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 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

lc_kwargs

lc_kwargs: SerializedFields

lc_namespace

lc_namespace: string[] = ...

A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.

lc_serializable

lc_serializable: boolean = false

location

location: string = "us-central1"

Region where the LLM is stored

removeDatapointClient

removeDatapointClient: RemoveDatapointConnection

upsertDatapointClient

upsertDatapointClient: UpsertDatapointConnection

Accessors

lc_aliases

get lc_aliases(): undefined | {
[key: string]: string;
}
A map of aliases for constructor args. Keys are the attribute names, e.g. "foo". Values are the alias that will replace the key in serialization. This is used to eg. make argument names match Python.

Returns undefined | {
[key: string]: string;
}
Inherited from VectorStore.lc_aliases
- Defined in langchain/src/load/serializable.ts:132

lc_attributes

get lc_attributes(): undefined | SerializedFields
A map of additional attributes to merge with constructor args. Keys are the attribute names, e.g. "foo". Values are the attribute values, which will be serialized. These attributes need to be accepted by the constructor as arguments.

Returns undefined | SerializedFields
Inherited from VectorStore.lc_attributes
- Defined in langchain/src/load/serializable.ts:122

lc_id

get lc_id(): string[]
The final serialized identifier for the module.

Returns string[]
Inherited from VectorStore.lc_id
- Defined in langchain/src/load/serializable.ts:100

lc_secrets

get lc_secrets(): undefined | {
[key: string]: string;
}
A map of secrets, which will be omitted from serialization. Keys are paths to the secret in constructor args, e.g. "foo.bar.baz". Values are the secret ids, which will be used when deserializing.

Returns undefined | {
[key: string]: string;
}
Inherited from VectorStore.lc_secrets
- Defined in langchain/src/load/serializable.ts:112

Methods

_vectorstoreType

_vectorstoreType(): string
Returns string
Overrides VectorStore._vectorstoreType
- Defined in langchain/src/vectorstores/googlevertexai.ts:418

addDocuments

addDocuments(documents): Promise<void>
Parameters
- documents: Document<Record<string, any>>[]
Returns Promise<void>
Overrides VectorStore.addDocuments
- Defined in langchain/src/vectorstores/googlevertexai.ts:422

addVectors

addVectors(vectors, documents): Promise<void>
Parameters
- vectors: number[][]
- documents: Document<Record<string, any>>[]
Returns Promise<void>
Overrides VectorStore.addVectors
- Defined in langchain/src/vectorstores/googlevertexai.ts:428

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 langchain/src/vectorstores/base.ts:246

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 langchain/src/vectorstores/googlevertexai.ts:563

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 langchain/src/vectorstores/googlevertexai.ts:455

delete

delete(params): Promise<void>
Parameters
- params: MatchingEngineDeleteParams
Returns Promise<void>
Overrides VectorStore.delete
- Defined in langchain/src/vectorstores/googlevertexai.ts:579

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 langchain/src/vectorstores/googlevertexai.ts:665

getDeployedIndexId

getDeployedIndexId(): Promise<string>
Returns Promise<string>
- Defined in langchain/src/vectorstores/googlevertexai.ts:706

getPublicAPIEndpoint

getPublicAPIEndpoint(): Promise<string>
Returns Promise<string>
- Defined in langchain/src/vectorstores/googlevertexai.ts:700

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 langchain/src/vectorstores/googlevertexai.ts:519

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 langchain/src/vectorstores/base.ts:174

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 langchain/src/vectorstores/googlevertexai.ts:584

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 langchain/src/vectorstores/base.ts:189

toJSON

toJSON(): Serialized
Returns Serialized
Inherited from VectorStore.toJSON
- Defined in langchain/src/load/serializable.ts:140

toJSONNotImplemented

toJSONNotImplemented(): SerializedNotImplemented
Returns SerializedNotImplemented
Inherited from VectorStore.toJSONNotImplemented
- Defined in langchain/src/load/serializable.ts:214

`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 langchain/src/vectorstores/base.ts:217

`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 langchain/src/vectorstores/googlevertexai.ts:728

`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 langchain/src/vectorstores/googlevertexai.ts:713

`Static` lc_name

lc_name(): string
The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.

Implemented as a static method to support loading logic.

Returns string
Inherited from VectorStore.lc_name
- Defined in langchain/src/load/serializable.ts:93

Class MatchingEngine

Hierarchy

Implements

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

Parameters

embeddings: Embeddings

args: MatchingEngineArgs

Returns MatchingEngine

Properties

FilterType

apiEndpoint

apiVersion

caller

callerOptions

callerParams

deployedIndexId

docstore

embeddings

endpoint

index

indexEndpoint

indexEndpointClient

lc_kwargs

lc_namespace

lc_serializable

location

removeDatapointClient

upsertDatapointClient

Accessors

lc_aliases

Returns undefined | { [key: string]: string; }

lc_attributes

Returns undefined | SerializedFields

lc_id

Returns string[]

lc_secrets

Returns undefined | { [key: string]: string; }

Methods

_vectorstoreType

Returns string

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>

Returns undefined | {
[key: string]: string;
}

Returns undefined | {
[key: string]: string;
}

`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

`Static` lc_name