature with a body of twisted vines and thorns", "Snake-like". -Let's search by "habitat" and "behaviour" simultaneously: +Now, let's do another search, ignoring appearance, and looking instead for monsters that are similar in terms of "habitat" and "behavior" simultaneously: ```python weights = { @@ -413,9 +405,9 @@ weights = { | Dreamweaver Octopus | 0.357656 | Cephalopod with tentacles that shimmer like auroras | Deep ocean trenches and underwater caves | Influences the dreams of nearby creatures | | Mist Phantom | 0.288106 | Ethereal, fog-like humanoid with shifting features | Swamps, moors, and foggy coastlines | Lures travelers astray with illusions and whispers | -All three monsters live in some watery habitats and have mind-controlling abilities. +Again, the Superlinked method produces great results. All three monsters live in watery habitats and possess mind-controlling abilities. -Lastly, let's try to use all attributes with different weights: +Finally, let's try another search, weighting all three attributes differently - to find monsters that in comparison to Harmonic Coral look somewhat similar, have very different habitats, and very similar behavior: ```python weights = { @@ -425,57 +417,37 @@ weights = { } ``` -Such set of weights means that we want to find monsters that have similar behaviour, somewhat similar look, but live in different habitats for the one Harmonic Coral prefers. - | id | score | look | habitat | behavior | |:---------------|---------:|:---------------------------------------------------------------------|:----------------------------------------------------|:---------------------------------------------------------------| | Harmonic Coral | 0.19245 | Branching, musical instrument-like structure with vibrating tendrils | Shallow seas and tidal pools | Creates complex melodies to communicate and influence emotions | | Luminoth | 0.149196 | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | | Zephyr Dancer | 0.136456 | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | -Two retrievel monsters (Luminoth and Zephyr Dancer) are indeed similar to the Harmonic Coral in terms of behaviour, but live in different habitats. -In terms of look, there is little similarity between Harmonic Coral and Luminoth since antenna could look like tendrils, but it's not the same for sure. -Anyway, this attribute was half-important for us, so we can compensate here. +Great results again! Our two other retrieved monsters - Luminoth and Zephyr Dancer - have behavior similar to Harmonic Coral, and live in habitats different from Harmonic Coral's. They also look very different from Harmonic Coral. (Harmonic's tendrils and Luminoth's antenna are similar features, but we only down-weighted `look_weight` by 0.5, and the resemblance ends there.) ## Conclusion -Multi-attribute vector search represents a significant advancement in information retrieval, offering a more nuanced and effective approach compared to traditional search methods. -As demonstrated through our D&D monster example, this technique excels at finding relevant results when multiple criteria are involved. +Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive method (above) - embedding data objects (e.g., D&D monsters) as single vectors, doing *separate searches* for different attributes, then weighting and combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. -Key takeaways: -1. Vector embeddings capture semantic meaning, allowing for more intelligent searches. -2. The Superlinked framework provides an efficient way to implement multi-attribute vector search. -3. Attribute weighting adds flexibility, allowing users to prioritize certain criteria over others. +To handle scenarios like this, and where users want to weight different attributes differently, it's better to embed attributes separately, weight and combine attribute vectors at query time, and perform *a single search* - the Superlinked method. This approach is more accurate, efficient, and scalable than the naive method for any application - from D&D monster-finding to production e-commerce and recommendation systems - that requires fast, reliable, nuanced, multi-attribute vector retrieval. -As data continues to grow in complexity and volume, multi-attribute vector search will play an increasingly crucial role in various applications, from e-commerce to content recommendation systems. -By understanding and implementing these techniques, developers and data scientists can create more powerful, intuitive, and user-friendly search experiences. - -Whether you're battling monsters in a fantasy realm or tackling real-world data challenges, multi-attribute vector search offers a