Tony20100/langchain_full huggingface dataset:
afrom_texts(texts, embedding[, metadatas, ...])
Construct Qdrant wrapper from a list of texts.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :param lambda_mult: Number between 0 and 1 that determines the degree                     of diversity among the results with 0 corresponding                     to maximum diversity and 1 to minimum diversity. Defaults to 0.5. :param filter: Filter by metadata. Defaults to None. :param search_params: Additional search params :param score_threshold: Define a minimal score threshold for the result. If defined, less similar results will not be returned. Score of the returned result might be higher or smaller than the                         threshold depending on the Distance function used. E.g. for cosine similarity only higher scores will be returned. :param consistency: Read consistency of the search. Defines how many replicas should be                     queried before returning the result. Values:                     - int - number of replicas to query, values should present in all                             queried replicas                     - 'majority' - query all replicas, but return values present in the                                    majority of replicas                     - 'quorum' - query the majority of replicas, return values present in                                  all of them                     - 'all' - query all replicas, and return values present in all replicas :param **kwargs: Any other named arguments to pass through to                    AsyncQdrantClient.Search().
amax_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :param lambda_mult: Number between 0 and 1 that determines the degree                     of diversity among the results with 0 corresponding                     to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k, filter])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(query[, k, ...])
Return docs most similar to query.
asimilarity_search_with_score_by_vector(...)
Return docs most similar to embedding vector.
construct_instance(texts, embedding[, ...])
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_existing_collection(embedding[, path, ...])
Get instance of an existing Qdrant collection.
from_texts(texts, embedding[, metadatas, ...])
Construct Qdrant wrapper from a list of texts.
Construct Qdrant wrapper from a list of texts.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :param lambda_mult: Number between 0 and 1 that determines the degree                     of diversity among the results with 0 corresponding                     to maximum diversity and 1 to minimum diversity. Defaults to 0.5. :param filter: Filter by metadata. Defaults to None. :param search_params: Additional search params :param score_threshold: Define a minimal score threshold for the result. If defined, less similar results will not be returned. Score of the returned result might be higher or smaller than the                         threshold depending on the Distance function used. E.g. for cosine similarity only higher scores will be returned. :param consistency: Read consistency of the search. Defines how many replicas should be                     queried before returning the result. Values:                     - int - number of replicas to query, values should present in all                             queried replicas                     - 'majority' - query all replicas, but return values present in the                                    majority of replicas                     - 'quorum' - query the majority of replicas, return values present in                                  all of them                     - 'all' - query all replicas, and return values present in all replicas :param **kwargs: Any other named arguments to pass through to QdrantClient.search().
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter, ...])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to embedding vector.
Parameters
client (Any) – 
collection_name (str) – 
embeddings (Optional[Embeddings]) – 
content_payload_key (str) – 
metadata_payload_key (str) – 
distance_strategy (str) – 
vector_name (Optional[str]) – 
async_client (Optional[Any]) – 
embedding_function (Optional[Callable]) – 
__init__(client: Any, collection_name: str, embeddings: Optional[Embeddings] = None, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', distance_strategy: str = 'COSINE', vector_name: Optional[str] = None, async_client: Optional[Any] = None, embedding_function: Optional[Callable] = None)[source]¶
Initialize with necessary components.
Parameters
client (Any) – 
collection_name (str) – 
embeddings (Optional[Embeddings]) – 
content_payload_key (str) – 
metadata_payload_key (str) – 
distance_strategy (str) – 
vector_name (Optional[str]) – 
async_client (Optional[Any]) – 
embedding_function (Optional[Callable]) –
async_client (Optional[Any]) – 
embedding_function (Optional[Callable]) – 
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[Sequence[str]] = None, batch_size: int = 64, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[Sequence[str]]) – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size (int) – How many vectors upload per-request.
Default: 64
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async classmethod aconstruct_instance(texts: List[str], embedding: Embeddings, location: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: Optional[bool] = None, api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, host: Optional[str] = None, path: Optional[str] = None, collection_name: Optional[str] = None, distance_func: str = 'Cosine', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: Optional[str] = None, shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, on_disk_payload: Optional[bool] = None, hnsw_config: Optional[HnswConfigDiff] = None, optimizers_config: Optional[OptimizersConfigDiff] = None, wal_config: Optional[WalConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, init_from: Optional[InitFrom] = None, on_disk: Optional[bool] = None, force_recreate: bool = False, **kwargs: Any) → Qdrant[source]¶
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
location (Optional[str]) – 
url (Optional[str]) – 
port (Optional[int]) – 
grpc_port (int) – 
prefer_grpc (bool) – 
https (Optional[bool]) – 
api_key (Optional[str]) –
https (Optional[bool]) – 
api_key (Optional[str]) – 
prefix (Optional[str]) – 
timeout (Optional[int]) – 
host (Optional[str]) – 
path (Optional[str]) – 
collection_name (Optional[str]) – 
distance_func (str) – 
content_payload_key (str) – 
metadata_payload_key (str) – 
vector_name (Optional[str]) – 
shard_number (Optional[int]) – 
replication_factor (Optional[int]) – 
write_consistency_factor (Optional[int]) – 
on_disk_payload (Optional[bool]) – 
hnsw_config (Optional[HnswConfigDiff]) – 
optimizers_config (Optional[OptimizersConfigDiff]) – 
wal_config (Optional[WalConfigDiff]) – 
quantization_config (Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]]) – 
init_from (Optional[InitFrom]) – 
on_disk (Optional[bool]) – 
force_recreate (bool) – 
kwargs (Any) – 
Return type
Qdrant
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[Sequence[str]] = None, batch_size: int = 64, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[Sequence[str]]) – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size (int) – How many vectors upload per-request.
