Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences.

You canself-host Meilisearch or run onMeilisearch Cloud.

Meilisearch v1.3 supports vector search. This page guides you through integrating Meilisearch as a vector store and using it to perform vector search.

You'll need to installlangchain-community withpip install -qU langchain-community to use this integration

Setup​

Launching a Meilisearch instance​

You will need a running Meilisearch instance to use as your vector store. You can runMeilisearch in local or create aMeilisearch Cloud account.

As of Meilisearch v1.3, vector storage is an experimental feature. After launching your Meilisearch instance, you need toenable vector storage. For self-hosted Meilisearch, read the docs onenabling experimental features. OnMeilisearch Cloud, enableVector Store via your projectSettings page.

You should now have a running Meilisearch instance with vector storage enabled. 🎉

Credentials​

To interact with your Meilisearch instance, the Meilisearch SDK needs a host (URL of your instance) and an API key.

Host

• Inlocal, the default host islocalhost:7700
• OnMeilisearch Cloud, find the host in your projectSettings page

API keys

Meilisearch instance provides you with three API keys out of the box:

• AMASTER KEY — it should only be used to create your Meilisearch instance
• AADMIN KEY — use it only server-side to update your database and its settings
• ASEARCH KEY — a key that you can safely share in front-end applications

You can createadditional API keys as needed.

Installing dependencies​

This guide uses theMeilisearch Python SDK. You can install it by running:

%pip install--upgrade--quiet  meilisearch

For more information, refer to theMeilisearch Python SDK documentation.

Examples​

There are multiple ways to initialize the Meilisearch vector store: providing a Meilisearch client or theURL andAPI key as needed. In our examples, the credentials will be loaded from the environment.

You can make environment variables available in your Notebook environment by usingos andgetpass. You can use this technique for all the following examples.

import getpass
import os

if"MEILI_HTTP_ADDR"notin os.environ:
    os.environ["MEILI_HTTP_ADDR"]= getpass.getpass(
"Meilisearch HTTP address and port:"
)
if"MEILI_MASTER_KEY"notin os.environ:
    os.environ["MEILI_MASTER_KEY"]= getpass.getpass("Meilisearch API Key:")

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

if"OPENAI_API_KEY"notin os.environ:
    os.environ["OPENAI_API_KEY"]= getpass.getpass("OpenAI API Key:")

Adding text and embeddings​

This example adds text to the Meilisearch vector database without having to initialize a Meilisearch vector store.

from langchain_community.vectorstoresimport Meilisearch
from langchain_openaiimport OpenAIEmbeddings
from langchain_text_splittersimport CharacterTextSplitter

embeddings= OpenAIEmbeddings()
embedders={
"default":{
"source":"userProvided",
"dimensions":1536,
}
}
embedder_name="default"

withopen("../../how_to/state_of_the_union.txt")as f:
    state_of_the_union= f.read()
text_splitter= CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts= text_splitter.split_text(state_of_the_union)

# Use Meilisearch vector store to store texts & associated embeddings as vector
vector_store= Meilisearch.from_texts(
    texts=texts, embedding=embeddings, embedders=embedders, embedder_name=embedder_name
)

Behind the scenes, Meilisearch will convert the text to multiple vectors. This will bring us to the same result as the following example.

Adding documents and embeddings​

In this example, we'll use Langchain TextSplitter to split the text in multiple documents. Then, we'll store these documents along with their embeddings.

from langchain_community.document_loadersimport TextLoader

# Load text
loader= TextLoader("../../how_to/state_of_the_union.txt")
documents= loader.load()
text_splitter= CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)

# Create documents
docs= text_splitter.split_documents(documents)

# Import documents & embeddings in the vector store
vector_store= Meilisearch.from_documents(
    documents=documents,
    embedding=embeddings,
    embedders=embedders,
    embedder_name=embedder_name,
)

# Search in our vector store
query="What did the president say about Ketanji Brown Jackson"
docs= vector_store.similarity_search(query, embedder_name=embedder_name)
print(docs[0].page_content)

Add documents by creating a Meilisearch Vectorstore​

In this approach, we create a vector store object and add documents to it.

import meilisearch
from langchain_community.vectorstoresimport Meilisearch

client= meilisearch.Client(url="http://127.0.0.1:7700", api_key="***")
vector_store= Meilisearch(
    embedding=embeddings,
    embedders=embedders,
    client=client,
    index_name="langchain_demo",
    text_key="text",
)
vector_store.add_documents(documents)

Similarity Search with score​

This specific method allows you to return the documents and the distance score of the query to them.embedder_name is the name of the embedder that should be used for semantic search, defaults to "default".

docs_and_scores= vector_store.similarity_search_with_score(
    query, embedder_name=embedder_name
)
docs_and_scores[0]

Similarity Search by vector​

embedder_name is the name of the embedder that should be used for semantic search, defaults to "default".

embedding_vector= embeddings.embed_query(query)
docs_and_scores= vector_store.similarity_search_by_vector(
    embedding_vector, embedder_name=embedder_name
)
docs_and_scores[0]

Additional resources​

Documentation

• Meilisearch
• Meilisearch Python SDK

Open-source repositories

• Meilisearch repository
• Meilisearch Python SDK

Related​

• Vector storeconceptual guide
• Vector storehow-to guides