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]¶
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.
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.
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])Delete vector data from the TiDB Vector Store.
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.
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.
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
(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, 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]) –
embedding (Embeddings) –
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]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
VST
- 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.
- Parameters
query (str) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
- 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
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]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) 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, **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
- classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ¶
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) –
embedding (Embeddings) –
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
- 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
- max_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.
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) –
search_type (str) –
kwargs (Any) –
- Return type
List[Document]
- similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) List[Document] [source]¶
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]]