langchain_community.vectorstores.yellowbrick.Yellowbrick

class langchain_community.vectorstores.yellowbrick.Yellowbrick(embedding: Embeddings, connection_string: str, table: str, *, schema: Optional[str] = None, logger: Optional[Logger] = None, drop: bool = False)[source]

Yellowbrick as a vector database. .. rubric:: Example

Initialize with yellowbrick client. :param embedding: Embedding operator :param connection_string: Format ‘postgres://username:password@host:port/database’ :param table: Table used to store / retrieve embeddings from

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding, connection_string, table, *)

Initialize with yellowbrick client.

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])

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_index(index_params)

Create index from existing vectors

delete([ids, delete_all])

Delete vectors by uuids.

drop(table[, schema, cursor])

Helper function: Drop data.

drop_index(index_params)

Drop an index

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas, ...])

Add texts to the vectorstore index.

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.

migrate_schema_v1_to_v2()

search(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

similarity_search(query[, k])

Perform a similarity search with Yellowbrick

similarity_search_by_vector(embedding[, k])

Perform a similarity search with Yellowbrick by vectors

similarity_search_with_relevance_scores(query)

Return docs and relevance scores in the range [0, 1].

similarity_search_with_score(query[, k])

Perform a similarity search with Yellowbrick

similarity_search_with_score_by_vector(embedding)

Perform a similarity search with Yellowbrick with vector

Parameters
  • embedding (Embeddings) –

  • connection_string (str) –

  • table (str) –

  • schema (Optional[str]) –

  • logger (Optional[logging.Logger]) –

  • drop (bool) –

__init__(embedding: Embeddings, connection_string: str, table: str, *, schema: Optional[str] = None, logger: Optional[Logger] = None, drop: bool = False) None[source]

Initialize with yellowbrick client. :param embedding: Embedding operator :param connection_string: Format ‘postgres://username:password@host:port/database’ :param table: Table used to store / retrieve embeddings from

Parameters
  • embedding (Embeddings) –

  • connection_string (str) –

  • table (str) –

  • schema (Optional[str]) –

  • logger (Optional[Logger]) –

  • drop (bool) –

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

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = 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 associated with the texts.

  • **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
  • 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 VectorStore initialized from texts and embeddings.

Parameters
  • texts (List[str]) – Texts to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

  • embedding (Embeddings) – Embedding function to use.

  • kwargs (Any) –

Return type

VST

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]

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]

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]

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

  • kwargs (Any) –

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.

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 docs most similar to query.

Parameters
  • query (str) – Input text.

  • k (int) – Number of Documents to return. Defaults to 4.

  • kwargs (Any) –

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]

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]]

create_index(index_params: IndexParams) None[source]

Create index from existing vectors

Parameters

index_params (IndexParams) –

Return type

None

delete(ids: Optional[List[str]] = None, delete_all: Optional[bool] = None, **kwargs: Any) None[source]

Delete vectors by uuids.

Parameters
  • ids (Optional[List[str]]) – List of ids to delete, where each id is a uuid string.

  • delete_all (Optional[bool]) –

  • kwargs (Any) –

Return type

None

drop(table: str, schema: Optional[str] = None, cursor: Optional['PgCursor'] = None) None[source]

Helper function: Drop data. If a cursor is provided, use it; otherwise, obtain a new cursor for the operation.

Parameters
  • table (str) –

  • schema (Optional[str]) –

  • cursor (Optional['PgCursor']) –

Return type

None

drop_index(index_params: IndexParams) None[source]

Drop an index

Parameters

index_params (IndexParams) –

Return type

None

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) –

Return type

VST

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, connection_string: str = '', table: str = 'langchain', schema: str = 'public', drop: bool = False, **kwargs: Any) Yellowbrick[source]

Add texts to the vectorstore index. :param texts: Iterable of strings to add to the vectorstore. :param metadatas: Optional list of metadatas associated with the texts. :param connection_string: URI to Yellowbrick instance :param embedding: Embedding function :param table: table to store embeddings :param kwargs: vectorstore specific parameters

Parameters
  • texts (List[str]) –

  • embedding (Embeddings) –

  • metadatas (Optional[List[dict]]) –

  • connection_string (str) –

  • table (str) –

  • schema (str) –

  • drop (bool) –

  • kwargs (Any) –

Return type

Yellowbrick

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.

  • kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

migrate_schema_v1_to_v2() None[source]
Return type

None

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]

Perform a similarity search with Yellowbrick

Parameters
  • query (str) – query string

  • k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

  • NOTE – Please do not let end-user fill this and always be aware of SQL injection.

  • kwargs (Any) –

Returns

List of Documents

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document][source]

Perform a similarity search with Yellowbrick by vectors

Parameters
  • embedding (List[float]) – query embedding

  • k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

  • NOTE – Please do not let end-user fill this and always be aware of SQL injection.

  • kwargs (Any) –

Returns

List of documents

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, **kwargs: Any) List[Tuple[Document, float]][source]

Perform a similarity search with Yellowbrick

Parameters
  • query (str) – query string

  • k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

  • NOTE – Please do not let end-user fill this and always be aware of SQL injection.

  • kwargs (Any) –

Returns

List of (Document, similarity)

Return type

List[Document]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Tuple[Document, float]][source]

Perform a similarity search with Yellowbrick with vector

Parameters
  • embedding (List[float]) – query embedding

  • k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

  • NOTE – Please do not let end-user fill this and always be aware of SQL injection.

  • kwargs (Any) –

Returns

List of Documents and scores

Return type

List[Document, float]

Examples using Yellowbrick