1. Describe your data using Python classes with the @schema decorator.
  2. Describe your vector embeddings from building blocks with Spaces.
  3. Combine your embeddings into a queryable Index.
  4. Define your search with dynamic parameters and weights as a Query.
  5. Load your data using a Source.
  6. Define your transformations with a Parser (e.g.: from pd.DataFrame).
  7. Run your configuration with an Executor.

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Colab notebooks explaining the concepts

access-vector-parts

Accessing stored vector parts

Use query interface to return vector-parts based on our needs
Categorical%20Embeddings

Categorical Embeddings

Efficiently represent and compare categorical data in vector space for similarity searches.
Combine%20Multiple%20Embeddings

Combine Multiple Embeddings

Merge different types of embeddings to create a unified representation for complex objects.
Custom%20Spaces

Custom Spaces

Create and manage custom vector spaces for specialized similarity searches.
Dynamic%20Parameters

Dynamic Parameters

Adjust query parameters dynamically to fine-tune search results.
Event%20Effects

Event Effects

Model and apply the impact of events on vector representations over time.
Hard%20Filtering

Hard Filtering

Apply strict criteria to narrow down search results before similarity ranking.
Image%20Embedding

Image Embedding

Embed text or images into a multi-modal vector space.
Natural%20Language%20Querying

Natural Language Querying

Perform similarity searches using natural language queries instead of vector representations.
Number%20Embedding%20Minmax

Number Embedding Minmax

Embed numerical values within a specified range for effective similarity comparisons.
Number%20Embedding%20Similar

Number Embedding Similar

Embed numbers to find similar values based on relative closeness rather than exact matches.
optional-schema-fields

Optional Schema Fields

Define optional fields in your schema.
query-result

Query Result

Customize the result object for your queries.
Query%20Time%20Weights

Query Time Weights

Adjust the importance of different embedding components during query execution.
Querying%20Options

Querying Options

Customize search behavior with various querying options for refined results.
Recency%20Embedding

Recency Embedding

Incorporate time-based relevance into vector representations for up-to-date search results.
Text%20Embedding

Text Embedding

Convert text data into vector representations for semantic similarity searches.
Vector%20Sampling

Vector Sampler

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