Tony20100/langchain_full huggingface dataset:
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return documents most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Document]
similarity_search_by_vector(embedding: Union[List[float], str], k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return documents closest to the given embedding.
Parameters
embedding (Union[List[float], str]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents most similar to the query
Return type
List[Document]
similarity_search_by_vector_with_score(embedding: Union[List[float], str], k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return texts whose embedding is closest to the given embedding
Parameters
embedding (Union[List[float], str]) – 
k (int) – 
filter (Optional[str]) –
k (int) – 
filter (Optional[str]) – 
namespace (Optional[str]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
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: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Retrieve texts most similar to query and
convert the result to Document objects.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
Examples using UpstashVectorStore¶
Upstash Vector
langchain_community.vectorstores.tencentvectordb.translate_filter¶
langchain_community.vectorstores.tencentvectordb.translate_filter(lc_filter: str, allowed_fields: Optional[Sequence[str]] = None) → str[source]¶
Translate LangChain filter to Tencent VectorDB filter.
Parameters
lc_filter (str) – LangChain filter.
allowed_fields (Optional[Sequence[str]]) – Allowed fields for filter.
Returns
Translated filter.
Return type
str
langchain_elasticsearch.vectorstores.ElasticsearchStore¶
class langchain_elasticsearch.vectorstores.ElasticsearchStore(index_name: str, *, embedding: ~typing.Optional[~langchain_core.embeddings.embeddings.Embeddings] = None, es_connection: ~typing.Optional[~elasticsearch.Elasticsearch] = None, es_url: ~typing.Optional[str] = None, es_cloud_id: ~typing.Optional[str] = None, es_user: ~typing.Optional[str] = None, es_api_key: ~typing.Optional[str] = None, es_password: ~typing.Optional[str] = None, vector_query_field: str = 'vector', query_field: str = 'text', distance_strategy: ~typing.Optional[~typing.Literal[<DistanceStrategy.COSINE: 'COSINE'>, <DistanceStrategy.DOT_PRODUCT: 'DOT_PRODUCT'>, <DistanceStrategy.EUCLIDEAN_DISTANCE: 'EUCLIDEAN_DISTANCE'>, <DistanceStrategy.MAX_INNER_PRODUCT: 'MAX_INNER_PRODUCT'>]] = None, strategy: ~typing.Union[~langchain_elasticsearch.vectorstores.BaseRetrievalStrategy, ~elasticsearch.helpers.vectorstore._sync.strategies.RetrievalStrategy] = <langchain_elasticsearch.vectorstores.ApproxRetrievalStrategy object>, es_params: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None)[source]¶
Elasticsearch vector store.
Example
from langchain_elasticsearch.vectorstores import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
store = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_url="http://localhost:9200"
)
Parameters
index_name (str) – Name of the Elasticsearch index to create.
es_url (Optional[str]) – URL of the Elasticsearch instance to connect to.
es_url (Optional[str]) – URL of the Elasticsearch instance to connect to.
cloud_id – Cloud ID of the Elasticsearch instance to connect to.
es_user (Optional[str]) – Username to use when connecting to Elasticsearch.
es_password (Optional[str]) – Password to use when connecting to Elasticsearch.
es_api_key (Optional[str]) – API key to use when connecting to Elasticsearch.
es_connection (Optional[Elasticsearch]) – Optional pre-existing Elasticsearch connection.
vector_query_field (str) – Optional. Name of the field to store
the embedding vectors in.
query_field (str) – Optional. Name of the field to store the texts in.
strategy (Union[BaseRetrievalStrategy, RetrievalStrategy]) – Optional. Retrieval strategy to use when searching the index.
Defaults to ApproxRetrievalStrategy. Can be one of
ExactRetrievalStrategy, ApproxRetrievalStrategy,
or SparseRetrievalStrategy.
distance_strategy (Optional[Literal[<DistanceStrategy.COSINE: 'COSINE'>, <DistanceStrategy.DOT_PRODUCT: 'DOT_PRODUCT'>, <DistanceStrategy.EUCLIDEAN_DISTANCE: 'EUCLIDEAN_DISTANCE'>, <DistanceStrategy.MAX_INNER_PRODUCT: 'MAX_INNER_PRODUCT'>]]) – Optional. Distance strategy to use when
searching the index.
Defaults to COSINE. Can be one of COSINE,
EUCLIDEAN_DISTANCE, MAX_INNER_PRODUCT or DOT_PRODUCT.
embedding (Optional[Embeddings]) – 
es_cloud_id (Optional[str]) – 
es_params (Optional[Dict[str, Any]]) – 
If you want to use a cloud hosted Elasticsearch instance, you can pass in the
cloud_id argument instead of the es_url argument.
Example
from langchain_elasticsearch.vectorstores import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
store = ElasticsearchStore(
from langchain_openai import OpenAIEmbeddings
store = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_cloud_id="<cloud_id>"
    es_user="elastic",
    es_password="<password>"
)
You can also connect to an existing Elasticsearch instance by passing in a
pre-existing Elasticsearch connection via the es_connection argument.
