langchain_community.vectorstores.tencentvectordb.TencentVectorDB

class langchain_community.vectorstores.tencentvectordb.TencentVectorDB(embedding: ~langchain_core.embeddings.embeddings.Embeddings, connection_params: ~langchain_community.vectorstores.tencentvectordb.ConnectionParams, index_params: ~langchain_community.vectorstores.tencentvectordb.IndexParams = <langchain_community.vectorstores.tencentvectordb.IndexParams object>, database_name: str = 'LangChainDatabase', collection_name: str = 'LangChainCollection', drop_old: ~typing.Optional[bool] = False, collection_description: ~typing.Optional[str] = 'Collection for LangChain', meta_fields: ~typing.Optional[~typing.List[~langchain_community.vectorstores.tencentvectordb.MetaField]] = None, t_vdb_embedding: ~typing.Optional[str] = 'bge-base-zh')[source]

Tencent VectorDB as a vector store.

In order to use this you need to have a database instance. See the following documentation for details: https://cloud.tencent.com/document/product/1709/94951

Attributes

embeddings

Access the query embedding object if available.

field_id

field_metadata

field_text

field_vector

Methods

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

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, timeout, ...])

Insert text data into TencentVectorDB.

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.

delete([ids, filter_expr])

Delete documents from the collection.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

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

Create a collection, indexes it with HNSW, and insert data.

max_marginal_relevance_search(query[, k, ...])

Perform a search and return results that are reordered by MMR.

max_marginal_relevance_search_by_vector(...)

Perform a search and return results that are reordered by MMR.

search(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

similarity_search(query[, k, param, expr, ...])

Perform a similarity search against the query string.

similarity_search_by_vector(embedding[, k, ...])

Perform a similarity search against the query string.

similarity_search_with_relevance_scores(query)

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

similarity_search_with_score(query[, k, ...])

Perform a search on a query string and return results with score.

similarity_search_with_score_by_vector(embedding)

Perform a search on a query string and return results with score.
Parameters

  • embedding (Embeddings) –

  • connection_params (ConnectionParams) –

  • index_params (IndexParams) –

  • database_name (str) –

  • collection_name (str) –

  • drop_old (Optional[bool]) –

  • collection_description (Optional[str]) –

  • meta_fields (Optional[List[MetaField]]) –

  • t_vdb_embedding (Optional[str]) –

__init__(embedding: ~langchain_core.embeddings.embeddings.Embeddings, connection_params: ~langchain_community.vectorstores.tencentvectordb.ConnectionParams, index_params: ~langchain_community.vectorstores.tencentvectordb.IndexParams = <langchain_community.vectorstores.tencentvectordb.IndexParams object>, database_name: str = 'LangChainDatabase', collection_name: str = 'LangChainCollection', drop_old: ~typing.Optional[bool] = False, collection_description: ~typing.Optional[str] = 'Collection for LangChain', meta_fields: ~typing.Optional[~typing.List[~langchain_community.vectorstores.tencentvectordb.MetaField]] = None, t_vdb_embedding: ~typing.Optional[str] = 'bge-base-zh')[source]
Parameters

  • embedding (Embeddings) –

  • connection_params (ConnectionParams) –

  • index_params (IndexParams) –

  • database_name (str) –

  • collection_name (str) –

  • drop_old (Optional[bool]) –

  • collection_description (Optional[str]) –

  • meta_fields (Optional[List[MetaField]]) –

  • t_vdb_embedding (Optional[str]) –

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.

  • documents (List[Document]) –

  • 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]) –

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

  • kwargs (Any) –

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

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

  • documents (List[Document]) –

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, timeout: Optional[int] = None, batch_size: int = 1000, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]

Insert text data into TencentVectorDB.

Parameters

  • texts (Iterable[str]) –

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

  • timeout (Optional[int]) –

  • batch_size (int) –

  • ids (Optional[List[str]]) –

  • kwargs (Any) –

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
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]) –

  • embedding (Embeddings) –

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

  • 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.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

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) –

  • search_type (str) –

  • kwargs (Any) –

Return type

List[Document]

Return docs most similar to query.

Parameters

  • query (str) –

  • k (int) –

  • kwargs (Any) –

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]) –

  • k (int) –

  • kwargs (Any) –

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], asynchronously.

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 asynchronously.

Parameters

  • args (Any) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

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

Delete documents from the collection.

Parameters

  • ids (Optional[List[str]]) –

  • filter_expr (Optional[str]) –

  • kwargs (Any) –

Return type

Optional[bool]

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

Parameters
Return type

VST

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, connection_params: Optional[ConnectionParams] = None, index_params: Optional[IndexParams] = None, database_name: str = 'LangChainDatabase', collection_name: str = 'LangChainCollection', drop_old: Optional[bool] = False, collection_description: Optional[str] = 'Collection for LangChain', meta_fields: Optional[List[MetaField]] = None, t_vdb_embedding: Optional[str] = 'bge-base-zh', **kwargs: Any) → TencentVectorDB[source]

Create a collection, indexes it with HNSW, and insert data.

Parameters

  • texts (List[str]) –

  • embedding (Embeddings) –

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

  • connection_params (Optional[ConnectionParams]) –

  • index_params (Optional[IndexParams]) –

  • database_name (str) –

  • collection_name (str) –

  • drop_old (Optional[bool]) –

  • collection_description (Optional[str]) –

  • meta_fields (Optional[List[MetaField]]) –

  • t_vdb_embedding (Optional[str]) –

  • kwargs (Any) –

Return type

TencentVectorDB

Perform a search and return results that are reordered by MMR.

Parameters

  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • param (Optional[dict]) –

  • expr (Optional[str]) –

  • timeout (Optional[int]) –

  • kwargs (Any) –

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, param: Optional[dict] = None, expr: Optional[str] = None, filter: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]

Perform a search and return results that are reordered by MMR.

Parameters

  • embedding (list[float]) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • param (Optional[dict]) –

  • expr (Optional[str]) –

  • filter (Optional[str]) –

  • timeout (Optional[int]) –

  • kwargs (Any) –

Return type

List[Document]

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

Parameters

  • query (str) –

  • search_type (str) –

  • kwargs (Any) –

Return type

List[Document]

Perform a similarity search against the query string.

Parameters

  • query (str) –

  • k (int) –

  • param (Optional[dict]) –

  • expr (Optional[str]) –

  • timeout (Optional[int]) –

  • kwargs (Any) –

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]

Perform a similarity search against the query string.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • param (Optional[dict]) –

  • expr (Optional[str]) –

  • timeout (Optional[int]) –

  • kwargs (Any) –

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, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]

Perform a search on a query string and return results with score.

Parameters

  • query (str) –

  • k (int) –

  • param (Optional[dict]) –

  • expr (Optional[str]) –

  • timeout (Optional[int]) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, filter: Optional[str] = None, timeout: Optional[int] = None, query: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]

Perform a search on a query string and return results with score.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • param (Optional[dict]) –

  • expr (Optional[str]) –

  • filter (Optional[str]) –

  • timeout (Optional[int]) –

  • query (Optional[str]) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

Examples using TencentVectorDB