langchain_community.vectorstores.tidb_vector.TiDBVectorStore

class langchain_community.vectorstores.tidb_vector.TiDBVectorStore(connection_string: str, embedding_function: Embeddings, table_name: str = 'langchain_vector', distance_strategy: str = 'cosine', *, engine_args: Optional[Dict[str, Any]] = None, drop_existing_table: bool = False, **kwargs: Any)[source]

TiDB Vector Store.

Initialize a TiDB Vector Store in Langchain with a flexible and standardized table structure for storing vector data which remains fixed regardless of the dynamic table name setting.

The vector table schema includes: - ‘id’: a UUID for each entry. - ‘embedding’: stores vector data in a VectorType column. - ‘document’: a Text column for the original data or additional information. - ‘meta’: a JSON column for flexible metadata storage. - ‘create_time’ and ‘update_time’: timestamp columns for tracking data changes.

This table structure caters to general use cases and complex scenarios where the table serves as a semantic layer for advanced data integration and analysis, leveraging SQL for join queries.

Parameters
  • connection_string (str) – The connection string for the TiDB database, format: “mysql+pymysql://root@34.212.137.91:4000/test”.

  • embedding_function (Embeddings) – The embedding function used to generate embeddings.

  • table_name (str, optional) – The name of the table that will be used to store vector data. If you do not provide a table name, a default table named langchain_vector will be created automatically.

  • distance_strategy (str) – The strategy used for similarity search, defaults to “cosine”, valid values: “l2”, “cosine”, “inner_product”.

  • engine_args (Optional[Dict]) – Additional arguments for the database engine, defaults to None.

  • drop_existing_table (bool) – Drop the existing TiDB table before initializing, defaults to False.

  • **kwargs (Any) – Additional keyword arguments.

Examples


from langchain_community.vectorstores import TiDBVectorStore from langchain_openai import OpenAIEmbeddings

embeddingFunc = OpenAIEmbeddings() CONNECTION_STRING = “mysql+pymysql://root@34.212.137.91:4000/test”

vs = TiDBVector.from_texts(

embedding=embeddingFunc, texts = […, …], connection_string=CONNECTION_STRING, distance_strategy=”l2”, table_name=”tidb_vector_langchain”,

)

query = “What did the president say about Ketanji Brown Jackson” docs = db.similarity_search_with_score(query)

Attributes

distance_strategy

Returns the current distance strategy.

embeddings

Return the function used to generate embeddings.

tidb_vector_client

Return the TiDB Vector Client.

Methods

__init__(connection_string, embedding_function)

Initialize a TiDB Vector Store in Langchain with a flexible and standardized table structure for storing vector data which remains fixed regardless of the dynamic table name setting.

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, ids])

Add texts to TiDB Vector Store.

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.

delete([ids])

Delete vector data from the TiDB Vector Store.

drop_vectorstore()

Drop the Vector Store from the TiDB database.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_existing_vector_table(embedding, ...[, ...])

Create a VectorStore instance from an existing TiDB Vector Store in TiDB.

from_texts(texts, embedding[, metadatas])

Create a VectorStore from a list of texts.

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, filter])

Perform a similarity search using the given 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, filter])

Perform a similarity search with score based on the given query.

__init__(connection_string: str, embedding_function: Embeddings, table_name: str = 'langchain_vector', distance_strategy: str = 'cosine', *, engine_args: Optional[Dict[str, Any]] = None, drop_existing_table: bool = False, **kwargs: Any) None[source]

Initialize a TiDB Vector Store in Langchain with a flexible and standardized table structure for storing vector data which remains fixed regardless of the dynamic table name setting.

The vector table schema includes: - ‘id’: a UUID for each entry. - ‘embedding’: stores vector data in a VectorType column. - ‘document’: a Text column for the original data or additional information. - ‘meta’: a JSON column for flexible metadata storage. - ‘create_time’ and ‘update_time’: timestamp columns for tracking data changes.

This table structure caters to general use cases and complex scenarios where the table serves as a semantic layer for advanced data integration and analysis, leveraging SQL for join queries.

Parameters
  • connection_string (str) – The connection string for the TiDB database, format: “mysql+pymysql://root@34.212.137.91:4000/test”.

  • embedding_function (Embeddings) – The embedding function used to generate embeddings.

  • table_name (str, optional) – The name of the table that will be used to store vector data. If you do not provide a table name, a default table named langchain_vector will be created automatically.

  • distance_strategy (str) – The strategy used for similarity search, defaults to “cosine”, valid values: “l2”, “cosine”, “inner_product”.

