langchain_community.vectorstores.vectara.Vectara

class langchain_community.vectorstores.vectara.Vectara(vectara_customer_id: Optional[str] = None, vectara_corpus_id: Optional[str] = None, vectara_api_key: Optional[str] = None, vectara_api_timeout: int = 120, source: str = 'langchain')[source]

Vectara API vector store.

Example

from langchain_community.vectorstores import Vectara

vectorstore = Vectara(
    vectara_customer_id=vectara_customer_id,
    vectara_corpus_id=vectara_corpus_id,
    vectara_api_key=vectara_api_key
)

Initialize with Vectara API.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__([vectara_customer_id, ...])

Initialize with Vectara API.

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_files(files_list[, metadatas])

Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain

add_texts(texts[, metadatas, doc_metadata])

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])

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 ID or other criteria.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_files(files[, embedding, metadatas])

Construct Vectara wrapper from raw documents.

from_texts(texts[, embedding, metadatas])

Construct Vectara wrapper from raw documents.

max_marginal_relevance_search(query[, ...])

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, **kwargs)

Return Vectara documents most similar to query, along with scores.

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, **kwargs)

Return Vectara documents most similar to query, along with scores.

vectara_query(query, config, **kwargs)

Run a Vectara query

Parameters
  • vectara_customer_id (Optional[str]) –

  • vectara_corpus_id (Optional[str]) –

  • vectara_api_key (Optional[str]) –

  • vectara_api_timeout (int) –

  • source (str) –

__init__(vectara_customer_id: Optional[str] = None, vectara_corpus_id: Optional[str] = None, vectara_api_key: Optional[str] = None, vectara_api_timeout: int = 120, source: str = 'langchain')[source]

Initialize with Vectara API.

Parameters
  • vectara_customer_id (Optional[str]) –

  • vectara_corpus_id (Optional[str]) –

  • vectara_api_key (Optional[str]) –

  • vectara_api_timeout (int) –

  • source (str) –

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.

  • documents (List[Document]) –

  • 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, **kwargs: Any) List[str]

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

Parameters
  • texts (Iterable[str]) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

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

  • documents (List[Document]) –

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

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

Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain

Parameters
  • files_list (Iterable[str]) – Iterable of strings, each representing a local file path. Files could be text, HTML, PDF, markdown, doc/docx, ppt/pptx, etc. see API docs for full list

  • metadatas (Optional[List[dict]]) – Optional list of metadatas associated with each file

  • kwargs (Any) –

Returns

List of ids associated with each of the files indexed

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, doc_metadata: Optional[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.

  • doc_metadata (Optional[dict]) – optional metadata for the document

  • kwargs (Any) –

Return type

List[str]

This function indexes all the input text strings in the Vectara corpus as a single Vectara document, where each input text is considered a “section” and the metadata are associated with each section. if ‘doc_metadata’ is provided, it is associated with the Vectara document.

Returns

document ID of the document added

Parameters
  • texts (Iterable[str]) –

  • metadatas (Optional[List[dict]]) –

  • doc_metadata (Optional[dict]) –

  • kwargs (Any) –

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
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]) –

  • embedding (Embeddings) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

Return type

VST

Return docs selected using the maximal marginal relevance.

Parameters
  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

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.

Parameters
  • embedding (List[float]) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

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:

    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) –

  • search_type (str) –

  • kwargs (Any) –

Return type

List[Document]

Return docs most similar to query.

Parameters
  • query (str) –

  • k (int) –

  • kwargs (Any) –

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]) –

  • k (int) –

  • kwargs (Any) –

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], asynchronously.

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

Run similarity search with distance asynchronously.

Parameters
  • args (Any) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

delete(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]

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

Return VectorStore initialized from documents and embeddings.

Parameters
Return type

VST

classmethod from_files(files: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any) Vectara[source]

Construct Vectara wrapper from raw documents. This is intended to be a quick way to get started. .. rubric:: Example

from langchain_community.vectorstores import Vectara
vectara = Vectara.from_files(
    files_list,
    vectara_customer_id=customer_id,
    vectara_corpus_id=corpus_id,
    vectara_api_key=api_key,
)
Parameters
  • files (List[str]) –

  • embedding (Optional[Embeddings]) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

Return type

Vectara

classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any) Vectara[source]

Construct Vectara wrapper from raw documents. This is intended to be a quick way to get started. .. rubric:: Example

from langchain_community.vectorstores import Vectara
vectara = Vectara.from_texts(
    texts,
    vectara_customer_id=customer_id,
    vectara_corpus_id=corpus_id,
    vectara_api_key=api_key,
)
Parameters
  • texts (List[str]) –

  • embedding (Optional[Embeddings]) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

Return type

Vectara

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 – Number of Documents to return. Defaults to 5.

  • fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Defaults to 50

  • 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) – any other querying variable in VectaraQueryConfig

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) –

  • search_type (str) –

  • kwargs (Any) –

Return type

List[Document]

Return Vectara documents most similar to query, along with scores.

Parameters
  • query (str) – Text to look up documents similar to.

  • VectaraQueryConfig (any other querying variable in) –

  • 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]

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, **kwargs: Any) List[Tuple[Document, float]][source]

Return Vectara documents most similar to query, along with scores.

Parameters
  • query (str) – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 10.

  • like (any other querying variable in VectaraQueryConfig) –

  • lambda_val (-) – lexical match parameter for hybrid search.

  • filter (-) – filter string

  • score_threshold (-) – minimal score threshold for the result.

  • n_sentence_context (-) – number of sentences before/after the matching segment

  • mmr_config (-) – optional configuration for MMR (see MMRConfig dataclass)

  • summary_config (-) – optional configuration for summary (see SummaryConfig dataclass)

  • kwargs (Any) –

Returns

List of Documents most similar to the query and score for each.

Return type

List[Tuple[Document, float]]

vectara_query(query: str, config: VectaraQueryConfig, **kwargs: Any) List[Tuple[Document, float]][source]

Run a Vectara query

Parameters
  • query (str) – Text to look up documents similar to.

  • config (VectaraQueryConfig) – VectaraQueryConfig object

  • kwargs (Any) –

Returns

A list of k Documents matching the given query If summary is enabled, last document is the summary text with ‘summary’=True

Return type

List[Tuple[Document, float]]

Examples using Vectara