IsaREPL 0.2.0

Isabelle REPL client

Isabelle REPL

Unofficial support for Isabelle's Read-Eval-Print-Loop.

• Python Client, easy for Machine Learning.
• Full support for all Isabelle commands
• Tracing proof state and output panel for each step of evaluation.
  • Plugin: inserting Isabelle/ML code to collect any data you want directly from Isabelle's internal representations.
• Socket based remote communication
• Concurrency

Installation

Ensuring <ISABELLE-BASE-DIRECTORY>/bin is in your $PATH environment

	isabelle components -u <THE-BASE-DIRECTORY-OF-OUR-PROGRAM>
pip install IsaREPL

Start up the REPL server

# merely an example:
./repl_server.sh 127.0.0.1:6666 HOL ./tmp

Run ./repl_server.sh to see the full explanation of the arguments and options.

Example Clients

Every interface of our client is will documented (it is highly recommended to read our source). Some examples are given in the example folder.

# Run an example
./examples/example_eval.py 127.0.0.1:6666

Notes

Documents

All documents are given as docstrings attaching to Python client interfaces, see the source.

Concurrency

This REPL supports concurrent evaluation of multiple files. However, to ensure proper error tracking and result tracing, it disables Isabelle's concurrency between commands within a single theory file. While Isabelle typically executes by commands asynchronously (continuing to the next command before completion), this REPL enforces synchronous evaluation. Each command must complete before the next begins, allowing for accurate capture and reporting of any failures.

Sledgehammer (via our auto_sledgehamemr wrapper, see example) is still concurrent.

Communication Protocol

It is possible to implement a client in other languages. However, the document for the communication protocol is not provided and I refer you to read our source code, as it should be simple enough.

Contribution

Feel free to open any GitHub issue if you have any feature requests.

I am not a professional python developer, so contribution is highly welcome to enrich the client's features.