Description
Following a discussion I had with @mishig25, we could simplify the usage of this Python library by having a Meilisearch binary automatically downloaded and runned just like SQLite.
For example, on instanciating of the client instance meilisearch.Client()
, when no api-key or url is given to the client, the library could use the meilisearch
binary and exec it from the PATH
. If no meilisearch binary is available in PATH
, it could download and install it in the PATH
We might want to focus onFollowing a conversation with @mishig25, we believe we can simplify the developer experience for this Python SDK by having it automatically handle the Meilisearch binary—similar to how SQLite is used in Python.
🔧 Suggested behavior
When a meilisearch.Client()
is instantiated without specifying a URL or API key, the SDK should:
1. Check for a local Meilisearch binary in the system PATH.
2. If not found, automatically download and install the appropriate Meilisearch binary into PATH.
3. Launch the binary and use it as the underlying instance for the client.
I suggest focusing on Unix-based platforms first (Linux/macOS) to keep the initial implementation simple.
💡 Why this matters
We’re seeing a growing number of developers using Python to build prototypes (e.g. AI agents, RAG pipelines) with Meilisearch. Reducing the setup friction—especially around installing and running a Meilisearch instance—could significantly boost adoption and ease-of-use. Unix platform first to make the implementation easier.
This would be important for adoption as we see more and more people using Python and building POC of agents or RAG pipelines using Meilisearch. If we can lighten the workload to get started with Meilisearch, it would be awesome 🔥