Marqo#

class langchain_community.vectorstores.marqo.Marqo(client: marqo.Client, index_name: str, add_documents_settings: Dict[str, Any] | None = None, searchable_attributes: List[str] | None = None, page_content_builder: Callable[[Dict[str, Any]], str] | None = None)[source]#

Marqo vector store.

Marqo indexes have their own models associated with them to generate your embeddings. This means that you can selected from a range of different models and also use CLIP models to create multimodal indexes with images and text together.

Marqo also supports more advanced queries with multiple weighted terms, see See https://docs.marqo.ai/latest/#searching-using-weights-in-queries. This class can flexibly take strings or dictionaries for weighted queries in its similarity search methods.

To use, you should have the marqo python package installed, you can do this with pip install marqo.

Example

import marqo
from langchain_community.vectorstores import Marqo
client = marqo.Client(url=os.environ["MARQO_URL"], ...)
vectorstore = Marqo(client, index_name)

Initialize with Marqo client.

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(client, index_name[, ...])	Initialize with Marqo client.
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas])	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])	Upload texts with metadata (properties) to Marqo.
`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])	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.
`amax_marginal_relevance_search_by_vector`(...)	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.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`bulk_similarity_search`(queries[, k])	Search the marqo index for the most similar documents in bulk with multiple queries.
`bulk_similarity_search_with_score`(queries[, k])	Return documents from Marqo that are similar to the query as well as their scores using a batch of queries.
`delete`([ids])	Delete by vector ID or other criteria.
`from_documents`(documents[, embedding])	Return VectorStore initialized from documents.
`from_texts`(texts[, embedding, metadatas, ...])	Return Marqo initialized from texts.
`get_by_ids`(ids, /)	Get documents by their IDs.
`get_indexes`()	Helper to see your available indexes in marqo, useful if the from_texts method was used without an index name specified
`get_number_of_documents`()	Helper to see the number of documents in the index
`marqo_bulk_similarity_search`(queries[, k])	Return documents from Marqo using a bulk search, exposes Marqo's output directly
`marqo_similarity_search`(query[, k])	Return documents from Marqo exposing Marqo's output directly
`max_marginal_relevance_search`(query[, k, ...])	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`search`(query, search_type, **kwargs)	Return docs most similar to query using a specified search type.
`similarity_search`(query[, k])	Search the marqo index for the most similar documents.
`similarity_search_by_vector`(embedding[, k])	Return docs most similar to embedding vector.
`similarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1].
`similarity_search_with_score`(query[, k])	Return documents from Marqo that are similar to the query as well as their scores.

Parameters:

client (marqo.Client)
index_name (str)
add_documents_settings (Optional[Dict[str, Any]])
searchable_attributes (Optional[List[str]])
page_content_builder (Optional[Callable[[Dict[str, Any]], str]])

__init__(client: marqo.Client, index_name: str, add_documents_settings: Dict[str, Any] | None = None, searchable_attributes: List[str] | None = None, page_content_builder: Callable[[Dict[str, Any]], str] | None = None)[source]#

Initialize with Marqo client.

Parameters:

client (marqo.Client)
index_name (str)
add_documents_settings (Optional[Dict[str, Any]])
searchable_attributes (Optional[List[str]])
page_content_builder (Optional[Callable[[Dict[str, Any]], str]])

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, **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.
**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, **kwargs: Any) → List[str][source]#

Upload texts with metadata (properties) to Marqo.

You can either have marqo generate ids for each document or you can provide your own by including a “_id” field in the metadata objects.

Parameters:

texts (Iterable[str]) – am iterator of texts - assumed to preserve an
metadatas. (order that matches the)
metadatas (Optional[List[dict]], optional) – a list of metadatas.
kwargs (Any)

Raises:

ValueError – if metadatas is provided and the number of metadatas differs
from the number of texts. –

Returns:

The list of ids that were added.

Return type:

List[str]

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:

VectorStore

async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | 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.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

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:

VectorStoreRetriever

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]]

bulk_similarity_search(queries: Iterable[str | Dict[str, float]], k: int = 4, **kwargs: Any) → List[List[Document]][source]#

Search the marqo index for the most similar documents in bulk with multiple queries.

Parameters:

queries (Iterable[Union[str, Dict[str, float]]]) – An iterable of queries to
bulk (execute in)
of (queries in the list can be strings or dictionaries)
queries. (weighted)
k (int, optional) – The number of documents to return for each query.
4. (Defaults to)
kwargs (Any)

Returns:

A list of results for each query.

Return type:

List[List[Document]]

bulk_similarity_search_with_score(queries: Iterable[str | Dict[str, float]], k: int = 4, **kwargs: Any) → List[List[Tuple[Document, float]]][source]#

Return documents from Marqo that are similar to the query as well as their scores using a batch of queries.

