`langchain_community.vectorstores.annoy`.Annoy¶

class langchain_community.vectorstores.annoy.Annoy(embedding_function: Callable, index: Any, metric: str, docstore: Docstore, index_to_docstore_id: Dict[int, str])[source]¶

Annoy vector store.

To use, you should have the annoy python package installed.

Example

from langchain_community.vectorstores import Annoy
db = Annoy(embedding_function, index, docstore, index_to_docstore_id)

Initialize with necessary components.

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(embedding_function, index, metric, ...)	Initialize with necessary components.
`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])	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_embeddings`(text_embeddings, embedding)	Construct Annoy wrapper from embeddings.
`from_texts`(texts, embedding[, metadatas, ...])	Construct Annoy wrapper from raw documents.
`load_local`(folder_path, embeddings, *[, ...])	Load Annoy index, docstore, and index_to_docstore_id to 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.
`process_index_results`(idxs, dists)	Turns annoy results into a list of documents and scores.
`save_local`(folder_path[, prefault])	Save Annoy index, docstore, and index_to_docstore_id to disk.
`search`(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
`similarity_search`(query[, k, search_k])	Return docs most similar to query.
`similarity_search_by_index`(docstore_index[, ...])	Return docs most similar to docstore_index.
`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[, k, ...])	Return docs most similar to query.
`similarity_search_with_score_by_index`(...[, ...])	Return docs most similar to query.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to query.

Parameters

embedding_function (Callable) –
index (Any) –
metric (str) –
docstore (Docstore) –
index_to_docstore_id (Dict[int, str]) –

__init__(embedding_function: Callable, index: Any, metric: str, docstore: Docstore, index_to_docstore_id: Dict[int, str])[source]¶

Initialize with necessary components.

Parameters

embedding_function (Callable) –
index (Any) –
metric (str) –
docstore (Docstore) –
index_to_docstore_id (Dict[int, 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_texts(texts: Iterable[str], metadatas: Optional[List[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.
kwargs (Any) – vectorstore specific parameters

Returns

List of ids from adding the texts into the vectorstore.

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]) –
embedding (Embeddings) –
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]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
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.

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]

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → 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

documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –

Return type

VST

classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, metric: str = 'angular', trees: int = 100, n_jobs: int = -1, **kwargs: Any) → Annoy[source]¶

Construct Annoy wrapper from embeddings.

Parameters

text_embeddings (List[Tuple[str, List[float]]]) – List of tuples of (text, embedding)
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – List of metadata dictionaries to associate with documents.
metric (str) – Metric to use for indexing. Defaults to “angular”.
trees (int) – Number of trees to use for indexing. Defaults to 100.
n_jobs (int) – Number of jobs to use for indexing. Defaults to -1
kwargs (Any) –

Return type

Annoy

This is a user friendly interface that:

Creates an in memory docstore with provided embeddings
Initializes the Annoy database

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

Example

from langchain_community.vectorstores import Annoy
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, metric: str = 'angular', trees: int = 100, n_jobs: int = -1, **kwargs: Any) → Annoy[source]¶

Construct Annoy wrapper from raw documents.

Parameters

texts (List[str]) – List of documents to index.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – List of metadata dictionaries to associate with documents.
metric (str) – Metric to use for indexing. Defaults to “angular”.
trees (int) – Number of trees to use for indexing. Defaults to 100.
n_jobs (int) – Number of jobs to use for indexing. Defaults to -1.
kwargs (Any) –

Return type

Annoy

This is a user friendly interface that:

Embeds documents.
Creates an in memory docstore
Initializes the Annoy database

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

Example

from langchain_community.vectorstores import Annoy
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = Annoy.from_texts(texts, embeddings)

classmethod load_local(folder_path: str, embeddings: Embeddings, *, allow_dangerous_deserialization: bool = False) → Annoy[source]¶

Load Annoy index, docstore, and index_to_docstore_id to disk.

Parameters

folder_path (str) – folder path to load index, docstore, and index_to_docstore_id from.
embeddings (Embeddings) – Embeddings to use when generating queries.
allow_dangerous_deserialization (bool) – whether to allow deserialization of the data which involves loading a pickle file. Pickle files can be modified by malicious actors to deliver a malicious payload that results in execution of arbitrary code on your machine.

Return type

Annoy

max_marginal_relevance_search(query: str, 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

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]

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 (List[float]) – Embedding to look up documents similar to.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
k (int) – Number of Documents to return. Defaults to 4.
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]

process_index_results(idxs: List[int], dists: List[float]) → List[Tuple[Document, float]][source]¶

Turns annoy results into a list of documents and scores.

Parameters

idxs (List[int]) – List of indices of the documents in the index.
dists (List[float]) – List of distances of the documents in the index.

Returns

List of Documents and scores.

Return type

List[Tuple[Document, float]]

save_local(folder_path: str, prefault: bool = False) → None[source]¶

Save Annoy index, docstore, and index_to_docstore_id to disk.

Parameters

folder_path (str) – folder path to save index, docstore, and index_to_docstore_id to.
prefault (bool) – Whether to pre-load the index into memory.

Return type

None

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]

similarity_search(query: str, k: int = 4, search_k: int = -1, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_k (int) – inspect up to search_k nodes which defaults to n_trees * n if not provided
kwargs (Any) –

Returns

List of Documents most similar to the query.

Return type

List[Document]

similarity_search_by_index(docstore_index: int, k: int = 4, search_k: int = -1, **kwargs: Any) → List[Document][source]¶

Return docs most similar to docstore_index.

Parameters

docstore_index (int) – Index of document in docstore
k (int) – Number of Documents to return. Defaults to 4.
search_k (int) – inspect up to search_k nodes which defaults to n_trees * n if not provided
kwargs (Any) –

Returns

List of Documents most similar to the embedding.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, search_k: int = -1, **kwargs: Any) → List[Document][source]¶

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.
search_k (int) – inspect up to search_k nodes which defaults to n_trees * n if not provided
kwargs (Any) –

Returns

List of Documents most similar to the embedding.

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, k: int = 4, search_k: int = -1) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_k (int) – inspect up to search_k nodes which defaults to n_trees * n if not provided

Returns

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

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_index(docstore_index: int, k: int = 4, search_k: int = -1) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

query – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_k (int) – inspect up to search_k nodes which defaults to n_trees * n if not provided
docstore_index (int) –

Returns

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

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, search_k: int = -1) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

query – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_k (int) – inspect up to search_k nodes which defaults to n_trees * n if not provided
embedding (List[float]) –

Returns

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

Return type

List[Tuple[Document, float]]

Examples using Annoy¶

Annoy

langchain_community.vectorstores.annoy.Annoy¶

Examples using Annoy¶

`langchain_community.vectorstores.annoy`.Annoy¶