QdrantVectorStore#
- class langchain_qdrant.qdrant.QdrantVectorStore(client: QdrantClient, collection_name: str, embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, vector_name: str = '', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', distance: Distance = Distance.COSINE, sparse_embedding: SparseEmbeddings | None = None, sparse_vector_name: str = 'langchain-sparse', validate_embeddings: bool = True, validate_collection_config: bool = True)[source]#
Qdrant vector store integration.
- Setup:
Install
langchain-qdrant
package.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:
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:
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:
vector_store.delete(ids=[ids[-1]])
- Search:
results = vector_store.similarity_search(query="thud",k=1) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]")
* thud [{'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}]
- Search with filter:
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}]")
* thud [{'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}]
- Search with score:
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}]")
* [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]
- Async:
# 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}]")
* [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]
- Use as Retriever:
retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud")
[Document(metadata={'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}, page_content='thud')]
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(),
)
Attributes
CONTENT_KEY
METADATA_KEY
SPARSE_VECTOR_NAME
VECTOR_NAME
client
Get the Qdrant client instance that is being used.
embeddings
Get the dense embeddings instance that is being used.
sparse_embeddings
Get the sparse embeddings instance that is being used.
Methods
__init__
(client, collection_name[, ...])Initialize a new instance of QdrantVectorStore.
aadd_documents
(documents, **kwargs)Async run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas, ids])Async run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Add or update documents in the vectorstore.
add_texts
(texts[, metadatas, ids, batch_size])Add texts with embeddings to the vectorstore.
adelete
([ids])Async delete by vector ID or other criteria.
afrom_documents
(documents, embedding, **kwargs)Async return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas, ids])Async return VectorStore initialized from texts and embeddings.
aget_by_ids
(ids, /)Async get documents by their IDs.
amax_marginal_relevance_search
(query[, k, ...])Async return docs selected using the maximal marginal relevance.
Async return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Async return docs most similar to query using a specified search type.
asimilarity_search
(query[, k])Async return docs most similar to query.
asimilarity_search_by_vector
(embedding[, k])Async return docs most similar to embedding vector.
Async return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score
(*args, **kwargs)Async run similarity search with distance.
construct_instance
([embedding, ...])delete
([ids])Delete documents by their ids.
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_existing_collection
(collection_name[, ...])Construct an instance of QdrantVectorStore from an existing collection without adding any data.
from_texts
(texts[, embedding, metadatas, ...])Construct an instance of QdrantVectorStore from a list of texts.
get_by_ids
(ids, /)Get documents by their IDs.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance with dense vectors.
Return docs selected using the maximal marginal relevance with dense vectors.
Return docs selected using the maximal marginal relevance.
search
(query, search_type, **kwargs)Return docs most similar to query using a 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.
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 (QdrantClient)
collection_name (str)
embedding (Optional[Embeddings])
retrieval_mode (RetrievalMode)
vector_name (str)
content_payload_key (str)
metadata_payload_key (str)
distance (models.Distance)
sparse_embedding (Optional[SparseEmbeddings])
sparse_vector_name (str)
validate_embeddings (bool)
validate_collection_config (bool)
- __init__(client: QdrantClient, collection_name: str, embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, vector_name: str = '', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', distance: Distance = Distance.COSINE, sparse_embedding: SparseEmbeddings | None = None, sparse_vector_name: str = 'langchain-sparse', validate_embeddings: bool = True, validate_collection_config: bool = True)[source]#
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(),
)
- Parameters:
client (QdrantClient)
collection_name (str)
embedding (Embeddings | None)
retrieval_mode (RetrievalMode)
vector_name (str)
content_payload_key (str)
metadata_payload_key (str)
distance (Distance)
sparse_embedding (SparseEmbeddings | None)
sparse_vector_name (str)
validate_embeddings (bool)
validate_collection_config (bool)
- async aadd_documents(documents: list[Document], **kwargs: Any) list[str] #
Async run more documents through the embeddings and add to the vectorstore.
- Parameters:
documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns:
List of IDs of the added texts.
