langchain-qdrant

Modules:

Name	Description
fastembed_sparse
qdrant
sparse_embeddings
vectorstores

Classes:

Name	Description
FastEmbedSparse	An interface for sparse embedding models to use with Qdrant.
QdrantVectorStore	Qdrant vector store integration.
RetrievalMode	Modes for retrieving vectors from Qdrant.
SparseEmbeddings	An interface for sparse embedding models to use with Qdrant.
SparseVector	Sparse vector structure.
Qdrant	Qdrant vector store.

FastEmbedSparse

Bases: SparseEmbeddings

An interface for sparse embedding models to use with Qdrant.

Methods:

Name	Description
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.
__init__	Sparse encoder implementation using FastEmbed.

aembed_documents async

aembed_documents(texts: list[str]) -> list[SparseVector]

Asynchronous Embed search docs.

aembed_query async

aembed_query(text: str) -> SparseVector

Asynchronous Embed query text.

__init__

__init__(
    model_name: str = "Qdrant/bm25",
    batch_size: int = 256,
    cache_dir: Optional[str] = None,
    threads: Optional[int] = None,
    providers: Optional[Sequence[Any]] = None,
    parallel: Optional[int] = None,
    **kwargs: Any
) -> None

Sparse encoder implementation using FastEmbed.

Uses FastEmbed <https://qdrant.github.io/fastembed/>__ for sparse text embeddings. For a list of available models, see the Qdrant docs <https://qdrant.github.io/fastembed/examples/Supported_Models/>__.

Parameters:

Name	Type	Description	Default
model_name	str	The name of the model to use. Defaults to "Qdrant/bm25".	'Qdrant/bm25'
batch_size	int	Batch size for encoding. Defaults to 256.	256
cache_dir	str	The path to the model cache directory. Can also be set using the FASTEMBED_CACHE_PATH env variable.	None
threads	int	The number of threads onnxruntime session can use.	None
providers	Sequence[Any]	List of ONNX execution providers. parallel (int, optional): If >1, data-parallel encoding will be used, r Recommended for encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead. Defaults to None.	None
kwargs	Any	Additional options to pass to fastembed.SparseTextEmbedding	{}

Raises: ValueError: If the model_name is not supported in SparseTextEmbedding.

QdrantVectorStore

Bases: VectorStore

Qdrant vector store integration.

Setup

Install langchain-qdrant package.

.. code-block:: bash

pip install -qU langchain-qdrant

Key init args — indexing params: collection_name: str Name of the collection. embedding: Embeddings Embedding function to use. sparse_embedding: SparseEmbeddings Optional sparse embedding function to use.

Key init args — client params: client: QdrantClient Qdrant client to use. retrieval_mode: RetrievalMode Retrieval mode to use.

Instantiate

.. code-block:: python

from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_openai import OpenAIEmbeddings

client = QdrantClient(":memory:")

client.create_collection(
    collection_name="demo_collection",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)

vector_store = QdrantVectorStore(
    client=client,
    collection_name="demo_collection",
    embedding=OpenAIEmbeddings(),
)

Add Documents

.. code-block:: python

from langchain_core.documents import Document
from uuid import uuid4

document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")

documents = [document_1, document_2, document_3]
ids = [str(uuid4()) for _ in range(len(documents))]
vector_store.add_documents(documents=documents, ids=ids)

Delete Documents

.. code-block:: python

vector_store.delete(ids=[ids[-1]])

Search

.. code-block:: python

results = vector_store.similarity_search(
    query="thud",
    k=1,
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

.. code-block:: python

*thud[
    {
        "bar": "baz",
        "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
        "_collection_name": "demo_collection",
    }
]

Search with filter

.. code-block:: python

from qdrant_client.http import models

results = vector_store.similarity_search(
    query="thud",
    k=1,
    filter=models.Filter(
        must=[
            models.FieldCondition(
                key="metadata.bar",
                match=models.MatchValue(value="baz"),
            )
        ]
    ),
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

.. code-block:: python

*thud[
    {
        "bar": "baz",
        "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
        "_collection_name": "demo_collection",
    }
]

Search with score

.. code-block:: python

results = vector_store.similarity_search_with_score(query="qux", k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]

Async

.. code-block:: python

# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)

# delete documents
# await vector_store.adelete(ids=["3"])

# search
# results = vector_store.asimilarity_search(query="thud",k=1)

# search with score
results = await vector_store.asimilarity_search_with_score(query="qux", k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]

Use as Retriever

.. code-block:: python

retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")

.. code-block:: python

[
    Document(
        metadata={
            "bar": "baz",
            "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
            "_collection_name": "demo_collection",
        },
        page_content="thud",
    )
]

Methods:

Name	Description
aget_by_ids	Async get documents by their IDs.
adelete	Async delete by vector ID or other criteria.
aadd_texts	Async run more texts through the embeddings and add to the vectorstore.
add_documents	Add or update documents in the vectorstore.
aadd_documents	Async run more documents through the embeddings and add to the vectorstore.
search	Return docs most similar to query using a specified search type.
asearch	Async return docs most similar to query using a specified search type.
asimilarity_search_with_score	Async run similarity search with distance.
similarity_search_with_relevance_scores	Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_relevance_scores	Async return docs and relevance scores in the range [0, 1].
asimilarity_search	Async return docs most similar to query.
asimilarity_search_by_vector	Async return docs most similar to embedding vector.
amax_marginal_relevance_search	Async return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector	Async return docs selected using the maximal marginal relevance.
from_documents	Return VectorStore initialized from documents and embeddings.
afrom_documents	Async return VectorStore initialized from documents and embeddings.
afrom_texts	Async return VectorStore initialized from texts and embeddings.
as_retriever	Return VectorStoreRetriever initialized from this VectorStore.
__init__	Initialize a new instance of QdrantVectorStore.
from_texts	Construct an instance of QdrantVectorStore from a list of texts.
from_existing_collection	Construct QdrantVectorStore from existing collection without adding data.
add_texts	Add texts with embeddings to the vectorstore.
similarity_search	Return docs most similar to query.
similarity_search_with_score	Return docs most similar to query.
similarity_search_with_score_by_vector	Return docs most similar to embedding vector.
similarity_search_by_vector	Return docs most similar to embedding vector.
max_marginal_relevance_search	Return docs selected using the maximal marginal relevance with dense vectors.
max_marginal_relevance_search_by_vector	Return docs selected using the maximal marginal relevance with dense vectors.
max_marginal_relevance_search_with_score_by_vector	Return docs selected using the maximal marginal relevance.
delete	Delete documents by their ids.

Attributes:

Name	Type	Description
client	QdrantClient	Get the Qdrant client instance that is being used.
embeddings	Optional[Embeddings]	Get the dense embeddings instance that is being used.
sparse_embeddings	SparseEmbeddings	Get the sparse embeddings instance that is being used.

client property

client: QdrantClient

Get the Qdrant client instance that is being used.

Returns:

Name	Type	Description
QdrantClient	QdrantClient	An instance of QdrantClient.

embeddings property

embeddings: Optional[Embeddings]

Get the dense embeddings instance that is being used.

Returns:

Name	Type	Description
Embeddings	Optional[Embeddings]	An instance of Embeddings, or None for SPARSE mode.

sparse_embeddings property

sparse_embeddings: SparseEmbeddings

Get the sparse embeddings instance that is being used.

Raises:

Type	Description
ValueError	If sparse embeddings are None.

Returns:

Name	Type	Description
SparseEmbeddings	SparseEmbeddings	An instance of SparseEmbeddings.

aget_by_ids async

aget_by_ids(ids: Sequence[str]) -> list[Document]

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

Name	Type	Description	Default
ids	Sequence[str]	List of ids to retrieve.	required

Returns:

Type	Description
list[Document]	List of Documents.

Added in version 0.2.11

adelete async

adelete(
    ids: list[str] | None = None, **kwargs: Any
) -> bool | None

Async delete by vector ID or other criteria.

Parameters:

Name	Type	Description	Default
ids	list[str] | None	List of ids to delete. If None, delete all. Default is None.	None
**kwargs	Any	Other keyword arguments that subclasses might use.	{}

Returns:

Type	Description
bool | None	Optional[bool]: True if deletion is successful,
bool | None	False otherwise, None if not implemented.

aadd_texts async

aadd_texts(
    texts: Iterable[str],
    metadatas: list[dict] | None = None,
    *,
    ids: list[str] | None = None,
    **kwargs: Any
) -> list[str]

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
texts	Iterable[str]	Iterable of strings to add to the vectorstore.	required
metadatas	list[dict] | None	Optional list of metadatas associated with the texts. Default is None.	None
ids	list[str] | None	Optional list	None
**kwargs	Any	vectorstore specific parameters.	{}

Returns:

Type	Description
list[str]	List of ids from adding the texts into the vectorstore.

Raises:

Type	Description
ValueError	If the number of metadatas does not match the number of texts.
ValueError	If the number of ids does not match the number of texts.

add_documents

add_documents(
    documents: list[Document], **kwargs: Any
) -> list[str]

Add or update documents in the vectorstore.

Parameters:

Name	Type	Description	Default
documents	list[Document]	Documents to add to the vectorstore.	required
kwargs	Any	Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.	{}

Returns:

Type	Description
list[str]	List of IDs of the added texts.

aadd_documents async

aadd_documents(
    documents: list[Document], **kwargs: Any
) -> list[str]

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
documents	list[Document]	Documents to add to the vectorstore.	required
kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
list[str]	List of IDs of the added texts.

search

search(
    query: str, search_type: str, **kwargs: Any
) -> list[Document]

Return docs most similar to query using a specified search type.

