Tony20100/langchain_full huggingface dataset:
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, ...)
Return connection string from database parameters.
create_collection()
create_table_if_not_exists()
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.
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)
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
embedding_dimension (int) – 
collection_name (str) – 
pre_delete_collection (bool) – 
logger (Optional[logging.Logger]) – 
engine_args (Optional[dict]) – 
__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]¶
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
embedding_dimension (int) – 
collection_name (str) – 
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]
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
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]¶
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
str
create_collection() → None[source]¶
Return type
None
create_table_if_not_exists() → None[source]¶
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
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) → AnalyticDB[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
AnalyticDB
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) → AnalyticDB[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]) –
pre_delete_collection (bool) – 
engine_args (Optional[dict]) – 
kwargs (Any) – 
Return type
AnalyticDB
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.
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.
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]
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
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
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]]
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 AnalyticDB¶
Alibaba Cloud
AnalyticDB
langchain_community.vectorstores.kinetica.Kinetica¶
class langchain_community.vectorstores.kinetica.Kinetica(config: KineticaSettings, embedding_function: Embeddings, collection_name: str = 'langchain_kinetica_embeddings', schema_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶
Kinetica vector store.
To use, you should have the gpudb python package installed.
Parameters
kinetica_settings – Kinetica connection settings class.
embedding_function (Embeddings) – Any embedding function implementing
langchain.embeddings.base.Embeddings interface.
collection_name (str) – 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.
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.
engine_args – SQLAlchemy’s create engine arguments.
config (KineticaSettings) – 
schema_name (str) – 
logger (Optional[logging.Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
Example
from langchain_community.vectorstores import Kinetica, KineticaSettings
from langchain_community.embeddings.openai import OpenAIEmbeddings
kinetica_settings = KineticaSettings(
kinetica_settings = KineticaSettings(
    host="http://127.0.0.1", username="", password=""
    )
COLLECTION_NAME = "kinetica_store"
embeddings = OpenAIEmbeddings()
vectorstore = Kinetica.from_documents(
    documents=docs,
    embedding=embeddings,
    collection_name=COLLECTION_NAME,
    config=kinetica_settings,
)
Constructor for the Kinetica class
Parameters
config (KineticaSettings) – a KineticaSettings instance
embedding_function (Embeddings) – embedding function to use
collection_name (str, optional) – the Kinetica table name.
Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
schema_name (str, optional) – the Kinetica table name.
Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
distance_strategy (DistanceStrategy, optional) – _description_.
Defaults to DEFAULT_DISTANCE_STRATEGY.
pre_delete_collection (bool, optional) – _description_. Defaults to False.
logger (Optional[logging.Logger], optional) – _description_.
Defaults to None.
relevance_score_fn (Optional[Callable[[float], float]]) – 
Attributes
distance_strategy
embeddings
Access the query embedding object if available.
Methods
__init__(config, embedding_function[, ...])
Constructor for the Kinetica class
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_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
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_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.
create_schema()
Create a new Kinetica schema
create_tables_if_not_exists()
Create the table to store the texts and embeddings
delete([ids])
Delete by vector ID or other criteria.
delete_schema()
Delete a Kinetica schema with cascade set to true This method will delete a schema with all tables in it.
drop_tables()
Delete the table
from_documents(documents, embedding[, ...])
Adds the list of Document passed in to the vector store and returns it
from_embeddings(text_embeddings, embedding)
from_embeddings(text_embeddings, embedding)
Adds the embeddings passed in to the vector store and returns it
from_texts(texts, embedding[, metadatas, ...])
Adds the texts passed in to the vector store and returns it
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance
max_marginal_relevance_search_with_score(query)
Return docs selected using the maximal marginal relevance with score.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance with score
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with Kinetica 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__(config: KineticaSettings, embedding_function: Embeddings, collection_name: str = 'langchain_kinetica_embeddings', schema_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶
Constructor for the Kinetica class
Parameters
config (KineticaSettings) – a KineticaSettings instance
Parameters
config (KineticaSettings) – a KineticaSettings instance
embedding_function (Embeddings) – embedding function to use
collection_name (str, optional) – the Kinetica table name.
Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
schema_name (str, optional) – the Kinetica table name.
Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
distance_strategy (DistanceStrategy, optional) – _description_.
Defaults to DEFAULT_DISTANCE_STRATEGY.
pre_delete_collection (bool, optional) – _description_. Defaults to False.
logger (Optional[logging.Logger], optional) – _description_.
Defaults to None.
relevance_score_fn (Optional[Callable[[float], float]]) – 
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
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]
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[str]) – Iterable of strings to add to the vectorstore.
embeddings (List[List[float]]) – List of list of embedding vectors.
metadatas (Optional[List[dict]]) – List of metadatas associated with the texts.
ids (Optional[List[str]]) – List of ids for the text embedding pairs
kwargs (Any) – vectorstore specific parameters
Return type
List[str]
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[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas (JSON data) associated with the texts.
ids (Optional[List[str]]) – List of IDs (UUID) for the texts supplied; will be generated if None
kwargs (Any) – vectorstore specific parameters
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
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]
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
filter (Optional[Dict[str, str]]) – 
kwargs (Any) – 
Return type
List[Document]
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;