langchain_community.vectorstores.epsilla.Epsilla

class langchain_community.vectorstores.epsilla.Epsilla(client: Any, embeddings: Embeddings, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store')

Wrapper around Epsilla vector database.

As a prerequisite, you need to install pyepsilla package and have a running Epsilla vector database (for example, through our docker image) See the following documentation for how to run an Epsilla vector database: https://epsilla-inc.gitbook.io/epsilladb/quick-start

Parameters

• client (Any) – Epsilla client to connect to.

• embeddings (Embeddings) – Function used to embed the texts.

• db_path (Optional[str]) – The path where the database will be persisted. Defaults to “/tmp/langchain-epsilla”.

• db_name (Optional[str]) – Give a name to the loaded database. Defaults to “langchain_store”.

Example

from langchain_community.vectorstores import Epsilla
from pyepsilla import vectordb

client = vectordb.Client()
embeddings = OpenAIEmbeddings()
db_path = "/tmp/vectorstore"
db_name = "langchain_store"
epsilla = Epsilla(client, embeddings, db_path, db_name)

Initialize with necessary components.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(client, embeddings[, db_path, db_name])	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, ...])	Embed texts and add them to the database.
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.
clear_data([collection_name])	Clear data in a collection.
delete([ids])	Delete by vector ID or other criteria.
from_documents(documents, embedding[, ...])	Create an Epsilla vectorstore from a list of documents.
from_texts(texts, embedding[, metadatas, ...])	Create an Epsilla vectorstore from raw documents.
get([collection_name, response_fields])	Get the collection.
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, collection_name])	Return the documents that are semantically most relevant to the query.
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(*args, **kwargs)	Run similarity search with distance.
use_collection(collection_name)	Set default collection to use.

__init__(client: Any, embeddings: Embeddings, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store')

Initialize with necessary components.

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, collection_name: Optional[str] = '', drop_old: Optional[bool] = False, **kwargs: Any) → List[str]

Embed texts and add them to the database.

Parameters

• texts (Iterable[str]) – The texts to embed.

• metadatas (Optional[List[dict]]) – Metadata dicts attached to each of the texts. Defaults to None.

• collection_name (Optional[str]) – Which collection to use. Defaults to “langchain_collection”. If provided, default collection name will be set as well.

• drop_old (Optional[bool]) – Whether to drop the previous collection and create a new one. Defaults to False.

Returns

List of ids of the added texts.

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.

clear_data(collection_name: str = '') → None

Clear data in a collection.

Parameters

collection_name (Optional[str]) – The name of the collection. If not provided, the default collection will be used.

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, client: Any = None, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store', collection_name: Optional[str] = 'langchain_collection', drop_old: Optional[bool] = False, **kwargs: Any) → Epsilla

Create an Epsilla vectorstore from a list of documents.

Parameters

• texts (List[str]) – List of text data to be inserted.

• embeddings (Embeddings) – Embedding function.

• client (pyepsilla.vectordb.Client) – Epsilla client to connect to.

• metadatas (Optional[List[dict]]) – Metadata for each text. Defaults to None.

• db_path (Optional[str]) – The path where the database will be persisted. Defaults to “/tmp/langchain-epsilla”.

• db_name (Optional[str]) – Give a name to the loaded database. Defaults to “langchain_store”.

• collection_name (Optional[str]) – Which collection to use. Defaults to “langchain_collection”. If provided, default collection name will be set as well.

• drop_old (Optional[bool]) – Whether to drop the previous collection and create a new one. Defaults to False.

Returns

Epsilla vector store.

Return type

Epsilla

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Any = None, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store', collection_name: Optional[str] = 'langchain_collection', drop_old: Optional[bool] = False, **kwargs: Any) → Epsilla

Create an Epsilla vectorstore from raw documents.

Parameters

• texts (List[str]) – List of text data to be inserted.

• embeddings (Embeddings) – Embedding function.

• client (pyepsilla.vectordb.Client) – Epsilla client to connect to.

• metadatas (Optional[List[dict]]) – Metadata for each text. Defaults to None.

• db_path (Optional[str]) – The path where the database will be persisted. Defaults to “/tmp/langchain-epsilla”.

• db_name (Optional[str]) – Give a name to the loaded database. Defaults to “langchain_store”.

• collection_name (Optional[str]) – Which collection to use. Defaults to “langchain_collection”. If provided, default collection name will be set as well.

• drop_old (Optional[bool]) – Whether to drop the previous collection and create a new one. Defaults to False.

Returns

Epsilla vector store.

Return type

Epsilla

get(collection_name: str = '', response_fields: Optional[List[str]] = None) → List[dict]

Get the collection.

Parameters

• collection_name (Optional[str]) – The name of the collection to retrieve data from. If not provided, the default collection will be used.

• response_fields (Optional[List[str]]) – List of field names in the result. If not specified, all available fields will be responded.

Returns

A list of the retrieved data.

max_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.

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]

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, k: int = 4, collection_name: str = '', **kwargs: Any) → List[Document]

Return the documents that are semantically most relevant to the query.

Parameters

• query (str) – String to query the vectorstore with.

• k (Optional[int]) – Number of documents to return. Defaults to 4.

• collection_name (Optional[str]) – Collection to use. Defaults to “langchain_store” or the one provided before.

Returns

List of documents that are semantically most relevant 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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with distance.

use_collection(collection_name: str) → None

Set default collection to use.

Parameters

collection_name (str) – The name of the collection.

Examples using Epsilla

• Epsilla