`langchain_community.vectorstores.awadb`.AwaDB¶

class langchain_community.vectorstores.awadb.AwaDB(table_name: str = 'langchain_awadb', embedding: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = 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 – Name of the table created, default _DEFAULT_TABLE_NAME.
embedding – Optional Embeddings initially set.
log_and_data_dir – Optional the root directory of log and data.
client – Optional AwaDB client.
kwargs – 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)	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, is_duplicate_texts])	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], asynchronously.
`asimilarity_search_with_score`(args, *kwargs)	Run similarity search with distance asynchronously.
`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_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 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: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = 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 – Name of the table created, default _DEFAULT_TABLE_NAME.
embedding – Optional Embeddings initially set.
log_and_data_dir – Optional the root directory of log and data.
client – Optional AwaDB client.
kwargs – Any possible extend parameters in the future.

Returns

None.

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, is_duplicate_texts: Optional[bool] = 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.

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶

Delete by vector ID or other criteria.

Parameters

ids – List of ids to delete.
**kwargs – 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.

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, **kwargs: Any) → 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.

create_table(table_name: str, **kwargs: Any) → bool[source]¶: Create a new table.

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

Delete the documents which have the specified ids.

Parameters

ids – The ids of the embedding vectors.
**kwargs – 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: Optional[Embeddings] = None, table_name: str = 'langchain_awadb', log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = 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

Returns

AwaDB vectorstore.

Return type

AwaDB

classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, table_name: str = 'langchain_awadb', log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = 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

Returns

AwaDB vectorstore.

Return type

AwaDB

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

Return docs according ids.

Parameters

ids – The ids of the embedding vectors.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter – Filter by any metadata of the document.
not_include_fields – Not pack the specified fields of each document.
limit – The number of documents to return. Defaults to 5. Optional.

Returns

Documents which satisfy the input conditions.

get_current_table(**kwargs: Any) → str[source]¶: Get the current table.

list_tables(**kwargs: Any) → List[str][source]¶: List all the tables created by the client.

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

Load the local specified table.

Parameters

table_name – Table name
kwargs – Any possible extend parameters in the future.

Returns

Success or failure of loading the local specified table

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = 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

query – Text 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.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.

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, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = 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 – 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.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.

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, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query.

Parameters

query – Text query.
k – The maximum number of documents to return.
text_in_page_content – 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 possible extend parameters in the future.

Returns

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

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

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.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter – Filter by metadata. Defaults to None.
not_incude_fields_in_metadata – Not include meta fields of each document.

Returns

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

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

Parameters

query – Text query.
k – The k most similar documents to the text query.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter – Filter by metadata. Defaults to None.
kwargs – 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.

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

Update the documents which have the specified ids.

Parameters

ids – The id list of the updating embedding vector.
texts – The texts of the updating documents.
metadatas – The metadatas of the updating documents.

Returns

the ids of the updated documents.

use(table_name: str, **kwargs: Any) → bool[source]¶: Use the specified table. Don’t know the tables, please invoke list_tables.

Examples using AwaDB¶

AwaDB

langchain_community.vectorstores.awadb.AwaDB¶

Examples using AwaDB¶

`langchain_community.vectorstores.awadb`.AwaDB¶