Method●Since v0.0

afrom_texts

Construct Qdrant wrapper from a list of texts.

afrom_texts(
  cls: type[Qdrant],
  texts: list[str],
  embedding: Embeddings,
  metadatas: list[dict] | None = None,
  ids: Sequence[str] | None = None,
  location: str | None = None,
  url: str | None = None,
  port: int | None = 6333,
  grpc_port: int = 6334,
  prefer_grpc: bool = False,
  https: bool | None = None,
  api_key: str | None = None,
  prefix: str | None = None,
  timeout: int | None = None,
  host: str | None = None,
  path: str | None = None,
  collection_name: str | None = None,
  distance_func: str = 'Cosine',
  content_payload_key: str = CONTENT_KEY,
  metadata_payload_key: str = METADATA_KEY,
  vector_name: str | None = VECTOR_NAME,
  batch_size: int = 64,
  shard_number: int | None = None,
  replication_factor: int | None = None,
  write_consistency_factor: int | None = None,
  on_disk_payload: bool | None = None,
  hnsw_config: models.HnswConfigDiff | None = None,
  optimizers_config: models.OptimizersConfigDiff | None = None,
  wal_config: models.WalConfigDiff | None = None,
  quantization_config: models.QuantizationConfig | None = None,
  init_from: models.InitFrom | None = None,
  on_disk: bool | None = None,
  force_recreate: bool = False,
  **kwargs: Any = {}
) -> Qdrant

This is a user-friendly interface that:

Creates embeddings, one for each text
Initializes the Qdrant database as an in-memory docstore by default (and overridable to a remote docstore)
Adds the text embeddings to the Qdrant database

This is intended to be a quick way to get started.

from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")

Parameters

Name	Type	Description
`texts`*	`list[str]`	A list of texts to be indexed in Qdrant.
`embedding`*	`Embeddings`	A subclass of `Embeddings`, responsible for text vectorization.
`metadatas`	`list[dict] \| None`	Default:`None` An optional list of metadata. If provided it has to be of the same length as a list of texts.
`ids`	`Sequence[str] \| None`	Default:`None` Optional list of ids to associate with the texts. Ids have to be uuid-like strings.
`location`	`str \| None`	Default:`None` If ':memory:' - use in-memory Qdrant instance. If `str` - use it as a `url` parameter. If `None` - fallback to relying on `host` and `port` parameters.
`url`	`str \| None`	Default:`None` either host or str of "scheme \| None, host, port \| None, prefix \| None".
`port`	`int \| None`	Default:`6333` Port of the REST API interface. Default: 6333
`grpc_port`	`int`	Default:`6334` Port of the gRPC interface. Default: 6334
`prefer_grpc`	`bool`	Default:`False` If true - use gPRC interface whenever possible in custom methods. Default: False
`https`	`bool \| None`	Default:`None` If true - use HTTPS(SSL) protocol. Default: None
`api_key`	`str \| None`	Default:`None` API key for authentication in Qdrant Cloud. Default: None Can also be set via environment variable `QDRANT_API_KEY`.
`prefix`	`str \| None`	Default:`None` If not None - add prefix to the REST URL path. Example: service/v1 will result in http://localhost:6333/service/v1/{qdrant-endpoint} for REST API. Default: None