Parameters:

Name	Type	Description	Default
query	str	Input text	required
search_type	str	Type of search to perform. Can be "similarity", "mmr", or "similarity_score_threshold".	required
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

Raises:

Type	Description
ValueError	If search_type is not one of "similarity", "mmr", or "similarity_score_threshold".

asearch async

asearch(
    query: str, search_type: str, **kwargs: Any
) -> list[Document]

Async return docs most similar to query using a specified search type.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
search_type	str	Type of search to perform. Can be "similarity", "mmr", or "similarity_score_threshold".	required
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

Raises:

Type	Description
ValueError	If search_type is not one of "similarity", "mmr", or "similarity_score_threshold".

asimilarity_search_with_score async

asimilarity_search_with_score(
    *args: Any, **kwargs: Any
) -> list[tuple[Document, float]]

Async run similarity search with distance.

Parameters:

Name	Type	Description	Default
*args	Any	Arguments to pass to the search method.	()
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Tuples of (doc, similarity_score).

similarity_search_with_relevance_scores

similarity_search_with_relevance_scores(
    query: str, k: int = 4, **kwargs: Any
) -> list[tuple[Document, float]]

Return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
k	int	Number of Documents to return. Defaults to 4.	4
**kwargs	Any	kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs.	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Tuples of (doc, similarity_score).

asimilarity_search_with_relevance_scores async

asimilarity_search_with_relevance_scores(
    query: str, k: int = 4, **kwargs: Any
) -> list[tuple[Document, float]]

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
k	int	Number of Documents to return. Defaults to 4.	4
**kwargs	Any	kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Tuples of (doc, similarity_score)

asimilarity_search async

asimilarity_search(
    query: str, k: int = 4, **kwargs: Any
) -> list[Document]

Async return docs most similar to query.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
k	int	Number of Documents to return. Defaults to 4.	4
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

asimilarity_search_by_vector async

asimilarity_search_by_vector(
    embedding: list[float], k: int = 4, **kwargs: Any
) -> list[Document]

Async return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query vector.

amax_marginal_relevance_search async

amax_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    **kwargs: Any
) -> list[Document]

Async return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Default is 20.	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.	0.5
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

amax_marginal_relevance_search_by_vector async

amax_marginal_relevance_search_by_vector(
    embedding: list[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    **kwargs: Any
) -> list[Document]

Async return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Default is 20.	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.	0.5
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

from_documents classmethod

from_documents(
    documents: list[Document],
    embedding: Embeddings,
    **kwargs: Any
) -> Self

Return VectorStore initialized from documents and embeddings.

Parameters:

Name	Type	Description	Default
documents	list[Document]	List of Documents to add to the vectorstore.	required
embedding	Embeddings	Embedding function to use.	required
kwargs	Any	Additional keyword arguments.	{}

Returns:

Name	Type	Description
VectorStore	Self	VectorStore initialized from documents and embeddings.

afrom_documents async classmethod

afrom_documents(
    documents: list[Document],
    embedding: Embeddings,
    **kwargs: Any
) -> Self

Async return VectorStore initialized from documents and embeddings.

Parameters:

Name	Type	Description	Default
documents	list[Document]	List of Documents to add to the vectorstore.	required
embedding	Embeddings	Embedding function to use.	required
kwargs	Any	Additional keyword arguments.	{}

Returns:

Name	Type	Description
VectorStore	Self	VectorStore initialized from documents and embeddings.

afrom_texts async classmethod

afrom_texts(
    texts: list[str],
    embedding: Embeddings,
    metadatas: list[dict] | None = None,
    *,
    ids: list[str] | None = None,
    **kwargs: Any
) -> Self

Async return VectorStore initialized from texts and embeddings.

Parameters:

Name	Type	Description	Default
texts	list[str]	Texts to add to the vectorstore.	required
embedding	Embeddings	Embedding function to use.	required
metadatas	list[dict] | None	Optional list of metadatas associated with the texts. Default is None.	None
ids	list[str] | None	Optional list of IDs associated with the texts.	None
kwargs	Any	Additional keyword arguments.	{}

Returns:

Name	Type	Description
VectorStore	Self	VectorStore initialized from texts and embeddings.

as_retriever

as_retriever(**kwargs: Any) -> VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Keyword arguments to pass to the search function. Can include: search_type (Optional[str]): Defines the type of search that the Retriever should perform. Can be "similarity" (default), "mmr", or "similarity_score_threshold". search_kwargs (Optional[Dict]): Keyword arguments to pass to the search function. Can include things like: k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold for similarity_score_threshold fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR; 1 for minimum diversity and 0 for maximum. (Default: 0.5) filter: Filter by document metadata	{}

Returns:

Name	Type	Description
VectorStoreRetriever	VectorStoreRetriever	Retriever class for VectorStore.

