Interface: IVectorStore
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:269
Interface
IVectorStore
Description
Defines the contract for interacting with a specific vector database or storage backend. Implementations will wrap specific clients (e.g., Pinecone client, Weaviate client, in-memory store logic).
Methods
checkHealth()
checkHealth():
Promise<{details?:any;isHealthy:boolean; }>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:412
Async
Checks the operational health of the vector store provider. This might involve pinging the service, checking connection status, or verifying authentication.
Returns
Promise<{ details?: any; isHealthy: boolean; }>
A promise that resolves with the health status.
details can include specific error messages or status information.
collectionExists()?
optionalcollectionExists(collectionName):Promise<boolean>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:402
Async
Checks if a collection with the given name exists in the vector store.
Parameters
collectionName
string
The name of the collection to check.
Returns
Promise<boolean>
A promise that resolves with true if the collection exists, false otherwise.
Throws
If the check fails for reasons other than existence (e.g., connection issue).
createCollection()?
optionalcreateCollection(collectionName,dimension,options?):Promise<void>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:378
Async
Creates a new collection (or index, class, etc.) in the vector store. This is often necessary before documents can be upserted into it, depending on the provider.
Parameters
collectionName
string
The name of the collection to create.
dimension
number
The dimensionality of the vector embeddings that will be stored in this collection.
options?
Optional parameters for collection creation, such as similarity metric or provider-specific settings.
Returns
Promise<void>
A promise that resolves when the collection is successfully created.
Throws
If collection creation fails (e.g., name conflict and not overwriting, invalid parameters).
delete()
delete(
collectionName,ids?,options?):Promise<DeleteResult>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:361
Async
Deletes documents from a specified collection by their IDs or by a metadata filter.
Parameters
collectionName
string
The name of the collection to delete documents from.
ids?
string[]
An array of document IDs to delete.
options?
Optional parameters, including metadata filters or a deleteAll flag.
If ids are provided, options.filter might be ignored or combined,
depending on store behavior. Use with caution.
Returns
Promise<DeleteResult>
A promise that resolves with the result of the delete operation.
Throws
If the delete operation fails.
deleteCollection()?
optionaldeleteCollection(collectionName):Promise<void>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:392
Async
Deletes an entire collection from the vector store. This is a destructive operation.
Parameters
collectionName
string
The name of the collection to delete.
Returns
Promise<void>
A promise that resolves when the collection is successfully deleted.
Throws
If collection deletion fails.
getStats()?
optionalgetStats(collectionName?):Promise<Record<string,any>>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:433
Async
Optional: Retrieves statistics about a specific collection or the store itself. The structure of the returned statistics is provider-dependent.
Parameters
collectionName?
string
Optional: The name of the collection to get stats for. If omitted, may return store-wide stats if supported.
Returns
Promise<Record<string, any>>
A promise that resolves with a statistics object.
Throws
If fetching statistics fails.
hybridSearch()?
optionalhybridSearch(collectionName,queryEmbedding,queryText,options?):Promise<QueryResult>
Defined in: packages/agentos/src/core/vector-store/IVectorStore.ts:333
Optional: Hybrid retrieval combining dense vector similarity with lexical search.
This is typically implemented using a store-native full-text index (e.g., SQLite FTS5), or a store-side BM25 implementation, then fusing dense and lexical rankings (e.g., RRF).
If not implemented, callers should fall back to query() (dense similarity).
Parameters
collectionName
string
queryEmbedding
number[]
queryText
string
options?
QueryOptions & object