HanaDB#

class langchain_community.vectorstores.hanavector.HanaDB(connection: dbapi.Connection, embedding: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, table_name: str = 'EMBEDDINGS', content_column: str = 'VEC_TEXT', metadata_column: str = 'VEC_META', vector_column: str = 'VEC_VECTOR', vector_column_length: int = -1, *, specific_metadata_columns: List[str] | None = None)[source]#

SAP HANA Cloud Vector Engine

The prerequisite for using this class is the installation of the hdbcli Python package.

The HanaDB vectorstore can be created by providing an embedding function and an existing database connection. Optionally, the names of the table and the columns to use.

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(connection, embedding[, ...])
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas])	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`(documents, **kwargs)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas, embeddings])	Add more texts to the vectorstore.
`adelete`([ids, filter])	Delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Async 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`(...)	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])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async 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`(args, *kwargs)	Async run similarity search with distance.
`delete`([ids, filter])	Delete entries by filter with metadata values
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_texts`(texts, embedding[, metadatas, ...])	Create a HanaDB instance from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a table if it does not yet exist. 3. Adds the documents to the table. This is intended to be a quick way to get started.
`get_by_ids`(ids, /)	Get documents by their IDs.
`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.
`similarity_search`(query[, k, filter])	Return docs most similar to query.
`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 documents and score values most similar to query.
`similarity_search_with_score_and_vector_by_vector`(...)	Return docs most similar to the given embedding.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to the given embedding.

Parameters:

connection (dbapi.Connection) –
embedding (Embeddings) –
distance_strategy (DistanceStrategy) –
table_name (str) –
content_column (str) –
metadata_column (str) –
vector_column (str) –
vector_column_length (int) –
specific_metadata_columns (Optional[List[str]]) –

__init__(connection: dbapi.Connection, embedding: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, table_name: str = 'EMBEDDINGS', content_column: str = 'VEC_TEXT', metadata_column: str = 'VEC_META', vector_column: str = 'VEC_VECTOR', vector_column_length: int = -1, *, specific_metadata_columns: List[str] | None = None)[source]#

Parameters:

connection (dbapi.Connection) –
embedding (Embeddings) –
distance_strategy (DistanceStrategy) –
table_name (str) –
content_column (str) –
metadata_column (str) –
vector_column (str) –
vector_column_length (int) –
specific_metadata_columns (Optional[List[str]]) –

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_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str]#

Async 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. Default is None.
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

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

Add more texts to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas. Defaults to None.
embeddings (Optional[List[List[float]]], optional) – Optional pre-generated embeddings. Defaults to None.
kwargs (Any) –

Returns:

empty list

Return type:

List[str]

async adelete(ids: List[str] | None = None, filter: dict | None = None) → bool | None[source]#

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete.
filter (dict | None) –

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_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

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]

New 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) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Parameters:

embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –

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, **kwargs: Any) → List[Document]#

Async return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]#

Async 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) – Arguments to pass to the search method.

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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]#

Async run similarity search with distance.

Parameters:

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

delete(ids: List[str] | None = None, filter: dict | None = None) → bool | None[source]#

Delete entries by filter with metadata values

Parameters:

ids (List[str] | None) – Deletion with ids is not supported! A ValueError will be raised.
filter (dict | None) – A dictionary of metadata fields and values to filter by. An empty filter ({}) will delete all entries in the table.

Returns:

True, if deletion is technically successful. Deletion of zero entries, due to non-matching filters is a success.

Return type:

Optional[bool]

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_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, connection: dbapi.Connection = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, table_name: str = 'EMBEDDINGS', content_column: str = 'VEC_TEXT', metadata_column: str = 'VEC_META', vector_column: str = 'VEC_VECTOR', vector_column_length: int = -1, *, specific_metadata_columns: List[str] | None = None)[source]#

Create a HanaDB instance from raw documents. This is a user-friendly interface that:

Embeds documents.

Creates a table if it does not yet exist.

Adds the documents to the table.

This is intended to be a quick way to get started.

Parameters:

texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
connection (dbapi.Connection) –
distance_strategy (DistanceStrategy) –
table_name (str) –
content_column (str) –
metadata_column (str) –
vector_column (str) –
vector_column_length (int) –
specific_metadata_columns (Optional[List[str]]) –

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]

New in version 0.2.11.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: dict | None = None) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

query (str) – search query text.
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.
filter (dict | None) –
Filter on metadata properties, e.g. {

”str_property”: “foo”, “int_property”: 123

}

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, filter: dict | None = None) → List[Document][source]#

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 – Arguments to pass to the search method.
filter (dict | None) –

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]

similarity_search(query: str, k: int = 4, filter: dict | None = None) → List[Document][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 (dict | None) – A dictionary of metadata fields and values to filter by. Defaults to None.

Returns:

List of Documents most similar to the query

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: dict | None = None) → 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 (dict | None) – A dictionary of metadata fields and values to filter by. Defaults to None.

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 | None = None) → List[Tuple[Document, float]][source]#

Return documents and score values 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 (dict | None) – A dictionary of metadata fields and values to filter by. Defaults to None.

Returns:

List of tuples (containing a Document and a score) that are most similar to the query

Return type:

List[Tuple[Document, float]]

similarity_search_with_score_and_vector_by_vector(embedding: List[float], k: int = 4, filter: dict | None = None) → List[Tuple[Document, float, List[float]]][source]#

Return docs most similar to the given embedding.

Parameters:

query – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (dict | None) – A dictionary of metadata fields and values to filter by. Defaults to None.
embedding (List[float]) –

Returns:

List of Documents most similar to the query and score and the document’s embedding vector for each

Return type:

List[Tuple[Document, float, List[float]]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: dict | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to the given embedding.

Parameters:

query – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (dict | None) – A dictionary of metadata fields and values to filter by. Defaults to None.
embedding (List[float]) –

Returns:

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

Return type:

List[Tuple[Document, float]]

Examples using HanaDB