Examples:

.. code-block:: python

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr", search_kwargs={"k": 6, "lambda_mult": 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr", search_kwargs={"k": 5, "fetch_k": 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"score_threshold": 0.8},
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={"k": 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={"filter": {"paper_title": "GPT-4 Technical Report"}}
)

__init__

__init__(
    client: QdrantClient,
    collection_name: str,
    embedding: Optional[Embeddings] = None,
    retrieval_mode: RetrievalMode = DENSE,
    vector_name: str = VECTOR_NAME,
    content_payload_key: str = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    distance: Distance = COSINE,
    sparse_embedding: Optional[SparseEmbeddings] = None,
    sparse_vector_name: str = SPARSE_VECTOR_NAME,
    validate_embeddings: bool = True,
    validate_collection_config: bool = True,
) -> None

Initialize a new instance of QdrantVectorStore.

Example

.. code-block:: python qdrant = Qdrant( client=client, collection_name="my-collection", embedding=OpenAIEmbeddings(), retrieval_mode=RetrievalMode.HYBRID, sparse_embedding=FastEmbedSparse(), )

from_texts classmethod

from_texts(
    texts: list[str],
    embedding: Optional[Embeddings] = None,
    metadatas: Optional[list[dict]] = None,
    ids: Optional[Sequence[str | int]] = None,
    collection_name: Optional[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,
    distance: Distance = COSINE,
    content_payload_key: str = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    vector_name: str = VECTOR_NAME,
    retrieval_mode: RetrievalMode = DENSE,
    sparse_embedding: Optional[SparseEmbeddings] = None,
    sparse_vector_name: str = SPARSE_VECTOR_NAME,
    collection_create_options: Optional[
        dict[str, Any]
    ] = None,
    vector_params: Optional[dict[str, Any]] = None,
    sparse_vector_params: Optional[dict[str, Any]] = None,
    batch_size: int = 64,
    force_recreate: bool = False,
    validate_embeddings: bool = True,
    validate_collection_config: bool = True,
    **kwargs: Any
) -> QdrantVectorStore

Construct an instance of QdrantVectorStore from a list of texts.

This is a user-friendly interface that: 1. Creates embeddings, one for each text 2. Creates a Qdrant collection if it doesn't exist. 3. Adds the text embeddings to the Qdrant database

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

Example

.. code-block:: python

from langchain_qdrant import Qdrant from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() qdrant = Qdrant.from_texts(texts, embeddings, url="http://localhost:6333")

from_existing_collection classmethod

from_existing_collection(
    collection_name: str,
    embedding: Optional[Embeddings] = None,
    retrieval_mode: RetrievalMode = DENSE,
    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,
    distance: Distance = COSINE,
    content_payload_key: str = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    vector_name: str = VECTOR_NAME,
    sparse_vector_name: str = SPARSE_VECTOR_NAME,
    sparse_embedding: Optional[SparseEmbeddings] = None,
    validate_embeddings: bool = True,
    validate_collection_config: bool = True,
    **kwargs: Any
) -> QdrantVectorStore

Construct QdrantVectorStore from existing collection without adding data.

Returns:

Name	Type	Description
QdrantVectorStore	QdrantVectorStore	A new instance of QdrantVectorStore.

add_texts

add_texts(
    texts: Iterable[str],
    metadatas: Optional[list[dict]] = None,
    ids: Optional[Sequence[str | int]] = None,
    batch_size: int = 64,
    **kwargs: Any
) -> list[str | int]

Add texts with embeddings to the vectorstore.

Returns:

Type	Description
list[str | int]	List of ids from adding the texts into the vectorstore.

similarity_search

similarity_search(
    query: str,
    k: int = 4,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    hybrid_fusion: Optional[FusionQuery] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to query.

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

similarity_search_with_score

similarity_search_with_score(
    query: str,
    k: int = 4,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    hybrid_fusion: Optional[FusionQuery] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs most similar to query.

Returns:

Type	Description
list[tuple[Document, float]]	List of documents most similar to the query text and distance for each.

similarity_search_with_score_by_vector

similarity_search_with_score_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs most similar to embedding vector.

Returns:

Type	Description
list[tuple[Document, float]]	List of Documents most similar to the query and distance for each.

similarity_search_by_vector

similarity_search_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to embedding vector.

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

max_marginal_relevance_search

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs selected using the maximal marginal relevance with dense vectors.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_by_vector

max_marginal_relevance_search_by_vector(
    embedding: list[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs selected using the maximal marginal relevance with dense vectors.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_with_score_by_vector

max_marginal_relevance_search_with_score_by_vector(
    embedding: list[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: Optional[Filter] = None,
    search_params: Optional[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Returns:

Type	Description
list[tuple[Document, float]]	List of Documents selected by maximal marginal relevance and distance for
list[tuple[Document, float]]	each.

delete

delete(
    ids: Optional[list[str | int]] = None, **kwargs: Any
) -> Optional[bool]

Delete documents by their ids.

Parameters:

Name	Type	Description	Default
ids	Optional[list[str | int]]	List of ids to delete.	None
**kwargs	Any	Other keyword arguments that subclasses might use.	{}

Returns:

Type	Description
Optional[bool]	True if deletion is successful, False otherwise.