- Raises:
ValueError – If the number of IDs does not match the number of documents.
- Return type:
list[str]
- 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:
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list
**kwargs (Any) – vectorstore specific parameters.
- Returns:
List of ids from adding the texts into the vectorstore.
- Raises:
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.
- Return type:
list[str]
- add_documents(documents: list[Document], **kwargs: Any) list[str] #
Add or update documents in the vectorstore.
- Parameters:
documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.
- Returns:
List of IDs of the added texts.
- Raises:
ValueError – If the number of ids does not match the number of documents.
- Return type:
list[str]
- add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: Sequence[str | int] | None = None, batch_size: int = 64, **kwargs: Any) List[str | int] [source]#
Add texts with embeddings to the vectorstore.
- Returns:
List of ids from adding the texts into the vectorstore.
- Parameters:
texts (Iterable[str])
metadatas (List[dict] | None)
ids (Sequence[str | int] | None)
batch_size (int)
kwargs (Any)
- Return type:
List[str | int]
- async adelete(ids: list[str] | None = None, **kwargs: Any) bool | None #
Async delete by vector ID or other criteria.
- Parameters:
ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.
**kwargs (Any) – Other keyword arguments that subclasses might use.
- Returns:
True if deletion is successful, False otherwise, None if not implemented.
- Return type:
Optional[bool]
- async classmethod afrom_documents(documents: list[Document], embedding: Embeddings, **kwargs: Any) VST #
Async 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) – Additional keyword arguments.
- Returns:
VectorStore initialized from documents and embeddings.
- Return type:
- async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, *, ids: list[str] | None = None, **kwargs: Any) VST #
Async return VectorStore initialized from texts and embeddings.
- Parameters:
texts (list[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list of IDs associated with the texts.
kwargs (Any) – Additional keyword arguments.
- Returns:
VectorStore initialized from texts and embeddings.
- Return type:
- 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:
ids (Sequence[str]) – List of ids to retrieve.
- Returns:
List of Documents.
- Return type:
list[Document]
Added in version 0.2.11.
- 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:
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. Default is 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.
kwargs (Any)
- 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, **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:
embedding (list[float]) – Embedding 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. Default is 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.
**kwargs (Any) – Arguments to pass to the search method.
- Returns:
List of Documents selected by maximal marginal relevance.
- Return type:
list[Document]
- as_retriever(**kwargs: Any) VectorStoreRetriever #
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters:
**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:
Retriever class for VectorStore.
- Return type:
Examples:
# 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'}} )
- async asearch(query: str, search_type: str, **kwargs: Any) list[Document] #
Async return docs most similar to query using a specified search type.
- Parameters:
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns:
List of Documents most similar to the query.
- Raises:
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type:
list[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) list[Document] #
Async return docs most similar to query.
- Parameters:
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns:
List of Documents most similar to the query.
- Return type:
list[Document]
- async asimilarity_search_by_vector(embedding: list[float], k: int = 4, **kwargs: Any) list[Document] #
Async return docs most similar to embedding vector.
- Parameters:
embedding (list[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns:
List of Documents most similar to the query vector.
- Return type:
list[Document]
- 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:
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 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:
List of Tuples of (doc, similarity_score)
- Return type:
list[tuple[Document, float]]
- async asimilarity_search_with_score(*args: Any, **kwargs: Any) list[tuple[Document, float]] #
Async run similarity search with distance.
- Parameters:
*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.
- Returns:
List of Tuples of (doc, similarity_score).
