💡 Roadmap

Below is a very high-level roadmap for HeimdaLLM.

Tip

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More SQL statement types

SELECT gets the biggest bang for the buck, but other SQL statements are also very useful and would benefit tremendously from a Bifrost. For example, INSERT:

traverse("Add a new calendar entry for Dinner at 7 on friday")

Could produce a validated SQL query:

INSERT INTO calendar (title, when, user_id)
VALUES ('Dinner at 7 on friday', '2023-07-01 19:00:00', 123)

Generalized constraint spec

The current implementation requires a Python application to use HeimdaLLM, because constraint validators are defined by subclassing a Python class. A future implementation could be language agnostic by providing an api and a JSON or YAML spec for constraining LLM output.

More LLM integrations

Currently we support OpenAI, but I intend to add support for all major LLM API services, and private LLMs, as they become more capable.

More databases

I will be adding support for more SQL-based databases:

SQL Server
Oracle
Snowflake

Note

Want us to prioritize a specific database? Let us know by voting here.

More Bifrosts

Bifrosts are not limited to converting human input to trusted SQL statements. HeimdaLLM is generalized enough to support many kinds of structured output. I intend to develop more Bifrosts that facilitate natural language interactions with your application. Stay tuned!