powerful tool for finding the perfect match in a sea of possibilities. +Whether you're battling monsters or tackling real-world data challenges, [a multi-vector indexing approach like Superlinked's] is a powerful tool for finding the perfect match in a sea of possibilities, especially as data continues to grow in complexity and volume. ## Glossary - **Vector Embedding**: A numerical representation of data (like text or images) in a high-dimensional space, capturing semantic meaning. - - **Cosine Similarity**: A measure of similarity between two vectors, often used in vector search to determine how closely related two items are. - - **Multi-attribute Search**: A search method that considers multiple characteristics or properties of items simultaneously. - - **Semantic Meaning**: The underlying concept or idea represented by data, rather than just its literal interpretation. - - **Nearest Neighbors**: In vector search, the items that are most similar or closest to a given query in the vector space. - - **Large Language Model (LLM)**: An AI model trained on vast amounts of text data, capable of generating human-like text and performing various language tasks. - - **Sentence Transformer**: A type of model that converts sentences or short texts into vector embeddings. - - **Index**: In the context of vector search, a data structure that organizes vector embeddings for efficient retrieval. - - **Query**: A request for information from a search system, often represented as a vector in vector search. - - **Attribute Weighting**: Assigning different levels of importance to various attributes in a multi-attribute search. ## Contributors -- [Andrey Pikunov](https://www.linkedin.com/in/andrey-pikunov/) +- [Andrey Pikunov, Author](https://www.linkedin.com/in/andrey-pikunov/) - [Mór Kapronczay, Editor](https://www.linkedin.com/in/mór-kapronczay-49447692) - [Robert Turner, Editor](https://www.linkedin.com/in/robertdhayanturner/) \ No newline at end of file From a1162f288fded7dac90ee4db6fcee4cbaaf30989 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Thu, 22 Aug 2024 12:27:33 -0700 Subject: [PATCH 14/19] hand off to author for final review --- .../multi-attribute-semantic-search.md | 69 +++++++------------ 1 file changed, 25 insertions(+), 44 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 575d37626..84f3740d8 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -2,24 +2,22 @@ ## Why do multi-attribute vector search -[Vector search](https://superlinked.com/vectorhub/building-blocks/vector-search/introduction) represents a revolution in information retrieval. Vector embedding, by taking account of context and semantic meaning, empowers vector search to return more relevant and accurate results, handle unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications, we often need to assign different weights to specific attributes of our data objects. +[Vector search](https://superlinked.com/vectorhub/building-blocks/vector-search/introduction) represents a revolution in information retrieval. Vector embedding - by taking account of context and semantic meaning - empowers vector search to return more relevant and accurate results, handle not just structured but also unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications (e.g., e-comm, recSys, etc.), we often need to assign different weights to specific attributes of our data objects. -There are two common approaches to multi-attribute vector search: +There are two common approaches to multi-attribute vector search. In both approaches, we start by embedding each attribute of a data object separately. The main difference between these two approaches is in how our embeddings are *stored* and *searched*. -1. embed objects as undifferentiated single vectors, search attributes separately, then weight and combine your results -2. embed attributes separately, weight and combine attribute vectors, then perform a single search +1. the *naive* approach - store each attribute vector in separate vector stores (one per attribute), perform a separate search for each attribute, combine search results, and post-process (e.g., weight) as required. +2. the *Superlinked* approach - concatenate and store all attribute vectors in the same vector store (using Superlinked's built-in funtionality), which allows us to *search just once*, with attendant efficiency gains. Superlinked's `spaces` *also* let us weight each attribute at query time to surface more relevant results, with no post-processing. -Below, we'll walk through an implementation (using a role-play game scenario) of the first approach. Despite its intuitive appeal, this "naive method" performs poorly even in simple cases. Next, for