- Return type:
list[tuple[Document, float]]
- classmethod construct_instance(embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, sparse_embedding: SparseEmbeddings | None = None, client_options: Dict[str, Any] = {}, collection_name: str | None = None, distance: Distance = Distance.COSINE, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: str = '', sparse_vector_name: str = 'langchain-sparse', force_recreate: bool = False, collection_create_options: Dict[str, Any] = {}, vector_params: Dict[str, Any] = {}, sparse_vector_params: Dict[str, Any] = {}, validate_embeddings: bool = True, validate_collection_config: bool = True) QdrantVectorStore [source]#
- Parameters:
embedding (Embeddings | None)
retrieval_mode (RetrievalMode)
sparse_embedding (SparseEmbeddings | None)
client_options (Dict[str, Any])
collection_name (str | None)
distance (Distance)
content_payload_key (str)
metadata_payload_key (str)
vector_name (str)
sparse_vector_name (str)
force_recreate (bool)
collection_create_options (Dict[str, Any])
vector_params (Dict[str, Any])
sparse_vector_params (Dict[str, Any])
validate_embeddings (bool)
validate_collection_config (bool)
- Return type:
- delete(ids: List[str | int] | None = None, **kwargs: Any) bool | None [source]#
Delete documents by their ids.
- Parameters:
ids (List[str | int] | None) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
- Returns:
True if deletion is successful, False otherwise.
- Return type:
bool | None
- classmethod from_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) – Additional keyword arguments.
- Returns:
VectorStore initialized from documents and embeddings.
- Return type:
- classmethod from_existing_collection(collection_name: str, embedding: Embeddings | None = None, retrieval_mode: RetrievalMode = RetrievalMode.DENSE, location: str | None = None, url: str | None = None, port: int | None = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: bool | None = None, api_key: str | None = None, prefix: str | None = None, timeout: int | None = None, host: str | None = None, path: str | None = None, distance: Distance = Distance.COSINE, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: str = '', sparse_vector_name: str = 'langchain-sparse', sparse_embedding: SparseEmbeddings | None = None, validate_embeddings: bool = True, validate_collection_config: bool = True, **kwargs: Any) QdrantVectorStore [source]#
Construct an instance of QdrantVectorStore from an existing collection without adding any data.
- Returns:
A new instance of QdrantVectorStore.
- Return type:
- Parameters:
collection_name (str)
embedding (Embeddings | None)
retrieval_mode (RetrievalMode)
location (str | None)
url (str | None)
port (int | None)
grpc_port (int)
prefer_grpc (bool)
https (bool | None)
api_key (str | None)
prefix (str | None)
timeout (int | None)
host (str | None)
path (str | None)
distance (Distance)
content_payload_key (str)
metadata_payload_key (str)
vector_name (str)
sparse_vector_name (str)
sparse_embedding (SparseEmbeddings | None)
validate_embeddings (bool)
validate_collection_config (bool)
kwargs (Any)
- classmethod from_texts(texts: List[str], embedding: Embeddings | None = None, metadatas: List[dict] | None = None, ids: Sequence[str | int] | None = None, collection_name: str | None = None, location: str | None = None, url: str | None = None, port: int | None = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: bool | None = None, api_key: str | None = None, prefix: str | None = None, timeout: int | None = None, host: str | None = None, path: str | None = None, distance: Distance = Distance.COSINE, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: str = '', retrieval_mode: RetrievalMode = RetrievalMode.DENSE, sparse_embedding: SparseEmbeddings | None = None, sparse_vector_name: str = 'langchain-sparse', collection_create_options: Dict[str, Any] = {}, vector_params: Dict[str, Any] = {}, sparse_vector_params: Dict[str, Any] = {}, batch_size: int = 64, force_recreate: bool = False, validate_embeddings: bool = True, validate_collection_config: bool = True, **kwargs: Any) QdrantVectorStore [source]#
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
from langchain_qdrant import Qdrant from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() qdrant = Qdrant.from_texts(texts, embeddings, url=”http://localhost:6333”)
- Parameters:
texts (List[str])
embedding (Embeddings | None)
metadatas (List[dict] | None)
ids (Sequence[str | int] | None)
collection_name (str | None)
location (str | None)
url (str | None)
port (int | None)
grpc_port (int)
prefer_grpc (bool)
https (bool | None)
api_key (str | None)
prefix (str | None)
timeout (int | None)
host (str | None)
path (str | None)
distance (Distance)
content_payload_key (str)
metadata_payload_key (str)
vector_name (str)
retrieval_mode (RetrievalMode)
sparse_embedding (SparseEmbeddings | None)
sparse_vector_name (str)
collection_create_options (Dict[str, Any])
vector_params (Dict[str, Any])
sparse_vector_params (Dict[str, Any])
batch_size (int)
force_recreate (bool)
validate_embeddings (bool)
validate_collection_config (bool)
kwargs (Any)
- Return type:
- get_by_ids(ids: Sequence[str | int], /) List[Document] [source]#
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:
ids (Sequence[str | int]) – List of ids to retrieve.
