`langchain-db2`¶

Reference docs

This page contains reference documentation for IBM DB2. See the docs for conceptual guides, tutorials, and examples on using DB2 modules.

langchain_db2 ¶

DB2VS ¶

Bases: VectorStore

DB2VS vector store.

PARAMETER	DESCRIPTION
`embedding_function`	The embedding backend used to generate vectors for stored texts and queries TYPE: `Callable[[str], list[float]] \| Embeddings`
`table_name`	DB2 table name TYPE: `str`
`client`	Existing DB2 connection. Required if `connection_args` is not provided TYPE: `Connection \| None` DEFAULT: `None`
`distance_strategy`	Similarity metric used by Db2 `VECTOR_DISTANCE` when ranking results TYPE: `DistanceStrategy` DEFAULT: `EUCLIDEAN_DISTANCE`
`query`	Probe text used once to infer embedding dimension TYPE: `str \| None` DEFAULT: `'What is a Db2 database'`
`params`	Extra options TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`connection_args`	Connection parameters used when `client` is not supplied. Expected keys: `{"database": str, "host": str, "port": str, "username": str, "password": str, "security": bool}` TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`text_field`	Column name for the raw text (CLOB) TYPE: `str` DEFAULT: `'text'`

Setup

To use, you should have:

the langchain_db2 python package installed
a connection to db2 database with vector store feature (v12.1.2+)

pip install -U langchain-db2

# or using uv
uv add langchain-db2

Instantiate

Create a Vector Store instance with ibm_db_dbi.Connection object

from langchain_db2 import DB2VS

db2vs = DB2VS(
    embedding_function=embeddings, table_name=table_name, client=db_client
)

Create a Vector Store instance with connection_args

from langchain_db2 import DB2VS

db2vs = DB2VS(
    embedding_function=embeddings,
    table_name=table_name,
    connection_args={
        "database": "<DATABASE>",
        "host": "<HOST>",
        "port": "<PORT>",
        "username": "<USERNAME>",
        "password": "<PASSWORD>",
        "security": False,
    },
)

METHOD	DESCRIPTION
`get_by_ids`	Get documents by their IDs.
`aget_by_ids`	Async get documents by their IDs.
`adelete`	Async delete by vector ID or other criteria.
`aadd_texts`	Async run more texts through the embeddings and add to the `VectorStore`.
`add_documents`	Add or update documents in the `VectorStore`.
`aadd_documents`	Async run more documents through the embeddings and add to the `VectorStore`.
`search`	Return docs most similar to query using a specified search type.
`asearch`	Async return docs most similar to query using a specified search type.
`asimilarity_search_with_score`	Async run similarity search with distance.
`similarity_search_with_relevance_scores`	Return docs and relevance scores in the range `[0, 1]`.
`asimilarity_search_with_relevance_scores`	Async return docs and relevance scores in the range `[0, 1]`.
`asimilarity_search`	Async return docs most similar to query.
`asimilarity_search_by_vector`	Async return docs most similar to embedding vector.
`amax_marginal_relevance_search`	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`	Async return docs selected using the maximal marginal relevance.
`from_documents`	Return `VectorStore` initialized from documents and embeddings.
`afrom_documents`	Async return `VectorStore` initialized from documents and embeddings.
`afrom_texts`	Async return `VectorStore` initialized from texts and embeddings.
`as_retriever`	Return `VectorStoreRetriever` initialized from this `VectorStore`.
`__init__`	`DB2VS` vector store.
`get_embedding_dimension`	Embed the single document by wrapping it in a list.
`add_texts`	Add more texts to the vectorstore.
`similarity_search`	Return docs most similar to query.
`similarity_search_by_vector`	Return documents most similar to a query embedding.
`similarity_search_with_score`	Return the top-k documents most similar to a text query, with scores.
`similarity_search_by_vector_with_relevance_scores`	Return top-k documents for a query embedding, with relevance scores.
`similarity_search_by_vector_returning_embeddings`	Return top-k documents, their distances, and stored embeddings.
`max_marginal_relevance_search_with_score_by_vector`	Return docs and their similarity scores selected.
`max_marginal_relevance_search_by_vector`	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search`	Return docs selected using the maximal marginal relevance.
`delete`	Delete by vector IDs.
`from_texts`	Return VectorStore initialized from texts and embeddings.
`get_pks`	Get primary keys, optionally filtered by expr.

embeddings `property` ¶

embeddings: Embeddings | None

A property that returns an Embeddings instance.

