The host to connect to for queries and upserts.
The version of the API functions. Part of the path.
Explicitly set Google Auth credentials if you cannot get them from google auth application-default login This is useful for serverless or autoscaling environments like Fargate
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 that retains the document, stored by ID
Hostname for the API call
The id for the index
The id for the index endpoint
Region where the LLM is stored
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<MatchingEngine>>Optional filter: Restriction[]Optional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanCreate an index datapoint for the vector and document id. If an id does not exist, create it and set the document to its value.
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.
The URL
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).
The metadata from a document
a Restriction[] (or an array of a subclass, from the FilterType)
Optional filter: Restriction[]Optional maxReturn documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Text to look up documents similar to.
Static fromStatic fromGenerated using TypeDoc
A class that represents a connection to a Google Vertex AI Matching Engine instance.