Tony20100/langchain_full huggingface dataset:
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][source]¶
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[Sequence[str]] = None, **kwargs: Any) → List[str][source]¶
Add texts to the store.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[Sequence[str]]) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[Sequence[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[Sequence[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.
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
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → InMemoryVectorStore[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
InMemoryVectorStore
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
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.
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, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
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]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **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.
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
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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[Sequence[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[Sequence[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]
dump(path: str) → None[source]¶
Parameters
path (str) – 
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_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → InMemoryVectorStore[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
InMemoryVectorStore
classmethod load(path: str, embedding: Embeddings, **kwargs: Any) → InMemoryVectorStore[source]¶
Parameters
path (str) – 
embedding (Embeddings) – 
kwargs (Any) – 
Return type
InMemoryVectorStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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) – 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][source]¶
Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – 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) – Input text.
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_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
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 = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Callable[[Document], bool]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) –
langchain_community.vectorstores.astradb.AstraDB¶
class langchain_community.vectorstores.astradb.AstraDB(*, embedding: Embeddings, collection_name: str, token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[LibAstraDB] = None, async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, metric: Optional[str] = None, batch_size: Optional[int] = None, bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, setup_mode: SetupMode = SetupMode.SYNC, pre_delete_collection: bool = False)[source]¶
[Deprecated]
Notes
Deprecated since version 0.0.21.
Wrapper around DataStax Astra DB for vector-store workloads.
For quickstart and details, visit
https://docs.datastax.com/en/astra/astra-db-vector/
Example
from langchain_community.vectorstores import AstraDB
from langchain_openai.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = AstraDB(
    embedding=embeddings,
    collection_name="my_store",
    token="AstraCS:...",
    api_endpoint="https://<DB-ID>-<REGION>.apps.astra.datastax.com"
)
vectorstore.add_texts(["Giraffes", "All good here"])
results = vectorstore.similarity_search("Everything's ok", k=1)
Parameters
embedding (Embeddings) – embedding function to use.
collection_name (str) – name of the Astra DB collection to create/use.
token (Optional[str]) – API token for Astra DB usage.
token (Optional[str]) – API token for Astra DB usage.
api_endpoint (Optional[str]) – full URL to the API endpoint, such as
https://<DB-ID>-us-east1.apps.astra.datastax.com.
astra_db_client (Optional[LibAstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AstraDB’ instance.
async_astra_db_client (Optional[AsyncAstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance.
namespace (Optional[str]) – namespace (aka keyspace) where the collection is created.
Defaults to the database’s “default namespace”.
metric (Optional[str]) – similarity function to use out of those available in Astra DB.
If left out, it will use Astra DB API’s defaults (i.e. “cosine” - but,
for performance reasons, “dot_product” is suggested if embeddings are
normalized to one).
batch_size (Optional[int]) – Size of batches for bulk insertions.
bulk_insert_batch_concurrency (Optional[int]) – Number of threads or coroutines to insert
batches concurrently.
bulk_insert_overwrite_concurrency (Optional[int]) – Number of threads  or coroutines in a
batch to insert pre-existing entries.
bulk_delete_concurrency (Optional[int]) – Number of threads (for deleting multiple rows
concurrently).
pre_delete_collection (bool) – whether to delete the collection before creating it.
If False and the collection already exists, the collection will be used
as is.
setup_mode (SetupMode) – 
Note
For concurrency in synchronous add_texts():, as a rule of thumb, on a
typical client machine it is suggested to keep the quantity
typical client machine it is suggested to keep the quantity
bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency
much below 1000 to avoid exhausting the client multithreading/networking
resources. The hardcoded defaults are somewhat conservative to meet
most machines’ specs, but a sensible choice to test may be:
bulk_insert_batch_concurrency = 80
bulk_insert_overwrite_concurrency = 10
A bit of experimentation is required to nail the best results here,
depending on both the machine/network specs and the expected workload
(specifically, how often a write is an update of an existing id).
Remember you can pass concurrency settings to individual calls to
add_texts() and add_documents() as well.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(*, embedding, collection_name[, ...])
Wrapper around DataStax Astra DB for vector-store workloads.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas, ids, ...])
Run texts through the embeddings and add them to the vectorstore.
aclear()
Empty the collection of all its stored entries.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, ...])
Run texts through the embeddings and add them to the vectorstore.
adelete([ids, concurrency])
Delete by vector ids.
adelete_by_document_id(document_id)
Remove a single document from the store, given its document ID.
adelete_collection()
Completely delete the collection from the database (as opposed to aclear(), which empties it only).
