TimescaleVector#

class langchain_community.vectorstores.timescalevector.TimescaleVector(service_url: str, embedding: Embeddings, collection_name: str = 'langchain_store', num_dimensions: int = 1536, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, logger: Logger | None = None, relevance_score_fn: Callable[[float], float] | None = None, time_partition_interval: timedelta | None = None, **kwargs: Any)[source]#

Timescale Postgres vector store

To use, you should have the timescale_vector python package installed.

Parameters:

service_url (str) – Service url on timescale cloud.
embedding (Embeddings) – Any embedding function implementing langchain.embeddings.base.Embeddings interface.
collection_name (str) – The name of the collection to use. (default: langchain_store) This will become the table name used for the collection.
distance_strategy (DistanceStrategy) – The distance strategy to use. (default: COSINE)
pre_delete_collection (bool) – If True, will delete the collection if it exists. (default: False). Useful for testing.
num_dimensions (int)
logger (Optional[logging.Logger])
relevance_score_fn (Optional[Callable[[float], float]])
time_partition_interval (Optional[timedelta])
kwargs (Any)

Example

from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings.openai import OpenAIEmbeddings

SERVICE_URL = "postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require"
COLLECTION_NAME = "state_of_the_union_test"
embeddings = OpenAIEmbeddings()
vectorestore = TimescaleVector.from_documents(
    embedding=embeddings,
    documents=docs,
    collection_name=COLLECTION_NAME,
    service_url=SERVICE_URL,
)

Attributes

`DEFAULT_INDEX_TYPE`
`embeddings`	Access the query embedding object if available.

Methods

`__init__`(service_url, embedding[, ...])
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_embeddings`(texts, embeddings[, ...])	Add embeddings to the vectorstore.
`aadd_texts`(texts[, metadatas, ids])	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[, ...])	Add embeddings to the vectorstore.
`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_embeddings`(text_embeddings, embedding)	Construct TimescaleVector wrapper from raw documents and pre- generated embeddings.
`afrom_texts`(texts, embedding[, metadatas, ...])	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, filter, ...])	Run similarity search with TimescaleVector with distance.
`asimilarity_search_by_vector`(embedding[, k, ...])	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`(query[, k, ...])	Return docs most similar to query.
`asimilarity_search_with_score_by_vector`(...)
`create_index`([index_type])
`date_to_range_filter`(**kwargs)
`delete`([ids])	Delete by vector ID or other criteria.
`delete_by_metadata`(filter, **kwargs)	Delete by vector ID or other criteria.
`drop_index`()
`drop_tables`()
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_embeddings`(text_embeddings, embedding)	Construct TimescaleVector wrapper from raw documents and pre- generated embeddings.
`from_existing_index`(embedding[, ...])	Get instance of an existing TimescaleVector store.This method will return the instance of the store without inserting any new embeddings
`from_texts`(texts, embedding[, metadatas, ...])	Return VectorStore initialized from texts and embeddings.
`get_by_ids`(ids, /)	Get documents by their IDs.
`get_service_url`(kwargs)
`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 a specified search type.
`service_url_from_db_params`(host, port, ...)	Return connection string from database parameters.
`similarity_search`(query[, k, filter, predicates])	Run similarity search with TimescaleVector with distance.
`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 most similar to query.
`similarity_search_with_score_by_vector`(embedding)

__init__(service_url: str, embedding: Embeddings, collection_name: str = 'langchain_store', num_dimensions: int = 1536, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, logger: Logger | None = None, relevance_score_fn: Callable[[float], float] | None = None, time_partition_interval: timedelta | None = None, **kwargs: Any) → None[source]#

Parameters:

service_url (str)
embedding (Embeddings)
collection_name (str)
num_dimensions (int)
distance_strategy (DistanceStrategy)
pre_delete_collection (bool)
logger (Logger | None)
relevance_score_fn (Callable[[float], float] | None)
time_partition_interval (timedelta | None)
kwargs (Any)

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

Add embeddings to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
embeddings (List[List[float]]) – List of list of embedding vectors.
metadatas (List[dict] | None) – List of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
ids (List[str] | None)

Return type:

List[str]

async aadd_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
ids (List[str] | None)

Returns:

List of ids from adding the texts into the vectorstore.