Parameters:

query (Iterable[Union[str, Dict[str, float]]]) – An iterable of queries
bulk (to execute in)
dictionaries (queries in the list can be strings or)
queries. (of weighted)
k (int, optional) – The number of documents to return. Defaults to 4.
queries (Iterable[str | Dict[str, float]])
kwargs (Any)

Returns:

A list of lists of the matching documents and their scores for each query

Return type:

List[Tuple[Document, float]]

delete(ids: list[str] | None = None, **kwargs: Any) → bool | None#

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]

classmethod from_documents(documents: List[Document], embedding: Embeddings | None = None, **kwargs: Any) → Marqo[source]#

Return VectorStore initialized from documents. Note that Marqo does not need embeddings, we retain the parameter to adhere to the Liskov substitution principle.

Parameters:

documents (List[Document]) – Input documents
embedding (Any, optional) – Embeddings (not required). Defaults to None.
kwargs (Any)

Returns:

A Marqo vectorstore

Return type:

VectorStore

classmethod from_texts(texts: List[str], embedding: Any = None, metadatas: List[dict] | None = None, index_name: str = '', url: str = 'http://localhost:8882', api_key: str = '', add_documents_settings: Dict[str, Any] | None = None, searchable_attributes: List[str] | None = None, page_content_builder: Callable[[Dict[str, str]], str] | None = None, index_settings: Dict[str, Any] | None = None, verbose: bool = True, **kwargs: Any) → Marqo[source]#

Return Marqo initialized from texts. Note that Marqo does not need embeddings, we retain the parameter to adhere to the Liskov substitution principle.

This is a quick way to get started with marqo - simply provide your texts and metadatas and this will create an instance of the data store and index the provided data.

To know the ids of your documents with this approach you will need to include them in under the key “_id” in your metadatas for each text

Example: .. code-block:: python

from langchain_community.vectorstores import Marqo

datastore = Marqo(texts=[‘text’], index_name=’my-first-index’, url=’http://localhost:8882’)

Parameters:

texts (List[str]) – A list of texts to index into marqo upon creation.
embedding (Any, optional) – Embeddings (not required). Defaults to None.
index_name (str, optional) – The name of the index to use, if none is
None. (accompany the texts. Defaults to)
url (str, optional) – The URL for Marqo. Defaults to “http://localhost:8882”.
api_key (str, optional) – The API key for Marqo. Defaults to “”.
metadatas (Optional[List[dict]], optional) – A list of metadatas, to
None.
Can (this is only used when a new index is being created. Defaults to "cpu".)
"cuda". (be "cpu" or)
add_documents_settings (Optional[Dict[str, Any]], optional) – Settings
documents (for adding)
see
https – //docs.marqo.ai/0.0.16/API-Reference/documents/#query-parameters.
{}. (Defaults to)
index_settings (Optional[Dict[str, Any]], optional) – Index settings if
exist (the index doesn't)
see
https – //docs.marqo.ai/0.0.16/API-Reference/indexes/#index-defaults-object.
{}.
searchable_attributes (List[str] | None)
page_content_builder (Callable[[Dict[str, str]], str] | None)
verbose (bool)
kwargs (Any)

Returns:

An instance of the Marqo vector store

Return type:

Marqo

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:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

get_indexes() → List[Dict[str, str]][source]#

Helper to see your available indexes in marqo, useful if the from_texts method was used without an index name specified

Returns:: The list of indexes
Return type:: List[Dict[str, str]]

get_number_of_documents() → int[source]#

Helper to see the number of documents in the index

Returns:: The number of documents
Return type:: int

marqo_bulk_similarity_search(queries: Iterable[str | Dict[str, float]], k: int = 4) → Dict[str, List[Dict[str, List[Dict[str, str]]]]][source]#

Return documents from Marqo using a bulk search, exposes Marqo’s output directly

Parameters:

queries (Iterable[Union[str, Dict[str, float]]]) – A list of queries.
k (int, optional) – The number of documents to return for each query.
4. (Defaults to)

Returns:

A bulk search results object

Return type:

Dict[str, Dict[List[Dict[str, Dict[str, Any]]]]]

marqo_similarity_search(query: str | Dict[str, float], k: int = 4) → Dict[str, List[Dict[str, str]]][source]#

Return documents from Marqo exposing Marqo’s output directly

Parameters:

query (str) – The query to search with.
k (int, optional) – The number of documents to return. Defaults to 4.

Returns:

This hits from marqo.

Return type:

List[Dict[str, Any]]

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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:

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) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

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, **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:

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]

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 | Dict[str, float], k: int = 4, **kwargs: Any) → List[Document][source]#

Search the marqo index for the most similar documents.

Parameters:

query (Union[str, Dict[str, float]]) – The query for the search, either
query. (as a string or a weighted)
k (int, optional) – The number of documents to return. Defaults to 4.
kwargs (Any)

Returns:

k documents ordered from best to worst match.

Return type:

List[Document]

similarity_search_by_vector(embedding: list[float], k: int = 4, **kwargs: Any) → list[Document]#

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]

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 | Dict[str, float], k: int = 4) → List[Tuple[Document, float]][source]#

Return documents from Marqo that are similar to the query as well as their scores.

Parameters:

query (str) – The query to search with, either as a string or a weighted
query.
k (int, optional) – The number of documents to return. Defaults to 4.

Returns:

The matching documents and their scores, ordered by descending score.

Return type:

List[Tuple[Document, float]]

Examples using Marqo

Marqo