comparison, we'll implement the second approach using the Superlinked framework. We'll explain why the Superlinked method works better. +Below, we'll build a monster finder tool for Dungeons and Dragons using each of these two approaches to see how they compare. These simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. -These simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. - -If you're new to vector similarity search, don't worry, we've got you covered! You can find definitions for key terms in the "Glossary" section at the end of this article. You can also check out our [building blocks articles](https://superlinked.com/vectorhub/building-blocks). +If you're new to vector similarity search, don't worry! We've got you covered - check out our [building blocks articles](https://superlinked.com/vectorhub/building-blocks). Okay, let's get into it. ## A Dungeons & Dragons use case -It's game night, your friends are perched around the games table, waiting to see what Dungeons & Dragons (D&D) character they'll become and quest they'll embark on. Tonight, you're Dungeon Master (storyteller and guide), crafter of thrilling encounters that challenge and enthrall your players. Your trusty Monster Manual contains *thousands* of creatures. Finding the perfect monster for each situation among the myriad options can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment. +It's game night, your friends are perched around the games table, waiting to see what Dungeons & Dragons (D&D) character they'll become and quest they'll embark on. Tonight, you're Dungeon Master (storyteller and guide), crafter of thrilling encounters to challenge and enthrall your players. Your trusty D&D Monster Manual contains *thousands* of creatures. Finding the perfect monster for each situation among the myriad options can be overwhelming. The ideal foe needs to match the setting, difficulty, and narrative of the moment. What if we could create a tool that instantly finds the monster most suited to each scenario? A **tool that considers multiple factors simultaneously**, ensuring each encounter is as immersive and exciting as possible? @@ -84,18 +82,18 @@ See original dataset and query examples [here](https://github.com/superlinked/ar ## Retrieval -Let's set up parameters we'll use in both of our implementations - the naive method and the Superlinked method - below. +Let's set up parameters we'll use in both of our approaches - naive and Superlinked - below. -We generate our vector embeddings with `sentence-transformers/all-mpnet-base-v2`. For simplicity's sake, we'll limit our output to the top 3 matches. (For the complete code, including necessary imports and helper functions, refer to the [accompanying notebook](https://github.com/superlinked/article-multi-attribute-search).) +We generate our vector embeddings with `sentence-transformers/all-mpnet-base-v2`. For simplicity's sake, we'll limit our output to the top 3 matches. (For complete code, including necessary imports and helper functions, see the [notebook](https://github.com/superlinked/article-multi-attribute-search).) ```python LIMIT = 3 MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" ``` -Now, let's get our multi-attribute monster search under way! First, the *naive* multi-attribute search method. +Now, let's get our multi-attribute monster search under way! First, we'll try the *naive* approach. -## Naive approach +### Naive approach We start by defining a class `NaiveRetriever` to perform similarity-based search on our dataset, using our `all-mpnet-base-v2`-generated embeddings. In our naive approach, we embed a single vector per data object. @@ -209,7 +207,7 @@ And here, the limits of the naive approach become obvious. Let's evaluate: - No single monster was retrieved for all three attributes simultaneously. - The results are fragmented: different monsters are relevant for different attributes. -In short, the naive search approach fails to find monsters that satisfy all criteria at once. **Maybe we can fix this issue by proactively retrieving more monsters for each attribute?** Let's try it with 6 monsters for each attribute instead of 3. +In short, the naive search approach fails to find monsters that satisfy all criteria at once. **Maybe we can fix this issue by proactively retrieving more monsters for each attribute?