AwaDB#

class langchain_community.vectorstores.awadb.AwaDB(table_name: str = 'langchain_awadb', embedding: Embeddings | None = None, log_and_data_dir: str | None = None, client: awadb.Client | None = None, **kwargs: Any)[source]#

AwaDB vector store.

Initialize with AwaDB client.

If table_name is not specified, a random table name of _DEFAULT_TABLE_NAME + last segment of uuid would be created automatically.

Parameters:

  • table_name (str) – Name of the table created, default _DEFAULT_TABLE_NAME.

  • embedding (Optional[Embeddings]) – Optional Embeddings initially set.

  • log_and_data_dir (Optional[str]) – Optional the root directory of log and data.

  • client (Optional[awadb.Client]) – Optional AwaDB client.

  • kwargs (Any) – Any possible extend parameters in the future.

Returns:

None.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__([table_name, embedding, ...])

Initialize with AwaDB client.

aadd_documents(documents, **kwargs)

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

aadd_texts(texts[, metadatas, ids])

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, is_duplicate_texts])

Run more texts through the embeddings and add to the vectorstore.

adelete([ids])

Async 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, ids])

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(...)

Async 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.

create_table(table_name, **kwargs)

Create a new table.

delete([ids])

Delete the documents which have the specified ids.

from_documents(documents[, embedding, ...])

Create an AwaDB vectorstore from a list of documents.

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

Create an AwaDB vectorstore from a raw documents.

get([ids, text_in_page_content, ...])

Return docs according ids.

get_by_ids(ids, /)

Get documents by their IDs.

get_current_table(**kwargs)

Get the current table.

list_tables(**kwargs)

List all the tables created by the client.

load_local(table_name, **kwargs)

Load the local specified table.

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, ...])

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, ...])

The most k similar documents and scores of the specified query.

update(ids, texts[, metadatas])

Update the documents which have the specified ids.

use(table_name, **kwargs)

Use the specified table.
__init__(table_name: str = 'langchain_awadb', embedding: Embeddings | None = None, log_and_data_dir: str | None = None, client: awadb.Client | None = None, **kwargs: Any) → None[source]#
Initialize with AwaDB client.

If table_name is not specified, a random table name of _DEFAULT_TABLE_NAME + last segment of uuid would be created automatically.

Parameters:

  • table_name (str) – Name of the table created, default _DEFAULT_TABLE_NAME.

  • embedding (Optional[Embeddings]) – Optional Embeddings initially set.

  • log_and_data_dir (Optional[str]) – Optional the root directory of log and data.

  • client (Optional[awadb.Client]) – Optional AwaDB client.

  • kwargs (Any) – Any possible extend parameters in the future.

Returns:

None.

Return type:

None

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, *, ids: list[str] | 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.

  • ids (list[str] | None) – Optional list

  • **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, is_duplicate_texts: bool | None = None, **kwargs: Any) → List[str][source]#

Run more texts through the embeddings and add to the vectorstore. :param texts: Iterable of strings to add to the vectorstore. :param metadatas: Optional list of metadatas associated with the texts. :param is_duplicate_texts: Optional whether to duplicate texts. Defaults to True. :param kwargs: any possible extend parameters in the future.

Returns:

List of ids from adding the texts into the vectorstore.

Parameters:

  • texts (Iterable[str])

  • metadatas (List[dict] | None)

  • is_duplicate_texts (bool | None)

  • kwargs (Any)

Return type:

List[str]

async adelete(ids: list[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

  • ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.

  • **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#

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, *, ids: list[str] | 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.

  • ids (list[str] | None) – Optional list of IDs associated with the texts.

  • 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]

Added in version 0.2.11.

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, **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:

  • 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 (Any) – Arguments to pass to the search method.

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:

**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 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]]

create_table(table_name: str, **kwargs: Any) → bool[source]#

Create a new table.

Parameters:

  • table_name (str)

  • kwargs (Any)

Return type:

bool

delete(ids: List[str] | None = None, **kwargs: Any) → bool | None[source]#

Delete the documents which have the specified ids.

Parameters:

  • ids (List[str] | None) – The ids of the embedding vectors.

  • **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]

classmethod from_documents(documents: List[Document], embedding: Embeddings | None = None, table_name: str = 'langchain_awadb', log_and_data_dir: str | None = None, client: awadb.Client | None = None, **kwargs: Any) → AwaDB[source]#

Create an AwaDB vectorstore from a list of documents.

If a log_and_data_dir specified, the table will be persisted there.

Parameters:

  • documents (List[Document]) – List of documents to add to the vectorstore.

  • embedding (Optional[Embeddings]) – Embedding function. Defaults to None.

  • table_name (str) – Name of the table to create.

  • log_and_data_dir (Optional[str]) – Directory to persist the table.

  • client (Optional[awadb.Client]) – AwaDB client.

  • Any – Any possible parameters in the future

  • kwargs (Any)

Returns:

AwaDB vectorstore.

