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  1. Concepts

Overview

Explore the core ideas and principles behind Superlinked's functionality..

PreviousQdrantNextCombining Multiple Embeddings for Better Retrieval Outcomes

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  1. Describe your data using Python classes with the decorator.

  2. Describe your vector embeddings from building blocks with .

  3. Combine your embeddings into a queryable .

  4. Define your search with dynamic parameters and weights as a .

  5. Load your data using a .

  6. Define your transformations with a (e.g.: from ).

  7. Run your configuration with an .

Colab notebooks explaining the concepts

@schema
Spaces
Index
Query
Source
Parser
pd.DataFrame
Executor
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Accessing stored vector parts

Use query interface to return vector-parts based on our needs

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Categorical Embeddings

Efficiently represent and compare categorical data in vector space for similarity searches.

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Combine Multiple Embeddings

Merge different types of embeddings to create a unified representation for complex objects.

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Custom Spaces

Create and manage custom vector spaces for specialized similarity searches.

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Dynamic Parameters

Adjust query parameters dynamically to fine-tune search results.

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Event Effects

Model and apply the impact of events on vector representations over time.

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Hard Filtering

Apply strict criteria to narrow down search results before similarity ranking.

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Image Embedding

Embed text or images into a multi-modal vector space.

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Natural Language Querying

Perform similarity searches using natural language queries instead of vector representations.

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Number Embedding Minmax

Embed numerical values within a specified range for effective similarity comparisons.

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Number Embedding Similar

Embed numbers to find similar values based on relative closeness rather than exact matches.

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Optional Schema Fields

Define optional fields in your schema.

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Query Result

Customize the result object for your queries.

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Query Time Weights

Adjust the importance of different embedding components during query execution.

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Querying Options

Customize search behavior with various querying options for refined results.

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Recency Embedding

Incorporate time-based relevance into vector representations for up-to-date search results.

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Text Embedding

Convert text data into vector representations for semantic similarity searches.

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Vector Sampler

Generate diverse vector samples to explore and understand the embedding space.