** Let's try it with 6 monsters per attribute, instead of 3. Let's take a look at what this approach generates: | id | score_look | score_habitat | score_behavior | |:--------------------|-------------:|----------------:|-----------------:| @@ -227,9 +225,10 @@ In short, the naive search approach fails to find monsters that satisfy all crit | Crystalwing Drake | | | 0.385211 | | Aqua Wraith | | | 0.283581 | -We've now retrieved 13 monsters (more than half of our tiny dataset!), and still have the same issue: not one of these monsters was retrieved for all three attributes. +We've now retrieved 13 monsters (more than half of our tiny dataset!), and *still* have the same issue: not one of these monsters was retrieved for all three attributes. + +Increasing the number of retrieved monsters (beyond 6) *might* solve our problem, but it creates additional issues: -Increasing the number of retrieved monsters *might* solve our problem, but it creates additional issues: 1. In production, retrieving more results (multiple KNN searches) lengthens search time noticeably. 2. For each new attribute we introduce, we have to retrieve nearest neighbors (monsters), making the total number of retrieved monsters grow exponentially. 3. We still have no guarantee we'll retrieve monsters that possess all our desired attributes. @@ -239,7 +238,7 @@ In sum, the naive approach is too uncertain and inefficient for viable multi-att ### Superlinked approach -Let's implement our second approach - multi-vector indexing - using the Superlinked framework. +Let's implement our second approach to see if it does any better than the naive one. First, we define the schema, spaces, index, and query: @@ -295,7 +294,7 @@ app = executor.run() source.put([df]) ``` -Let's run the same query we ran using our first, naive approach (above): +Let's run the same query we ran in our naive approach implementation above: ```python query = { @@ -320,7 +319,7 @@ app.query( Et voila! This time, every one of our returned monsters possesses all three attributes at once! What an improvement. -Let's see if we can replicate. We'll try another query. +Can we can replicate our results? Let's try another query and find out. ```python query = { @@ -338,10 +337,7 @@ query = { Great! Our outcomes are excellent again. -What if we want to find monsters that are similar to a specific monster from our dataset? -Let's try it with the "Harmonic Coral" (very last row in our dataset). -We *could* extract attributes for this monster and create query parameters manually. But Superlinked provides a better way to query records similar to already stored ones. -We can do it with `with_vector` method of the query object. Since each monster's id is its name, we can express our request as simply as: +What if we want to find monsters that are similar to a specific monster from our dataset? Let's try it with a monster we haven't seen yet - "Harmonic Coral". We *could* extract attributes for this monster and create query parameters manually. But Superlinked has a `with_vector` method we can use on the query object. Because each monster's id is its name, we can express our request as simply as: ```python app.query( @@ -363,7 +359,7 @@ The top result is the most relevant one, Harmonic Coral itself, as expected. The 2. Ability to influence or manipulate their environment (*behavior*) 3. Dynamic or fluid visual characteristics (*look*) -## Attribute weighing +## Attribute weighting Suppose, now, that we want to give more importance to the "look" attribute. The Superlinked framework lets us easily adjust weights at query time. For easy comparison, we'll search for monsters similar to Harmonic Coral, but with our weights adjusted to favor "look". @@ -405,9 +401,9 @@ weights = { | Dreamweaver Octopus | 0.357656 | Cephalopod with tentacles that shimmer like auroras | Deep ocean trenches and underwater caves | Influences the dreams of nearby creatures | | Mist Phantom | 0.288106 | Ethereal, fog-like humanoid with shifting features | Swamps, moors, and foggy coastlines | Lures travelers astray with illusions and whispers | -Again, the Superlinked method produces great results. All three monsters live in watery habitats and possess mind-controlling abilities. +Again, the Superlinked approach produces great results. All three monsters live in watery habitats and possess mind-controlling abilities. -Finally, let's try another search, weighting all three attributes differently - to find monsters that in comparison to Harmonic Coral look somewhat similar, have very different habitats, and very similar behavior: +Finally, let's try another search, weighting all three attributes differently - to find monsters that in comparison to Harmonic Coral look somewhat similar, live in very different habitats, and possess very similar behavior: ```python weights = { @@ -423,28 +419,13 @@ weights = { | Luminoth | 0.149196 | Moth-like creature with glowing wings and antenna | Dense forests and jungles with bioluminescent flora | Emits soothing light patterns to communicate