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.
Method to add documents to the Weaviate index. It first generates vectors for the documents using the embeddings, then adds the vectors and documents to the index.
Array of documents to be added.
Optional options: { Optional parameter that can include specific IDs for the documents.
Optional ids?: string[]An array of document IDs.
Method to add vectors and corresponding documents to the Weaviate index.
Array of vectors to be added.
Array of documents corresponding to the vectors.
Optional options: { Optional parameter that can include specific IDs for the documents.
Optional ids?: string[]An array of document IDs.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<WeaviateStore>>Optional filter: WeaviateFilterOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanMethod to delete data from the Weaviate index. It can delete data based on specific IDs or a filter.
Object that includes either an array of IDs or a filter for the data to be deleted.
Optional filter?: WeaviateFilterOptional ids?: string[]Promise that resolves when the deletion is complete.
Method to perform a similarity search on the stored vectors in the Weaviate index. It returns the top k most similar documents and their similarity scores.
The query vector.
The number of most similar documents to return.
Optional filter: WeaviateFilterOptional filter to apply to the search.
An array of tuples, where each tuple contains a document 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 fromStatic method to create a new WeaviateStore instance from a list of
documents. It adds the documents to the Weaviate index.
Array of documents.
Embeddings to be used for the documents.
Arguments required to create a new WeaviateStore instance.
A new WeaviateStore instance.
Static fromStatic method to create a new WeaviateStore instance from an existing
Weaviate index.
Embeddings to be used for the Weaviate index.
Arguments required to create a new WeaviateStore instance.
A new WeaviateStore instance.
Static fromStatic method to create a new WeaviateStore instance from a list of
texts. It first creates documents from the texts and metadata, then
adds the documents to the Weaviate index.
Array of texts.
Metadata for the texts. Can be a single object or an array of objects.
Embeddings to be used for the texts.
Arguments required to create a new WeaviateStore instance.
A new WeaviateStore instance.
Static lc_Generated using TypeDoc
Class that extends the
VectorStorebase class. It provides methods to interact with a Weaviate index, including adding vectors and documents, deleting data, and performing similarity searches.