RetrievalMode

Bases: str, Enum

Modes for retrieving vectors from Qdrant.

SparseEmbeddings

Bases: ABC

An interface for sparse embedding models to use with Qdrant.

Methods:

Name	Description
embed_documents	Embed search docs.
embed_query	Embed query text.
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.

embed_documents abstractmethod

embed_documents(texts: list[str]) -> list[SparseVector]

Embed search docs.

embed_query abstractmethod

embed_query(text: str) -> SparseVector

Embed query text.

aembed_documents async

aembed_documents(texts: list[str]) -> list[SparseVector]

Asynchronous Embed search docs.

aembed_query async

aembed_query(text: str) -> SparseVector

Asynchronous Embed query text.

SparseVector

Bases: BaseModel

Sparse vector structure.

Qdrant

Bases: VectorStore

Qdrant vector store.

Example

.. code-block:: python

from qdrant_client import QdrantClient
from langchain_qdrant import Qdrant

client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)

Methods:

Name	Description
get_by_ids	Get documents by their IDs.
aget_by_ids	Async get documents by their IDs.
add_documents	Add or update documents in the vectorstore.
aadd_documents	Async run more documents through the embeddings and add to the vectorstore.
search	Return docs most similar to query using a specified search type.
asearch	Async return docs most similar to query using a specified search type.
similarity_search_with_relevance_scores	Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_relevance_scores	Async return docs and relevance scores in the range [0, 1].
from_documents	Return VectorStore initialized from documents and embeddings.
afrom_documents	Async return VectorStore initialized from documents and embeddings.
as_retriever	Return VectorStoreRetriever initialized from this VectorStore.
__init__	Initialize with necessary components.
add_texts	Run more texts through the embeddings and add to the vectorstore.
aadd_texts	Run more texts through the embeddings and add to the vectorstore.
similarity_search	Return docs most similar to query.
asimilarity_search	Return docs most similar to query.
similarity_search_with_score	Return docs most similar to query.
asimilarity_search_with_score	Return docs most similar to query.
similarity_search_by_vector	Return docs most similar to embedding vector.
asimilarity_search_by_vector	Return docs most similar to embedding vector.
similarity_search_with_score_by_vector	Return docs most similar to embedding vector.
asimilarity_search_with_score_by_vector	Return docs most similar to embedding vector.
max_marginal_relevance_search	Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search	Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector	Return docs selected using the maximal marginal relevance.
amax_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.
amax_marginal_relevance_search_with_score_by_vector	Return docs selected using the maximal marginal relevance.
delete	Delete by vector ID or other criteria.
adelete	Delete by vector ID or other criteria.
from_texts	Construct Qdrant wrapper from a list of texts.
from_existing_collection	Get instance of an existing Qdrant collection.
afrom_texts	Construct Qdrant wrapper from a list of texts.

get_by_ids

get_by_ids(ids: Sequence[str]) -> list[Document]

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

Name	Type	Description	Default
ids	Sequence[str]	List of ids to retrieve.	required

Returns:

Type	Description
list[Document]	List of Documents.

Added in version 0.2.11

aget_by_ids async

aget_by_ids(ids: Sequence[str]) -> list[Document]

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

Name	Type	Description	Default
ids	Sequence[str]	List of ids to retrieve.	required

Returns:

Type	Description
list[Document]	List of Documents.

Added in version 0.2.11

add_documents

add_documents(
    documents: list[Document], **kwargs: Any
) -> list[str]

Add or update documents in the vectorstore.

Parameters:

Name	Type	Description	Default
documents	list[Document]	Documents to add to the vectorstore.	required
kwargs	Any	Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.	{}

Returns:

Type	Description
list[str]	List of IDs of the added texts.

aadd_documents async

aadd_documents(
    documents: list[Document], **kwargs: Any
) -> list[str]

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
documents	list[Document]	Documents to add to the vectorstore.	required
kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
list[str]	List of IDs of the added texts.

search

search(
    query: str, search_type: str, **kwargs: Any
) -> list[Document]

Return docs most similar to query using a specified search type.

Parameters:

Name	Type	Description	Default
query	str	Input text	required
search_type	str	Type of search to perform. Can be "similarity", "mmr", or "similarity_score_threshold".	required
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

Raises:

Type	Description
ValueError	If search_type is not one of "similarity", "mmr", or "similarity_score_threshold".

asearch async

asearch(
    query: str, search_type: str, **kwargs: Any
) -> list[Document]

Async return docs most similar to query using a specified search type.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
search_type	str	Type of search to perform. Can be "similarity", "mmr", or "similarity_score_threshold".	required
**kwargs	Any	Arguments to pass to the search method.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

Raises:

Type	Description
ValueError	If search_type is not one of "similarity", "mmr", or "similarity_score_threshold".

similarity_search_with_relevance_scores

similarity_search_with_relevance_scores(
    query: str, k: int = 4, **kwargs: Any
) -> list[tuple[Document, float]]

