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.
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.
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.
The final serialized identifier for the module.
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.
Adds an array of documents to the collection. The documents are first
converted to vectors using the embedDocuments method of the
embeddings instance.
Array of Document instances to be added to the collection.
Promise that resolves when the documents are added.
Adds an array of vectors and corresponding documents to the collection. The vectors and documents are batch inserted into the database.
Array of vectors to be added to the collection.
Array of Document instances corresponding to the vectors.
Promise that resolves when the vectors and documents are added.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<AnalyticDBVectorStore>>Optional filter: Record<string, any>Optional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanCreates a new table in the database if it does not already exist. The table is created with columns for id, embedding, document, and metadata. An index is also created on the embedding column if it does not already exist.
Promise that resolves when the table and index are created.
Performs a similarity search on the vectors in the collection. The search is performed using the given query vector and returns the top k most similar vectors along with their corresponding documents and similarity scores.
Query vector for the similarity search.
Number of top similar vectors to return.
Optional filter: Record<string, any>Optional. Filter to apply on the metadata of the documents.
Promise that resolves to an array of tuples, each containing a Document instance and its similarity score.
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 fromCreates an instance of AnalyticDBVectorStore from an array of
Document instances. The documents are added to the collection.
Array of Document instances to be added to the collection.
Embeddings instance used to convert the documents to vectors.
Configuration for the AnalyticDB.
Promise that resolves to an instance of AnalyticDBVectorStore.
Static fromCreates an instance of AnalyticDBVectorStore from an existing index
in the database. A new collection is created in the database.
Embeddings instance used to convert the documents to vectors.
Configuration for the AnalyticDB.
Promise that resolves to an instance of AnalyticDBVectorStore.
Static fromCreates an instance of AnalyticDBVectorStore from an array of texts
and corresponding metadata. The texts are first converted to Document
instances before being added to the collection.
Array of texts to be added to the collection.
Array or object of metadata corresponding to the texts.
Embeddings instance used to convert the texts to vectors.
Configuration for the AnalyticDB.
Promise that resolves to an instance of AnalyticDBVectorStore.
Static lc_Generated using TypeDoc
Class that provides methods for creating and managing a collection of documents in an AnalyticDB, adding documents or vectors to the collection, performing similarity search on vectors, and creating an instance of
AnalyticDBVectorStorefrom texts or documents.