Tony20100/langchain_full huggingface dataset:
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, timeout, ...])
Add text to the collection.
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Creates an instance of the VST class from the given texts.
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.
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, param, expr, ...])
Perform a similarity search on the query string.
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, ...])
Performs a search on the query string and returns results with scores.
similarity_search_with_score_by_vector(embedding)
Performs a search on the query string and returns results with scores.
__init__(embedding_function: Embeddings, table_name: str = 'test', database_name: str = 'default', number_of_shards: int = 1, number_of_replicas: int = 1, connection_args: Optional[Dict[str, Any]] = None, index_params: Optional[dict] = None, drop_old: Optional[bool] = False)[source]¶
Parameters
embedding_function (Embeddings) – 
table_name (str) – 
database_name (str) – 
number_of_shards (int) – 
number_of_replicas (int) – 
connection_args (Optional[Dict[str, Any]]) – 
index_params (Optional[dict]) – 
drop_old (Optional[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
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, timeout: Optional[int] = None, batch_size: int = 1000, **kwargs: Any) → List[str][source]¶
Add text to the collection.
Parameters
texts (Iterable[str]) – An iterable that contains the text to be added.
metadatas (Optional[List[dict]]) – An optional list of dictionaries,
text. (each dictionary contains the metadata associated with a) – 
timeout (Optional[int]) – Optional timeout, in seconds.
batch_size (int) – The number of texts inserted in each batch, defaults to 1000.
**kwargs (Any) – Other optional parameters.
Returns
A list of strings, containing the unique identifiers of the inserted texts.
Return type
List[str]
Note
Return type
List[str]
Note
If the collection has not yet been created,
this method will create a new collection.
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.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
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.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, table_name: str = 'test', database_name: str = 'default', connection_args: Dict[str, Any] = {'host': 'localhost', 'password': 'admin', 'port': '7788', 'username': 'admin'}, index_params: Optional[Dict[Any, Any]] = None, search_params: Optional[Dict[str, Any]] = None, drop_old: bool = False, **kwargs: Any) → Hippo[source]¶
Creates an instance of the VST class from the given texts.
Parameters
texts (List[str]) – List of texts to be added.
embedding (Embeddings) – Embedding model for the texts.
metadatas (List[dict], optional) – 
None. (List of metadata dictionaries for each text.Defaults to) – 
table_name (str) – Name of the table. Defaults to “test”.
database_name (str) – Name of the database. Defaults to “default”.
connection_args (dict[str, Any]) – Connection parameters.
DEFAULT_HIPPO_CONNECTION. (Defaults to) – 
index_params (dict) – Indexing parameters. Defaults to None.
search_params (dict) – Search parameters. Defaults to an empty dictionary.
drop_old (bool) – Whether to drop the old collection. Defaults to False.
kwargs (Any) – Other arguments.
Returns
An instance of the VST class.
Return type
Hippo
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search on the query string.
Parameters
query (str) – The text to search for.
k (int, optional) – The number of results to return. Default is 4.
param (dict, optional) – Specifies the search parameters for the index.
None. (Defaults to) – 
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – Time to wait before a timeout error.
None. – 
kwargs (Any) – Keyword arguments for Collection.search().
Returns
The document results of the search.
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: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Performs a search on the query string and returns results with scores.
Parameters
query (str) – The text being searched.
k (int, optional) – The number of results to return.
4. (Default is) – 
param (dict) – Specifies the search parameters for the index.
None. (Default is) – 
expr (str, optional) – Filtering expression. Default is None.
timeout (int, optional) – The waiting time before a timeout error.
None. – 
kwargs (Any) – Keyword arguments for Collection.search().
Return type
List[float], List[Tuple[Document, any, any]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Performs a search on the query string and returns results with scores.
Parameters
embedding (List[float]) – The embedding vector being searched.
k (int, optional) – The number of results to return.
4. (Default is) – 
param (dict) – Specifies the search parameters for the index.
None. (Default is) – 
expr (str, optional) – Filtering expression. Default is None.
timeout (int, optional) – The waiting time before a timeout error.
None. – 
kwargs (Any) – Keyword arguments for Collection.search().
Returns
Resulting documents and scores.
Return type
List[Tuple[Document, float]]
Examples using Hippo¶
Hippo
langchain_community.vectorstores.elastic_vector_search.ElasticVectorSearch¶
class langchain_community.vectorstores.elastic_vector_search.ElasticVectorSearch(elasticsearch_url: str, index_name: str, embedding: Embeddings, *, ssl_verify: Optional[Dict[str, Any]] = None)[source]¶
[Deprecated] ElasticVectorSearch uses the brute force method of searching on vectors.
Recommended to use ElasticsearchStore instead, which gives you the option
to uses the approx  HNSW algorithm which performs better on large datasets.
ElasticsearchStore also supports metadata filtering, customising the
query retriever and much more!
You can read more on ElasticsearchStore:
https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
To connect to an Elasticsearch instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Example
from langchain_community.vectorstores import ElasticVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch(
    elasticsearch_url="http://localhost:9200",
    index_name="test_index",
    embedding=embedding
)
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the “Deployments” page.
To obtain your Elastic Cloud password for the default “elastic” user:
To obtain your Elastic Cloud password for the default “elastic” user:
Log in to the Elastic Cloud console at https://cloud.elastic.co
Go to “Security” > “Users”
Locate the “elastic” user and click “Edit”
Click “Reset password”
Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example
from langchain_community.vectorstores import ElasticVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
    elasticsearch_url=elasticsearch_url,
    index_name="test_index",
    embedding=embedding
)
Parameters
elasticsearch_url (str) – The URL for the Elasticsearch instance.
index_name (str) – The name of the Elasticsearch index for the embeddings.
embedding (Embeddings) – An object that provides the ability to embed text.
It should be an instance of a class that subclasses the Embeddings
abstract base class, such as OpenAIEmbeddings()
ssl_verify (Optional[Dict[str, Any]]) – 
Raises
ValueError – If the elasticsearch python package is not installed.
Notes
Deprecated since version 0.0.27: Use Use ElasticsearchStore class in langchain-elasticsearch package instead.
Initialize with necessary components.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(elasticsearch_url, index_name, ...)
Initialize with necessary components.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
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, ids, ...])
Run more texts through the embeddings and add to the vectorstore.
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
client_search(client, index_name, ...)
create_index(client, index_name, mapping)
delete([ids])
Delete by vector IDs.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct ElasticVectorSearch wrapper from raw documents.
get_user_agent()
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, filter])
Return docs most similar to query.
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, filter])
Return docs most similar to query.
__init__(elasticsearch_url: str, index_name: str, embedding: Embeddings, *, ssl_verify: Optional[Dict[str, Any]] = None)[source]¶
Initialize with necessary components.
Parameters
elasticsearch_url (str) – 
index_name (str) – 
embedding (Embeddings) – 
ssl_verify (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]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, **kwargs: Any) → List[str][source]¶
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.
ids (Optional[List[str]]) – Optional list of unique IDs.
refresh_indices (bool) – bool to refresh ElasticSearch indices
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
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,
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.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
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.
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.
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]¶
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]
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
kwargs (Any) – 
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.
Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”,