Lantern#

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

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)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas])	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`(documents, **kwargs)	Add or update documents in 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])	Async delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Async return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`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.
`get_by_ids`(ids, /)	Get documents by their IDs.
`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 a 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)

Parameters:

connection_string (str) –
embedding_function (Embeddings) –
distance_strategy (DistanceStrategy) –
collection_name (str) –
collection_metadata (Optional[dict]) –
pre_delete_collection (bool) –
logger (Optional[logging.Logger]) –
relevance_score_fn (Optional[Callable[[float], float]]) –

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

Parameters:

connection_string (str) –
embedding_function (Embeddings) –
distance_strategy (DistanceStrategy) –
collection_name (str) –
collection_metadata (dict | None) –
pre_delete_collection (bool) –
logger (Logger | None) –
relevance_score_fn (Callable[[float], float] | None) –

Return type:

None

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

List[str]

async aadd_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]#

Add or update documents in the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

List[str]

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

Parameters:

texts (List[str]) –
embeddings (List[List[float]]) –
metadatas (List[dict]) –
ids (List[str]) –
kwargs (Any) –

Return type:

None

add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: List[str] | None = None, **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 (List[dict] | None) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters. One of the kwargs should be ids which is a list of ids associated with the texts.
ids (List[str] | None) –
**kwargs –

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

List[str]

async adelete(ids: List[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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#

Async 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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

New in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]#

Async 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. Default is 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.
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]#

Async 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. Default is 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.
**kwargs (Any) – Arguments to pass to the search method.

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:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: 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]#

Async return docs most similar to query using a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]#

Async return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]#

Async 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) – Arguments to pass to the search method.

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]]#

Async 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]]#

Async run similarity search with distance.

Parameters:

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

connect() → Connection[source]#

Return type:: Connection

classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str[source]#

Return connection string from database parameters.

Parameters:

driver (str) –
host (str) –
port (int) –
database (str) –
user (str) –
password (str) –

Return type:

str

create_collection() → None[source]#

Return type:: None

create_hnsw_extension() → None[source]#

Return type:: None

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

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

Parameters:

dims (int) –
m (int) –
ef_construction (int) –
ef_search (int) –
_kwargs (Any) –

Return type:

None

create_tables_if_not_exists() → None[source]#

Return type:: None

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

Delete vectors by ids or uuids.

Parameters:

ids (List[str] | None) – List of ids to delete.
kwargs (Any) –

Return type:

None

delete_collection() → None[source]#

Return type:: None

drop_index() → None[source]#

Return type:: None

drop_table() → None[source]#

Return type:: None

drop_tables() → None[source]#

Return type:: None

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

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.

Parameters:

documents (List[Document]) –
embedding (Embeddings) –
collection_name (str) –
distance_strategy (DistanceStrategy) –
ids (List[str] | None) –
pre_delete_collection (bool) –
kwargs (Any) –

Return type:

Lantern

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

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.

Parameters:

text_embeddings (List[Tuple[str, List[float]]]) –
embedding (Embeddings) –
metadatas (List[dict] | None) –
collection_name (str) –
ids (List[str] | None) –
pre_delete_collection (bool) –
distance_strategy (DistanceStrategy) –
kwargs (Any) –

Return type:

Lantern

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

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.

Parameters:

embedding (Embeddings) –
collection_name (str) –
pre_delete_collection (bool) –
distance_strategy (DistanceStrategy) –
kwargs (Any) –

Return type:

Lantern

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

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.

Parameters:

texts (List[str]) –
embedding (Embeddings) –
metadatas (List[dict] | None) –
collection_name (str) –
distance_strategy (DistanceStrategy) –
ids (List[str] | None) –
pre_delete_collection (bool) –
kwargs (Any) –

Return type:

Lantern

get_by_ids(ids: Sequence[str], /) → List[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

New in version 0.2.11.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, str] | None = None, **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. 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.
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, filter: Dict[str, str] | None = None, **kwargs: Any) → List[Document][source]#

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.
kwargs (Any) –

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: dict | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

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.
kwargs (Any) –

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: Dict[str, str] | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

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 (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. 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.
kwargs (Any) –

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 a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

similarity_search(query: str, k: int = 4, filter: dict | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
filter (dict | None) –
**kwargs –

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: dict | None = None, **kwargs: Any) → List[Document][source]#

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) – Arguments to pass to the search method.
filter (dict | None) –
**kwargs –

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, filter: dict | None = None) → List[Tuple[Document, float]][source]#

Run similarity search with distance.

Parameters:

*args – Arguments to pass to the search method.
**kwargs – Arguments to pass to the search method.
query (str) –
k (int) –
filter (dict | None) –

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

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

Parameters:

embedding (List[float]) –
k (int) –
filter (dict | None) –

Return type:

List[Tuple[Document, float]]

Examples using Lantern

Lantern