- Returns:
List of Documents.
- Return type:
List[Document]
Added in version 0.2.11.
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Filter | None = None, search_params: SearchParams | None = None, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = None, **kwargs: Any) List[Document] [source]#
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:
List of Documents selected by maximal marginal relevance.
- Parameters:
query (str)
k (int)
fetch_k (int)
lambda_mult (float)
filter (Filter | None)
search_params (SearchParams | None)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
kwargs (Any)
- Return type:
List[Document]
- max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Filter | None = None, search_params: SearchParams | None = None, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = None, **kwargs: Any) List[Document] [source]#
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:
List of Documents selected by maximal marginal relevance.
- Parameters:
embedding (List[float])
k (int)
fetch_k (int)
lambda_mult (float)
filter (Filter | None)
search_params (SearchParams | None)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
kwargs (Any)
- Return type:
List[Document]
- max_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Filter | None = None, search_params: SearchParams | None = None, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = 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.
- Returns:
List of Documents selected by maximal marginal relevance and distance for each.
- Parameters:
embedding (List[float])
k (int)
fetch_k (int)
lambda_mult (float)
filter (Filter | None)
search_params (SearchParams | None)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
kwargs (Any)
- Return type:
List[Tuple[Document, float]]
- search(query: str, search_type: str, **kwargs: Any) list[Document] #
Return docs most similar to query using a specified search type.
- Parameters:
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns:
List of Documents most similar to the query.
- Raises:
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type:
list[Document]
- similarity_search(query: str, k: int = 4, filter: Filter | None = None, search_params: SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = None, hybrid_fusion: FusionQuery | None = None, **kwargs: Any) List[Document] [source]#
Return docs most similar to query.
- Returns:
List of Documents most similar to the query.
- Parameters:
query (str)
k (int)
filter (Filter | None)
search_params (SearchParams | None)
offset (int)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
hybrid_fusion (FusionQuery | None)
kwargs (Any)
- Return type:
List[Document]
- similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Filter | None = None, search_params: SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = None, **kwargs: Any) List[Document] [source]#
Return docs most similar to embedding vector.
- Returns:
List of Documents most similar to the query.
- Parameters:
embedding (List[float])
k (int)
filter (Filter | None)
search_params (SearchParams | None)
offset (int)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
kwargs (Any)
- Return type:
List[Document]
- 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:
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 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:
List of Tuples of (doc, similarity_score).
- Return type:
list[tuple[Document, float]]
- similarity_search_with_score(query: str, k: int = 4, filter: Filter | None = None, search_params: SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = None, hybrid_fusion: FusionQuery | None = None, **kwargs: Any) List[Tuple[Document, float]] [source]#
Return docs most similar to query.
- Returns:
List of documents most similar to the query text and distance for each.
- Parameters:
query (str)
k (int)
filter (Filter | None)
search_params (SearchParams | None)
offset (int)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
hybrid_fusion (FusionQuery | None)
kwargs (Any)
- Return type:
List[Tuple[Document, float]]
- similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Filter | None = None, search_params: SearchParams | None = None, offset: int = 0, score_threshold: float | None = None, consistency: Annotated[int, Strict(strict=True)] | ReadConsistencyType | None = None, **kwargs: Any) List[tuple[Document, float]] [source]#
Return docs most similar to embedding vector.
- Returns:
List of Documents most similar to the query and distance for each.
- Parameters:
embedding (List[float])
k (int)
filter (Filter | None)
search_params (SearchParams | None)
offset (int)
score_threshold (float | None)
consistency (Annotated[int, Strict(strict=True)] | ReadConsistencyType | None)
kwargs (Any)
- Return type:
List[tuple[Document, float]]