Return type:

AwaDB

classmethod from_texts(texts: List[str], embedding: Embeddings | None = None, metadatas: List[dict] | None = None, table_name: str = 'langchain_awadb', log_and_data_dir: str | None = None, client: awadb.Client | None = None, **kwargs: Any) → AwaDB[source]#

Create an AwaDB vectorstore from a raw documents.

Parameters:

  • texts (List[str]) – List of texts to add to the table.

  • embedding (Optional[Embeddings]) – Embedding function. Defaults to None.

  • metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.

  • table_name (str) – Name of the table to create.

  • log_and_data_dir (Optional[str]) – Directory of logging and persistence.

  • client (Optional[awadb.Client]) – AwaDB client

  • kwargs (Any)

Returns:

AwaDB vectorstore.

Return type:

AwaDB

get(ids: List[str] | None = None, text_in_page_content: str | None = None, meta_filter: dict | None = None, not_include_fields: Set[str] | None = None, limit: int | None = None, **kwargs: Any) → Dict[str, Document][source]#

Return docs according ids.

Parameters:

  • ids (List[str] | None) – The ids of the embedding vectors.

  • text_in_page_content (str | None) – Filter by the text in page_content of Document.

  • meta_filter (dict | None) – Filter by any metadata of the document.

  • not_include_fields (Set[str] | None) – Not pack the specified fields of each document.

  • limit (int | None) – The number of documents to return. Defaults to 5. Optional.

  • kwargs (Any)

Returns:

Documents which satisfy the input conditions.

Return type:

Dict[str, Document]

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]

Added in version 0.2.11.

get_current_table(**kwargs: Any) → str[source]#

Get the current table.

Parameters:

kwargs (Any)

Return type:

str

list_tables(**kwargs: Any) → List[str][source]#

List all the tables created by the client.

Parameters:

kwargs (Any)

Return type:

List[str]

load_local(table_name: str, **kwargs: Any) → bool[source]#

Load the local specified table.

Parameters:

  • table_name (str) – Table name

  • kwargs (Any) – Any possible extend parameters in the future.

Returns:

Success or failure of loading the local specified table

Return type:

bool

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.

  • text_in_page_content (str | None) – Filter by the text in page_content of Document.

  • meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.

  • 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, text_in_page_content: str | None = None, meta_filter: dict | None = None, **kwargs: Any) → 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.

  • 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.

  • text_in_page_content (str | None) – Filter by the text in page_content of Document.

  • meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.

  • 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 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]

Return docs most similar to query.

Parameters:

  • query (str) – Text query.

  • k (int) – The maximum number of documents to return.

  • text_in_page_content (str | None) – Filter by the text in page_content of Document.

  • meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.

  • `{"color" (E.g.) –

    ”red”, “price”: 4.20}`. Optional.

  • `{"max_price" (E.g.) –

    15.66, “min_price”: 4.20}`

  • field (price is the metadata)

  • filter (means range)

  • `{"maxe_price" (E.g.) –

    15.66, “mine_price”: 4.20}`

  • field

  • filter

  • kwargs (Any) – Any possible extend parameters in the future.

Returns:

Returns the k most similar documents to the specified text query.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float] | None = None, k: int = 4, text_in_page_content: str | None = None, meta_filter: dict | None = None, not_include_fields_in_metadata: Set[str] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to embedding vector.

Parameters:

  • embedding (List[float] | None) – Embedding to look up documents similar to.

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

  • text_in_page_content (str | None) – Filter by the text in page_content of Document.

  • meta_filter (dict | None) – Filter by metadata. Defaults to None.

  • not_incude_fields_in_metadata – Not include meta fields of each document.

  • not_include_fields_in_metadata (Set[str] | None)

  • kwargs (Any)

Returns:

List of Documents which are the 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, text_in_page_content: str | None = None, meta_filter: dict | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

The most k similar documents and scores of the specified query.

Parameters:

  • query (str) – Text query.

  • k (int) – The k most similar documents to the text query.

  • text_in_page_content (str | None) – Filter by the text in page_content of Document.

  • meta_filter (dict | None) – Filter by metadata. Defaults to None.

  • kwargs (Any) – Any possible extend parameters in the future.

Returns:

The k most similar documents to the specified text query. 0 is dissimilar, 1 is the most similar.

Return type:

List[Tuple[Document, float]]

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

Update the documents which have the specified ids.

Parameters:

  • ids (List[str]) – The id list of the updating embedding vector.

  • texts (Iterable[str]) – The texts of the updating documents.

  • metadatas (List[dict] | None) – The metadatas of the updating documents.

  • kwargs (Any)

Returns:

the ids of the updated documents.

Return type:

List[str]

use(table_name: str, **kwargs: Any) → bool[source]#

Use the specified table. Don’t know the tables, please invoke list_tables.

Parameters:

  • table_name (str)

  • kwargs (Any)

Return type:

bool

Examples using AwaDB