Example
from langchain_elasticsearch.vectorstores import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
from elasticsearch import Elasticsearch
es_connection = Elasticsearch("http://localhost:9200")
store = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_connection=es_connection
)
ElasticsearchStore by default uses the ApproxRetrievalStrategy, which uses the
HNSW algorithm to perform approximate nearest neighbor search. This is the
fastest and most memory efficient algorithm.
If you want to use the Brute force / Exact strategy for searching vectors, you
can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor.
Example
from langchain_elasticsearch.vectorstores import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
store = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_url="http://localhost:9200",
    strategy=ElasticsearchStore.ExactRetrievalStrategy()
)
Both strategies require that you know the similarity metric you want to use
when creating the index. The default is cosine similarity, but you can also
use dot product or euclidean distance.
Example
from langchain_elasticsearch.vectorstores import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy
store = ElasticsearchStore(
    "langchain-demo",
store = ElasticsearchStore(
    "langchain-demo",
    embedding=OpenAIEmbeddings(),
    es_url="http://localhost:9200",
    distance_strategy="DOT_PRODUCT"
)
Attributes
embeddings
Access the query embedding object if available.
Methods
ApproxRetrievalStrategy([query_model_id, ...])
Used to perform approximate nearest neighbor search using the HNSW algorithm.
BM25RetrievalStrategy([k1, b])
Used to apply BM25 without vector search.
ExactRetrievalStrategy()
Used to perform brute force / exact nearest neighbor search via script_score.
SparseVectorRetrievalStrategy([model_id])
Used to perform sparse vector search via text_expansion.
__init__(index_name, *[, embedding, ...])
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_embeddings(text_embeddings[, metadatas, ...])
Add the given texts and embeddings to the store.
add_texts(texts[, metadatas, ids, ...])
Run more texts through the embeddings and add to the store.
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.
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.
close()
connect_to_elasticsearch(*[, es_url, ...])
delete([ids, refresh_indices])
Delete documents from the Elasticsearch index.
from_documents(documents[, embedding, ...])
Construct ElasticsearchStore wrapper from documents.
from_texts(texts[, embedding, metadatas, ...])
Construct ElasticsearchStore 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, fetch_k, ...])
Return Elasticsearch documents most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)
Return Elasticsearch documents most similar to query, along with scores.
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 Elasticsearch documents most similar to query, along with scores.
static ApproxRetrievalStrategy(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True) → ApproxRetrievalStrategy[source]¶
Used to perform approximate nearest neighbor search
using the HNSW algorithm.
At build index time, this strategy will create a
dense vector field in the index and store the
embedding vectors in the index.
At query time, the text will either be embedded using the
provided embedding function or the query_model_id
will be used to embed the text using the model
deployed to Elasticsearch.
if query_model_id is used, do not provide an embedding function.
Parameters
query_model_id (Optional[str]) – Optional. ID of the model to use to
embed the query text within the stack. Requires
embedding model to be deployed to Elasticsearch.
hybrid (Optional[bool]) – Optional. If True, will perform a hybrid search
using both the knn query and a text query.
Defaults to False.
rrf (Optional[Union[dict, bool]]) – Optional. rrf is Reciprocal Rank Fusion.
When hybrid is True,
and rrf is True, then rrf: {}.
and rrf is False, then rrf is omitted.
and isinstance(rrf, dict) is True, then pass in the dict values.
rrf could be passed for adjusting ‘rank_constant’ and ‘window_size’.
Return type
ApproxRetrievalStrategy
Return type
ApproxRetrievalStrategy
static BM25RetrievalStrategy(k1: Optional[float] = None, b: Optional[float] = None) → BM25RetrievalStrategy[source]¶
Used to apply BM25 without vector search.
Parameters
k1 (Optional[float]) – Optional. This corresponds to the BM25 parameter, k1. Default is None,
which uses the default setting of Elasticsearch.
b (Optional[float]) – Optional. This corresponds to the BM25 parameter, b. Default is None,
which uses the default setting of Elasticsearch.
Return type
BM25RetrievalStrategy
static ExactRetrievalStrategy() → ExactRetrievalStrategy[source]¶
Used to perform brute force / exact
nearest neighbor search via script_score.
Return type
ExactRetrievalStrategy
static SparseVectorRetrievalStrategy(model_id: Optional[str] = None) → SparseRetrievalStrategy[source]¶
Used to perform sparse vector search via text_expansion.
Used for when you want to use ELSER model to perform document search.
At build index time, this strategy will create a pipeline that
will embed the text using the ELSER model and store the
resulting tokens in the index.
At query time, the text will be embedded using the ELSER
model and the resulting tokens will be used to
perform a text_expansion query.
Parameters
model_id (Optional[str]) – Optional. Default is “.elser_model_1”.
ID of the model to use to embed the query text
within the stack. Requires embedding model to be
deployed to Elasticsearch.
Return type
SparseRetrievalStrategy
deployed to Elasticsearch.