RETURNS	DESCRIPTION
`Embeddings \| None`	Embeddings instance if embedding_function is an instance of Embeddings, otherwise returns None

get_by_ids ¶

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.

PARAMETER	DESCRIPTION
`ids`	List of IDs to retrieve. TYPE: `Sequence[str]`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects.

aget_by_ids `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.

PARAMETER	DESCRIPTION
`ids`	List of IDs to retrieve. TYPE: `Sequence[str]`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects.

adelete `async` ¶

adelete(ids: list[str] | None = None, **kwargs: Any) -> bool | None

Async delete by vector ID or other criteria.

PARAMETER	DESCRIPTION
`ids`	List of IDs to delete. If `None`, delete all. TYPE: `list[str] \| None` DEFAULT: `None`
`**kwargs`	Other keyword arguments that subclasses might use. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`bool \| None`	`True` if deletion is successful, `False` otherwise, `None` if not implemented.

aadd_texts `async` ¶

aadd_texts(
    texts: Iterable[str],
    metadatas: list[dict] | None = None,
    *,
    ids: list[str] | None = None,
    **kwargs: Any,
) -> list[str]

Async run more texts through the embeddings and add to the VectorStore.

PARAMETER	DESCRIPTION
`texts`	Iterable of strings to add to the `VectorStore`. TYPE: `Iterable[str]`
`metadatas`	Optional list of metadatas associated with the texts. TYPE: `list[dict] \| None` DEFAULT: `None`
`ids`	Optional list TYPE: `list[str] \| None` DEFAULT: `None`
`**kwargs`	`VectorStore` specific parameters. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[str]`	List of IDs from adding the texts into the `VectorStore`.

RAISES	DESCRIPTION
`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.

add_documents ¶

add_documents(documents: list[Document], **kwargs: Any) -> list[str]

Add or update documents in the VectorStore.

PARAMETER	DESCRIPTION
`documents`	Documents to add to the `VectorStore`. TYPE: `list[Document]`
`**kwargs`	Additional keyword arguments. If kwargs contains IDs and documents contain ids, the IDs in the kwargs will receive precedence. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[str]`	List of IDs of the added texts.

aadd_documents `async` ¶

aadd_documents(documents: list[Document], **kwargs: Any) -> list[str]

Async run more documents through the embeddings and add to the VectorStore.

PARAMETER	DESCRIPTION
`documents`	Documents to add to the `VectorStore`. TYPE: `list[Document]`
`**kwargs`	Additional keyword arguments. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[str]`	List of IDs of the added texts.

search ¶

search(query: str, search_type: str, **kwargs: Any) -> list[Document]

Return docs most similar to query using a specified search type.

PARAMETER	DESCRIPTION
`query`	Input text. TYPE: `str`
`search_type`	Type of search to perform. Can be `'similarity'`, `'mmr'`, or `'similarity_score_threshold'`. TYPE: `str`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects most similar to the query.

RAISES	DESCRIPTION
`ValueError`	If `search_type` is not one of `'similarity'`, `'mmr'`, or `'similarity_score_threshold'`.

asearch `async` ¶

asearch(query: str, search_type: str, **kwargs: Any) -> list[Document]

Async return docs most similar to query using a specified search type.

PARAMETER	DESCRIPTION
`query`	Input text. TYPE: `str`
`search_type`	Type of search to perform. Can be `'similarity'`, `'mmr'`, or `'similarity_score_threshold'`. TYPE: `str`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects most similar to the query.

RAISES	DESCRIPTION
`ValueError`	If `search_type` is not one of `'similarity'`, `'mmr'`, or `'similarity_score_threshold'`.

asimilarity_search_with_score `async` ¶

asimilarity_search_with_score(
    *args: Any, **kwargs: Any
) -> list[tuple[Document, float]]

Async run similarity search with distance.

PARAMETER	DESCRIPTION
`*args`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `()`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[tuple[Document, float]]`	List of tuples of `(doc, similarity_score)`.

similarity_search_with_relevance_scores ¶

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.

PARAMETER	DESCRIPTION
`query`	Input text. TYPE: `str`
`k`	Number of `Document` objects to return. TYPE: `int` DEFAULT: `4`
`**kwargs`	Kwargs to be passed to similarity search. Should include `score_threshold`, an optional floating point value between `0` to `1` to filter the resulting set of retrieved docs. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[tuple[Document, float]]`	List of tuples of `(doc, similarity_score)`.

asimilarity_search_with_relevance_scores `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.

