`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.

See (https://vectara.com).

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

__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.

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_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 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 of metadatas associated with each file

Returns

List of ids associated with each of the files indexed

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 of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
doc_metadata – optional metadata for the document

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

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.

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.

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.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶: 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) → 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]

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶: Return VectorStore initialized from documents and embeddings.

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,
)

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,
)

max_marginal_relevance_search(query: str, fetch_k: int = 50, 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 – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 5.
fetch_k – Number of Documents to fetch to pass to MMR algorithm. Defaults to 50
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.
kwargs – any other querying variable in VectaraQueryConfig

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]¶

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.

similarity_search(query: str, **kwargs: Any) → List[Document][source]¶

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

Parameters

query – Text to look up documents similar to.
VectaraQueryConfig (any other querying variable in) –

Returns

List of Documents most similar to the query

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

Return docs most similar to embedding vector.

Parameters

embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query vector.

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

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

Parameters

query – 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)

Returns

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

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

Run a Vectara query

Parameters

query – Text to look up documents similar to.
config – VectaraQueryConfig object

Returns

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

langchain_community.vectorstores.vectara.Vectara¶

Examples using Vectara¶

`langchain_community.vectorstores.vectara`.Vectara¶