Vector Store

Vector stores.

class llama_index.vector_stores.ChatGPTRetrievalPluginClient(endpoint_url: str, bearer_token: Optional[str] = None, retries: Optional[Retry] = None, batch_size: int = 100, **kwargs: Any)

ChatGPT Retrieval Plugin Client.

In this client, we make use of the endpoints defined by ChatGPT.

Parameters

• endpoint_url (str) – URL of the ChatGPT Retrieval Plugin.

• bearer_token (Optional[str]) – Bearer token for the ChatGPT Retrieval Plugin.

• retries (Optional[Retry]) – Retry object for the ChatGPT Retrieval Plugin.

• batch_size (int) – Batch size for the ChatGPT Retrieval Plugin.

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding_results to index.

property client: None

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Get nodes for response.

class llama_index.vector_stores.ChromaVectorStore(chroma_collection: Any, **kwargs: Any)

Chroma vector store.

In this vector store, embeddings are stored within a ChromaDB collection.

During query time, the index uses ChromaDB to query for the top k most similar nodes.

Parameters

chroma_collection (chromadb.api.models.Collection.Collection) – ChromaDB collection instance

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Return client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

class llama_index.vector_stores.DeepLakeVectorStore(dataset_path: str = 'llama_index', token: Optional[str] = None, read_only: Optional[bool] = False, ingestion_batch_size: int = 1024, ingestion_num_workers: int = 4, overwrite: bool = False)

The DeepLake Vector Store.

In this vector store we store the text, its embedding and a few pieces of its metadata in a deeplake dataset. This implemnetation allows the use of an already existing deeplake dataset if it is one that was created this vector store. It also supports creating a new one if the dataset doesnt exist or if overwrite is set to True.

Parameters

• deeplake_path (str, optional) – Path to the deeplake dataset, where data will be

• "llama_index". (stored. Defaults to) –

• overwrite (bool, optional) – Whether to overwrite existing dataset with same name. Defaults to False.

• token (str, optional) – the deeplake token that allows you to access the dataset with proper access. Defaults to None.

• read_only (bool, optional) – Whether to open the dataset with read only mode.

• ingestion_batch_size (bool, 1024) – used for controlling batched data injestion to deeplake dataset. Defaults to 1024.

• injestion_num_workers (int, 1) – number of workers to use during data injestion. Defaults to 4.

• overwrite – Whether to overwrite existing dataset with the new dataset with the same name.

Raises

• ImportError – Unable to import deeplake.

• UserNotLoggedinException – When user is not logged in with credentials or token.

• TokenPermissionError – When dataset does not exist or user doesn’t have enough permissions to modify the dataset.

• InvalidTokenException – If the specified token is invalid

Returns

Vectorstore that supports add, delete, and query.

Return type

DeepLakeVectorstore

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add the embeddings and their nodes into DeepLake.

Parameters

embedding_results (List[NodeWithEmbedding]) – The embeddings and their data to insert.

Raises

• UserNotLoggedinException – When user is not logged in with credentials or token.

• TokenPermissionError – When dataset does not exist or user doesn’t have enough permissions to modify the dataset.

• InvalidTokenException – If the specified token is invalid

Returns

List of ids inserted.

Return type

List[str]

property client: None

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

class llama_index.vector_stores.DocArrayHnswVectorStore(work_dir: str, dim: int = 1536, dist_metric: Literal['cosine', 'ip', 'l2'] = 'cosine', max_elements: int = 1024, ef_construction: int = 200, ef: int = 10, M: int = 16, allow_replace_deleted: bool = True, num_threads: int = 1)

Class representing a DocArray HNSW vector store.

This class is a lightweight Document Index implementation provided by Docarray. It stores vectors on disk in hnswlib, and stores all other data in SQLite.

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Adds embedding results to the vector store.

Parameters

• embedding_results (List[NodeWithEmbedding]) – List of nodes

• embeddings. (with corresponding) –

Returns

List of document IDs added to the vector store.

Return type

List[str]

property client: Any

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Deletes a document from the vector store.

Parameters

• ref_doc_id (str) – Document ID to be deleted.

• **delete_kwargs (Any) – Additional arguments to pass to the delete method.

num_docs() → int

Retrieves the number of documents in the index.

Returns

The number of documents in the index.

Return type

int

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Queries the vector store and retrieves the results.

Parameters

query (VectorStoreQuery) – Query for the vector store.

Returns

Result of the query from vector store.

Return type

VectorStoreQueryResult

class llama_index.vector_stores.DocArrayInMemoryVectorStore(index_path: Optional[str] = None, metric: Literal['cosine_sim', 'euclidian_dist', 'sgeuclidean_dist'] = 'cosine_sim')

Class representing a DocArray In-Memory vector store.

This class is a document index provided by Docarray that stores documents in memory.

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Adds embedding results to the vector store.

Parameters

• embedding_results (List[NodeWithEmbedding]) – List of nodes

• embeddings. (with corresponding) –

Returns

List of document IDs added to the vector store.

Return type

List[str]

property client: Any

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Deletes a document from the vector store.

Parameters

• ref_doc_id (str) – Document ID to be deleted.

• **delete_kwargs (Any) – Additional arguments to pass to the delete method.

num_docs() → int

Retrieves the number of documents in the index.

Returns

The number of documents in the index.

Return type

int

persist(persist_path: str, fs: Optional[AbstractFileSystem] = None) → None

Persists the in-memory vector store to a file.

Parameters

• persist_path (str) – The path to persist the index.

• fs (fsspec.AbstractFileSystem, optional) – Filesystem to persist to. (doesn’t apply)

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Queries the vector store and retrieves the results.

Parameters

query (VectorStoreQuery) – Query for the vector store.

Returns

Result of the query from vector store.

Return type

VectorStoreQueryResult

class llama_index.vector_stores.FaissVectorStore(faiss_index: Any)

Faiss Vector Store.

Embeddings are stored within a Faiss index.

During query time, the index uses Faiss to query for the top k embeddings, and returns the corresponding indices.

Parameters

faiss_index (faiss.Index) – Faiss index instance

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

NOTE: in the Faiss vector store, we do not store text in Faiss.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Return the faiss index.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

persist(persist_path: str = './storage/vector_store.json', fs: Optional[AbstractFileSystem] = None) → None

Save to file.

This method saves the vector store to disk.

Parameters

persist_path (str) – The save_path of the file.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

class llama_index.vector_stores.LanceDBVectorStore(uri: str, table_name: str = 'vectors', nprobes: int = 20, refine_factor: Optional[int] = None, **kwargs: Any)

The LanceDB Vector Store.

Stores text and embeddings in LanceDB. The vector store will open an existing

LanceDB dataset or create the dataset if it does not exist.

Parameters

• uri (str, required) – Location where LanceDB will store its files.

• table_name (str, optional) – The table name where the embeddings will be stored. Defaults to “vectors”.

• nprobes (int, optional) – The number of probes used. A higher number makes search more accurate but also slower. Defaults to 20.

• refine_factor – (int, optional): Refine the results by reading extra elements and re-ranking them in memory. Defaults to None

Raises

ImportError – Unable to import lancedb.

Returns

VectorStore that supports creating LanceDB datasets and

querying it.

Return type

LanceDBVectorStore

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to vector store.

property client: None

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

class llama_index.vector_stores.MetalVectorStore(api_key: str, client_id: str, index_id: str)

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeEmbeddingResult]: list of embedding results

property client: Any

Return Metal client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query vector store.

class llama_index.vector_stores.MilvusVectorStore(collection_name: str = 'llamalection', index_params: Optional[dict] = None, search_params: Optional[dict] = None, dim: Optional[int] = None, host: str = 'localhost', port: int = 19530, user: str = '', password: str = '', use_secure: bool = False, overwrite: bool = False, **kwargs: Any)

The Milvus Vector Store.

In this vector store we store the text, its embedding and a few pieces of its metadata in a Milvus collection. This implemnetation allows the use of an already existing collection if it is one that was created this vector store. It also supports creating a new one if the collection doesnt exist or if overwrite is set to True.

Parameters

• collection_name (str, optional) – The name of the collection where data will be stored. Defaults to “llamalection”.

• index_params (dict, optional) – The index parameters for Milvus, if none are provided an HNSW index will be used. Defaults to None.

• search_params (dict, optional) – The search parameters for a Milvus query. If none are provided, default params will be generated. Defaults to None.

• dim (int, optional) – The dimension of the embeddings. If it is not provided, collection creation will be done on first insert. Defaults to None.

• host (str, optional) – The host address of Milvus. Defaults to “localhost”.

• port (int, optional) – The port of Milvus. Defaults to 19530.

• user (str, optional) – The username for RBAC. Defaults to “”.

• password (str, optional) – The password for RBAC. Defaults to “”.

• use_secure (bool, optional) – Use https. Required for Zilliz Cloud. Defaults to False.

• overwrite (bool, optional) – Whether to overwrite existing collection with same name. Defaults to False.

Raises

• ImportError – Unable to import pymilvus.

• MilvusException – Error communicating with Milvus, more can be found in logging under Debug.

Returns

Vectorstore that supports add, delete, and query.

Return type

MilvusVectorstore

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add the embeddings and their nodes into Milvus.

Parameters

embedding_results (List[NodeWithEmbedding]) – The embeddings and their data to insert.

Raises

MilvusException – Failed to insert data.

Returns

List of ids inserted.

Return type

List[str]

property client: Any

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

Raises

MilvusException – Failed to delete the doc.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

• doc_ids (Optional[List[str]]) – list of doc_ids to filter by

class llama_index.vector_stores.MyScaleVectorStore(myscale_client: Optional[Any] = None, table: str = 'llama_index', database: str = 'default', index_type: str = 'IVFFLAT', metric: str = 'cosine', batch_size: int = 32, index_params: Optional[dict] = None, search_params: Optional[dict] = None, service_context: Optional[ServiceContext] = None, **kwargs: Any)

MyScale Vector Store.

In this vector store, embeddings and docs are stored within an existing MyScale cluster.

During query time, the index uses MyScale to query for the top k most similar nodes.

Parameters

• myscale_client (httpclient) – clickhouse-connect httpclient of an existing MyScale cluster.

• table (str, optional) – The name of the MyScale table where data will be stored. Defaults to “llama_index”.

• database (str, optional) – The name of the MyScale database where data will be stored. Defaults to “default”.

• index_type (str, optional) – The type of the MyScale vector index. Defaults to “IVFFLAT”.

• metric (str, optional) – The metric type of the MyScale vector index. Defaults to “cosine”.

• batch_size (int, optional) – the size of documents to insert. Defaults to 32.

• index_params (dict, optional) – The index parameters for MyScale. Defaults to None.

• search_params (dict, optional) – The search parameters for a MyScale query. Defaults to None.

• service_context (ServiceContext, optional) – Vector store service context. Defaults to None

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

drop() → None

Drop MyScale Index and table

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

query (VectorStoreQuery) – query

class llama_index.vector_stores.OpensearchVectorClient(endpoint: str, index: str, dim: int, embedding_field: str = 'embedding', text_field: str = 'content', extra_info_field: str = 'extra_info', method: Optional[dict] = None, auth: Optional[dict] = None)

Object encapsulating an Opensearch index that has vector search enabled.

If the index does not yet exist, it is created during init. Therefore, the underlying index is assumed to either: 1) not exist yet or 2) be created due to previous usage of this class.

Parameters

• endpoint (str) – URL (http/https) of elasticsearch endpoint

• index (str) – Name of the elasticsearch index

• dim (int) – Dimension of the vector

• embedding_field (str) – Name of the field in the index to store embedding array in.

• text_field (str) – Name of the field to grab text from

• method (Optional[dict]) – Opensearch “method” JSON obj for configuring the KNN index. This includes engine, metric, and other config params. Defaults to: {“name”: “hnsw”, “space_type”: “l2”, “engine”: “faiss”, “parameters”: {“ef_construction”: 256, “m”: 48}}

delete_doc_id(doc_id: str) → None

Delete a document.

Parameters

doc_id (str) – document id

do_approx_knn(query_embedding: List[float], k: int) → VectorStoreQueryResult

Do approximate knn.

index_results(results: List[NodeWithEmbedding]) → List[str]

Store results in the index.

class llama_index.vector_stores.OpensearchVectorStore(client: OpensearchVectorClient)

Elasticsearch/Opensearch vector store.

Parameters

client (OpensearchVectorClient) – Vector index client to use for data insertion/querying.

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

class llama_index.vector_stores.PineconeVectorStore(pinecone_index: Optional[Any] = None, index_name: Optional[str] = None, environment: Optional[str] = None, namespace: Optional[str] = None, insert_kwargs: Optional[Dict] = None, add_sparse_vector: bool = False, tokenizer: Optional[Callable] = None, **kwargs: Any)

Pinecone Vector Store.

In this vector store, embeddings and docs are stored within a Pinecone index.

During query time, the index uses Pinecone to query for the top k most similar nodes.

Parameters

• pinecone_index (Optional[pinecone.Index]) – Pinecone index instance

• insert_kwargs (Optional[Dict]) – insert kwargs during upsert call.

• add_sparse_vector (bool) – whether to add sparse vector to index.

• tokenizer (Optional[Callable]) – tokenizer to use to generate sparse

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Return Pinecone client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

• query_embedding (List[float]) – query embedding

• similarity_top_k (int) – top k most similar nodes

class llama_index.vector_stores.QdrantVectorStore(collection_name: str, client: Optional[Any] = None, **kwargs: Any)

Qdrant Vector Store.

In this vector store, embeddings and docs are stored within a Qdrant collection.

During query time, the index uses Qdrant to query for the top k most similar nodes.

Parameters

• collection_name – (str): name of the Qdrant collection

• client (Optional[Any]) – QdrantClient instance from qdrant-client package

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Return the Qdrant client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

query (VectorStoreQuery) – query

class llama_index.vector_stores.RedisVectorStore(index_name: str, index_prefix: str = 'llama_index', index_args: Optional[Dict[str, Any]] = None, metadata_fields: Optional[List[str]] = None, redis_url: str = 'redis://localhost:6379', overwrite: bool = False, **kwargs: Any)

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to the index.

Parameters

embedding_results (List[NodeWithEmbedding]) – List of embedding results to add to the index.

Returns

List of ids of the documents added to the index.

Return type

List[str]

Raises

ValueError – If the index already exists and overwrite is False.

property client: RedisType

Return the redis client instance

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

delete_index() → None

Delete the index and all documents.

persist(persist_path: str, fs: Optional[AbstractFileSystem] = None, in_background: bool = True) → None

Persist the vector store to disk.

Parameters

• persist_path (str) – Path to persist the vector store to. (doesn’t apply)

• in_background (bool, optional) – Persist in background. Defaults to True.

• fs (fsspec.AbstractFileSystem, optional) – Filesystem to persist to. (doesn’t apply)

Raises

redis.exceptions.RedisError – If there is an error persisting the index to disk.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query the index.

Parameters

query (VectorStoreQuery) – query object

Returns

query result

Return type

VectorStoreQueryResult

Raises

• ValueError – If query.query_embedding is None.

• redis.exceptions.RedisError – If there is an error querying the index.

• redis.exceptions.TimeoutError – If there is a timeout querying the index.

• ValueError – If no documents are found when querying the index.

class llama_index.vector_stores.SimpleVectorStore(data: Optional[SimpleVectorStoreData] = None, fs: Optional[AbstractFileSystem] = None, **kwargs: Any)

Simple Vector Store.

In this vector store, embeddings are stored within a simple, in-memory dictionary.

Parameters

simple_vector_store_data_dict (Optional[dict]) – data dict containing the embeddings and doc_ids. See SimpleVectorStoreData for more details.

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding_results to index.

property client: None

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

classmethod from_persist_dir(persist_dir: str = './storage', fs: Optional[AbstractFileSystem] = None) → SimpleVectorStore

Load from persist dir.

classmethod from_persist_path(persist_path: str, fs: Optional[AbstractFileSystem] = None) → SimpleVectorStore

Create a SimpleKVStore from a persist directory.

get(text_id: str) → List[float]

Get embedding.

persist(persist_path: str = './storage/vector_store.json', fs: Optional[AbstractFileSystem] = None) → None

Persist the SimpleVectorStore to a directory.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Get nodes for response.

class llama_index.vector_stores.SupabaseVectorStore(postgres_connection_string: str, collection_name: str, dimension: int = 1536, **kwargs: Any)

Supbabase Vector.

In this vector store, embeddings are stored in Postgres table using pgvector.

During query time, the index uses pgvector/Supabase to query for the top k most similar nodes.

Parameters

• postgres_connection_string (str) – postgres connection string

• collection_name (str) – name of the collection to store the embeddings in

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: None

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete doc.

Parameters

doc_id (str) – document id

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters

query (List[float]) – query embedding

class llama_index.vector_stores.TairVectorStore(tair_url: str, index_name: str, index_type: str = 'HNSW', index_args: Optional[Dict[str, Any]] = None, overwrite: bool = False, **kwargs: Any)

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to the index.

Parameters

embedding_results (List[NodeWithEmbedding]) – List of embedding results to add to the index.

Returns

List of ids of the documents added to the index.

Return type

List[str]

property client: Tair

Return the Tair client instance

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete a document.

Parameters

doc_id (str) – document id

delete_index() → None

Delete the index and all documents.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query the index.

Parameters

query (VectorStoreQuery) – query object

Returns

query result

Return type

VectorStoreQueryResult

Raises

ValueError – If query.query_embedding is None.

class llama_index.vector_stores.WeaviateVectorStore(weaviate_client: Optional[Any] = None, class_prefix: Optional[str] = None, **kwargs: Any)

Weaviate vector store.

In this vector store, embeddings and docs are stored within a Weaviate collection.

During query time, the index uses Weaviate to query for the top k most similar nodes.

Parameters

• weaviate_client (weaviate.Client) – WeaviateClient instance from weaviate-client package

• class_prefix (Optional[str]) – prefix for Weaviate classes

add(embedding_results: List[NodeWithEmbedding]) → List[str]

Add embedding results to index.

Args

embedding_results: List[NodeWithEmbedding]: list of embedding results

property client: Any

Get client.

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query index for top k most similar nodes.