and attract prey | | Zephyr Dancer | 0.136456 | Graceful avian creature with iridescent feathers | High mountain peaks and wind-swept plains | Creates mesmerizing aerial displays to attract mates | -Great results again! Our two other retrieved monsters - Luminoth and Zephyr Dancer - have behavior similar to Harmonic Coral, and live in habitats different from Harmonic Coral's. They also look very different from Harmonic Coral. (Harmonic's tendrils and Luminoth's antenna are similar features, but we only down-weighted `look_weight` by 0.5, and the resemblance ends there.) +Great results again! Our two other retrieved monsters - Luminoth and Zephyr Dancer - have behavior similar to Harmonic Coral, and live in habitats different from Harmonic Coral's. They also look very different from Harmonic Coral. (Harmonic's tendrils and Luminoth's antenna are similar features, but we only down-weighted `look_weight` by 0.5, and the resemblance between the two monsters ends there.) ## Conclusion -Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive method (above) - embedding data objects (e.g., D&D monsters) as single vectors, doing *separate searches* for different attributes, then weighting and combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. - -To handle scenarios like this, and where users want to weight different attributes differently, it's better to embed attributes separately, weight and combine attribute vectors at query time, and perform *a single search* - the Superlinked method. This approach is more accurate, efficient, and scalable than the naive method for any application - from D&D monster-finding to production e-commerce and recommendation systems - that requires fast, reliable, nuanced, multi-attribute vector retrieval. - -Whether you're battling monsters or tackling real-world data challenges, [a multi-vector indexing approach like Superlinked's] is a powerful tool for finding the perfect match in a sea of possibilities, especially as data continues to grow in complexity and volume. - -## Glossary +Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive approach (above) - storing and searching attribute vectors separately, *then* combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. (Moreover, [multiple KNN searches take more time than a single one with concatenated vectors](https://redis.io/blog/benchmarking-results-for-vector-databases/).) -- **Vector Embedding**: A numerical representation of data (like text or images) in a high-dimensional space, capturing semantic meaning. -- **Cosine Similarity**: A measure of similarity between two vectors, often used in vector search to determine how closely related two items are. -- **Multi-attribute Search**: A search method that considers multiple characteristics or properties of items simultaneously. -- **Semantic Meaning**: The underlying concept or idea represented by data, rather than just its literal interpretation. -- **Nearest Neighbors**: In vector search, the items that are most similar or closest to a given query in the vector space. -- **Large Language Model (LLM)**: An AI model trained on vast amounts of text data, capable of generating human-like text and performing various language tasks. -- **Sentence Transformer**: A type of model that converts sentences or short texts into vector embeddings. -- **Index**: In the context of vector search, a data structure that organizes vector embeddings for efficient retrieval. -- **Query**: A request for information from a search system, often represented as a vector in vector search. -- **Attribute Weighting**: Assigning different levels of importance to various attributes in a multi-attribute search. +To handle scenarios like this, it's better to store all your attribute vectors in the same vector store and perform *a single search*, weighting your attributes at query time. The Superlinked approach is more accurate, efficient, and scalable than the naive approach for any application that requires fast, reliable, nuanced, multi-attribute vector retrieval - whether your use case is tackling real world data challenges in your e-commerce, recommendation system, or something entirely different, like battling monsters. ## Contributors From a271e3943a2af0e7c9cdda25bb5179a6693b4212 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Thu, 22 Aug 2024 12:40:33 -0700 Subject: [PATCH 15/19] couple small changes to review commit --- docs/articles/multi-attribute-semantic-search.