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas, ids])
Create an Astra DB vectorstore from raw texts.
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, filter])
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(query[, k, filter])
Return docs most similar to query with score.
asimilarity_search_with_score_by_vector(...)
Return docs most similar to embedding vector with score.
asimilarity_search_with_score_id(query[, k, ...])
Return docs most similar to the query with score and id.
asimilarity_search_with_score_id_by_vector(...)
Return docs most similar to embedding vector with score and id.
clear()
Empty the collection of all its stored entries.
delete([ids, concurrency])
Delete by vector ids.
delete_by_document_id(document_id)
Remove a single document from the store, given its document ID.
delete_collection()
Completely delete the collection from the database (as opposed to clear(), which empties it only).
from_documents(documents, embedding, **kwargs)
Create an Astra DB vectorstore from a document list.
from_texts(texts, embedding[, metadatas, ids])
Create an Astra DB vectorstore from raw 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)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Return docs 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 docs most similar to query with score.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to embedding vector with score.
similarity_search_with_score_id(query[, k, ...])
Return docs most similar to the query with score and id.
similarity_search_with_score_id_by_vector(...)
Return docs most similar to embedding vector with score and id.
Return docs most similar to embedding vector with score and id.
__init__(*, embedding: Embeddings, collection_name: str, token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[LibAstraDB] = None, async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, metric: Optional[str] = None, batch_size: Optional[int] = None, bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, setup_mode: SetupMode = SetupMode.SYNC, pre_delete_collection: bool = False) → None[source]¶
Wrapper around DataStax Astra DB for vector-store workloads.
For quickstart and details, visit
https://docs.datastax.com/en/astra/astra-db-vector/
Example
from langchain_community.vectorstores import AstraDB
from langchain_openai.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = AstraDB(
    embedding=embeddings,
    collection_name="my_store",
    token="AstraCS:...",
    api_endpoint="https://<DB-ID>-<REGION>.apps.astra.datastax.com"
)
vectorstore.add_texts(["Giraffes", "All good here"])
results = vectorstore.similarity_search("Everything's ok", k=1)
Parameters
embedding (Embeddings) – embedding function to use.
collection_name (str) – name of the Astra DB collection to create/use.
token (Optional[str]) – API token for Astra DB usage.
api_endpoint (Optional[str]) – full URL to the API endpoint, such as
api_endpoint (Optional[str]) – full URL to the API endpoint, such as
https://<DB-ID>-us-east1.apps.astra.datastax.com.
astra_db_client (Optional[LibAstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AstraDB’ instance.
async_astra_db_client (Optional[AsyncAstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance.
namespace (Optional[str]) – namespace (aka keyspace) where the collection is created.
Defaults to the database’s “default namespace”.
metric (Optional[str]) – similarity function to use out of those available in Astra DB.
If left out, it will use Astra DB API’s defaults (i.e. “cosine” - but,
for performance reasons, “dot_product” is suggested if embeddings are
normalized to one).
batch_size (Optional[int]) – Size of batches for bulk insertions.
bulk_insert_batch_concurrency (Optional[int]) – Number of threads or coroutines to insert
batches concurrently.
bulk_insert_overwrite_concurrency (Optional[int]) – Number of threads  or coroutines in a
batch to insert pre-existing entries.
bulk_delete_concurrency (Optional[int]) – Number of threads (for deleting multiple rows
concurrently).
pre_delete_collection (bool) – whether to delete the collection before creating it.
If False and the collection already exists, the collection will be used
as is.
setup_mode (SetupMode) – 
Return type
None
Note
For concurrency in synchronous add_texts():, as a rule of thumb, on a
typical client machine it is suggested to keep the quantity
bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency
bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency
much below 1000 to avoid exhausting the client multithreading/networking
resources. The hardcoded defaults are somewhat conservative to meet
most machines’ specs, but a sensible choice to test may be:
bulk_insert_batch_concurrency = 80
bulk_insert_overwrite_concurrency = 10
A bit of experimentation is required to nail the best results here,
depending on both the machine/network specs and the expected workload
(specifically, how often a write is an update of an existing id).
Remember you can pass concurrency settings to individual calls to
add_texts() and add_documents() as well.
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, 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.
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
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 aclear() → None[source]¶
Empty the collection of all its stored entries.
Return type
None
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]