`langchain.vectorstores.opensearch_vector_search`.OpenSearchVectorSearch¶

class langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch(opensearch_url: str, index_name: str, embedding_function: Embeddings, **kwargs: Any)[source]¶

Amazon OpenSearch Vector Engine vector store.

Example

from langchain.vectorstores import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
    "http://localhost:9200",
    "embeddings",
    embedding_function
)

Initialize with necessary components.

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(opensearch_url, index_name, ...)	Initialize with necessary components.
`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 vectorstore.
`add_texts`(texts[, metadatas, ids, bulk_size])	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], asynchronously.
`asimilarity_search_with_score`(args, *kwargs)	Run similarity search with distance asynchronously.
`delete`([ids])	Delete by vector ID or other criteria.
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_embeddings`(embeddings, texts, embedding)	Construct OpenSearchVectorSearch wrapper from pre-vectorized embeddings.
`from_texts`(texts, embedding[, metadatas, ...])	Construct OpenSearchVectorSearch wrapper from raw 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.
`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_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 docs and it's scores most similar to query.

__init__(opensearch_url: str, index_name: str, embedding_function: Embeddings, **kwargs: Any)[source]¶: Initialize with necessary components.

async aadd_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]

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_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, bulk_size: int = 500, **kwargs: Any) → List[str][source]¶

Add the given texts and embeddings to the vectorstore.

Parameters

text_embeddings – Iterable pairs of string and embedding to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of ids to associate with the texts.
bulk_size – Bulk API request count; Default: 500

Returns

List of ids from adding the texts into the vectorstore.

Optional Args:

vector_field: Document field embeddings are stored in. Defaults to “vector_field”.

text_field: Document field the text of the document is stored in. Defaults to “text”.

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, bulk_size: int = 500, **kwargs: Any) → List[str][source]¶

Run more texts through the embeddings and add to the vectorstore.

Parameters

texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of ids to associate with the texts.
bulk_size – Bulk API request count; Default: 500

Returns

List of ids from adding the texts into the vectorstore.

Optional Args:

vector_field: Document field embeddings are stored in. Defaults to “vector_field”.

text_field: Document field the text of the document is stored in. Defaults to “text”.

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.
**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

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.

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.

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: Embeddings, **kwargs: Any) → VST¶: Return VectorStore initialized from documents and embeddings.

classmethod from_embeddings(embeddings: List[List[float]], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, bulk_size: int = 500, ids: Optional[List[str]] = None, **kwargs: Any) → OpenSearchVectorSearch[source]¶

Construct OpenSearchVectorSearch wrapper from pre-vectorized embeddings.

Example

from langchain.vectorstores import OpenSearchVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedder = OpenAIEmbeddings()
embeddings = embedder.embed_documents(["foo", "bar"])
opensearch_vector_search = OpenSearchVectorSearch.from_embeddings(
    embeddings,
    texts,
    embedder,
    opensearch_url="http://localhost:9200"
)

OpenSearch by default supports Approximate Search powered by nmslib, faiss and lucene engines recommended for large datasets. Also supports brute force search through Script Scoring and Painless Scripting.

Optional Args:

vector_field: Document field embeddings are stored in. Defaults to “vector_field”.

text_field: Document field the text of the document is stored in. Defaults to “text”.

Optional Keyword Args for Approximate Search:

engine: “nmslib”, “faiss”, “lucene”; default: “nmslib”

space_type: “l2”, “l1”, “cosinesimil”, “linf”, “innerproduct”; default: “l2”

ef_search: Size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches; default: 512

ef_construction: Size of the dynamic list used during k-NN graph creation. Higher values lead to more accurate graph but slower indexing speed; default: 512

m: Number of bidirectional links created for each new element. Large impact on memory consumption. Between 2 and 100; default: 16

Keyword Args for Script Scoring or Painless Scripting:

is_appx_search: False

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, bulk_size: int = 500, ids: Optional[List[str]] = None, **kwargs: Any) → OpenSearchVectorSearch[source]¶

Construct OpenSearchVectorSearch wrapper from raw texts.

Example

from langchain.vectorstores import OpenSearchVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
opensearch_vector_search = OpenSearchVectorSearch.from_texts(
    texts,
    embeddings,
    opensearch_url="http://localhost:9200"
)

Optional Args:

vector_field: Document field embeddings are stored in. Defaults to “vector_field”.

text_field: Document field the text of the document is stored in. Defaults to “text”.

Optional Keyword Args for Approximate Search:

engine: “nmslib”, “faiss”, “lucene”; default: “nmslib”

space_type: “l2”, “l1”, “cosinesimil”, “linf”, “innerproduct”; default: “l2”

ef_search: Size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches; default: 512

ef_construction: Size of the dynamic list used during k-NN graph creation. Higher values lead to more accurate graph but slower indexing speed; default: 512

m: Number of bidirectional links created for each new element. Large impact on memory consumption. Between 2 and 100; default: 16

Keyword Args for Script Scoring or Painless Scripting:

is_appx_search: False

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → list[langchain_core.documents.base.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 – 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. Defaults to 20.
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: str, k: int = 4, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query.

By default, supports Approximate Search. Also supports Script Scoring and Painless Scripting.

Parameters

query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query.

Optional Args:

vector_field: Document field embeddings are stored in. Defaults to “vector_field”.

text_field: Document field the text of the document is stored in. Defaults to “text”.

metadata_field: Document field that metadata is stored in. Defaults to “metadata”. Can be set to a special value “*” to include the entire document.

Optional Args for Approximate Search:

search_type: “approximate_search”; default: “approximate_search”

boolean_filter: A Boolean filter is a post filter consists of a Boolean query that contains a k-NN query and a filter.

subquery_clause: Query clause on the knn vector field; default: “must”

lucene_filter: the Lucene algorithm decides whether to perform an exact k-NN search with pre-filtering or an approximate search with modified post-filtering. (deprecated, use efficient_filter)

efficient_filter: the Lucene Engine or Faiss Engine decides whether to perform an exact k-NN search with pre-filtering or an approximate search with modified post-filtering.

Optional Args for Script Scoring Search:

search_type: “script_scoring”; default: “approximate_search”

space_type: “l2”, “l1”, “linf”, “cosinesimil”, “innerproduct”, “hammingbit”; default: “l2”

pre_filter: script_score query to pre-filter documents before identifying nearest neighbors; default: {“match_all”: {}}

Optional Args for Painless Scripting Search:

search_type: “painless_scripting”; default: “approximate_search”

space_type: “l2Squared”, “l1Norm”, “cosineSimilarity”; default: “l2Squared”

pre_filter: script_score query to pre-filter documents before identifying nearest neighbors; default: {“match_all”: {}}

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: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Return docs and it’s scores most similar to query.

By default, supports Approximate Search. Also supports Script Scoring and Painless Scripting.

Parameters

query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.

Returns

List of Documents along with its scores most similar to the query.

Optional Args:: same as similarity_search

Examples using OpenSearchVectorSearch¶

OpenSearch

langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch¶

Examples using OpenSearchVectorSearch¶

`langchain.vectorstores.opensearch_vector_search`.OpenSearchVectorSearch¶