Return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
k	int	Number of Documents to return. Defaults to 4.	4
**kwargs	Any	kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs.	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Tuples of (doc, similarity_score).

asimilarity_search_with_relevance_scores async

asimilarity_search_with_relevance_scores(
    query: str, k: int = 4, **kwargs: Any
) -> list[tuple[Document, float]]

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

Name	Type	Description	Default
query	str	Input text.	required
k	int	Number of Documents to return. Defaults to 4.	4
**kwargs	Any	kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Tuples of (doc, similarity_score)

from_documents classmethod

from_documents(
    documents: list[Document],
    embedding: Embeddings,
    **kwargs: Any
) -> Self

Return VectorStore initialized from documents and embeddings.

Parameters:

Name	Type	Description	Default
documents	list[Document]	List of Documents to add to the vectorstore.	required
embedding	Embeddings	Embedding function to use.	required
kwargs	Any	Additional keyword arguments.	{}

Returns:

Name	Type	Description
VectorStore	Self	VectorStore initialized from documents and embeddings.

afrom_documents async classmethod

afrom_documents(
    documents: list[Document],
    embedding: Embeddings,
    **kwargs: Any
) -> Self

Async return VectorStore initialized from documents and embeddings.

Parameters:

Name	Type	Description	Default
documents	list[Document]	List of Documents to add to the vectorstore.	required
embedding	Embeddings	Embedding function to use.	required
kwargs	Any	Additional keyword arguments.	{}

Returns:

Name	Type	Description
VectorStore	Self	VectorStore initialized from documents and embeddings.

as_retriever

as_retriever(**kwargs: Any) -> VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Keyword arguments to pass to the search function. Can include: search_type (Optional[str]): Defines the type of search that the Retriever should perform. Can be "similarity" (default), "mmr", or "similarity_score_threshold". search_kwargs (Optional[Dict]): Keyword arguments to pass to the search function. Can include things like: k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold for similarity_score_threshold fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR; 1 for minimum diversity and 0 for maximum. (Default: 0.5) filter: Filter by document metadata	{}

Returns:

Name	Type	Description
VectorStoreRetriever	VectorStoreRetriever	Retriever class for VectorStore.

Examples:

.. code-block:: python

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr", search_kwargs={"k": 6, "lambda_mult": 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr", search_kwargs={"k": 5, "fetch_k": 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"score_threshold": 0.8},
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={"k": 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={"filter": {"paper_title": "GPT-4 Technical Report"}}
)

__init__

__init__(
    client: Any,
    collection_name: str,
    embeddings: Optional[Embeddings] = None,
    content_payload_key: str = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    distance_strategy: str = "COSINE",
    vector_name: Optional[str] = VECTOR_NAME,
    async_client: Optional[Any] = None,
    embedding_function: Optional[Callable] = None,
) -> None

Initialize with necessary components.

add_texts

add_texts(
    texts: Iterable[str],
    metadatas: Optional[list[dict]] = None,
    ids: Optional[Sequence[str]] = None,
    batch_size: int = 64,
    **kwargs: Any
) -> list[str]

Run more texts through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
texts	Iterable[str]	Iterable of strings to add to the vectorstore.	required
metadatas	Optional[list[dict]]	Optional list of metadatas associated with the texts.	None
ids	Optional[Sequence[str]]	Optional list of ids to associate with the texts. Ids have to be uuid-like strings.	None
batch_size	int	How many vectors upload per-request. Default: 64	64
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
list[str]	List of ids from adding the texts into the vectorstore.

aadd_texts async

aadd_texts(
    texts: Iterable[str],
    metadatas: Optional[list[dict]] = None,
    ids: Optional[Sequence[str]] = None,
    batch_size: int = 64,
    **kwargs: Any
) -> list[str]

Run more texts through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
texts	Iterable[str]	Iterable of strings to add to the vectorstore.	required
metadatas	Optional[list[dict]]	Optional list of metadatas associated with the texts.	None
ids	Optional[Sequence[str]]	Optional list of ids to associate with the texts. Ids have to be uuid-like strings.	None
batch_size	int	How many vectors upload per-request. Default: 64	64
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
list[str]	List of ids from adding the texts into the vectorstore.

similarity_search

similarity_search(
    query: str,
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to query.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

asimilarity_search async

asimilarity_search(
    query: str,
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to query.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

similarity_search_with_score

similarity_search_with_score(
    query: str,
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs most similar to query.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of documents most similar to the query text and distance for each.

asimilarity_search_with_score async

asimilarity_search_with_score(
    query: str,
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs most similar to query.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to AsyncQdrantClient.Search().	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of documents most similar to the query text and distance for each.

similarity_search_by_vector

similarity_search_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

asimilarity_search_by_vector async

asimilarity_search_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to AsyncQdrantClient.Search().	{}

Returns:

Type	Description
list[Document]	List of Documents most similar to the query.