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: Iterable[str], embeddings: List[List[float]], metadatas: List[dict] | None = None, ids: List[str] | None = None, **kwargs: Any) → List[str][source]#

Add embeddings to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
embeddings (List[List[float]]) – List of list of embedding vectors.
metadatas (List[dict] | None) – List of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
ids (List[str] | None)

Return type:

List[str]

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
ids (List[str] | None)

Returns:

List of ids from adding the texts into the vectorstore.

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

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

Return VectorStore initialized from documents and embeddings. Postgres connection string is required “Either pass it as a parameter or set the TIMESCALE_SERVICE_URL environment variable.

Example

from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)

Parameters:

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

Return type:

TimescaleVector

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

Return VectorStore initialized from texts and embeddings. Postgres connection string is required “Either pass it as a parameter or set the TIMESCALE_SERVICE_URL environment variable.

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:

TimescaleVector

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]

Added 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, filter: dict | list | None = None, predicates: Predicates | None = None, **kwargs: Any) → List[Document][source]#

Run similarity search with TimescaleVector with distance.

Parameters:

query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
predicates (Optional[Predicates])
kwargs (Any)

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

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

Return docs most similar to embedding vector.

Parameters:

embedding (Optional[List[float]]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
predicates (Optional[Predicates])
kwargs (Any)

Returns:

List of Documents most similar to the query vector.

Return type:

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → list[tuple[Document, float]]#

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

Return docs most similar to query.

Parameters:

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
predicates (Optional[Predicates])
kwargs (Any)

Returns:

List of Documents most similar to the query and score for each

Return type:

List[Tuple[Document, float]]

async asimilarity_search_with_score_by_vector(embedding: List[float] | None, k: int = 4, filter: dict | list | None = None, predicates: Predicates | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Parameters:

embedding (Optional[List[float]])
k (int)
filter (Optional[Union[dict, list]])
predicates (Optional[Predicates])
kwargs (Any)

Return type:

List[Tuple[Document, float]]

create_index(index_type: IndexType | str = IndexType.TIMESCALE_VECTOR, **kwargs: Any) → None[source]#

Parameters:

index_type (Union[IndexType, str])
kwargs (Any)

Return type:

None

date_to_range_filter(**kwargs: Any) → Any[source]#

Parameters:: kwargs (Any)
Return type:: Any

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

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – 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]

delete_by_metadata(filter: Dict[str, str] | List[Dict[str, str]], **kwargs: Any) → bool | None[source]#

Delete by vector ID or other criteria.

Parameters:

ids – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
filter (Dict[str, str] | List[Dict[str, str]])
**kwargs

Returns:

True if deletion is successful, False otherwise, None if not implemented.

Return type:

Optional[bool]

drop_index() → None[source]#

Return type:: None

drop_tables() → None[source]#

Return type:: None

classmethod from_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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

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

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

Return VectorStore initialized from documents and embeddings. Postgres connection string is required “Either pass it as a parameter or set the TIMESCALE_SERVICE_URL environment variable.

Example

from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)

Parameters:

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

Return type:

TimescaleVector

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

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

Parameters:

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

Return type:

TimescaleVector

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

Return VectorStore initialized from texts and embeddings. Postgres connection string is required “Either pass it as a parameter or set the TIMESCALE_SERVICE_URL environment variable.

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:

TimescaleVector

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]

Added in version 0.2.11.

classmethod get_service_url(kwargs: Dict[str, Any]) → str[source]#

Parameters:: kwargs (Dict[str, Any])
Return type:: str

max_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. 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]

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 (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]

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]

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

Return connection string from database parameters.

Parameters:

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

Return type:

str

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

Run similarity search with TimescaleVector with distance.

Parameters:

query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
predicates (Optional[Predicates])
kwargs (Any)

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

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

Return docs most similar to embedding vector.

Parameters:

embedding (Optional[List[float]]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
predicates (Optional[Predicates])
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, filter: dict | list | None = None, predicates: Predicates | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Return docs most similar to query.

Parameters:

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
predicates (Optional[Predicates])
kwargs (Any)

Returns:

List of Documents most similar to the query and score for each

Return type:

List[Tuple[Document, float]]

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

Parameters:

embedding (Optional[List[float]])
k (int)
filter (Optional[Union[dict, list]])
predicates (Optional[Predicates])
kwargs (Any)

Return type:

List[Tuple[Document, float]]

Examples using TimescaleVector