langchain_community.vectorstores.lantern.Lantern

class langchain_community.vectorstores.lantern.Lantern(connection_string: str, embedding_function: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)

Postgres with the lantern extension as a vector store.

lantern uses sequential scan by default. but you can create a HNSW index using the create_hnsw_index method. - connection_string is a postgres connection string. - embedding_function any embedding function implementing

langchain.embeddings.base.Embeddings interface.

• collection_name is the name of the collection to use. (default: langchain)

  • NOTE: This is the name of the table in which embedding data will be stored

    The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.

• distance_strategy is the distance strategy to use. (default: EUCLIDEAN)

  • EUCLIDEAN is the euclidean distance.

  • COSINE is the cosine distance.

  • HAMMING is the hamming distance.

• pre_delete_collection if True, will delete the collection if it exists.

  (default: False) - Useful for testing.

Attributes

distance_function
distance_strategy
embeddings	Access the query embedding object if available.

Methods

__init__(connection_string, embedding_function)
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(texts, embeddings, metadatas, ...)
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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)	Run similarity search with distance asynchronously.
connect()
connection_string_from_db_params(driver, ...)	Return connection string from database parameters.
create_collection()
create_hnsw_extension()
create_hnsw_index([dims, m, ...])	Create HNSW index on collection.
create_tables_if_not_exists()
delete([ids])	Delete vectors by ids or uuids.
delete_collection()
drop_index()
drop_table()
drop_tables()
from_documents(documents, embedding[, ...])	Initialize a vector store with a set of documents.
from_embeddings(text_embeddings, embedding)	Construct Lantern wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ...])	Get instance of an existing Lantern store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])	Initialize Lantern vectorstore from list of 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
max_marginal_relevance_search_with_score(query)	Return docs selected using the maximal marginal relevance with score.
max_marginal_relevance_search_with_score_by_vector(...)	Return docs selected using the maximal marginal relevance with score
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])	Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)

__init__(connection_string: str, embedding_function: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None

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(texts: List[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None

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

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.

• kwargs – vectorstore specific parameters

Returns

List of ids from adding the texts into the vectorstore.

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.

connect() → Connection

classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str

Return connection string from database parameters.

create_collection() → None

create_hnsw_extension() → None

create_hnsw_index(dims: int = 1536, m: int = 16, ef_construction: int = 64, ef_search: int = 64, **_kwargs: Any) → None

Create HNSW index on collection.

Optional Keyword Args for HNSW Index:

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

ef: Size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches; default: 64

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: 64

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

dims: Dimensions of the vectors in collection. default: 1536

create_tables_if_not_exists() → None

delete(ids: Optional[List[str]] = None, **kwargs: Any) → None

Delete vectors by ids or uuids.

Parameters

ids – List of ids to delete.

delete_collection() → None

drop_index() → None

drop_table() → None

drop_tables() → None

classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Lantern

Initialize a vector store with a set of documents.

Postgres connection string is required “Either pass it as connection_string parameter or set the LANTERN_CONNECTION_STRING environment variable.

• connection_string is a postgres connection string.

• documents is list of Document to initialize the vector store with

• embedding is Embeddings that will be used for

  embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.

• collection_name is the name of the collection to use. (default: langchain)

  • NOTE: This is the name of the table in which embedding data will be stored

    The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.

• distance_strategy is the distance strategy to use. (default: EUCLIDEAN)

  • EUCLIDEAN is the euclidean distance.

  • COSINE is the cosine distance.

  • HAMMING is the hamming distance.

• ids row ids to insert into collection.

• pre_delete_collection if True, will delete the collection if it exists.

  (default: False) - Useful for testing.

classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, **kwargs: Any) → Lantern

Construct Lantern wrapper from raw documents and pre- generated embeddings.

Postgres connection string is required “Either pass it as connection_string parameter or set the LANTERN_CONNECTION_STRING environment variable.

Order of elements for lists ids, text_embeddings, metadatas should match, so each row will be associated with correct values.

• connection_string is fully populated connection string for postgres database

• text_embeddings is array with tuples (text, embedding)

  to insert into collection.

• embedding is Embeddings that will be used for

  embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.

• metadatas row metadata to insert into collection.

• collection_name is the name of the collection to use. (default: langchain)

  • NOTE: This is the name of the table in which embedding data will be stored

    The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.

• ids row ids to insert into collection.

• pre_delete_collection if True, will delete the collection if it exists.

  (default: False) - Useful for testing.

• distance_strategy is the distance strategy to use. (default: EUCLIDEAN)

  • EUCLIDEAN is the euclidean distance.

  • COSINE is the cosine distance.

  • HAMMING is the hamming distance.

classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', pre_delete_collection: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, **kwargs: Any) → Lantern

Get instance of an existing Lantern store.This method will return the instance of the store without inserting any new embeddings

Postgres connection string is required “Either pass it as connection_string parameter or set the LANTERN_CONNECTION_STRING environment variable.

• connection_string is a postgres connection string.

• embedding is Embeddings that will be used for

  embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.

• collection_name is the name of the collection to use. (default: langchain)

  • NOTE: This is the name of the table in which embedding data will be stored

    The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.

• ids row ids to insert into collection.

• pre_delete_collection if True, will delete the collection if it exists.

  (default: False) - Useful for testing.

• distance_strategy is the distance strategy to use. (default: EUCLIDEAN)

  • EUCLIDEAN is the euclidean distance.

  • COSINE is the cosine distance.

  • HAMMING is the hamming distance.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Lantern

Initialize Lantern vectorstore from list of texts. The embeddings will be generated using embedding class provided.

Order of elements for lists ids, texts, metadatas should match, so each row will be associated with correct values.

Postgres connection string is required “Either pass it as connection_string parameter or set the LANTERN_CONNECTION_STRING environment variable.

• connection_string is fully populated connection string for postgres database

• texts texts to insert into collection.

• embedding is Embeddings that will be used for

  embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.

• metadatas row metadata to insert into collection.

• collection_name is the name of the collection to use. (default: langchain)

  • NOTE: This is the name of the table in which embedding data will be stored

    The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.

• distance_strategy is the distance strategy to use. (default: EUCLIDEAN)

  • EUCLIDEAN is the euclidean distance.

  • COSINE is the cosine distance.

  • HAMMING is the hamming distance.

• ids row ids to insert into collection.

• pre_delete_collection if True, will delete the collection if it exists.

  (default: False) - Useful for testing.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **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. Defaults to 20.

• 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.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

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, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance

to embedding vector.

Maximal marginal relevance optimizes for similarity to query AND diversity

among selected documents.

Parameters

• embedding (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. Defaults to 20.

• 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.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs selected using the maximal marginal relevance with score.

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. Defaults to 20.

• 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.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

List of Documents selected by maximal marginal

relevance to the query and score for each.

Return type

List[Tuple[Document, float]]

max_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs selected using the maximal marginal relevance with score

to embedding vector.

Maximal marginal relevance optimizes for similarity to query AND diversity

among selected documents.

Parameters

• embedding – 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. Defaults to 20.

• 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.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

List of Documents selected by maximal marginal

relevance to the query and score for each.

Return type

List[Tuple[Document, float]]

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[dict] = None, **kwargs: Any) → List[Document]

Return docs most similar to query.

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **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, filter: Optional[dict] = None) → List[Tuple[Document, float]]

Run similarity search with distance.

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]]