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 84f3740d8..000e0a270 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -95,7 +95,9 @@ Now, let's get our multi-attribute monster search under way! First, we'll try th ### Naive approach -We start by defining a class `NaiveRetriever` to perform similarity-based search on our dataset, using our `all-mpnet-base-v2`-generated embeddings. In our naive approach, we embed a single vector per data object. +In our naive approach, we embed attributes independently and store them in different indices. At query time, we run multiple kNN-searches on all the indices, and then combine all our partial results into one. + +We start by defining a class `NaiveRetriever` to perform similarity-based search on our dataset, using our `all-mpnet-base-v2`-generated embeddings. ```python class NaiveRetriever: @@ -229,7 +231,7 @@ We've now retrieved 13 monsters (more than half of our tiny dataset!), and *stil Increasing the number of retrieved monsters (beyond 6) *might* solve our problem, but it creates additional issues: -1. In production, retrieving more results (multiple KNN searches) lengthens search time noticeably. +1. In production, retrieving more results (multiple kNN searches) lengthens search time noticeably. 2. For each new attribute we introduce, we have to retrieve nearest neighbors (monsters), making the total number of retrieved monsters grow exponentially. 3. We still have no guarantee we'll retrieve monsters that possess all our desired attributes. 4. If we do manage to retrieve monsters that satisfy all criteria at once, we'll have to expend additional overhead reconciling results. @@ -423,7 +425,7 @@ Great results again! Our two other retrieved monsters - Luminoth and Zephyr Danc ## Conclusion -Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive approach (above) - storing and searching attribute vectors separately, *then* combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. (Moreover, [multiple KNN searches take more time than a single one with concatenated vectors](https://redis.io/blog/benchmarking-results-for-vector-databases/).) +Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive approach (above) - storing and searching attribute vectors separately, *then* combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. (Moreover, [multiple kNN searches take more time than a single one with concatenated vectors](https://redis.io/blog/benchmarking-results-for-vector-databases/).) To handle scenarios like this, it's better to store all your attribute vectors in the same vector store and perform *a single search*, weighting your attributes at query time. The Superlinked approach is more accurate, efficient, and scalable than the naive approach for any application that requires fast, reliable, nuanced, multi-attribute vector retrieval - whether your use case is tackling real world data challenges in your e-commerce, recommendation system, or something entirely different, like battling monsters. From 4c8355d482414534e6414e239739d5f0bbda9924 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Thu, 22 Aug 2024 12:54:36 -0700 Subject: [PATCH 16/19] final small comment resolutions - update article; hand off to author for final review --- docs/articles/multi-attribute-semantic-search.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index 000e0a270..c3080bed9 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -2,18 +2,18 @@ ## Why do multi-attribute vector search -[Vector search](https://superlinked.com/vectorhub/building-blocks/vector-search/introduction) represents a revolution in information retrieval. Vector embedding - by taking account of context and semantic meaning - empowers vector search to return more relevant and accurate results, handle not just structured but also unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications (e.g., e-comm, recSys, etc.), we often need to assign different weights to specific attributes of our data objects. +[Vector search](https://superlinked.com/vectorhub/building-blocks/vector-search/introduction) represents a revolution in information retrieval. Vector embedding - by taking account of context and semantic meaning - empowers vector search to return more relevant and accurate results, handle not just structured but also unstructured data and multiple languages, and scale. But to generate high quality responses in real-world applications, we often need to assign different weights to specific attributes of our data objects. There are two common approaches to multi-attribute vector search. In both approaches, we start by embedding each attribute of a data object separately. The main difference between these two approaches is in how our embeddings are *stored* and *searched*. 1. the *naive* approach - store each attribute vector in separate vector stores (one per attribute), perform a separate search for each attribute, combine search results, and post-process (e.g., weight) as required. 