Tony20100/langchain_full huggingface dataset:
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, *, batch_size: int = 32, text_ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Insert more texts through the embeddings and add to the VectorStore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the VectorStore
metadata – Optional column data to be inserted
batch_size (int) – Batch size of insertion
ids – Optional list of ids to associate with the texts
metadatas (Optional[List[dict]]) – 
text_ids (Optional[List[str]]) – 
kwargs (Any) – 
Returns
List of ids from adding the texts into the VectorStore.
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
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) → 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]
drop() → None[source]¶
Helper function: Drop data
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, *, config: Optional[ManticoreSearchSettings] = None, text_ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → ManticoreSearch[source]¶
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.
config (Optional[ManticoreSearchSettings]) – 
text_ids (Optional[List[str]]) – 
batch_size (int) – 
kwargs (Any) – 
Return type
ManticoreSearch
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, *, config: Optional[ManticoreSearchSettings] = None, text_ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → ManticoreSearch[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[Dict[Any, Any]]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
config (Optional[ManticoreSearchSettings]) – 
text_ids (Optional[List[str]]) – 
batch_size (int) – 
kwargs (Any) – 
Return type
ManticoreSearch
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, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with ManticoreSearch
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with ManticoreSearch by vectors
Parameters
embedding (List[float]) – Embedding vector
Parameters
embedding (List[float]) – Embedding vector
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
kwargs (Any) – 
Returns
List of documents
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(*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]]
Examples using ManticoreSearch¶
ManticoreSearch VectorStore
langchain_community.vectorstores.baiduvectordb.TableParams¶
class langchain_community.vectorstores.baiduvectordb.TableParams(dimension: int, replication: int = 3, partition: int = 1, index_type: str = 'HNSW', metric_type: str = 'L2', params: Optional[Dict] = None)[source]¶
Baidu VectorDB table params.
See the following documentation for details:
https://cloud.baidu.com/doc/VDB/s/mlrsob0p6
Methods
__init__(dimension[, replication, ...])
Parameters
dimension (int) – 
replication (int) – 
partition (int) – 
index_type (str) – 
metric_type (str) – 
params (Optional[Dict]) – 
__init__(dimension: int, replication: int = 3, partition: int = 1, index_type: str = 'HNSW', metric_type: str = 'L2', params: Optional[Dict] = None)[source]¶
Parameters
dimension (int) – 
replication (int) – 
partition (int) – 
index_type (str) – 
metric_type (str) – 
params (Optional[Dict]) –
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embedding 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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by vector ID or other criteria.
from_client_params(embedding, *[, host, ...])
Initialize Typesense directly from client parameters.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct Typesense wrapper from raw text.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
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])
Return typesense documents 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, filter])
Return typesense documents most similar to query, along with scores.
Parameters
typesense_client (Client) – 
embedding (Embeddings) – 
typesense_collection_name (Optional[str]) – 
text_key (str) – 
__init__(typesense_client: Client, embedding: Embeddings, *, typesense_collection_name: Optional[str] = None, text_key: str = 'text')[source]¶
Initialize with Typesense client.
Parameters
typesense_client (Client) – 
embedding (Embeddings) – 
typesense_collection_name (Optional[str]) – 
text_key (str) – 
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
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]¶
Run more texts through the embedding 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.
ids (Optional[List[str]]) – Optional list of ids to associate with the texts.
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
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]
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) → 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]
classmethod from_client_params(embedding: Embeddings, *, host: str = 'localhost', port: Union[str, int] = '8108', protocol: str = 'http', typesense_api_key: Optional[str] = None, connection_timeout_seconds: int = 2, **kwargs: Any) → Typesense[source]¶
Initialize Typesense directly from client parameters.
