Tony20100/langchain_full huggingface dataset:
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, *, batch_size: Optional[int] = None, batch_concurrency: Optional[int] = None, overwrite_concurrency: Optional[int] = None, **kwargs: Any) → List[str][source]¶
Run texts through the embeddings and add them to the vectorstore.
If passing explicit ids, those entries whose id is in the store already
will be replaced.
Parameters
texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas.
ids (Optional[List[str]]) – Optional list of ids.
batch_size (Optional[int]) – Number of documents in each API call.
Check the underlying Astra DB HTTP API specs for the max value
(20 at the time of writing this). If not provided, defaults
to the instance-level setting.
batch_concurrency (Optional[int]) – number of threads to process
insertion batches concurrently. Defaults to instance-level
setting if not provided.
overwrite_concurrency (Optional[int]) – number of threads to process
pre-existing documents in each batch (which require individual
API calls). Defaults to instance-level setting if not provided.
kwargs (Any) – 
Return type
List[str]
Note
There are constraints on the allowed field names
in the metadata dictionaries, coming from the underlying Astra DB API.
For instance, the $ (dollar sign) cannot be used in the dict keys.
See this document for details:
https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html
Returns
The list of ids of the added texts.
Parameters
Returns
The list of ids of the added texts.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
batch_size (Optional[int]) – 
batch_concurrency (Optional[int]) – 
overwrite_concurrency (Optional[int]) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, concurrency: Optional[int] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ids.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
concurrency (Optional[int]) – max concurrency of single-doc delete requests.
Defaults to instance-level setting.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful, False otherwise.
Return type
Optional[bool]
async adelete_by_document_id(document_id: str) → bool[source]¶
Remove a single document from the store, given its document ID.
Parameters
document_id (str) – The document ID
Return type
bool
ReturnsTrue if a document has indeed been deleted, False if ID not found.
async adelete_collection() → None[source]¶
Completely delete the collection from the database (as opposed
to aclear(), which empties it only).
Stored data is lost and unrecoverable, resources are freed.
Use with caution.
Return type
None
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.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → ADBVST[source]¶
Create an Astra DB vectorstore from raw texts.
Parameters
texts (List[str]) – the texts to insert.
embedding (Embeddings) – the embedding function to use in the store.
metadatas (Optional[List[dict]]) – metadata dicts for the texts.
ids (Optional[List[str]]) – ids to associate to the texts.
**kwargs (Any) – you can pass any argument that you would
to add_texts() and/or to the ‘AstraDB’ constructor
(see these methods for details). These arguments will be
routed to the respective methods as they are.
Returns
an AstraDb vectorstore.
Return type
ADBVST
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = 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.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return.
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.
to maximum diversity and 1 to minimum diversity.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = 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.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return.
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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents selected by maximal marginal relevance.
Return type
List[Document]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
kwargs (Any) – 
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text.
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]
async asimilarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = 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, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The 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
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(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with score.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The 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, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector with score.
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, Any]]) – Filter on the metadata to apply.
Returns
The 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, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to the query with score and id.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query.
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, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to embedding vector with score and id.
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, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
clear() → None[source]¶
Empty the collection of all its stored entries.
Return type
None
delete(ids: Optional[List[str]] = None, concurrency: Optional[int] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ids.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
concurrency (Optional[int]) – max number of threads issuing single-doc delete requests.
Defaults to instance-level setting.
kwargs (Any) – 
Returns
True if deletion is successful, False otherwise.
Return type
Optional[bool]
delete_by_document_id(document_id: str) → bool[source]¶
Remove a single document from the store, given its document ID.
Parameters
document_id (str) – The document ID
Return type
bool
ReturnsTrue if a document has indeed been deleted, False if ID not found.
delete_collection() → None[source]¶
Completely delete the collection from the database (as opposed
to clear(), which empties it only).
Stored data is lost and unrecoverable, resources are freed.
Use with caution.
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → ADBVST[source]¶
Create an Astra DB vectorstore from a document list.
Utility method that defers to ‘from_texts’ (see that one).
Args: see ‘from_texts’, except here you have to supply ‘documents’in place of ‘texts’ and ‘metadatas’.
Returns
an AstraDB vectorstore.
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
kwargs (Any) – 
Return type
ADBVST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → ADBVST[source]¶
Create an Astra DB vectorstore from raw texts.
Parameters
texts (List[str]) – the texts to insert.
Parameters
texts (List[str]) – the texts to insert.
embedding (Embeddings) – the embedding function to use in the store.
metadatas (Optional[List[dict]]) – metadata dicts for the texts.
ids (Optional[List[str]]) – ids to associate to the texts.
**kwargs (Any) – you can pass any argument that you would
to add_texts() and/or to the ‘AstraDB’ constructor
(see these methods for details). These arguments will be
routed to the respective methods as they are.
Returns
an AstraDb vectorstore.
Return type
ADBVST
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = 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.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return.
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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents selected by maximal marginal relevance.
Return type
List[Document]
The 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, Any]] = 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.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return.
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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The 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, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
Return docs most similar to query.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = 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, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The 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]]
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with score.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector with score.
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, Any]]) – Filter on the metadata to apply.
Returns
The 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, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to the query with score and id.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query.
Return type
List[Tuple[Document, float, str]]
similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to embedding vector with score and id.
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, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
Examples using AstraDB¶
Astra DB (Cassandra)
langchain_community.vectorstores.kdbai.KDBAI¶
class langchain_community.vectorstores.kdbai.KDBAI(table: Any, embedding: Embeddings, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
KDB.AI vector store.
See [https://kdb.ai](https://kdb.ai)
To use, you should have the kdbai_client python package installed.
Parameters
table (Any) – kdbai_client.Table object to use as storage,
embedding (Embeddings) – Any embedding function implementing
langchain.embeddings.base.Embeddings interface,
distance_strategy (Optional[DistanceStrategy]) – One option from DistanceStrategy.EUCLIDEAN_DISTANCE,
DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE.
See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb).
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(table, embedding[, distance_strategy])
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[, batch_size])
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, batch_size])
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])
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_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Not implemented.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search from a query string.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(...)
Return documents most similar to embedding, along with scores.