2. the *Superlinked* approach - concatenate and store all attribute vectors in the same vector store (using Superlinked's built-in funtionality), which allows us to *search just once*, with attendant efficiency gains. Superlinked's `spaces` *also* let us weight each attribute at query time to surface more relevant results, with no post-processing. -Below, we'll build a monster finder tool for Dungeons and Dragons using each of these two approaches to see how they compare. These simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease. +Below, we'll use these two approaches to implement a multi-attribute vector search tool - a Dungeons and Dragons monster finder! Our simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease, whatever your use case. If you're new to vector similarity search, don't worry! We've got you covered - check out our [building blocks articles](https://superlinked.com/vectorhub/building-blocks). -Okay, let's get into it. +Okay, let's go monster hunting! ## A Dungeons & Dragons use case @@ -168,7 +168,7 @@ Our `naive_retriever` returns the following search results for each attribute: | Sandstorm Djinn | 0.407344 | Swirling vortex of sand with glowing symbols | | Luminoth | 0.378619 | Moth-like creature with glowing wings and antenna | -Awesome! Our returned monster results are relevant - they all have some "glowing" attribute. +Awesome! Our returned monster results are relevant - they all have some "glowing" characteristic. Let's see what the naive approach returns when we search the other two attributes. @@ -232,7 +232,7 @@ We've now retrieved 13 monsters (more than half of our tiny dataset!), and *stil Increasing the number of retrieved monsters (beyond 6) *might* solve our problem, but it creates additional issues: 1. In production, retrieving more results (multiple kNN searches) lengthens search time noticeably. -2. For each new attribute we introduce, we have to retrieve nearest neighbors (monsters), making the total number of retrieved monsters grow exponentially. +2. For each new attribute we introduce, our chances of finding a "complete" monster - with all the attributes in our query - drops exponentially. To prevent this, we have to retrieve many more nearest neighbors (monsters), making the total number of retrieved monsters grow exponentially. 3. We still have no guarantee we'll retrieve monsters that possess all our desired attributes. 4. If we do manage to retrieve monsters that satisfy all criteria at once, we'll have to expend additional overhead reconciling results. @@ -427,7 +427,7 @@ Great results again! Our two other retrieved monsters - Luminoth and Zephyr Danc Multi-attribute vector search is a significant advance in information retrieval, offering more accuracy, contextual understanding, and flexibility than basic semantic similarity search. Still, our naive approach (above) - storing and searching attribute vectors separately, *then* combining results - is limited in ability, subtlety, and efficiency when we need to retrieve objects with multiple simultaneous attributes. (Moreover, [multiple kNN searches take more time than a single one with concatenated vectors](https://redis.io/blog/benchmarking-results-for-vector-databases/).) -To handle scenarios like this, it's better to store all your attribute vectors in the same vector store and perform *a single search*, weighting your attributes at query time. The Superlinked approach is more accurate, efficient, and scalable than the naive approach for any application that requires fast, reliable, nuanced, multi-attribute vector retrieval - whether your use case is tackling real world data challenges in your e-commerce, recommendation system, or something entirely different, like battling monsters. +To handle scenarios like this, it's better to store all your attribute vectors in the same vector store and perform *a single search*, weighting your attributes at query time. The Superlinked approach is more accurate, efficient, and scalable than the naive approach for any application that requires fast, reliable, nuanced, multi-attribute vector retrieval - whether your use case is tackling real world data challenges in your e-commerce or recommendation system... or something entirely different, like battling monsters. ## Contributors From 41cef8004da52d78ded007bc95fcf30120eb7e01 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Thu, 22 Aug 2024 13:01:01 -0700 Subject: [PATCH 17/19] final small changes, add image --- .../multi-attribute-semantic-search.md | 2 ++ .../graphics.png | Bin 0 -> 698006 bytes 2 files changed, 2 insertions(+) create mode 100644 docs/assets/use_cases/multi-attribute-semantic-search/graphics.png diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index c3080bed9..b3b83dad8 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -9,6 +9,8 @@ There are two common approaches to multi-attribute vector search. In both approa 1. the *naive* approach - store each attribute vector in separate vector stores (one per attribute), perform a separate search for each attribute, combine search results, and post-process (e.g., weight) as required. 