langchain.vectorstores.meilisearch.Meilisearch

class langchain.vectorstores.meilisearch.Meilisearch(embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = 'langchain-demo', text_key: str = 'text', metadata_key: str = 'metadata')[source]

Meilisearch vector store.

To use this, you need to have meilisearch python package installed, and a running Meilisearch instance.

To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/.

See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start.

Example

from langchain.vectorstores import Meilisearch
from langchain.embeddings.openai import OpenAIEmbeddings
import meilisearch

# api_key is optional; provide it if your meilisearch instance requires it
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
embeddings = OpenAIEmbeddings()
vectorstore = Meilisearch(
    embedding=embeddings,
    client=client,
    index_name='langchain_demo',
    text_key='text')

Initialize with Meilisearch client.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding[, client, url, api_key, ...])

Initialize with Meilisearch 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, ids])

Run more texts through the embedding and add them to the vector store.

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

Construct Meilisearch 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, filter])

Return meilisearch documents most similar to the query.

similarity_search_by_vector(embedding[, k, ...])

Return meilisearch documents most similar to embedding vector.

similarity_search_by_vector_with_scores(...)

Return meilisearch documents 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, filter])

Return meilisearch documents most similar to the query, along with scores.

__init__(embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = 'langchain-demo', text_key: str = 'text', metadata_key: str = 'metadata')[source]

Initialize with Meilisearch 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, ids: Optional[List[str]] = None, **kwargs: Any) List[str][source]

Run more texts through the embedding and add them to the vector store.

Parameters
  • texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadata. Defaults to None.

  • Optional[List[str]] (ids) – Optional list of IDs. Defaults to None.

Returns

List of IDs of the texts added to 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 – 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) 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_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = 'langchain-demo', ids: Optional[List[str]] = None, text_key: Optional[str] = 'text', metadata_key: Optional[str] = 'metadata', **kwargs: Any) Meilisearch[source]

Construct Meilisearch wrapper from raw documents.

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

  2. Adds the documents to a provided Meilisearch index.

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

Example

from langchain.vectorstores import Meilisearch
from langchain.embeddings import OpenAIEmbeddings
import meilisearch

# The environment should be the one specified next to the API key
# in your Meilisearch console
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
embeddings = OpenAIEmbeddings()
docsearch = Meilisearch.from_texts(
    client=client,
    embeddings=embeddings,
)

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.

Return meilisearch documents most similar to the query.

Parameters
  • query (str) – Query text for which to find similar documents.

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

  • filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

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

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document][source]

Return meilisearch documents most similar to embedding vector.

Parameters
  • embedding (List[float]) – Embedding to look up similar documents.

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

  • filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

List of Documents most similar to the query

vector and score for each.

Return type

List[Document]

similarity_search_by_vector_with_scores(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Return meilisearch documents most similar to embedding vector.

Parameters
  • embedding (List[float]) – Embedding to look up similar documents.

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

  • filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

List of Documents most similar to the query

vector and score for each.

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 – 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, filter: Optional[Dict[str, str]] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Return meilisearch documents most similar to the query, along with scores.

Parameters
  • query (str) – Query text for which to find similar documents.

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

  • filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns

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

Return type

List[Document]

Examples using Meilisearch