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core/org.llm4s/org.llm4s.agent/org.llm4s.agent.memory/VectorMemoryStore

VectorMemoryStore

org.llm4s.agent.memory.VectorMemoryStore
See theVectorMemoryStore companion object
final class VectorMemoryStore extends MemoryStore

Memory store with vector embedding support for semantic search.

This store extends SQLite-based storage with embedding vectors, enabling semantic similarity search in addition to keyword search.

Embeddings are generated on-demand using the provided EmbeddingService and stored alongside memories for efficient retrieval.

Value parameters

config

Store configuration

dbPath

Path to SQLite database file

embeddingService

Service for generating embeddings

Attributes

Companion
object
Graph
Supertypes
trait MemoryStore
class Object
trait Matchable
class Any

Members list

Value members

Concrete methods

override def clear(): Result[MemoryStore]

Clear all memories from the store.

Clear all memories from the store.

Attributes

Returns

Empty store or error

Definition Classes
def close(): Unit

Close the database connection.

Close the database connection.

Attributes

override def count(filter: MemoryFilter): Result[Long]

Count memories matching a filter.

Count memories matching a filter.

Value parameters

filter

Criteria for counting memories

Attributes

Returns

Count or error

Definition Classes
override def delete(id: MemoryId): Result[MemoryStore]

Delete a memory by its ID.

Delete a memory by its ID.

Value parameters

id

The memory identifier

Attributes

Returns

Updated store or error

Definition Classes
override def deleteMatching(filter: MemoryFilter): Result[MemoryStore]

Delete all memories matching a filter.

Delete all memories matching a filter.

Value parameters

filter

Criteria for selecting memories to delete

Attributes

Returns

Updated store or error

Definition Classes
def embedAll(batchSize: Int): Result[Int]

Embed all memories that don't have embeddings.

Embed all memories that don't have embeddings.

This is useful for adding embeddings to memories imported without them.

Value parameters

batchSize

Number of memories to embed per batch

Attributes

Returns

Number of memories embedded

override def get(id: MemoryId): Result[Option[Memory]]

Retrieve a memory by its ID.

Retrieve a memory by its ID.

Value parameters

id

The memory identifier

Attributes

Returns

The memory if found, or error

Definition Classes
override def recall(filter: MemoryFilter, limit: Int): Result[Seq[Memory]]

Recall memories matching a filter.

Recall memories matching a filter.

This is the primary retrieval method for most use cases. Returns memories sorted by relevance (most relevant first).

Value parameters

filter

Criteria for filtering memories

limit

Maximum number of memories to return

Attributes

Returns

Matching memories or error

Definition Classes
override def recent(limit: Int, filter: MemoryFilter): Result[Seq[Memory]]

Get the most recent memories.

Get the most recent memories.

Value parameters

filter

Additional filter criteria

limit

Maximum number of memories

Attributes

Returns

Most recent memories

Definition Classes
override def search(query: String, topK: Int, filter: MemoryFilter): Result[Seq[ScoredMemory]]

Semantic search for memories similar to a query.

Semantic search for memories similar to a query.

This method uses embeddings for semantic similarity search. Falls back to keyword search if embeddings are not available.

Value parameters

filter

Additional filter criteria

query

The search query

topK

Number of results to return

Attributes

Returns

Most similar memories or error

Definition Classes
override def store(memory: Memory): Result[MemoryStore]

Store a new memory.

Store a new memory.

Value parameters

memory

The memory to store

Attributes

Returns

Updated store or error

Definition Classes
override def update(id: MemoryId, updateFn: Memory => Memory): Result[MemoryStore]

Update a memory.

Update a memory.

Value parameters

id

The memory identifier

update

Function to transform the memory

Attributes

Returns

Updated store or error

Definition Classes
def vectorStats: Result[VectorStoreStats]

Get statistics about the store including embedding coverage.

Get statistics about the store including embedding coverage.

Attributes

Inherited methods

def exists(id: MemoryId): Result[Boolean]

Check if a memory exists.

Check if a memory exists.

Value parameters

id

The memory identifier

Attributes

Returns

True if memory exists

Inherited from:
MemoryStore
def getConversation(conversationId: String, limit: Int): Result[Seq[Memory]]

Get conversation history.

Get conversation history.

Value parameters

conversationId

The conversation identifier

limit

Maximum messages to return

Attributes

Returns

Conversation memories in chronological order

Inherited from:
MemoryStore
def getEntityMemories(entityId: EntityId): Result[Seq[Memory]]

Get all memories for a specific entity.

Get all memories for a specific entity.

Value parameters

entityId

The entity identifier

Attributes

Returns

All memories related to this entity

Inherited from:
MemoryStore
def important(threshold: Double, limit: Int): Result[Seq[Memory]]

Get high-importance memories.

Get high-importance memories.

Value parameters

limit

Maximum number of memories

threshold

Minimum importance score (0.0 to 1.0)

Attributes

Returns

Important memories

Inherited from:
MemoryStore
def storeAll(memories: Seq[Memory]): Result[MemoryStore]

Store multiple memories in a batch.

Store multiple memories in a batch.

Value parameters

memories

The memories to store

Attributes

Returns

Updated store or error

Inherited from:
MemoryStore

Concrete fields

val dbPath: String
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