viruPVS/langchain-docs huggingface dataset:
filter – Doc fields filter conditions that meet the SQL where clause
specification.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter – Doc fields filter conditions that meet the SQL where clause
specification.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query – Text to search documents similar to.
k – Number of documents to return. Default to 4.
filter – Doc fields filter conditions that meet the SQL where clause
specification.
Returns
List of Documents most similar to the query text.
specification.
Returns
List of Documents most similar to the query text.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Doc fields filter conditions that meet the SQL where clause
specification.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query text , alone with relevance scores.
Less is more similar, more is more dissimilar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
filter – Doc fields filter conditions that meet the SQL where clause
specification.
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using DashVector¶
DashVector
langchain_community.vectorstores.marqo.Marqo¶
class langchain_community.vectorstores.marqo.Marqo(client: marqo.Client, index_name: str, add_documents_settings: Optional[Dict[str, Any]] = None, searchable_attributes: Optional[List[str]] = None, page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = 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)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
add_texts(texts[, metadatas])
Upload texts with metadata (properties) to Marqo.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
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_indexes()
Return Marqo initialized from texts.
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 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.
__init__(client: marqo.Client, index_name: str, add_documents_settings: Optional[Dict[str, Any]] = None, searchable_attributes: Optional[List[str]] = None, page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = None)[source]¶
Initialize with Marqo client.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = 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.
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: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – 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¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_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.
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]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
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
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]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_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], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – 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)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
bulk_similarity_search(queries: Iterable[Union[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) – 
Returns
A list of results for each query.
Return type
List[List[Document]]
Returns
A list of results for each query.
Return type
List[List[Document]]
bulk_similarity_search_with_score(queries: Iterable[Union[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.
Returns
A list of lists of the matching
documents and their scores for each query
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – 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: Optional[Embeddings] = 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.
Returns
A Marqo vectorstore
Return type
VectorStore
Returns
A Marqo vectorstore
Return type
VectorStore
classmethod from_texts(texts: List[str], embedding: Any = None, metadatas: Optional[List[dict]] = None, index_name: str = '', url: str = 'http://localhost:8882', api_key: str = '', add_documents_settings: Optional[Dict[str, Any]] = None, searchable_attributes: Optional[List[str]] = None, page_content_builder: Optional[Callable[[Dict[str, str]], str]] = None, index_settings: Optional[Dict[str, Any]] = 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) –
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.
{}. – 
Returns
An instance of the Marqo vector store
Return type
Marqo
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
Returns
The number of documents
Return type
int
marqo_bulk_similarity_search(queries: Iterable[Union[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: Union[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 – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: Union[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.
Returns
k documents ordered from best to worst match.
Return type
List[Document]
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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – 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)
similarity_search_with_score(query: Union[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
langchain_community.vectorstores.neo4j_vector.SearchType¶
class langchain_community.vectorstores.neo4j_vector.SearchType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies.
VECTOR = 'vector'¶
HYBRID = 'hybrid'¶
langchain_community.vectorstores.zilliz.Zilliz¶
class langchain_community.vectorstores.zilliz.Zilliz(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: Optional[bool] = False, *, primary_field: str = 'pk', text_field: str = 'text', vector_field: str = 'vector')[source]¶
Zilliz vector store.
You need to have pymilvus installed and a
running Zilliz database.
See the following documentation for how to run a Zilliz instance:
https://docs.zilliz.com/docs/create-cluster
IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
Parameters
embedding_function (Embeddings) – Function used to embed the text.
collection_name (str) – Which Zilliz collection to use. Defaults to
“LangChainCollection”.
connection_args (Optional[dict[str, any]]) – The connection args used for
this class comes in the form of a dict.
consistency_level (str) – The consistency level to use for a collection.
Defaults to “Session”.
index_params (Optional[dict]) – Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]) – Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]) – Whether to drop the current collection. Defaults
to False.
The connection args used for this class comes in the form of a dict,
here are a few of the options: