Tony20100/langchain_full huggingface dataset:
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]
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Text 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.
Return type
List[Document]
similarity_search_by_text(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Text 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.
Return type
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Look up similar documents by embedding vector in Weaviate.
Parameters
embedding (List[float]) – 
k (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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
Examples using Weaviate¶
Weaviate
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)
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)
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.
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]
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
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.
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
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
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]
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]
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
langchain_community.vectorstores.elasticsearch.BaseRetrievalStrategy¶
class langchain_community.vectorstores.elasticsearch.BaseRetrievalStrategy[source]¶
Base class for Elasticsearch retrieval strategies.
Methods
__init__()
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_field, ...)
Executes when the index is created.
query(query_vector, query, *, k, fetch_k, ...)
Executes when a search is performed on the store.
require_inference()
Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
__init__()¶
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None[source]¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client (Elasticsearch) – The Elasticsearch client.
text_field (str) – The field containing the text data in the index.
vector_query_field (str) – The field containing the vector
representations in the index.
Return type
None
abstract index(dims_length: Optional[int], vector_query_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when the index is created.
Parameters
dims_length (Optional[int]) – Numeric length of the embedding vectors,
or None if not using vector-based query.
vector_query_field (str) – The field containing the vector
representations in the index.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use,
or None if not using one.
Returns
The Elasticsearch settings and mappings for the strategy.
Return type
Dict
Returns
The Elasticsearch settings and mappings for the strategy.
Return type
Dict
abstract query(query_vector: Optional[List[float]], query: Optional[str], *, k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector (Optional[List[float]]) – The query vector,
or None if not using vector-based query.
query (Optional[str]) – The text query, or None if not using text-based query.
k (int) – The total number of results to retrieve.
fetch_k (int) – The number of results to fetch initially.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
filter (List[dict]) – List of filter clauses to apply to the query.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use, or None if not using one.
Returns
The Elasticsearch query body.
Return type
Dict
require_inference() → bool[source]¶
Returns whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Returns
Whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Return type
bool
langchain_community.vectorstores.lancedb.LanceDB¶
class langchain_community.vectorstores.lancedb.LanceDB(connection: Optional[Any] = None, embedding: Optional[Embeddings] = None, uri: Optional[str] = '/tmp/lancedb', vector_key: Optional[str] = 'vector', id_key: Optional[str] = 'id', text_key: Optional[str] = 'text', table_name: Optional[str] = 'vectorstore', api_key: Optional[str] = None, region: Optional[str] = None, mode: Optional[str] = 'overwrite')[source]¶
LanceDB vector store.
To use, you should have lancedb python package installed.
You can install it with pip install lancedb.
Parameters
connection (Optional[Any]) – LanceDB connection to use. If not provided, a new connection
will be created.
embedding (Optional[Embeddings]) – Embedding to use for the vectorstore.
vector_key (Optional[str]) – Key to use for the vector in the database. Defaults to vector.
id_key (Optional[str]) – Key to use for the id in the database. Defaults to id.
text_key (Optional[str]) – Key to use for the text in the database. Defaults to text.
table_name (Optional[str]) – Name of the table to use. Defaults to vectorstore.
api_key (Optional[str]) – API key to use for LanceDB cloud database.
region (Optional[str]) – Region to use for LanceDB cloud database.
mode (Optional[str]) – Mode to use for adding data to the table. Defaults to overwrite.
uri (Optional[str]) – 
Example
Initialize with Lance DB vectorstore
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([connection, embedding, uri, ...])
Initialize with Lance DB vectorstore