Example
from langchain_community.embedding.openai import OpenAIEmbeddings
from langchain_community.vectorstores import Typesense
# Pass in typesense_api_key as kwarg or set env var "TYPESENSE_API_KEY".
vectorstore = Typesense(
    OpenAIEmbeddings(),
    host="localhost",
    port="8108",
    protocol="http",
    typesense_collection_name="langchain-memory",
)
Parameters
embedding (Embeddings) – 
host (str) – 
port (Union[str, int]) – 
protocol (str) – 
typesense_api_key (Optional[str]) –
protocol (str) – 
typesense_api_key (Optional[str]) – 
connection_timeout_seconds (int) – 
kwargs (Any) – 
Return type
Typesense
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_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, typesense_client: Optional[Client] = None, typesense_client_params: Optional[dict] = None, typesense_collection_name: Optional[str] = None, text_key: str = 'text', **kwargs: Any) → Typesense[source]¶
Construct Typesense wrapper from raw text.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
typesense_client (Optional[Client]) – 
typesense_client_params (Optional[dict]) – 
typesense_collection_name (Optional[str]) – 
text_key (str) – 
kwargs (Any) – 
Return type
Typesense
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
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 = 10, filter: Optional[str] = '', **kwargs: Any) → List[Document][source]¶
Return typesense documents most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter (Optional[str]) – typesense filter_by expression to filter documents on
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
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.
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 = 10, filter: Optional[str] = '') → List[Tuple[Document, float]][source]¶
Return typesense documents most similar to query, along with scores.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter (Optional[str]) – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
Examples using Typesense¶
Typesense
langchain_community.vectorstores.tencentvectordb.ConnectionParams¶
class langchain_community.vectorstores.tencentvectordb.ConnectionParams(url: str, key: str, username: str = 'root', timeout: int = 10)[source]¶
Tencent vector DB Connection params.
See the following documentation for details:
https://cloud.tencent.com/document/product/1709/95820
Attribute:
url (str)The access address of the vector database serverthat the client needs to connect to.
key (str): API key for client to access the vector database server,which is used for authentication.
username (str) : Account for client to access the vector database server.
timeout (int) : Request Timeout.
Methods
__init__(url, key[, username, timeout])
Parameters
url (str) – 
key (str) – 
username (str) – 
timeout (int) – 
__init__(url: str, key: str, username: str = 'root', timeout: int = 10)[source]¶
Parameters
url (str) – 
key (str) – 
username (str) – 
timeout (int) – 
Examples using ConnectionParams¶
Baidu VectorDB
Tencent Cloud VectorDB
langchain_community.vectorstores.thirdai_neuraldb.NeuralDBVectorStore¶
class langchain_community.vectorstores.thirdai_neuraldb.NeuralDBVectorStore(db: Any)[source]¶
Vectorstore that uses ThirdAI’s NeuralDB.
To use, you should have the thirdai[neural_db] python package installed.
Example
from langchain_community.vectorstores import NeuralDBVectorStore
from thirdai import neural_db as ndb
db = ndb.NeuralDB()
vectorstore = NeuralDBVectorStore(db=db)
Attributes
db
NeuralDB instance
embeddings
Access the query embedding object if available.
Methods
__init__(db)
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])
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)
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.
associate(source, target)
The vectorstore associates a source phrase with a target phrase.
associate_batch(text_pairs)
Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase.
delete([ids])
Delete by vector ID or other criteria.
from_checkpoint(checkpoint[, thirdai_key])
Create a NeuralDBVectorStore with a base model from a saved checkpoint
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_scratch([thirdai_key])
Create a NeuralDBVectorStore from scratch.
from_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
insert(sources[, train, fast_mode])
Inserts files / document sources into the vectorstore.
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.
save(path)
Saves a NeuralDB instance to disk.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Retrieve {k} contexts with for a given query
similarity_search(query[, k])
Retrieve {k} contexts with for a 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(*args, **kwargs)
Run similarity search with distance.
upvote(query, document_id)
The vectorstore upweights the score of a document for a specific query.
upvote_batch(query_id_pairs)
Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries.
validate_environments(values)
Validate ThirdAI environment variables.
Parameters
db (Any) – 
__init__(db: Any) → None[source]¶
Parameters
db (Any) – 
Return type
None
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]