`timeout`	`int \| None`	Default:`None` Timeout for REST and gRPC API requests. Default: 5.0 seconds for REST and unlimited for gRPC
`host`	`str \| None`	Default:`None` Host name of Qdrant service. If url and host are None, set to 'localhost'. Default: None
`path`	`str \| None`	Default:`None` Path in which the vectors will be stored while using local mode. Default: None
`collection_name`	`str \| None`	Default:`None` Name of the Qdrant collection to be used. If not provided, it will be created randomly. Default: None
`distance_func`	`str`	Default:`'Cosine'` Distance function. One of: "Cosine" / "Euclid" / "Dot". Default: "Cosine"
`content_payload_key`	`str`	Default:`CONTENT_KEY` A payload key used to store the content of the document. Default: "page_content"
`metadata_payload_key`	`str`	Default:`METADATA_KEY` A payload key used to store the metadata of the document. Default: "metadata"
`vector_name`	`str \| None`	Default:`VECTOR_NAME` Name of the vector to be used internally in Qdrant. Default: None
`batch_size`	`int`	Default:`64` How many vectors upload per-request. Default: 64
`shard_number`	`int \| None`	Default:`None` Number of shards in collection. Default is 1, minimum is 1.
`replication_factor`	`int \| None`	Default:`None` Replication factor for collection. Default is 1, minimum is 1. Defines how many copies of each shard will be created. Have effect only in distributed mode.
`write_consistency_factor`	`int \| None`	Default:`None` Write consistency factor for collection. Default is 1, minimum is 1. Defines how many replicas should apply the operation for us to consider it successful. Increasing this number will make the collection more resilient to inconsistencies, but will also make it fail if not enough replicas are available. Does not have any performance impact. Have effect only in distributed mode.
`on_disk_payload`	`bool \| None`	Default:`None` If true - point`s payload will not be stored in memory. It will be read from the disk every time it is requested. This setting saves RAM by (slightly) increasing the response time. Note: those payload values that are involved in filtering and are indexed - remain in RAM.
`hnsw_config`	`models.HnswConfigDiff \| None`	Default:`None` Params for HNSW index
`optimizers_config`	`models.OptimizersConfigDiff \| None`	Default:`None` Params for optimizer
`wal_config`	`models.WalConfigDiff \| None`	Default:`None` Params for Write-Ahead-Log
`quantization_config`	`models.QuantizationConfig \| None`	Default:`None` Params for quantization, if None - quantization will be disabled
`init_from`	`models.InitFrom \| None`	Default:`None` Use data stored in another collection to initialize this collection
`on_disk`	`bool \| None`	Default:`None` If true - point`s payload will not be stored in memory. It will be read from the disk every time it is requested. This setting saves RAM by (slightly) increasing the response time. Note: those payload values that are involved in filtering and are indexed - remain in RAM.
`force_recreate`	`bool`	Default:`False` Force recreating the collection
`**kwargs`	`Any`	Default:`{}` Additional arguments passed directly into REST client initialization