  • engine_args (Optional[Dict]) – Additional arguments for the database engine, defaults to None.

  • drop_existing_table (bool) – Drop the existing TiDB table before initializing, defaults to False.

  • **kwargs (Any) – Additional keyword arguments.

Return type

None

Examples


from langchain_community.vectorstores import TiDBVectorStore from langchain_openai import OpenAIEmbeddings

embeddingFunc = OpenAIEmbeddings() CONNECTION_STRING = “mysql+pymysql://root@34.212.137.91:4000/test”

vs = TiDBVector.from_texts(

embedding=embeddingFunc, texts = […, …], connection_string=CONNECTION_STRING, distance_strategy=”l2”, table_name=”tidb_vector_langchain”,

)

query = “What did the president say about Ketanji Brown Jackson” docs = db.similarity_search_with_score(query)

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, ids: Optional[List[str]] = None, **kwargs: Any) List[str][source]

Add texts to TiDB Vector Store.

Parameters
  • texts (Iterable[str]) – The texts to be added.

  • metadatas (Optional[List[dict]]) – The metadata associated with each text, Defaults to None.

  • ids (Optional[List[str]]) – The IDs to be assigned to each text, Defaults to None, will be generated if not provided.

  • kwargs (Any) –

Returns

The IDs assigned to the added texts.

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]]

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

Delete vector data from the TiDB Vector Store.

Parameters
  • ids (Optional[List[str]]) – A list of vector IDs to delete.

  • **kwargs – Additional keyword arguments.

Return type

None

drop_vectorstore() None[source]

Drop the Vector Store from the TiDB database.

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_existing_vector_table(embedding: Embeddings, connection_string: str, table_name: str, distance_strategy: str = 'cosine', *, engine_args: Optional[Dict[str, Any]] = None, **kwargs: Any) VectorStore[source]

Create a VectorStore instance from an existing TiDB Vector Store in TiDB.

Parameters
  • embedding (Embeddings) – The function to use for generating embeddings.

  • connection_string (str) – The connection string for the TiDB database, format: “mysql+pymysql://root@34.212.137.91:4000/test”.

  • table_name (str, optional) – The name of table used to store vector data, defaults to “langchain_vector”.

  • distance_strategy (str) – The distance strategy used for similarity search, defaults to “cosine”, allowed: “l2”, “cosine”, ‘inner_product’.

  • engine_args (Optional[Dict[str, Any]]) – Additional arguments for the underlying database engine, defaults to None.

  • **kwargs (Any) – Additional keyword arguments.

Returns

The VectorStore instance.

Return type

VectorStore

Raises

NoSuchTableError – If the specified table does not exist in the TiDB.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) TiDBVectorStore[source]

Create a VectorStore from a list of texts.

Parameters
  • texts (List[str]) – The list of texts to be added to the TiDB Vector.

  • embedding (Embeddings) – The function to use for generating embeddings.

  • metadatas (Optional[List[dict]]) – The list of metadata dictionaries corresponding to each text, defaults to None.

  • **kwargs (Any) –

    Additional keyword arguments. connection_string (str): The connection string for the TiDB database,

    format: “mysql+pymysql://root@34.212.137.91:4000/test”.

    table_name (str, optional): The name of table used to store vector data,

    defaults to “langchain_vector”.

    distance_strategy: The distance strategy used for similarity search,

    defaults to “cosine”, allowed: “l2”, “cosine”, “inner_product”.

    ids (Optional[List[str]]): The list of IDs corresponding to each text,

    defaults to None.

    engine_args: Additional arguments for the underlying database engine,

    defaults to None.

    drop_existing_table: Drop the existing TiDB table before initializing,

    defaults to False.

Returns

The created TiDB Vector Store.

Return type

VectorStore

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]

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 using the given query.

Parameters
  • query (str) – The query string.

  • k (int, optional) – The number of results to retrieve. Defaults to 4.

  • filter (dict, optional) – A filter to apply to the search results. Defaults to None.

  • **kwargs – Additional keyword arguments.

Returns

A list of Document objects representing the search results.

Return type

List[Document]

similarity_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]

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 = 5, filter: Optional[dict] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Perform a similarity search with score based on the given query.

Parameters
  • query (str) – The query string.

  • k (int, optional) – The number of results to return. Defaults to 5.

  • filter (dict, optional) – A filter to apply to the search results. Defaults to None.

  • **kwargs – Additional keyword arguments.

Returns

A list of tuples containing relevant documents and their similarity scores.

Return type

List[Tuple[Document, float]]

Examples using TiDBVectorStore