Tony20100/langchain_full huggingface dataset:
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
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) – 
kwargs (Any) – 
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.
queries (Iterable[Union[str, Dict[str, float]]]) – 
kwargs (Any) – 
Returns
A list of lists of the matching
documents and their scores for each query
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 (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, 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.
kwargs (Any) – 
Returns
A Marqo vectorstore
Return type
VectorStore
classmethod from_texts(texts: List[str], embedding: Optional[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
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) –
{}. (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 (Optional[List[str]]) – 
page_content_builder (Optional[Callable[[Dict[str, str]], str]]) – 
verbose (bool) – 
kwargs (Any) – 
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
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]]]]]
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 (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.
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]
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.
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.
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]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using 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.
Return type
List[Document]
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.
kwargs (Any) – 
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 (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
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: 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
langchain_community.vectorstores.redis.schema.TextFieldSchema¶
class langchain_community.vectorstores.redis.schema.TextFieldSchema[source]¶
Bases: RedisField
Schema for text fields in Redis.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param name: str [Required]¶
param no_index: bool = False¶
param no_stem: bool = False¶
param phonetic_matcher: Optional[str] = None¶
param sortable: Optional[bool] = False¶
param weight: float = 1¶
param withsuffixtrie: bool = False¶
as_field() → TextField[source]¶
Return type
TextField
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
langchain_community.vectorstores.redis.filters.RedisTag¶
class langchain_community.vectorstores.redis.filters.RedisTag(field: str)[source]¶
RedisFilterField representing a tag in a Redis index.
Create a RedisTag FilterField.
Parameters
field (str) – The name of the RedisTag field in the index to be queried
against.
Attributes
OPERATORS
OPERATOR_MAP
SUPPORTED_VAL_TYPES
escaper
Methods
__init__(field)
Create a RedisTag FilterField.
equals(other)
__init__(field: str)[source]¶
Create a RedisTag FilterField.
Parameters
field (str) – The name of the RedisTag field in the index to be queried
against.
equals(other: RedisFilterField) → bool¶
Parameters
other (RedisFilterField) – 
Return type
bool
Examples using RedisTag¶
Redis
langchain_community.vectorstores.vectara.SummaryConfig¶
class langchain_community.vectorstores.vectara.SummaryConfig(is_enabled: bool = False, max_results: int = 7, response_lang: str = 'eng', prompt_name: str = 'vectara-summary-ext-v1.2.0', stream: bool = False)[source]¶
Configuration for summary generation.
is_enabled: True if summary is enabled, False otherwise
max_results: maximum number of results to summarize
response_lang: requested language for the summary
prompt_name: name of the prompt to use for summarization
(see https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer)
Attributes
is_enabled
max_results
prompt_name
response_lang
stream
Methods
__init__([is_enabled, max_results, ...])
Parameters
is_enabled (bool) – 
max_results (int) – 
response_lang (str) – 
prompt_name (str) – 
stream (bool) – 
__init__(is_enabled: bool = False, max_results: int = 7, response_lang: str = 'eng', prompt_name: str = 'vectara-summary-ext-v1.2.0', stream: bool = False) → None¶
Parameters
is_enabled (bool) – 
max_results (int) – 
response_lang (str) – 
prompt_name (str) – 
stream (bool) – 
Return type
None
langchain_postgres.vectorstores.DistanceStrategy¶
class langchain_postgres.vectorstores.DistanceStrategy(value)[source]¶
Enumerator of the Distance strategies.
EUCLIDEAN = 'l2'¶
COSINE = 'cosine'¶
MAX_INNER_PRODUCT = 'inner'¶
Examples using DistanceStrategy¶
Google BigQuery Vector Search
Kinetica Vectorstore API
Oracle AI Vector Search: Vector Store
SAP HANA Cloud Vector Engine
SemaDB
SingleStoreDB
langchain_community.vectorstores.weaviate.Weaviate¶
class langchain_community.vectorstores.weaviate.Weaviate(client: ~typing.Any, index_name: str, text_key: str, embedding: ~typing.Optional[~langchain_core.embeddings.embeddings.Embeddings] = None, attributes: ~typing.Optional[~typing.List[str]] = None, relevance_score_fn: ~typing.Optional[~typing.Callable[[float], float]] = <function _default_score_normalizer>, by_text: bool = True)[source]¶
Weaviate vector store.
