`langchain_community.vectorstores.meilisearch`.Meilisearch¶

class langchain_community.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', *, embedders: Optional[Dict[str, Any]] = None)[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_community.vectorstores import Meilisearch
from langchain_community.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()
embedders = {
    "theEmbedderName": {
        "source": "userProvided",
        "dimensions": "1536"
    }
}
vectorstore = Meilisearch(
    embedding=embeddings,
    embedders=embedders,
    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, embedder_name])	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, ...])	Return meilisearch documents most similar to the query, along with scores.

Parameters

embedding (Embeddings) –
client (Optional[Client]) –
url (Optional[str]) –
api_key (Optional[str]) –
index_name (str) –
text_key (str) –
metadata_key (str) –
embedders (Optional[Dict[str, Any]]) –

__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', *, embedders: Optional[Dict[str, Any]] = None)[source]¶

Initialize with Meilisearch client.

Parameters

embedding (Embeddings) –
client (Optional[Client]) –
url (Optional[str]) –
api_key (Optional[str]) –
index_name (str) –
text_key (str) –
metadata_key (str) –
embedders (Optional[Dict[str, Any]]) –

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, ids: Optional[List[str]] = None, embedder_name: Optional[str] = 'default', **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.
embedder_name (Optional[str]) – Name of the embedder. Defaults to “default”.
metadatas (Optional[List[dict]]) – Optional list of metadata. Defaults to None.
Optional[List[str]] (ids) – Optional list of IDs. Defaults to None.
ids (Optional[List[str]]) –
kwargs (Any) –

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 (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.

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]

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_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', embedders: Dict[str, Any] = {}, embedder_name: Optional[str] = 'default', **kwargs: Any) → Meilisearch[source]¶

Construct Meilisearch wrapper from raw documents.

This is a user-friendly interface that:

Embeds documents.
Adds the documents to a provided Meilisearch index.

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

Example

from langchain_community.vectorstores import Meilisearch
from langchain_community.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='***')
embedding = OpenAIEmbeddings()
embedders: Embedders index setting.
embedder_name: Name of the embedder. Defaults to "default".
docsearch = Meilisearch.from_texts(
    client=client,
    embedding=embedding,
)

Parameters

texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
client (Optional[Client]) –
url (Optional[str]) –
api_key (Optional[str]) –
index_name (str) –
ids (Optional[List[str]]) –
text_key (Optional[str]) –
metadata_key (Optional[str]) –
embedders (Dict[str, Any]) –
embedder_name (Optional[str]) –
kwargs (Any) –

Return type

Meilisearch

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 (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]¶

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]

similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, embedder_name: Optional[str] = 'default', **kwargs: Any) → List[Document][source]¶

Return meilisearch documents most similar to the query.

Parameters

query (str) – Query text for which to find similar documents.
embedder_name (Optional[str]) – Name of the embedder to be used. Defaults to “default”.
k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

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, embedder_name: Optional[str] = 'default', **kwargs: Any) → List[Document][source]¶

Return meilisearch documents most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up similar documents.
embedder_name (Optional[str]) – Name of the embedder to be used. Defaults to “default”.
k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

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], embedder_name: Optional[str] = 'default', 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.
embedder_name (Optional[str]) – Name of the embedder to be used. Defaults to “default”.
k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

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 (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, filter: Optional[Dict[str, str]] = None, embedder_name: Optional[str] = 'default', **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.
embedder_name (Optional[str]) – Name of the embedder to be used. Defaults to “default”.
k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

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

Return type

List[Document]

Examples using Meilisearch¶

langchain_community.vectorstores.meilisearch.Meilisearch¶

Examples using Meilisearch¶

`langchain_community.vectorstores.meilisearch`.Meilisearch¶