langchain_community.vectorstores.timescalevector.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: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, time_partition_interval: Optional[timedelta] = None, **kwargs: Any)

VectorStore implementation using the timescale vector client to store vectors in Postgres.

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

Parameters

• service_url – Service url on timescale cloud.

• embedding – Any embedding function implementing langchain.embeddings.base.Embeddings interface.

• collection_name – The name of the collection to use. (default: langchain_store) This will become the table name used for the collection.

• distance_strategy – The distance strategy to use. (default: COSINE)

• pre_delete_collection – If True, will delete the collection if it exists. (default: False). Useful for testing.

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)	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)	Run more documents through the embeddings and add to 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])	Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)	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.
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, 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)	Return docs and relevance scores in the range [0, 1], asynchronously.
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_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 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: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, time_partition_interval: Optional[timedelta] = None, **kwargs: Any) → 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_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

Add embeddings to the vectorstore.

Parameters

• texts – Iterable of strings to add to the vectorstore.

• embeddings – List of list of embedding vectors.

• metadatas – List of metadatas associated with the texts.

• kwargs – vectorstore specific parameters

async aadd_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.

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

Add embeddings to the vectorstore.

Parameters

• texts – Iterable of strings to add to the vectorstore.

• embeddings – List of list of embedding vectors.

• metadatas – List of metadatas associated with the texts.

• kwargs – vectorstore specific parameters

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

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)

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

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.

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, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document]

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.

Returns

List of Documents most similar to the query.

async asimilarity_search_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = 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.

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

Returns

List of Documents most similar to the query 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(query: str, k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

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

Returns

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

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

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

date_to_range_filter(**kwargs: Any) → Any

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]

delete_by_metadata(filter: Union[Dict[str, str], List[Dict[str, str]]], **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]

drop_index() → None

drop_tables() → None

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

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

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)

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

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

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

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.

classmethod get_service_url(kwargs: Dict[str, Any]) → 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 – 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.

• 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.

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

Return connection string from database parameters.

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

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.

Returns

List of Documents most similar to the query.

similarity_search_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = 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.

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

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[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

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

Returns

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

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