Class MatchingEngine

A class that represents a connection to a Google Vertex AI Matching Engine instance.

Hierarchy

VectorStore
- MatchingEngine

Implements

MatchingEngineArgs

Constructors

constructor

new MatchingEngine(embeddings, args): MatchingEngine
Parameters
- embeddings: Embeddings
- args: MatchingEngineArgs
Returns MatchingEngine
Overrides VectorStore.constructor
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:365

Properties

FilterType

FilterType: Restriction[]

apiEndpoint

apiEndpoint: string

The host to connect to for queries and upserts.

apiVersion

apiVersion: string = "v1"

The version of the API functions. Part of the path.

caller

caller: AsyncCaller

callerOptions

callerOptions: AsyncCallerCallOptions

callerParams

callerParams: AsyncCallerParams

deployedIndexId

deployedIndexId: string

The id for the "deployed index", which is an identifier in the index endpoint that references the index (but is not the index id)

docstore

docstore: Docstore

Docstore that retains the document, stored by ID

embeddings

embeddings: Embeddings

endpoint

endpoint: string = "us-central1-aiplatform.googleapis.com"

Hostname for the API call

index

index: string

The id for the index

indexEndpoint

indexEndpoint: string

The id for the index endpoint

indexEndpointClient

indexEndpointClient: IndexEndpointConnection

location

location: string = "us-central1"

Region where the LLM is stored

removeDatapointClient

removeDatapointClient: RemoveDatapointConnection

upsertDatapointClient

upsertDatapointClient: UpsertDatapointConnection

Methods

addDocuments

addDocuments(documents): Promise<void>
Parameters
- documents: Document<Record<string, any>>[]
Returns Promise<void>
Overrides VectorStore.addDocuments
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:423

addVectors

addVectors(vectors, documents): Promise<void>
Parameters
- vectors: number[][]
- documents: Document<Record<string, any>>[]
Returns Promise<void>
Overrides VectorStore.addVectors
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:429

asRetriever

asRetriever(kOrFields?, filter?, callbacks?, tags?, metadata?, verbose?): VectorStoreRetriever<MatchingEngine>
Parameters
- Optional kOrFields: number | Partial<VectorStoreRetrieverInput<MatchingEngine>>
- Optional filter: Restriction[]
- Optional callbacks: Callbacks
- Optional tags: string[]
- Optional metadata: Record<string, unknown>
- Optional verbose: boolean
Returns VectorStoreRetriever<MatchingEngine>
Inherited from VectorStore.asRetriever
- Defined in docs/api_refs/langchain/src/vectorstores/base.ts:252

buildDatapoint

buildDatapoint(vector, document): IndexDatapoint
Create an index datapoint for the vector and document id. If an id does not exist, create it and set the document to its value.
Parameters
- vector: number[]
- document: IdDocument
Returns IndexDatapoint
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:564

cleanMetadata

cleanMetadata(documentMetadata): {
[key: string]: string | number | boolean | string[] | null;
}
Parameters
- documentMetadata: Record<string, any>
Returns {
[key: string]: string | number | boolean | string[] | null;
}
- [key: string]: string | number | boolean | string[] | null
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:456

delete

delete(params): Promise<void>
Parameters
- params: MatchingEngineDeleteParams
Returns Promise<void>
Overrides VectorStore.delete
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:580

determinePublicAPIEndpoint

determinePublicAPIEndpoint(): Promise<PublicAPIEndpointInfo>
For this index endpoint, figure out what API Endpoint URL and deployed index ID should be used to do upserts and queries. Also sets the apiEndpoint and deployedIndexId property for future use.

Returns Promise<PublicAPIEndpointInfo>
The URL
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:666

getDeployedIndexId

getDeployedIndexId(): Promise<string>
Returns Promise<string>
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:707

getPublicAPIEndpoint

getPublicAPIEndpoint(): Promise<string>
Returns Promise<string>
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:701

metadataToRestrictions

metadataToRestrictions(documentMetadata): Restriction[]
Given the metadata from a document, convert it to an array of Restriction objects that may be passed to the Matching Engine and stored. The default implementation flattens any metadata and includes it as an "allowList". Subclasses can choose to convert some of these to "denyList" items or to add additional restrictions (for example, to format dates into a different structure or to add additional restrictions based on the date).
Parameters
- documentMetadata: Record<string, any>
  