Return type
SparseRetrievalStrategy
__init__(index_name: str, *, embedding: ~typing.Optional[~langchain_core.embeddings.embeddings.Embeddings] = None, es_connection: ~typing.Optional[~elasticsearch.Elasticsearch] = None, es_url: ~typing.Optional[str] = None, es_cloud_id: ~typing.Optional[str] = None, es_user: ~typing.Optional[str] = None, es_api_key: ~typing.Optional[str] = None, es_password: ~typing.Optional[str] = None, vector_query_field: str = 'vector', query_field: str = 'text', distance_strategy: ~typing.Optional[~typing.Literal[<DistanceStrategy.COSINE: 'COSINE'>, <DistanceStrategy.DOT_PRODUCT: 'DOT_PRODUCT'>, <DistanceStrategy.EUCLIDEAN_DISTANCE: 'EUCLIDEAN_DISTANCE'>, <DistanceStrategy.MAX_INNER_PRODUCT: 'MAX_INNER_PRODUCT'>]] = None, strategy: ~typing.Union[~langchain_elasticsearch.vectorstores.BaseRetrievalStrategy, ~elasticsearch.helpers.vectorstore._sync.strategies.RetrievalStrategy] = <langchain_elasticsearch.vectorstores.ApproxRetrievalStrategy object>, es_params: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None)[source]¶
Parameters
index_name (str) – 
embedding (Optional[Embeddings]) – 
es_connection (Optional[Elasticsearch]) – 
es_url (Optional[str]) – 
es_cloud_id (Optional[str]) – 
es_user (Optional[str]) – 
es_api_key (Optional[str]) – 
es_password (Optional[str]) – 
vector_query_field (str) – 
query_field (str) –
vector_query_field (str) – 
query_field (str) – 
distance_strategy (Optional[Literal[<DistanceStrategy.COSINE: 'COSINE'>, <DistanceStrategy.DOT_PRODUCT: 'DOT_PRODUCT'>, <DistanceStrategy.EUCLIDEAN_DISTANCE: 'EUCLIDEAN_DISTANCE'>, <DistanceStrategy.MAX_INNER_PRODUCT: 'MAX_INNER_PRODUCT'>]]) – 
strategy (Union[BaseRetrievalStrategy, RetrievalStrategy]) – 
es_params (Optional[Dict[str, Any]]) – 
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]
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]
Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, create_index_if_not_exists: bool = True, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → List[str][source]¶
Add the given texts and embeddings to the store.
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – Iterable pairs of string and embedding to
add to the store.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
refresh_indices (bool) – Whether to refresh the Elasticsearch indices
after adding the texts.
create_index_if_not_exists (bool) – Whether to create the Elasticsearch
index if it doesn’t already exist.
*bulk_kwargs (Optional[Dict]) – Additional arguments to pass to Elasticsearch bulk.
- chunk_size: Optional. Number of texts to add to the
index at a time. Defaults to 500.
kwargs (Any) – 
Returns
List of ids from adding the texts into the store.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, create_index_if_not_exists: bool = True, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the store.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the store.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the store.
metadatas (Optional[List[Dict[Any, Any]]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of ids to associate with the texts.
refresh_indices (bool) – Whether to refresh the Elasticsearch indices
after adding the texts.
create_index_if_not_exists (bool) – Whether to create the Elasticsearch
index if it doesn’t already exist.
*bulk_kwargs (Optional[Dict]) – Additional arguments to pass to Elasticsearch bulk.
- chunk_size: Optional. Number of texts to add to the
index at a time. Defaults to 500.
kwargs (Any) – 
Returns
List of ids from adding the texts into the store.
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]
async classmethod afrom_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
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
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]
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].
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]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
close() → None[source]¶
Return type
None
static connect_to_elasticsearch(*, es_url: Optional[str] = None, cloud_id: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None, es_params: Optional[Dict[str, Any]] = None) → Elasticsearch[source]¶
Parameters
Parameters
es_url (Optional[str]) – 
cloud_id (Optional[str]) – 
api_key (Optional[str]) – 
username (Optional[str]) – 
password (Optional[str]) – 
es_params (Optional[Dict[str, Any]]) – 
Return type
Elasticsearch
delete(ids: Optional[List[str]] = None, refresh_indices: Optional[bool] = True, **kwargs: Any) → Optional[bool][source]¶
Delete documents from the Elasticsearch index.
Parameters
ids (Optional[List[str]]) – List of ids of documents to delete.
refresh_indices (Optional[bool]) – Whether to refresh the index
after deleting documents. Defaults to True.
kwargs (Any) – 
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → ElasticsearchStore[source]¶
Construct ElasticsearchStore wrapper from documents.
Example
from langchain_elasticsearch.vectorstores import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
db = ElasticsearchStore.from_documents(
    texts,
    embeddings,
    index_name="langchain-demo",
    es_url="http://localhost:9200"
)
Parameters
texts – List of texts to add to the Elasticsearch index.
embedding (Optional[Embeddings]) – Embedding function to use to embed the texts.
Do not provide if using a strategy
that doesn’t require inference.
metadatas – Optional list of metadatas associated with the texts.
index_name – Name of the Elasticsearch index to create.
es_url – URL of the Elasticsearch instance to connect to.
cloud_id – Cloud ID of the Elasticsearch instance to connect to.
es_user – Username to use when connecting to Elasticsearch.