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Meilisearch

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 can self-host Meilisearch or run on Meilisearch 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 install langchain-community with pip 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 run Meilisearch in local or create a Meilisearch Cloud account.

As of Meilisearch v1.3, vector storage is an experimental feature. After launching your Meilisearch instance, you need to enable vector storage. For self-hosted Meilisearch, read the docs on enabling experimental features. On Meilisearch Cloud, enable Vector Store via your project Settings 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

  • In local, the default host is localhost:7700
  • On Meilisearch Cloud, find the host in your project Settings page

API keys

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

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

You can create additional API keys as needed.

Installing dependencies​

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

%pip install --upgrade --quiet  meilisearch

For more information, refer to the Meilisearch Python SDK documentation.

Examples​

There are multiple ways to initialize the Meilisearch vector store: providing a Meilisearch client or the URL and API 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 using os and getpass. You can use this technique for all the following examples.

import getpass
import os

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

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

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.vectorstores import Meilisearch
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

embeddings = OpenAIEmbeddings()
embedders = {
    "default": {
        "source": "userProvided",
        "dimensions": 1536,
    }
}
embedder_name = "default"
with open("../../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_loaders import 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)
API Reference:TextLoader

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.vectorstores import 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)
API Reference:Meilisearch

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

Open-source repositories

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