Tony20100/langchain_full huggingface dataset:
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
kwargs (Any) – 
Returns
List of Document, the most similar to the query vector.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
kwargs (Any) – 
Returns
List of Document, the 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
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(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score_id(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
async asimilarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
clear() → None[source]¶
List[Tuple[Document, float, str]]
clear() → None[source]¶
Empty the table.
Return type
None
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
delete_by_document_id(document_id: str) → None[source]¶
Delete by document ID.
Parameters
document_id (str) – the document ID to delete.
Return type
None
delete_collection() → None[source]¶
Just an alias for clear
(to better align with other VectorStore implementations).
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, metadata_indexing: Union[Tuple[str, Iterable[str]], str] = 'all', **kwargs: Any) → CVST[source]¶
Create a Cassandra vectorstore from a document list.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
session (Optional[Session]) – Cassandra driver session.
If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space.
If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
table_name (str) – Cassandra table (required).
ids (Optional[List[str]]) – Optional list of IDs associated with the documents.
batch_size (int) – Number of concurrent requests to send to the server.
Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added documents.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index.
Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
metadata_indexing (Union[Tuple[str, Iterable[str]], str]) – 
kwargs (Any) – 
Returns
a Cassandra vectorstore.
Return type
CVST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = '', ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, metadata_indexing: Union[Tuple[str, Iterable[str]], str] = 'all', **kwargs: Any) → CVST[source]¶
Create a Cassandra vectorstore from raw texts.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
session (Optional[Session]) – Cassandra driver session.
If not provided, it is resolved from cassio.
keyspace (Optional[str]) – Cassandra key space.
If not provided, it is resolved from cassio.
table_name (str) – Cassandra table (required).
table_name (str) – Cassandra table (required).
ids (Optional[List[str]]) – Optional list of IDs associated with the texts.
batch_size (int) – Number of concurrent requests to send to the server.
Defaults to 16.
ttl_seconds (Optional[int]) – Optional time-to-live for the added texts.
body_index_options (Optional[List[Tuple[str, Any]]]) – Optional options used to create the body index.
Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
metadata_indexing (Union[Tuple[str, Iterable[str]], str]) – 
kwargs (Any) – 
Returns
a Cassandra vectorstore.
Return type
CVST
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
Parameters
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
query (str) – 
k (int) – 
fetch_k (int) – 
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, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param embedding: Embedding to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
Parameters
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
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[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
kwargs (Any) – 
Returns
List of Document, the most similar to the query vector.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, **kwargs: Any) → List[Document][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
kwargs (Any) – 
Returns
List of Document, the 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 = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float]][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
similarity_search_with_score_id(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
Return type
List[Tuple[Document, float, str]]
similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None) → List[Tuple[Document, float, str]][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter on the metadata to apply.
body_search (Optional[Union[str, List[str]]]) – Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns
List of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
Examples using Cassandra¶
Apache Cassandra
Cassandra
Hybrid Search
langchain_community.vectorstores.neo4j_vector.collect_params¶
langchain_community.vectorstores.neo4j_vector.collect_params(input_data: List[Tuple[str, Dict[str, str]]]) → Tuple[List[str], Dict[str, Any]][source]¶
Transform the input data into the desired format.
Args:
- input_data (list of tuples): Input data to transform.
Each tuple contains a string and a dictionary.
Returns:
- tuple: A tuple containing a list of strings and a dictionary.
Parameters
input_data (List[Tuple[str, Dict[str, str]]]) – 
Return type
Tuple[List[str], Dict[str, Any]]
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 IDs.
delete_collection()
Delete the collection.
encode_image(uri)
Get base64 string from image URI.
from_documents(documents[, embedding, ids, ...])
Create a Chroma vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create a Chroma vectorstore from a raw documents.
get([ids, where, limit, offset, ...])
Gets the collection.
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.
persist()
[Deprecated] Persist the collection.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with Chroma.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)
Return docs most similar to embedding vector and similarity score.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with Chroma with distance.
update_document(document_id, document)
Update a document in the collection.
update_documents(ids, documents)
Update a document in the collection.
Parameters
collection_name (str) – 
embedding_function (Optional[Embeddings]) – 
persist_directory (Optional[str]) – 
client_settings (Optional[chromadb.config.Settings]) – 
collection_metadata (Optional[Dict]) – 
client (Optional[chromadb.Client]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
__init__(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[chromadb.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶
Initialize with a Chroma client.
Parameters
collection_name (str) – 
embedding_function (Optional[Embeddings]) – 
persist_directory (Optional[str]) – 
client_settings (Optional[chromadb.config.Settings]) – 
collection_metadata (Optional[Dict]) – 
client (Optional[chromadb.Client]) –
client (Optional[chromadb.Client]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
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]
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_images(uris: List[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more images through the embeddings and add to the vectorstore.
Parameters
List[str] (uris) – File path to the image.
Parameters
List[str] (uris) – File path to the image.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
ids (Optional[List[str]], optional) – Optional list of IDs.
uris (List[str]) – 
kwargs (Any) – 
Returns
List of IDs of the added images.
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 embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
ids (Optional[List[str]], optional) – Optional list of IDs.
kwargs (Any) – 
Returns
List of IDs of 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
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 by vector IDs.
Parameters