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.
Optional collectionA 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 Qdrant database. It generates vectors
from the documents using the Embeddings instance and then adds the
vectors to the database.
Array of Document instances to be added to the Qdrant database.
Promise that resolves when the documents have been added to the database.
Method to add vectors to the Qdrant database. Each vector is associated with a document, which is stored as the payload for a point in the database.
Array of vectors to be added to the Qdrant database.
Array of Document instances associated with the vectors.
Promise that resolves when the vectors have been added to the database.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<QdrantVectorStore>>Optional filter: string | objectOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanMethod to search for vectors in the Qdrant database that are similar to a given query vector. The search results include the score and payload (metadata and content) for each similar vector.
Query vector to search for similar vectors in the Qdrant database.
Optional k: numberOptional number of similar vectors to return. If not specified, all similar vectors are returned.
Optional filter: {}Optional filter to apply to the search results.
Promise that resolves with an array of tuples, where each tuple includes a Document instance and a score for a similar vector.
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 QdrantVectorStore instance from Document
instances. The documents are added to the Qdrant database.
Array of Document instances to be added to the Qdrant database.
Embeddings instance used to generate vectors from the documents.
QdrantLibArgs instance specifying the configuration for the Qdrant database.
Promise that resolves with a new QdrantVectorStore instance.
Static fromStatic method to create a QdrantVectorStore instance from an existing
collection in the Qdrant database.
Embeddings instance used to generate vectors from the documents in the collection.
QdrantLibArgs instance specifying the configuration for the Qdrant database.
Promise that resolves with a new QdrantVectorStore instance.
Static fromStatic method to create a QdrantVectorStore instance from texts. Each
text is associated with metadata and converted to a Document
instance, which is then added to the Qdrant database.
Array of texts to be converted to Document instances and added to the Qdrant database.
Array or single object of metadata to be associated with the texts.
Embeddings instance used to generate vectors from the texts.
QdrantLibArgs instance specifying the configuration for the Qdrant database.
Promise that resolves with a new QdrantVectorStore instance.
Static lc_Generated using TypeDoc
Class that extends the
VectorStorebase class to interact with a Qdrant database. It includes methods for adding documents and vectors to the Qdrant database, searching for similar vectors, and ensuring the existence of a collection in the database.