`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)[source]¶

Timescale Postgres vector store

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[source]¶

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][source]¶

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][source]¶

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

Parameters

texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
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][source]¶

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][source]¶

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

Parameters

texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
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[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)

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[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.

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][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.

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][source]¶

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]][source]¶

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]][source]¶

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

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

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

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][source]¶

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[source]¶

drop_tables() → None[source]¶

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[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)

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

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[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.

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

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[source]¶: 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][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.

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][source]¶

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]][source]¶

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]][source]¶

langchain_community.vectorstores.timescalevector.TimescaleVector¶

`langchain_community.vectorstores.timescalevector`.TimescaleVector¶