2. the *Superlinked* approach - concatenate and store all attribute vectors in the same vector store (using Superlinked's built-in funtionality), which allows us to *search just once*, with attendant efficiency gains. Superlinked's `spaces` *also* let us weight each attribute at query time to surface more relevant results, with no post-processing. +![Two approaches to multi-attribute vector search](../assets/use_cases/multi-attribute-vector-search/graphic.png) + Below, we'll use these two approaches to implement a multi-attribute vector search tool - a Dungeons and Dragons monster finder! Our simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease, whatever your use case. If you're new to vector similarity search, don't worry! 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z_xiGD_TsNhrv#l*S@7s~8%Czboy0CQImm@|O8Z+^UCy-2xlCnajM_W79JjYM8 z74TVmcDeSq1D3bMHPjzm{Fl)!7x?z0wUK%)2XIST*K52c;LL>!lz=<(x_*?vMhb{Z z=kUr&AfS+!G#xOlA;4{huuW$eaRyU_(`euZH=I7g8xFu-bSxoSk1o$7K3PH?^RB7- z0|Nt7qNj^v2&i6na5!p8y2C*QXTU_@T|tb9mJo3NX@`G0 z@wF9lPy#nAk{o7WS2?%>xAuvCeCdO18qsc0Snx=SU~)hT9z>E^;Q`zRsANaNSOapB zVhM?1#%e4<$A|*=p@tk;4q6FIOst@)Iq;u(;jECfV-@kw7=Xaj)z4*}Q$iB}klbNq literal 0 HcmV?d00001 From d5c11b1f378d506b62371d64f8dd087a68e8531a Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Sat, 24 Aug 2024 09:15:10 -0700 Subject: [PATCH 18/19] final commit, hand off to Arunesh - multi-attribute search article --- docs/articles/multi-attribute-semantic-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index b3b83dad8..ac55a439b 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -86,7 +86,7 @@ See original dataset and query examples [here](https://github.com/superlinked/ar Let's set up parameters we'll use in both of our approaches - naive and Superlinked - below. -We generate our vector embeddings with `sentence-transformers/all-mpnet-base-v2`. For simplicity's sake, we'll limit our output to the top 3 matches. (For complete code, including necessary imports and helper functions, see the [notebook](https://github.com/superlinked/article-multi-attribute-search).) +We generate our vector embeddings with `sentence-transformers/all-mpnet-base-v2`. For simplicity's sake, we'll limit our output to the top 3 matches. (For complete code, including necessary imports and helper functions, see the [notebook](https://github.com/superlinked/article-multi-attribute-search/blob/main/notebook.ipynb).) ```python LIMIT = 3 From 6377eaff450a4f3ec78f58689a0d0d7c09dc709a Mon Sep 17 00:00:00 2001 From: Arunesh Singh <43724007+AruneshSingh@users.noreply.github.com> Date: Tue, 27 Aug 2024 15:08:21 +0530 Subject: [PATCH 19/19] fix: image link --- docs/articles/multi-attribute-semantic-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/multi-attribute-semantic-search.md b/docs/articles/multi-attribute-semantic-search.md index ac55a439b..35635c20f 100644 --- a/docs/articles/multi-attribute-semantic-search.md +++ b/docs/articles/multi-attribute-semantic-search.md @@ -9,7 +9,7 @@ There are two common approaches to multi-attribute vector search. In both approa 1. the *naive* approach - store each attribute vector in separate vector stores (one per attribute), perform a separate search for each attribute, combine search results, and post-process (e.g., weight) as required. 2. the *Superlinked* approach - concatenate and store all attribute vectors in the same vector store (using Superlinked's built-in funtionality), which allows us to *search just once*, with attendant efficiency gains. Superlinked's `spaces` *also* let us weight each attribute at query time to surface more relevant results, with no post-processing. -![Two approaches to multi-attribute vector search](../assets/use_cases/multi-attribute-vector-search/graphic.png) +![Two approaches to multi-attribute vector search](../assets/use_cases/multi-attribute-semantic-search/graphics.png) Below, we'll use these two approaches to implement a multi-attribute vector search tool - a Dungeons and Dragons monster finder! Our simple implementations, especially the second, will illustrate how to create more powerful and flexible search systems, ones that can handle complex, multi-faceted queries with ease, whatever your use case.