To use, you should have the weaviate-client python package installed.
Example
import weaviate
from langchain_community.vectorstores import Weaviate
client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
weaviate = Weaviate(client, index_name, text_key)
Initialize with Weaviate client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(client, index_name, text_key[, ...])
Initialize with Weaviate 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])
Upload texts with metadata (properties) to Weaviate.
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.
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by vector IDs.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct Weaviate wrapper from raw documents.
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])
Return docs most similar to query.
similarity_search_by_text(query[, k])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
Look up similar documents by embedding vector in Weaviate.
similarity_search_with_relevance_scores(query)
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
Return list of documents most similar to the query text and cosine distance in float for each.
Parameters
client (Any) – 
index_name (str) – 
text_key (str) – 
embedding (Optional[Embeddings]) – 
attributes (Optional[List[str]]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
by_text (bool) – 
__init__(client: ~typing.Any, index_name: str, text_key: str, embedding: ~typing.Optional[~langchain_core.embeddings.embeddings.Embeddings] = None, attributes: ~typing.Optional[~typing.List[str]] = None, relevance_score_fn: ~typing.Optional[~typing.Callable[[float], float]] = <function _default_score_normalizer>, by_text: bool = True)[source]¶
Initialize with Weaviate client.
Parameters
client (Any) – 
index_name (str) – 
text_key (str) – 
embedding (Optional[Embeddings]) – 
attributes (Optional[List[str]]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
by_text (bool) – 
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
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.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Upload texts with metadata (properties) to Weaviate.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Return type
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) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, client: Optional[weaviate.Client] = None, weaviate_url: Optional[str] = None, weaviate_api_key: Optional[str] = None, batch_size: Optional[int] = None, index_name: Optional[str] = None, text_key: str = 'text', by_text: bool = False, relevance_score_fn: Optional[Callable[[float], float]] = <function _default_score_normalizer>, **kwargs: Any) → Weaviate[source]¶
Construct Weaviate wrapper from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in the Weaviate instance.
Embeds documents.
Creates a new index for the embeddings in the Weaviate instance.
Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Parameters
texts (List[str]) – Texts to add to vector store.
embedding (Embeddings) – Text embedding model to use.
metadatas (Optional[List[dict]]) – Metadata associated with each text.
client (Optional[weaviate.Client]) – weaviate.Client to use.
weaviate_url (Optional[str]) – The Weaviate URL. If using Weaviate Cloud Services get it
from the Details tab. Can be passed in as a named param or by
setting the environment variable WEAVIATE_URL. Should not be
specified if client is provided.
weaviate_api_key (Optional[str]) – The Weaviate API key. If enabled and using Weaviate Cloud
Services, get it from Details tab. Can be passed in as a named param
or by setting the environment variable WEAVIATE_API_KEY. Should
not be specified if client is provided.
batch_size (Optional[int]) – Size of batch operations.
index_name (Optional[str]) – Index name.
text_key (str) – Key to use for uploading/retrieving text to/from vectorstore.
by_text (bool) – Whether to search by text or by embedding.
relevance_score_fn (Optional[Callable[[float], float]]) – Function for converting whatever distance function the
vector store uses to a relevance score, which is a normalized similarity
score (0 means dissimilar, 1 means similar).
**kwargs (Any) – Additional named parameters to pass to Weaviate.__init__().
Return type
Weaviate
Example
from langchain_community.embeddings import OpenAIEmbeddings
Weaviate
Example
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Weaviate
embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
    texts,
    embeddings,
    weaviate_url="http://localhost:8080"
)
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
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]
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][source]¶
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.