PARAMETER	DESCRIPTION
`query`	Input text. TYPE: `str`
`k`	Number of `Document` objects to return. TYPE: `int` DEFAULT: `4`
`**kwargs`	Kwargs to be passed to similarity search. Should include `score_threshold`, an optional floating point value between `0` to `1` to filter the resulting set of retrieved docs. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[tuple[Document, float]]`	List of tuples of `(doc, similarity_score)`

asimilarity_search `async` ¶

asimilarity_search(query: str, k: int = 4, **kwargs: Any) -> list[Document]

Async return docs most similar to query.

PARAMETER	DESCRIPTION
`query`	Input text. TYPE: `str`
`k`	Number of `Document` objects to return. TYPE: `int` DEFAULT: `4`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects most similar to the query.

asimilarity_search_by_vector `async` ¶

asimilarity_search_by_vector(
    embedding: list[float], k: int = 4, **kwargs: Any
) -> list[Document]

Async return docs most similar to embedding vector.

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to. TYPE: `list[float]`
`k`	Number of `Document` objects to return. TYPE: `int` DEFAULT: `4`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects most similar to the query vector.

amax_marginal_relevance_search `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.

PARAMETER	DESCRIPTION
`query`	Text to look up documents similar to. TYPE: `str`
`k`	Number of `Document` objects to return. TYPE: `int` DEFAULT: `4`
`fetch_k`	Number of `Document` objects to fetch to pass to MMR algorithm. TYPE: `int` DEFAULT: `20`
`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. TYPE: `float` DEFAULT: `0.5`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects selected by maximal marginal relevance.

amax_marginal_relevance_search_by_vector `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]

Async return docs selected using the maximal marginal relevance.

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

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to. TYPE: `list[float]`
`k`	Number of `Document` objects to return. TYPE: `int` DEFAULT: `4`
`fetch_k`	Number of `Document` objects to fetch to pass to MMR algorithm. TYPE: `int` DEFAULT: `20`
`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. TYPE: `float` DEFAULT: `0.5`
`**kwargs`	Arguments to pass to the search method. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of `Document` objects selected by maximal marginal relevance.

from_documents `classmethod` ¶

from_documents(documents: list[Document], embedding: Embeddings, **kwargs: Any) -> Self

Return VectorStore initialized from documents and embeddings.

PARAMETER	DESCRIPTION
`documents`	List of `Document` objects to add to the `VectorStore`. TYPE: `list[Document]`
`embedding`	Embedding function to use. TYPE: `Embeddings`
`**kwargs`	Additional keyword arguments. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Self`	`VectorStore` initialized from documents and embeddings.

afrom_documents `async` `classmethod` ¶

afrom_documents(
    documents: list[Document], embedding: Embeddings, **kwargs: Any
) -> Self

Async return VectorStore initialized from documents and embeddings.

PARAMETER	DESCRIPTION
`documents`	List of `Document` objects to add to the `VectorStore`. TYPE: `list[Document]`
`embedding`	Embedding function to use. TYPE: `Embeddings`
`**kwargs`	Additional keyword arguments. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Self`	`VectorStore` initialized from documents and embeddings.

afrom_texts `async` `classmethod` ¶

afrom_texts(
    texts: list[str],
    embedding: Embeddings,
    metadatas: list[dict] | None = None,
    *,
    ids: list[str] | None = None,
    **kwargs: Any,
) -> Self

Async return VectorStore initialized from texts and embeddings.

PARAMETER	DESCRIPTION
`texts`	Texts to add to the `VectorStore`. TYPE: `list[str]`
`embedding`	Embedding function to use. TYPE: `Embeddings`
`metadatas`	Optional list of metadatas associated with the texts. TYPE: `list[dict] \| None` DEFAULT: `None`
`ids`	Optional list of IDs associated with the texts. TYPE: `list[str] \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Self`	`VectorStore` initialized from texts and embeddings.

as_retriever ¶

as_retriever(**kwargs: Any) -> VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

PARAMETER DESCRIPTION

**kwargs

Keyword arguments to pass to the search function.

Can include:

search_type: Defines the type of search that the Retriever should perform. Can be 'similarity' (default), 'mmr', or 'similarity_score_threshold'.
search_kwargs: 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

TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
`VectorStoreRetriever`	Retriever class for `VectorStore`.

