ExprAgent_Ollama Generate & Run FME Expressions with an LLM
Type: Template workspace (Form)
Purpose: Convert a plain-English task into an Arithmetic Editor expression using a local LLM (Ollama), then evaluate that expression on every feature and write the numeric result to _result.
What it does
Scans the input schema and builds a compact JSON description of attributes.
Calls an Ollama tools-enabled model via /api/chat with a single tool (eval_expression) so the model returns only an expression.
Cleans the returned text
Evaluates the expression for each feature and stores the value in _result.
Why it’s useful
Turn a request like:
add 5 to _creation_instance and multiply by 3
check if the id of my instance is even
into a valid expression:
(@Value(_creation_instance) + 5) * 3
…then apply it across your data—no manual expression authoring needed.
Inputs & outputs
Input: Any feature type with attributes referenced by your task.
Outputs:
_expr — expression returned by the model (cleaned).
_result — numeric value computed per feature.
Requirements
Ollama running locally with a tools-enabled model. Recommended:
qwen3:8b, llama3.1:8b, granite3.3:8b, qwen3:1.7b, qwen2.5:1.5b
FME Form 2025.1+.
Extending
Add other tools (run_sql, run_python) and loop the chat if multi-step reasoning is needed.
Feed the result back to LLM to conclude on the question, make a loop...
Version & compatibility
v1.0 — initial release (single-pass tool call, single _result).
Tested with FME Form 2025.1 and Ollama ≥ 0.1.40.
Tags
Ollama · LLM · Function Calling · Arithmetic Editor · ExpressionEvaluator · Automation · FME AI
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