Tony20100/langchain_full huggingface dataset:
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: 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.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
TimescaleVector
classmethod get_service_url(kwargs: Dict[str, Any]) → str[source]¶
Parameters
kwargs (Dict[str, Any]) – 
Return type
str
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.
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]
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.
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.
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]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using 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.
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: 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.
predicates (Optional[Predicates]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
Returns
List of Documents most similar to the query.
Return type
List[Document]
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 (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: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = 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: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = 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¶
Timescale Vector (Postgres)
Timescale Vector (Postgres)
langchain_community.vectorstores.relyt.Relyt¶
class langchain_community.vectorstores.relyt.Relyt(connection_string: str, embedding_function: Embeddings, embedding_dimension: int = 1536, collection_name: str = 'langchain_document', pre_delete_collection: bool = False, logger: Optional[Logger] = None, engine_args: Optional[dict] = None)[source]¶
Relyt (distributed PostgreSQL) vector store.
Relyt is a distributed full postgresql syntax cloud-native database.
- 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 not the name of the table, but the name of the collection.The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
Initialize a PGVecto_rs vectorstore.
Parameters
embedding – Embeddings to use.
dimension – Dimension of the embeddings.
db_url – Database URL.
collection_name (str) – Name of the collection.
new_table – Whether to create a new table or connect to an existing one.
true (If) – 
exists (the table will be dropped if) – 
recreated. (then) – 
False. (Defaults to) – 
connection_string (str) – 
embedding_function (Embeddings) – 
embedding_dimension (int) – 
pre_delete_collection (bool) – 
logger (Optional[logging.Logger]) – 
engine_args (Optional[dict]) – 
Attributes
embeddings
Access the query embedding object if available.
Methods
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(connection_string, embedding_function)
Initialize a PGVecto_rs vectorstore.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, batch_size])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
connection_string_from_db_params(driver, ...)
Run similarity search with distance.
connection_string_from_db_params(driver, ...)
Return connection string from database parameters.
create_collection()
create_table_if_not_exists()
Table(
delete([ids])
Delete by vector IDs.
delete_collection()
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_connection_string(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.
similarity_search(query[, k, filter])
Run similarity search with AnalyticDB 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, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(connection_string: str, embedding_function: Embeddings, embedding_dimension: int = 1536, collection_name: str = 'langchain_document', pre_delete_collection: bool = False, logger: Optional[Logger] = None, engine_args: Optional[dict] = None) → None[source]¶
Initialize a PGVecto_rs vectorstore.
Parameters
embedding – Embeddings to use.
dimension – Dimension of the embeddings.
db_url – Database URL.
collection_name (str) – Name of the collection.
db_url – Database URL.
collection_name (str) – Name of the collection.
new_table – Whether to create a new table or connect to an existing one.
true (If) – 
exists (the table will be dropped if) – 
recreated. (then) – 
False. (Defaults to) – 
connection_string (str) – 
embedding_function (Embeddings) – 
embedding_dimension (int) – 
pre_delete_collection (bool) – 
logger (Optional[Logger]) – 
engine_args (Optional[dict]) – 
Return type
None
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 500, **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 (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
ids (Optional[List[str]]) – 
batch_size (int) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – 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]
async classmethod afrom_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) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
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) – 
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]]¶
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]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
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
user (str) – 
password (str) – 
Return type
str
create_collection() → None[source]¶
Return type
None
create_table_if_not_exists() → None[source]¶
Table(self.collection_name,
Base.metadata,
Column(“id”, TEXT, primary_key=True, default=uuid.uuid4),
Column(“embedding”, Vector(self.embedding_dimension)),
Column(“document”, String, nullable=True),
Column(“metadata”, JSON, nullable=True),
extend_existing=True,
)
Return type
None
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Return type
Optional[bool]
delete_collection() → None[source]¶
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, embedding_dimension: int = 1536, collection_name: str = 'langchain_document', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, engine_args: Optional[dict] = None, **kwargs: Any) → Relyt[source]¶
Return VectorStore initialized from documents and embeddings.
Postgres Connection string is required
Either pass it as a parameter
or set the PG_CONNECTION_STRING environment variable.
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
embedding_dimension (int) – 
collection_name (str) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
engine_args (Optional[dict]) – 
kwargs (Any) – 
Return type
Relyt
kwargs (Any) – 
Return type
Relyt
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, embedding_dimension: int = 1536, collection_name: str = 'langchain_document', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, engine_args: Optional[dict] = None, **kwargs: Any) → Relyt[source]¶
Return VectorStore initialized from texts and embeddings.
Postgres Connection string is required
Either pass it as a parameter
or set the PG_CONNECTION_STRING environment variable.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
embedding_dimension (int) – 
collection_name (str) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
engine_args (Optional[dict]) – 
kwargs (Any) – 
Return type
Relyt
classmethod get_connection_string(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.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
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]
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.
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]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using 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.
Return type
List[Document]
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with AnalyticDB 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.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = 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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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
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: Optional[dict] = None) → 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.
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], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) – 
filter (Optional[dict]) – 
Return type
List[Tuple[Document, float]]
Examples using Relyt¶
Relyt
langchain_community.vectorstores.tencentvectordb.TencentVectorDB¶
class langchain_community.vectorstores.tencentvectordb.TencentVectorDB(embedding: ~langchain_core.embeddings.embeddings.Embeddings, connection_params: ~langchain_community.vectorstores.tencentvectordb.ConnectionParams, index_params: ~langchain_community.vectorstores.tencentvectordb.IndexParams = <langchain_community.vectorstores.tencentvectordb.IndexParams object>, database_name: str = 'LangChainDatabase', collection_name: str = 'LangChainCollection', drop_old: ~typing.Optional[bool] = False, collection_description: ~typing.Optional[str] = 'Collection for LangChain', meta_fields: ~typing.Optional[~typing.List[~langchain_community.vectorstores.tencentvectordb.MetaField]] = None, t_vdb_embedding: ~typing.Optional[str] = 'bge-base-zh')[source]¶
Tencent VectorDB as a vector store.
In order to use this you need to have a database instance.
See the following documentation for details:
https://cloud.tencent.com/document/product/1709/94951
Attributes
embeddings
Access the query embedding object if available.
field_id
field_metadata
field_text
field_vector
Methods
__init__(embedding, connection_params[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, timeout, ...])
Insert text data into TencentVectorDB.
adelete([ids])
Delete by vector ID or other criteria.