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"}}
)

init ¶

__init__(
    embedding_function: Callable[[str], list[float]] | Embeddings,
    table_name: str,
    client: Connection | None = None,
    distance_strategy: DistanceStrategy = EUCLIDEAN_DISTANCE,
    query: str | None = "What is a Db2 database",
    params: dict[str, Any] | None = None,
    connection_args: dict[str, Any] | None = None,
    text_field: str = "text",
)

DB2VS vector store.

get_embedding_dimension ¶

get_embedding_dimension() -> int

Embed the single document by wrapping it in a list.

add_texts ¶

add_texts(
    texts: Iterable[str],
    metadatas: list[dict[Any, Any]] | None = None,
    ids: list[str] | None = None,
    **kwargs: Any,
) -> list[str]

Add more texts to the vectorstore.

PARAMETER	DESCRIPTION
`texts`	Iterable of strings to add to the vectorstore TYPE: `Iterable[str]`
`metadatas`	Optional list of metadatas associated with the texts TYPE: `list[dict[Any, Any]] \| None` DEFAULT: `None`
`ids`	Optional list of ids for the texts that are being added to the vector store TYPE: `list[str] \| None` DEFAULT: `None`
`kwargs`	vectorstore specific parameters TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[str]`	List of ids from adding the texts into the vectorstore

similarity_search ¶

similarity_search(
    query: str, k: int = 4, filter: dict[str, Any] | None = None, **kwargs: Any
) -> list[Document]

Return docs most similar to query.

PARAMETER	DESCRIPTION
`query`	The natural-language text to search for TYPE: `str`
`k`	Number of Documents to return TYPE: `int` DEFAULT: `4`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	Documents most similar to a query

similarity_search_by_vector ¶

similarity_search_by_vector(
    embedding: list[float],
    k: int = 4,
    filter: dict[str, Any] | None = None,
    **kwargs: Any,
) -> list[Document]

Return documents most similar to a query embedding.

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to TYPE: `list[float]`
`k`	Number of Documents to return TYPE: `int` DEFAULT: `4`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	Documents ordered from most to least similar

similarity_search_with_score ¶

similarity_search_with_score(
    query: str, k: int = 4, filter: dict[str, Any] | None = None, **kwargs: Any
) -> list[tuple[Document, float]]

Return the top-k documents most similar to a text query, with scores.

PARAMETER	DESCRIPTION
`query`	Natural-language query to embed and search with TYPE: `str`
`k`	Number of results to return TYPE: `int` DEFAULT: `4`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[tuple[Document, float]]`	A list of (document, score) pairs ordered by similarity. The score is the vector distance; lower values indicate closer matches.

similarity_search_by_vector_with_relevance_scores ¶

similarity_search_by_vector_with_relevance_scores(
    embedding: list[float], k: int = 4, filter: dict[str, Any] | None = None
) -> list[tuple[Document, float]]

Return top-k documents for a query embedding, with relevance scores.

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to TYPE: `list[float]`
`k`	Number of Documents to return TYPE: `int` DEFAULT: `4`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`list[tuple[Document, float]]`	A list of `(Document, distance)` pairs ordered from most to least similar (smallest distance first).

similarity_search_by_vector_returning_embeddings ¶

similarity_search_by_vector_returning_embeddings(
    embedding: list[float], k: int, filter: dict[str, Any] | None = None
) -> list[tuple[Document, float, ndarray]]

Return top-k documents, their distances, and stored embeddings.

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to TYPE: `list[float]`
`k`	Number of Documents to return TYPE: `int`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`list[tuple[Document, float, ndarray]]`	Tuples of `(document, distance, embedding_array)`, ordered from most to least similar (ascending distance)

max_marginal_relevance_search_with_score_by_vector ¶

max_marginal_relevance_search_with_score_by_vector(
    embedding: list[float],
    *,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: dict[str, Any] | None = None,
) -> list[tuple[Document, float]]

Return docs and their similarity scores selected.

Return docs and their similarity scores selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to TYPE: `list[float]`
`k`	Number of Documents to return TYPE: `int` DEFAULT: `4`
`fetch_k`	Number of Documents to fetch before filtering to pass to MMR algorithm TYPE: `int` DEFAULT: `20`
`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 TYPE: `float` DEFAULT: `0.5`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`list[tuple[Document, float]]`	List of Documents and similarity scores selected by maximal marginal relevance and score for each.

max_marginal_relevance_search_by_vector ¶

max_marginal_relevance_search_by_vector(
    embedding: list[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: dict[str, Any] | None = None,
    **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.

