langchain_community.vectorstores.hanavector.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)

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)	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, embeddings])	Add more texts to the vectorstore.
adelete([ids, filter])	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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)	Run similarity search with distance asynchronously.
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.
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])	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.

__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)

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

Run more texts through the embeddings and add to 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_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None) → List[str]

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.

Returns

empty list

Return type

List[str]

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

Delete by vector ID or other criteria.

Parameters

ids – List of ids to delete.

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

Return VectorStore initialized from texts and embeddings.

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) → 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, **kwargs: Any) → List[Document]

Return docs most similar to query.

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

Return docs most similar to embedding 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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with distance asynchronously.

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

Delete entries by filter with metadata values

Parameters

• ids – Deletion with ids is not supported! A ValueError will be raised.

• filter – 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.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = 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)

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.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None) → 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 – search query text.

• 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.

• filter –

  Filter on metadata properties, e.g. {

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

  }

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, filter: Optional[dict] = None) → 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.

similarity_search(query: str, k: int = 4, filter: Optional[dict] = None) → List[Document]

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 – A dictionary of metadata fields and values to filter by. Defaults to None.

Returns

List of Documents most similar to the query

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

Return documents and score values most similar to query.

Parameters

• query – Text to look up documents similar to.

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

• filter – 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

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

Return docs most similar to the given embedding.

Parameters

• query – Text to look up documents similar to.

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

• filter – A dictionary of metadata fields and values to filter by. Defaults to None.

Returns

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

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

Return docs most similar to the given embedding.

Parameters

• query – Text to look up documents similar to.

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

• filter – A dictionary of metadata fields and values to filter by. Defaults to None.

Returns

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