Type Meanings

The following terms are fundamental to how Ahnlich AI requests are structured and processed.

Store Key

A one-dimensional vector that uniquely identifies an item in the store.

• Functions like a primary key in a database.

• Ensures that every stored entry has a distinct handle for retrieval and indexing.

• Source Code Source Code Example: a numerical vector representing an embedding for a product image.

Store Value

A dictionary containing texts or binary data associated with a StoreKey.

• Stores the payload of information that can be retrieved, searched, or filtered.

• May include metadata such as titles, descriptions, or binary content (like embeddings, files, or serialized objects).

• Think of it as the "body" of the data linked to the store key.

Store Predicates (Predicate Indices)

Special indices built on top of StoreValue fields to make filtering more efficient.

• They optimize lookups by pre-indexing specific fields.

• Useful when you need fast filtering by metadata like "job" or "rank".

• Without them, searches would be slower since the system would need to scan every entry.

Predicates

Operations that define how filtering is performed on data.

• Examples include:

  • Equals → match exact values.

  • NotEquals → exclude values.

  • Contains → substring or set membership checks.

• They are always tied to a key in a StoreValue and evaluated against a metadata value.

• Provide the basic building blocks for query logic.

PredicateConditions

Conditions that wrap predicates and allow combining them logically.

• A PredicateCondition can represent:

  • A single predicate (just one filter condition).

  • A compound condition using AND or OR.

• This makes it possible to construct complex filters, e.g., “all sorcerers who are chunin rank.”

Example – single predicate condition:

condition = predicates.PredicateCondition(
    value=predicates.Predicate(
        equals=predicates.Equals(
            key="job", value=metadata.MetadataValue(raw_string="sorcerer")
        )
    )
)

Example – binary metadata value:

condition = predicates.PredicateCondition(
    value=predicates.Predicate(
        equals=predicates.Equals(
            key="rank", value=metadata.MetadataValue(image=[2,2,3,4,5,6,7])
        )
    )
)

Example – compound condition with AND:

condition = predicates.PredicateCondition(
    and_=predicates.AndCondition(
        left=predicates.PredicateCondition(
            value=predicates.Predicate(
                equals=predicates.Equals(
                    key="job",
                    value=metadata.MetadataValue(raw_string="sorcerer")
                )
            )
        ),
        right=predicates.PredicateCondition(
            value=predicates.Predicate(
                equals=predicates.Equals(
                    key="rank",
                    value=metadata.MetadataValue(raw_string="chunin")
                )
            )
        )
    )
)

MetadataValue

The container used inside predicates to hold values.

• Supports both raw strings (like "sorcerer") and binary vectors (lists of bytes/integers).

• This makes it versatile enough to handle both structured text metadata and embeddings or binary payloads.

Search Input

The query input sent to Ahnlich AI for processing.

• Can be a string (text input, e.g., "What is AI?") or a binary file (like an image or audio file).

• The type of input depends on the AI model and the store configuration (string vs. binary store).

AIModels

The set of supported AI models within Ahnlich AI.

• Each model determines the type of input it can process (e.g., text-only, image, multimodal).

• Choosing the right model ensures that the search input is properly understood and processed.

AIStoreType

Defines the type of store being created.

• String Store - optimized for textual inputs and queries.

• Binary Store - optimized for binary data like embeddings, images, or raw vectors.

• Must be chosen carefully depending on whether you are working with text-based AI models or binary models.