Tony20100/langchain_full huggingface dataset:
namespace (str) : The instance data will be partitioned based on the “namespace”
field,If the namespace is enabled, you need to specify the namespace field
name during initialization, Otherwise, the queries cannot be executed
correctly.
embedding_field_separator(str): Delimiter specified for writing vectorfield data, default is comma.
output_fields: Specify the field list returned when invoking OpenSearch,by default it is the value list of the field mapping field.
Methods
__init__(endpoint, instance_id, username, ...)
Parameters
endpoint (str) – 
instance_id (str) – 
username (str) – 
password (str) – 
table_name (str) – 
field_name_mapping (Dict[str, str]) – 
protocol (str) – 
namespace (str) – 
embedding_field_separator (str) – 
output_fields (Optional[List[str]]) – 
__init__(endpoint: str, instance_id: str, username: str, password: str, table_name: str, field_name_mapping: Dict[str, str], protocol: str = 'http', namespace: str = '', embedding_field_separator: str = ',', output_fields: Optional[List[str]] = None) → None[source]¶
Parameters
endpoint (str) – 
instance_id (str) – 
username (str) – 
password (str) – 
table_name (str) – 
field_name_mapping (Dict[str, str]) – 
protocol (str) – 
namespace (str) – 
embedding_field_separator (str) – 
output_fields (Optional[List[str]]) – 
Return type
None
Examples using AlibabaCloudOpenSearchSettings¶
Alibaba Cloud
Alibaba Cloud OpenSearch
langchain_community.vectorstores.bagel.Bagel¶
class langchain_community.vectorstores.bagel.Bagel(cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, embedding_function: Optional[Embeddings] = None, cluster_metadata: Optional[Dict] = None, client: Optional[bagel.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶
Bagel.net Inference platform.
To use, you should have the bagelML python package installed.
Example
from langchain_community.vectorstores import Bagel
vectorstore = Bagel(cluster_name="langchain_store")
Initialize with bagel client
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([cluster_name, client_settings, ...])
Initialize with bagel client
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, embeddings])
Add texts along with their corresponding embeddings and optional metadata to the Bagel cluster.
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(...)
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 IDs.
delete_cluster()
Delete the cluster.
from_documents(documents[, embedding, ids, ...])
Create a Bagel vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create and initialize a Bagel instance from list of texts.
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.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, where])
Run a similarity search with Bagel.
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.
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, where])
Run a similarity search with Bagel and return documents with their corresponding similarity scores.
update_document(document_id, document)
Update a document in the cluster.
Parameters
cluster_name (str) – 
client_settings (Optional[bagel.config.Settings]) – 
embedding_function (Optional[Embeddings]) – 
cluster_metadata (Optional[Dict]) – 
client (Optional[bagel.Client]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
__init__(cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, embedding_function: Optional[Embeddings] = None, cluster_metadata: Optional[Dict] = None, client: Optional[bagel.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶
Initialize with bagel client
Parameters
cluster_name (str) – 
client_settings (Optional[bagel.config.Settings]) – 
embedding_function (Optional[Embeddings]) – 
cluster_metadata (Optional[Dict]) – 
client (Optional[bagel.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]
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → List[str][source]¶
Add texts along with their corresponding embeddings and optional
metadata to the Bagel cluster.
Parameters
texts (Iterable[str]) – Texts to be added.
embeddings (Optional[List[float]]) – List of embeddingvectors
metadatas (Optional[List[dict]]) – Optional list of metadatas.
ids (Optional[List[str]]) – List of unique ID for the texts.
kwargs (Any) – 
Returns
List of unique ID representing the added texts.
Return type
List[str]
Returns
List of unique ID representing 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
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
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
VST
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
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]
async amax_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
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]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
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:
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(
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.
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]¶
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]¶
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.
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 IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Return type
None
delete_cluster() → None[source]¶
Delete the cluster.
Return type
None
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, client: Optional[bagel.Client] = None, cluster_metadata: Optional[Dict] = None, **kwargs: Any) → Bagel[source]¶
Create a Bagel vectorstore from a list of documents.
Parameters
documents (List[Document]) – List of Document objects to add to the
Bagel vectorstore.
embedding (Optional[List[float]]) – List of embedding.
ids (Optional[List[str]]) – List of IDs. Defaults to None.
ids (Optional[List[str]]) – List of IDs. Defaults to None.
cluster_name (str) – The name of the Bagel cluster.
client_settings (Optional[bagel.config.Settings]) – Client settings.
client (Optional[bagel.Client]) – Bagel client instance.
cluster_metadata (Optional[Dict]) – Metadata associated with the
Bagel cluster. Defaults to None.
kwargs (Any) – 
Returns
Bagel vectorstore.
Return type
Bagel
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, cluster_metadata: Optional[Dict] = None, client: Optional[bagel.Client] = None, text_embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → Bagel[source]¶
Create and initialize a Bagel instance from list of texts.
Parameters
texts (List[str]) – List of text content to be added.
cluster_name (str) – The name of the Bagel cluster.
client_settings (Optional[bagel.config.Settings]) – Client settings.
cluster_metadata (Optional[Dict]) – Metadata of the cluster.
embeddings (Optional[Embeddings]) – List of embedding.
metadatas (Optional[List[dict]]) – List of metadata.
ids (Optional[List[str]]) – List of unique ID. Defaults to None.
client (Optional[bagel.Client]) – Bagel client instance.
embedding (Optional[Embeddings]) – 
text_embeddings (Optional[List[List[float]]]) – 
kwargs (Any) – 
Returns
Bagel vectorstore.
Return type
Bagel
Returns
Bagel vectorstore.
Return type
Bagel
get(ids: Optional[OneOrMany[ID]] = None, where: Optional[Where] = None, limit: Optional[int] = None, offset: Optional[int] = None, where_document: Optional[WhereDocument] = None, include: Optional[List[str]] = None) → Dict[str, Any][source]¶
Gets the collection.
Parameters
ids (Optional[OneOrMany[ID]]) – 
where (Optional[Where]) – 
limit (Optional[int]) – 
offset (Optional[int]) – 
where_document (Optional[WhereDocument]) – 
include (Optional[List[str]]) – 
Return type
Dict[str, Any]
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]
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
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 = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Run a similarity search with Bagel.
Parameters
query (str) – The query text to search for similar documents/texts.
Parameters
query (str) – The query text to search for similar documents/texts.
k (int) – The number of results to return.
where (Optional[Dict[str, str]]) – Metadata filters to narrow down.
kwargs (Any) – 
Returns
List of documents objects representing
the documents most similar to the query text.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – 
k (int) – 
where (Optional[Dict[str, str]]) – 
kwargs (Any) – 
Return type
List[Document]
similarity_search_by_vector_with_relevance_scores(query_embeddings: List[float], k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector and similarity score.
Parameters
query_embeddings (List[float]) – 
k (int) – 
where (Optional[Dict[str, str]]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
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:
**kwargs (Any) – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run a similarity search with Bagel and return documents with their
corresponding similarity scores.
Parameters
query (str) – The query text to search for similar documents.
k (int) – The number of results to return.
where (Optional[Dict[str, str]]) – Filter using metadata.
kwargs (Any) – 
Returns
List of tuples, each containing a
Document object representing a similar document and its
corresponding similarity score.
Return type
List[Tuple[Document, float]]
update_document(document_id: str, document: Document) → None[source]¶
Update a document in the cluster.
Parameters
document_id (str) – ID of the document to update.
document (Document) – Document to update.
Return type
None
Examples using Bagel¶
Bagel
BagelDB
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_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct ScaNN wrapper from raw documents.
from_texts(texts, embedding[, metadatas, ids])
Construct ScaNN wrapper from raw documents.
load_local(folder_path, embedding[, ...])
Load ScaNN index, docstore, and index_to_docstore_id from disk.
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_local(folder_path[, index_name])