similarity_search_with_score_by_vector

similarity_search_with_score_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of documents most similar to the query text and distance for each.

asimilarity_search_with_score_by_vector async

asimilarity_search_with_score_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    offset: int = 0,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
offset	int	Offset of the first result to return. May be used to paginate results. Note: large offset values may cause performance issues.	0
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to AsyncQdrantClient.Search().	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of documents most similar to the query text and distance for each.

max_marginal_relevance_search

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: Optional[MetadataFilter] = None,
    search_params: Optional[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.	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.	0.5
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

amax_marginal_relevance_search 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[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
query	str	Text to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.	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.	0.5
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to AsyncQdrantClient.Search().	{}

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_by_vector

max_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[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm.	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.	0.5
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance.

amax_marginal_relevance_search_by_vector 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[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.	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.	0.5
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to AsyncQdrantClient.Search().	{}

Returns:

Type	Description
list[Document]	List of Documents selected by maximal marginal relevance and distance for
list[Document]	each.

max_marginal_relevance_search_with_score_by_vector

max_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[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.	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.	0.5
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params	None
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.	None
consistency	Optional[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 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	None
**kwargs	Any	Any other named arguments to pass through to QdrantClient.search()	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Documents selected by maximal marginal relevance and distance for
list[tuple[Document, float]]	each.

amax_marginal_relevance_search_with_score_by_vector 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[SearchParams] = None,
    score_threshold: Optional[float] = None,
    consistency: Optional[ReadConsistency] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding vector to look up documents similar to.	required
k	int	Number of Documents to return. Defaults to 4.	4
fetch_k	int	Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.	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.	0.5
filter	Optional[MetadataFilter]	Filter by metadata. Defaults to None.	None
search_params	Optional[SearchParams]	Additional search params.	None
score_threshold	Optional[float]	Define a minimal score threshold for the result.	None
consistency	Optional[ReadConsistency]	Read consistency of the search.	None
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of Documents selected by maximal marginal relevance and distance for
list[tuple[Document, float]]	each.

delete

delete(
    ids: Optional[list[str]] = None, **kwargs: Any
) -> Optional[bool]

Delete by vector ID or other criteria.

Parameters:

Name	Type	Description	Default
ids	Optional[list[str]]	List of ids to delete.	None
**kwargs	Any	Other keyword arguments that subclasses might use.	{}

Returns:

Type	Description
Optional[bool]	True if deletion is successful, False otherwise.

adelete async

adelete(
    ids: Optional[list[str]] = None, **kwargs: Any
) -> Optional[bool]

Delete by vector ID or other criteria.

Parameters:

Name	Type	Description	Default
ids	Optional[list[str]]	List of ids to delete.	None
**kwargs	Any	Other keyword arguments that subclasses might use.	{}

Returns:

Type	Description
Optional[bool]	True if deletion is successful, False otherwise.

from_texts classmethod

from_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 = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    vector_name: Optional[str] = VECTOR_NAME,
    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[
        QuantizationConfig
    ] = None,
    init_from: Optional[InitFrom] = None,
    on_disk: Optional[bool] = None,
    force_recreate: bool = False,
    **kwargs: Any
) -> Qdrant

Construct Qdrant wrapper from a list of texts.

Parameters:

Name	Type	Description	Default
texts	list[str]	A list of texts to be indexed in Qdrant.	required
embedding	Embeddings	A subclass of Embeddings, responsible for text vectorization.	required
metadatas	Optional[list[dict]]	An optional list of metadata. If provided it has to be of the same length as a list of texts.	None
ids	Optional[Sequence[str]]	Optional list of ids to associate with the texts. Ids have to be uuid-like strings.	None
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.	None
url	Optional[str]	either host or str of "Optional[scheme], host, Optional[port], Optional[prefix]". Default: None	None
port	Optional[int]	Port of the REST API interface. Default: 6333	6333
grpc_port	int	Port of the gRPC interface. Default: 6334	6334
prefer_grpc	bool	If true - use gPRC interface whenever possible in custom methods. Default: False	False
https	Optional[bool]	If true - use HTTPS(SSL) protocol. Default: None	None
api_key	Optional[str]	API key for authentication in Qdrant Cloud. Default: None Can also be set via environment variable `QDRANT_API_KEY`.	None
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	None
timeout	Optional[int]	Timeout for REST and gRPC API requests. Default: 5.0 seconds for REST and unlimited for gRPC	None
host	Optional[str]	Host name of Qdrant service. If url and host are None, set to 'localhost'. Default: None	None
path	Optional[str]	Path in which the vectors will be stored while using local mode. Default: None	None
collection_name	Optional[str]	Name of the Qdrant collection to be used. If not provided, it will be created randomly. Default: None	None
distance_func	str	Distance function. One of: "Cosine" / "Euclid" / "Dot". Default: "Cosine"	'Cosine'
content_payload_key	str	A payload key used to store the content of the document. Default: "page_content"	CONTENT_KEY
metadata_payload_key	str	A payload key used to store the metadata of the document. Default: "metadata"	METADATA_KEY
vector_name	Optional[str]	Name of the vector to be used internally in Qdrant. Default: None	VECTOR_NAME
batch_size	int	How many vectors upload per-request. Default: 64	64
shard_number	Optional[int]	Number of shards in collection. Default is 1, minimum is 1.	None
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.	None
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.	None
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. 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.	None
hnsw_config	Optional[HnswConfigDiff]	Params for HNSW index	None
optimizers_config	Optional[OptimizersConfigDiff]	Params for optimizer	None
wal_config	Optional[WalConfigDiff]	Params for Write-Ahead-Log	None
quantization_config	Optional[QuantizationConfig]	Params for quantization, if None - quantization will be disabled	None
init_from	Optional[InitFrom]	Use data stored in another collection to initialize this collection	None
on_disk	Optional[bool]	If true - vectors will be stored on disk, reducing memory usage.	None
force_recreate	bool	Force recreating the collection	False
**kwargs	Any	Additional arguments passed directly into REST client initialization	{}

