enabled. üéâCredentials‚ÄãTo interact with your Meilisearch instance, the Meilisearch SDK needs a host (URL of your instance) and an API key.HostIn local, the default host is localhost:7700On Meilisearch Cloud, find the host in your project Settings pageAPI keysMeilisearch instance provides you with three API keys out of the box: A MASTER KEY ‚Äî it should only be used to create your Meilisearch instanceA ADMIN KEY ‚Äî use it only server-side to update your database and its settingsA SEARCH KEY ‚Äî a key that you can safely share in front-end applicationsYou can create additional API keys as needed.Installing dependencies‚ÄãThis guide uses the Meilisearch Python SDK. You can install it by running:pip install meilisearchFor more information, refer to the Meilisearch Python SDK documentation.Examples‚ÄãThere 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 osimport getpassos.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‚ÄãThis example adds text to the Meilisearch vector database without having to initialize a Meilisearch vector store.from langchain.vectorstores import Meilisearchfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterembeddings = OpenAIEmbeddings()with open("../../modules/state_of_the_union.txt") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts =
Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences.
Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. ->: enabled. üéâCredentials‚ÄãTo interact with your Meilisearch instance, the Meilisearch SDK needs a host (URL of your instance) and an API key.HostIn local, the default host is localhost:7700On Meilisearch Cloud, find the host in your project Settings pageAPI keysMeilisearch instance provides you with three API keys out of the box: A MASTER KEY ‚Äî it should only be used to create your Meilisearch instanceA ADMIN KEY ‚Äî use it only server-side to update your database and its settingsA SEARCH KEY ‚Äî a key that you can safely share in front-end applicationsYou can create additional API keys as needed.Installing dependencies‚ÄãThis guide uses the Meilisearch Python SDK. You can install it by running:pip install meilisearchFor more information, refer to the Meilisearch Python SDK documentation.Examples‚ÄãThere 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 osimport getpassos.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‚ÄãThis example adds text to the Meilisearch vector database without having to initialize a Meilisearch vector store.from langchain.vectorstores import Meilisearchfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterembeddings = OpenAIEmbeddings()with open("../../modules/state_of_the_union.txt") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts =