langchain.vectorstores.weaviate.Weaviate

class langchain.vectorstores.weaviate.Weaviate(client: ~typing.Any, index_name: str, text_key: str, embedding: ~typing.Optional[~langchain_core.embeddings.Embeddings] = None, attributes: ~typing.Optional[~typing.List[str]] = None, relevance_score_fn: ~typing.Optional[~typing.Callable[[float], float]] = <function _default_score_normalizer>, by_text: bool = True)[source]

Weaviate vector store.

To use, you should have the weaviate-client python package installed.

Example

import weaviate
from langchain.vectorstores import Weaviate

client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
weaviate = Weaviate(client, index_name, text_key)

Initialize with Weaviate client.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(client, index_name, text_key[, ...])

Initialize with Weaviate 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])

Upload texts with metadata (properties) to Weaviate.

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(...)

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], asynchronously.

asimilarity_search_with_score(*args, **kwargs)

Run similarity search with distance asynchronously.

delete([ids])

Delete by vector IDs.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas, ...])

Construct Weaviate wrapper from raw documents.

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])

Return docs most similar to query.

similarity_search_by_text(query[, k])

Return docs most similar to query.

similarity_search_by_vector(embedding[, k])

Look up similar documents by embedding vector in Weaviate.

similarity_search_with_relevance_scores(query)

Return docs and relevance scores in the range [0, 1].

similarity_search_with_score(query[, k])

Return list of documents most similar to the query text and cosine distance in float for each.

__init__(client: ~typing.Any, index_name: str, text_key: str, embedding: ~typing.Optional[~langchain_core.embeddings.Embeddings] = None, attributes: ~typing.Optional[~typing.List[str]] = None, relevance_score_fn: ~typing.Optional[~typing.Callable[[float], float]] = <function _default_score_normalizer>, by_text: bool = True)[source]

Initialize with Weaviate client.

async aadd_documents(documents: List[Document], **kwargs: Any) List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

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.

add_documents(documents: List[Document], **kwargs: Any) List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str][source]

Upload texts with metadata (properties) to Weaviate.

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool]

Delete by vector ID or other criteria.

Parameters
  • ids – List of ids to delete.

  • **kwargs – 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.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST

Return VectorStore initialized from texts and embeddings.

Return docs selected using the maximal marginal relevance.

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.

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:

    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

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.

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document]

Return docs most similar to embedding vector.

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], asynchronously.

0 is dissimilar, 1 is most similar.

Parameters
  • query – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs

    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)

async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]]

Run similarity search with distance asynchronously.

delete(ids: Optional[List[str]] = None, **kwargs: Any) None[source]

Delete by vector IDs.

Parameters

ids – List of ids to delete.

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST

Return VectorStore initialized from documents and embeddings.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, client: Optional[weaviate.Client] = None, weaviate_url: Optional[str] = None, weaviate_api_key: Optional[str] = None, batch_size: Optional[int] = None, index_name: Optional[str] = None, text_key: str = 'text', by_text: bool = False, relevance_score_fn: Optional[Callable[[float], float]] = <function _default_score_normalizer>, **kwargs: Any) Weaviate[source]

Construct Weaviate wrapper from raw documents.

This is a user-friendly interface that:
  1. Embeds documents.

  2. Creates a new index for the embeddings in the Weaviate instance.

  3. Adds the documents to the newly created Weaviate index.

This is intended to be a quick way to get started.

Parameters
  • texts – Texts to add to vector store.

  • embedding – Text embedding model to use.

  • metadatas – Metadata associated with each text.

  • client – weaviate.Client to use.

  • weaviate_url – The Weaviate URL. If using Weaviate Cloud Services get it from the Details tab. Can be passed in as a named param or by setting the environment variable WEAVIATE_URL. Should not be specified if client is provided.

  • weaviate_api_key – The Weaviate API key. If enabled and using Weaviate Cloud Services, get it from Details tab. Can be passed in as a named param or by setting the environment variable WEAVIATE_API_KEY. Should not be specified if client is provided.

  • batch_size – Size of batch operations.

  • index_name – Index name.

  • text_key – Key to use for uploading/retrieving text to/from vectorstore.

  • by_text – Whether to search by text or by embedding.

  • relevance_score_fn – Function for converting whatever distance function the vector store uses to a relevance score, which is a normalized similarity score (0 means dissimilar, 1 means similar).

  • **kwargs – Additional named parameters to pass to Weaviate.__init__().

Example

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Weaviate

embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
    texts,
    embeddings,
    weaviate_url="http://localhost:8080"
)

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters
  • query – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters
  • embedding – Embedding to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

search(query: str, search_type: str, **kwargs: Any) List[Document]

Return docs most similar to query using specified search type.

Return docs most similar to query.

Parameters
  • query – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query.

similarity_search_by_text(query: str, k: int = 4, **kwargs: Any) List[Document][source]

Return docs most similar to query.

Parameters
  • query – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query.

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document][source]

Look up similar documents by embedding vector in Weaviate.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs

    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)

similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]][source]

Return list of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Examples using Weaviate