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) 3. Adds the text embeddings to the Qdrant database

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

Example

.. code-block:: python

from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings

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

from_existing_collection classmethod

from_existing_collection(
    embedding: Embeddings,
    path: Optional[str] = None,
    collection_name: Optional[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,
    content_payload_key: str = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    distance_strategy: str = "COSINE",
    vector_name: Optional[str] = VECTOR_NAME,
    **kwargs: Any
) -> Qdrant

Get instance of an existing Qdrant collection.

This method will return the instance of the store without inserting any new embeddings.

afrom_texts 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 = CONTENT_KEY,
    metadata_payload_key: str = METADATA_KEY,
    vector_name: Optional[str] = VECTOR_NAME,
    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[
        QuantizationConfig
    ] = None,
    init_from: Optional[InitFrom] = None,
    on_disk: Optional[bool] = None,
    force_recreate: bool = False,
    **kwargs: Any
) -> Qdrant

Construct Qdrant wrapper from a list of texts.

Parameters:

Name	Type	Description	Default
texts	list[str]	A list of texts to be indexed in Qdrant.	required
embedding	Embeddings	A subclass of Embeddings, responsible for text vectorization.	required
metadatas	Optional[list[dict]]	An optional list of metadata. If provided it has to be of the same length as a list of texts.	None
ids	Optional[Sequence[str]]	Optional list of ids to associate with the texts. Ids have to be uuid-like strings.	None
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.	None
url	Optional[str]	either host or str of "Optional[scheme], host, Optional[port], Optional[prefix]". Default: None	None
port	Optional[int]	Port of the REST API interface. Default: 6333	6333
grpc_port	int	Port of the gRPC interface. Default: 6334	6334
prefer_grpc	bool	If true - use gPRC interface whenever possible in custom methods. Default: False	False
https	Optional[bool]	If true - use HTTPS(SSL) protocol. Default: None	None
api_key	Optional[str]	API key for authentication in Qdrant Cloud. Default: None Can also be set via environment variable `QDRANT_API_KEY`.	None
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	None
timeout	Optional[int]	Timeout for REST and gRPC API requests. Default: 5.0 seconds for REST and unlimited for gRPC	None
host	Optional[str]	Host name of Qdrant service. If url and host are None, set to 'localhost'. Default: None	None
path	Optional[str]	Path in which the vectors will be stored while using local mode. Default: None	None
collection_name	Optional[str]	Name of the Qdrant collection to be used. If not provided, it will be created randomly. Default: None	None
distance_func	str	Distance function. One of: "Cosine" / "Euclid" / "Dot". Default: "Cosine"	'Cosine'
content_payload_key	str	A payload key used to store the content of the document. Default: "page_content"	CONTENT_KEY
metadata_payload_key	str	A payload key used to store the metadata of the document. Default: "metadata"	METADATA_KEY
vector_name	Optional[str]	Name of the vector to be used internally in Qdrant. Default: None	VECTOR_NAME
batch_size	int	How many vectors upload per-request. Default: 64	64
shard_number	Optional[int]	Number of shards in collection. Default is 1, minimum is 1.	None
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.	None
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.	None
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. 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.	None
hnsw_config	Optional[HnswConfigDiff]	Params for HNSW index	None
optimizers_config	Optional[OptimizersConfigDiff]	Params for optimizer	None
wal_config	Optional[WalConfigDiff]	Params for Write-Ahead-Log	None
quantization_config	Optional[QuantizationConfig]	Params for quantization, if None - quantization will be disabled	None
init_from	Optional[InitFrom]	Use data stored in another collection to initialize this collection	None
on_disk	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. 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.	None
force_recreate	bool	Force recreating the collection	False
**kwargs	Any	Additional arguments passed directly into REST client initialization	{}

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) 3. Adds the text embeddings to the Qdrant database

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

Example

.. code-block:: python

from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings

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