Default: 64
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful, False otherwise.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[Sequence[str]] = None, location: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: Optional[bool] = None, api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[int] = None, host: Optional[str] = None, path: Optional[str] = None, collection_name: Optional[str] = None, distance_func: str = 'Cosine', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: Optional[str] = None, batch_size: int = 64, shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, on_disk_payload: Optional[bool] = None, hnsw_config: Optional[HnswConfigDiff] = None, optimizers_config: Optional[OptimizersConfigDiff] = None, wal_config: Optional[WalConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, init_from: Optional[InitFrom] = None, on_disk: Optional[bool] = None, force_recreate: bool = False, **kwargs: Any) → Qdrant[source]¶
Construct Qdrant wrapper from a list of texts.
Parameters
texts (List[str]) – A list of texts to be indexed in Qdrant.
embedding (Embeddings) – A subclass of Embeddings, responsible for text vectorization.
embedding (Embeddings) – A subclass of Embeddings, responsible for text vectorization.
metadatas (Optional[List[dict]]) – An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids (Optional[Sequence[str]]) – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location (Optional[str]) – 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 (Optional[str]) – either host or str of “Optional[scheme], host, Optional[port],
Optional[prefix]”. Default: None
port (Optional[int]) – Port of the REST API interface. Default: 6333
grpc_port (int) – Port of the gRPC interface. Default: 6334
prefer_grpc (bool) – If true - use gPRC interface whenever possible in custom methods.
Default: False
https (Optional[bool]) – If true - use HTTPS(SSL) protocol. Default: None
api_key (Optional[str]) – API key for authentication in Qdrant Cloud. Default: None
Can also be set via environment variable QDRANT_API_KEY.
prefix (Optional[str]) – 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 (Optional[int]) – Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host (Optional[str]) – Host name of Qdrant service. If url and host are None, set to
‘localhost’. Default: None
‘localhost’. Default: None
path (Optional[str]) – Path in which the vectors will be stored while using local mode.
Default: None
collection_name (Optional[str]) – Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func (str) – Distance function. One of: “Cosine” / “Euclid” / “Dot”.
Default: “Cosine”
content_payload_key (str) – A payload key used to store the content of the document.
Default: “page_content”
metadata_payload_key (str) – A payload key used to store the metadata of the document.
Default: “metadata”
vector_name (Optional[str]) – Name of the vector to be used internally in Qdrant.
Default: None
batch_size (int) – How many vectors upload per-request.
Default: 64
shard_number (Optional[int]) – Number of shards in collection. Default is 1, minimum is 1.
replication_factor (Optional[int]) – 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 (Optional[int]) – 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 (Optional[bool]) – If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
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 (Optional[HnswConfigDiff]) – Params for HNSW index
optimizers_config (Optional[OptimizersConfigDiff]) – Params for optimizer
wal_config (Optional[WalConfigDiff]) – Params for Write-Ahead-Log
quantization_config (Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]]) – Params for quantization, if None - quantization will be disabled
init_from (Optional[InitFrom]) – Use data stored in another collection to initialize this collection
force_recreate (bool) – Force recreating the collection
**kwargs (Any) – Additional arguments passed directly into REST client initialization
on_disk (Optional[bool]) – 
Return type
Qdrant
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. 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.
Example
from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.SearchParams]) – Additional search params
score_threshold (Optional[float]) – Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[models.ReadConsistency]) – Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
**kwargs (Any) – Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
Parameters
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.SearchParams]) – Additional search params
score_threshold (Optional[float]) – Define a minimal score threshold for the result.
score_threshold (Optional[float]) – Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[models.ReadConsistency]) – Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
**kwargs (Any) – Any other named arguments to pass through to
AsyncQdrantClient.Search().
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
Returns
List of Documents selected by maximal marginal relevance and distance for
each.
Return type
List[Document]
async amax_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param query: Text to look up documents similar to.