View source on GitHub

afrom_texts( cls: type[Qdrant], texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, ids: Sequence[str] | None = None, location: str | None = None, url: str | None = None, port: int | None = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: bool | None = None, api_key: str | None = None, prefix: str | None = None, timeout: int | None = None, host: str | None = None, path: str | None = None, collection_name: str | None = None, distance_func: str = 'Cosine', content_payload_key: str = CONTENT_KEY, metadata_payload_key: str = METADATA_KEY, vector_name: str | None = VECTOR_NAME, batch_size: int = 64, shard_number: int | None = None, replication_factor: int | None = None, write_consistency_factor: int | None = None, on_disk_payload: bool | None = None, hnsw_config: models.HnswConfigDiff | None = None, optimizers_config: models.OptimizersConfigDiff | None = None, wal_config: models.WalConfigDiff | None = None, quantization_config: models.QuantizationConfig | None = None, init_from: models.InitFrom | None = None, on_disk: bool | None = None, force_recreate: bool = False, **kwargs: Any = {} ) -> Qdrant

Parameters

Name	Type	Description
`texts`*	`list[str]`	A list of texts to be indexed in Qdrant.
`embedding`*	`Embeddings`	A subclass of `Embeddings`, responsible for text vectorization.
`metadatas`	`list[dict] \| None`	Default:`None` An optional list of metadata. If provided it has to be of the same length as a list of texts.
`ids`	`Sequence[str] \| None`	Default:`None` Optional list of ids to associate with the texts. Ids have to be uuid-like strings.
`location`	`str \| None`	Default:`None` If ':memory:' - use in-memory Qdrant instance. If `str` - use it as a `url` parameter. If `None` - fallback to relying on `host` and `port` parameters.
`url`	`str \| None`	Default:`None` either host or str of "scheme \| None, host, port \| None, prefix \| None".
`port`	`int \| None`	Default:`6333` Port of the REST API interface. Default: 6333
`grpc_port`	`int`	Default:`6334` Port of the gRPC interface. Default: 6334
`prefer_grpc`	`bool`	Default:`False` If true - use gPRC interface whenever possible in custom methods. Default: False
`https`	`bool \| None`	Default:`None` If true - use HTTPS(SSL) protocol. Default: None
`api_key`	`str \| None`	Default:`None` API key for authentication in Qdrant Cloud. Default: None Can also be set via environment variable `QDRANT_API_KEY`.
`prefix`	`str \| None`	Default:`None` If not None - add prefix to the REST URL path. Example: service/v1 will result in http://localhost:6333/service/v1/{qdrant-endpoint} for REST API. Default: None
`timeout`	`int \| None`	Default:`None` Timeout for REST and gRPC API requests. Default: 5.0 seconds for REST and unlimited for gRPC
`host`	`str \| None`	Default:`None` Host name of Qdrant service. If url and host are None, set to 'localhost'. Default: None
`path`	`str \| None`	Default:`None` Path in which the vectors will be stored while using local mode. Default: None
`collection_name`	`str \| None`	Default:`None` Name of the Qdrant collection to be used. If not provided, it will be created randomly. Default: None
`distance_func`	`str`	Default:`'Cosine'` Distance function. One of: "Cosine" / "Euclid" / "Dot". Default: "Cosine"
`content_payload_key`	`str`	Default:`CONTENT_KEY` A payload key used to store the content of the document. Default: "page_content"
`metadata_payload_key`	`str`	Default:`METADATA_KEY` A payload key used to store the metadata of the document. Default: "metadata"
`vector_name`	`str \| None`	Default:`VECTOR_NAME` Name of the vector to be used internally in Qdrant. Default: None
`batch_size`	`int`	Default:`64` How many vectors upload per-request. Default: 64
`shard_number`	`int \| None`	Default:`None` Number of shards in collection. Default is 1, minimum is 1.
`replication_factor`	`int \| None`	Default:`None` Replication factor for collection. Default is 1, minimum is 1. Defines how many copies of each shard will be created. Have effect only in distributed mode.
`write_consistency_factor`	`int \| None`	Default:`None` Write consistency factor for collection. Default is 1, minimum is 1. Defines how many replicas should apply the operation for us to consider it successful. Increasing this number will make the collection more resilient to inconsistencies, but will also make it fail if not enough replicas are available. Does not have any performance impact. Have effect only in distributed mode.
`on_disk_payload`	`bool \| None`	Default:`None` If true - point`s payload will not be stored in memory. It will be read from the disk every time it is requested. This setting saves RAM by (slightly) increasing the response time. Note: those payload values that are involved in filtering and are indexed - remain in RAM.
`hnsw_config`	`models.HnswConfigDiff \| None`	Default:`None` Params for HNSW index
`optimizers_config`	`models.OptimizersConfigDiff \| None`	Default:`None` Params for optimizer
`wal_config`	`models.WalConfigDiff \| None`	Default:`None` Params for Write-Ahead-Log
`quantization_config`	`models.QuantizationConfig \| None`	Default:`None` Params for quantization, if None - quantization will be disabled
`init_from`	`models.InitFrom \| None`	Default:`None` Use data stored in another collection to initialize this collection
`on_disk`	`bool \| None`	Default:`None` If true - point`s payload will not be stored in memory. It will be read from the disk every time it is requested. This setting saves RAM by (slightly) increasing the response time. Note: those payload values that are involved in filtering and are indexed - remain in RAM.
`force_recreate`	`bool`	Default:`False` Force recreating the collection
`**kwargs`	`Any`	Default:`{}` Additional arguments passed directly into REST client initialization

afrom_texts

Parameters

LangChain Assistant

Menu

afrom_texts

Parameters