PARAMETER	DESCRIPTION
`embedding`	Embedding to look up documents similar to TYPE: `list[float]`
`k`	Number of Documents to return TYPE: `int` DEFAULT: `4`
`fetch_k`	Number of Documents to fetch to pass to MMR algorithm. TYPE: `int` DEFAULT: `20`
`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 TYPE: `float` DEFAULT: `0.5`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of Documents selected by maximal marginal relevance

max_marginal_relevance_search ¶

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: dict[str, Any] | None = None,
    **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.

PARAMETER	DESCRIPTION
`query`	Text to look up documents similar to TYPE: `str`
`k`	Number of Documents to return TYPE: `int` DEFAULT: `4`
`fetch_k`	Number of Documents to fetch to pass to MMR algorithm. TYPE: `int` DEFAULT: `20`
`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 TYPE: `float` DEFAULT: `0.5`
`filter`	Filter by metadata TYPE: `dict[str, Any] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Document]`	List of Documents selected by maximal marginal relevance

max_marginal_relevance_search requires that query returns matched embeddings alongside the match documents.

delete ¶

delete(ids: list[str] | None = None, **kwargs: Any) -> None

Delete by vector IDs.

PARAMETER	DESCRIPTION
`ids`	List of ids to delete TYPE: `list[str] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

from_texts `classmethod` ¶

from_texts(
    texts: Iterable[str],
    embedding: Embeddings,
    metadatas: list[dict] | None = None,
    **kwargs: Any,
) -> DB2VS

Return VectorStore initialized from texts and embeddings.

PARAMETER	DESCRIPTION
`texts`	Iterable of strings to add to the vectorstore TYPE: `Iterable[str]`
`embedding`	Embedding to look up documents similar to TYPE: `Embeddings`
`metadatas`	Optional list of metadatas associated with the texts TYPE: `list[dict] \| None` DEFAULT: `None`
`kwargs`	Additional keyword args TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`DB2VS`	A ready-to-use vector store with the provided texts loaded

get_pks ¶

get_pks(expr: str | None = None) -> list[str]

Get primary keys, optionally filtered by expr.

PARAMETER	DESCRIPTION
`expr`	SQL boolean expression to filter rows, e.g.: `id IN ('ABC123','DEF456')` or `title LIKE 'Abc%'`. If None, returns all rows. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`list[str]`	List of matching primary-key values.

langchain-db2¶

langchain_db2 ¶

DB2VS ¶

embeddings property ¶

get_by_ids ¶

aget_by_ids async ¶

adelete async ¶

aadd_texts async ¶

add_documents ¶

aadd_documents async ¶

search ¶

asearch async ¶

asimilarity_search_with_score async ¶

similarity_search_with_relevance_scores ¶

asimilarity_search_with_relevance_scores async ¶

asimilarity_search async ¶

asimilarity_search_by_vector async ¶

amax_marginal_relevance_search async ¶

amax_marginal_relevance_search_by_vector async ¶

from_documents classmethod ¶

afrom_documents async classmethod ¶

afrom_texts async classmethod ¶

as_retriever ¶

__init__ ¶

get_embedding_dimension ¶

add_texts ¶

similarity_search ¶

similarity_search_by_vector ¶

similarity_search_with_score ¶

similarity_search_by_vector_with_relevance_scores ¶

similarity_search_by_vector_returning_embeddings ¶

max_marginal_relevance_search_with_score_by_vector ¶

max_marginal_relevance_search_by_vector ¶

max_marginal_relevance_search ¶

delete ¶

from_texts classmethod ¶

get_pks ¶

`langchain-db2`¶

embeddings `property` ¶

aget_by_ids `async` ¶

adelete `async` ¶

aadd_texts `async` ¶

aadd_documents `async` ¶

asearch `async` ¶

asimilarity_search_with_score `async` ¶

asimilarity_search_with_relevance_scores `async` ¶

asimilarity_search `async` ¶

asimilarity_search_by_vector `async` ¶

amax_marginal_relevance_search `async` ¶

amax_marginal_relevance_search_by_vector `async` ¶

from_documents `classmethod` ¶

afrom_documents `async` `classmethod` ¶

afrom_texts `async` `classmethod` ¶

init ¶

from_texts `classmethod` ¶