  The metadata from a document
Returns Restriction[]
a Restriction[] (or an array of a subclass, from the FilterType)
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:520

similaritySearch

similaritySearch(query, k?, filter?, _callbacks?): Promise<Document<Record<string, any>>[]>
Parameters
- query: string
- k: number = 4
- filter: undefined | Restriction[] = undefined
- _callbacks: undefined | Callbacks = undefined
Returns Promise<Document<Record<string, any>>[]>
Inherited from VectorStore.similaritySearch
- Defined in docs/api_refs/langchain/src/vectorstores/base.ts:180

similaritySearchVectorWithScore

similaritySearchVectorWithScore(query, k, filter?): Promise<[Document<Record<string, any>>, number][]>
Parameters
- query: number[]
- k: number
- Optional filter: Restriction[]
Returns Promise<[Document<Record<string, any>>, number][]>
Overrides VectorStore.similaritySearchVectorWithScore
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:585

similaritySearchWithScore

similaritySearchWithScore(query, k?, filter?, _callbacks?): Promise<[Document<Record<string, any>>, number][]>
Parameters
- query: string
- k: number = 4
- filter: undefined | Restriction[] = undefined
- _callbacks: undefined | Callbacks = undefined
Returns Promise<[Document<Record<string, any>>, number][]>
Inherited from VectorStore.similaritySearchWithScore
- Defined in docs/api_refs/langchain/src/vectorstores/base.ts:195

toJSON

toJSON(): Serialized
Returns Serialized
Inherited from VectorStore.toJSON
- Defined in docs/api_refs/langchain/src/load/serializable.ts:148

toJSONNotImplemented

toJSONNotImplemented(): SerializedNotImplemented
Returns SerializedNotImplemented
Inherited from VectorStore.toJSONNotImplemented
- Defined in docs/api_refs/langchain/src/load/serializable.ts:222

`Optional` maxMarginalRelevanceSearch

maxMarginalRelevanceSearch(query, options, _callbacks): Promise<Document<Record<string, any>>[]>
Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Parameters
- query: string
  
  Text to look up documents similar to.
- options: MaxMarginalRelevanceSearchOptions<Restriction[]>
- _callbacks: undefined | Callbacks
Returns Promise<Document<Record<string, any>>[]>
- List of documents selected by maximal marginal relevance.
Inherited from VectorStore.maxMarginalRelevanceSearch
- Defined in docs/api_refs/langchain/src/vectorstores/base.ts:223

`Static` fromDocuments

fromDocuments(docs, embeddings, dbConfig): Promise<VectorStore>
Parameters
- docs: Document<Record<string, any>>[]
- embeddings: Embeddings
- dbConfig: MatchingEngineArgs
Returns Promise<VectorStore>
Overrides VectorStore.fromDocuments
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:729

`Static` fromTexts

fromTexts(texts, metadatas, embeddings, dbConfig): Promise<VectorStore>
Parameters
- texts: string[]
- metadatas: object | object[]
- embeddings: Embeddings
- dbConfig: MatchingEngineArgs
Returns Promise<VectorStore>
Overrides VectorStore.fromTexts
- Defined in docs/api_refs/langchain/src/vectorstores/googlevertexai.ts:714

Class MatchingEngine

Hierarchy

Implements

Index

Constructors

Properties

Methods

Constructors

constructor

Parameters

embeddings: Embeddings

args: MatchingEngineArgs

Returns MatchingEngine

Properties

FilterType

apiEndpoint

apiVersion

caller

callerOptions

callerParams

deployedIndexId

docstore

embeddings

endpoint

index

indexEndpoint

indexEndpointClient

location

removeDatapointClient

upsertDatapointClient

Methods

addDocuments

Parameters

documents: Document<Record<string, any>>[]

Returns Promise<void>

addVectors

Parameters

vectors: number[][]

documents: Document<Record<string, any>>[]

Returns Promise<void>

asRetriever

Parameters

Optional kOrFields: number | Partial<VectorStoreRetrieverInput<MatchingEngine>>

Optional filter: Restriction[]

Optional callbacks: Callbacks

Optional tags: string[]

Optional metadata: Record<string, unknown>

Optional verbose: boolean

Returns VectorStoreRetriever<MatchingEngine>

buildDatapoint

Parameters

vector: number[]

document: IdDocument

Returns IndexDatapoint

cleanMetadata

Parameters

documentMetadata: Record<string, any>

Returns { [key: string]: string | number | boolean | string[] | null; }

[key: string]: string | number | boolean | string[] | null

delete

Parameters

params: MatchingEngineDeleteParams

Returns Promise<void>

determinePublicAPIEndpoint

Returns Promise<PublicAPIEndpointInfo>

getDeployedIndexId

Returns Promise<string>

getPublicAPIEndpoint

Returns Promise<string>

metadataToRestrictions

Parameters

documentMetadata: Record<string, any>

Returns Restriction[]

similaritySearch

Parameters

query: string

k: number = 4

filter: undefined | Restriction[] = undefined

_callbacks: undefined | Callbacks = undefined

Returns Promise<Document<Record<string, any>>[]>

`Optional` kOrFields: number | Partial<VectorStoreRetrieverInput<MatchingEngine>>

`Optional` filter: Restriction[]

`Optional` callbacks: Callbacks

`Optional` tags: string[]

`Optional` metadata: Record<string, unknown>

`Optional` verbose: boolean

Returns {
[key: string]: string | number | boolean | string[] | null;
}

`Optional` filter: Restriction[]

`Optional` maxMarginalRelevanceSearch

`Static` fromDocuments

`Static` fromTexts