A lot of "AI" in production pipelines isn't writing anything. It's deciding.
Which queue does this ticket go to? Does this message need a human? How urgent is this request: low, medium or high? The
answer is one item from a list you already know. Yet the usual way to get it is to send the text to a language model,
ask it to reply in JSON, parse the reply, retry when the JSON is broken, and hope the word "high" means the same thing
every time.
That works, but it's an odd fit. You pay for generation you don't need, you get text where you wanted a number, and you
have no clean way to say "only automate this when the model is at least 90% sure."
I built ej to do that one job differently. It's an open model that takes a piece of text plus a set of typed
questions, and returns one probability distribution per question, in one forward pass, without generating a single token. The whole model is one 11 MB file that runs on a CPU.
pip install ejai (the import name is ej)
This is version 0.0.1. Below is what it does, how I measured it, and, just as important, where it is weak.
You give ej a state (free text, or a JSON object written as text) and any number of questions. Each question has one
of three types:
| Type | You give | You get |
|---|---|---|
choice |
instructions + a list of option texts, chosen when you call it | a probability for each option |
noul |
a yes/no statement | P(false), P(true) |
score |
instructions + ordered levels | a distribution over the levels |
The options are plain text you write at call time. There is no fixed label set baked in at training.
import ej
model = ej.load("5ak3t/ej", revision="v0.0.1") # downloads model.ejpack and checks its SHA-256
record = {
"state": '{"customer_tier": "gold", "message": "The blender arrived with a cracked jug. Replace it before Friday."}',
"questions": {
"route": {"type": "choice", "instructions": "Which team should handle this request?",
"options": [{"key": "returns", "text": "Returns and replacements for damaged or wrong items"},
{"key": "billing", "text": "Billing, invoices and payment problems"}]},
"needs_human": {"type": "noul",
"instructions": "The customer is upset enough that a human agent should reply.",
"options": ej.NOUL_OPTIONS},
"urgency": {"type": "score", "instructions": "How urgent is this request?",
"options": [{"key": "0", "text": "Low: can wait a week"},
{"key": "1", "text": "Medium: answer within two days"},
{"key": "2", "text": "High: answer today"}]},
},
}
(probs,) = model.predict([record])
print(probs["route"]) # [p_returns, p_billing], sums to 1
print(probs["needs_human"]) # [p_false, p_true]
print(probs["urgency"]) # [p_low, p_medium, p_high]
All three questions are answered in the same pass. Because the output is a distribution, the routing logic in your code
is a threshold, not a string parser:
if max(probs["route"]) >= threshold:
auto_route(record, probs["route"])
else:
send_to_human(record)
(Pick that threshold on labelled examples of your workflow. More on why below.)
There are three common ways to make this kind of decision today. ej is a different trade-off from each, not a strict
upgrade.
Prompting an LLM. It's flexible and strong on new tasks. But it generates text you have to parse, it needs either an
API call (cost, latency, your data leaving your servers) or a local model that is hundreds of MB to several GB, and its
reply isn't a probability you can threshold. ej skips generation entirely and is small enough to ship inside a service.
NLI zero-shot classifiers (the "zero-shot-classification" pipeline). These are the closest relatives. They usually
run one forward pass per (text, option) pair, so cost grows with the number of options, and each question type needs its
own setup. ej reads the text once and answers choice, yes/no and score questions together. To be clear, though, a good
NLI model beats ej on workflows ej has never seen (numbers below).
Fine-tuning your own classifier (BERT, SetFit and friends). It's accurate on its own task, but the label set is fixed
at training, and every change means new labelled data and a retrain. With ej the options are input. It also has an
adapt method that takes a handful of labelled records:
adapted = model.adapt(examples=labelled[:8]) # per-option offsets; the weights don't change
probs = adapted.predict(new_records)
Briefly (a technical report with the full method is coming):
e5-small-v2, quantised to model.ejpack: On the test box (a shared 4-core Xeon, one thread), a warm call takes roughly 200 to 360 ms per record, depending on
the run and the inputs, and the process peaks at about 558 MB of RAM. Most of that is torch itself: the model's own tensors peak at 128 MB, or 35 MB with
ej.load(..., low_memory=True), which is about 1.5x slower and gives identical predictions.
I tried hard to make these numbers trustworthy rather than flattering:
| Test suite | ej 0.0.1 accuracy [95% CI] |
|---|---|
| Typed Decisions, workflows seen in training | .721 [.695, .746] |
| MASSIVE intents (leak-free split) | .832 [.808, .857] |
| Support tickets (in-house) | .712 [.679, .746] |
| Support tickets, new writing styles | .683 [.639, .726] |
| 105 workflows never seen in training | .419 [.379, .458] |
| One held-out Typed Decisions workflow (also used during model selection) | .422 [.388, .452] |
How that compares with the six rivals:
A few more things you should know before using it:
ej makes sense if all of these are true:
If your task is brand-new and you have no labelled data at all, a prompted LLM or an NLI zero-shot model will probably
serve you better today. I'd rather say that here than have you find out in production.
pip install ejai
python
import ej
model = ej.load("5ak3t/ej", revision="v0.0.1")
The repository also contains the training code (python -m ej.train), the evaluator that produced every number above
(python -m ej.eval), and the benchmark runner with the rival adapters, so you can check my numbers or train your own
pack.
If you run it on your own workflow, I'd love to hear what accuracy you get, good or bad. Results on real workflows are
exactly what version 0.0.2 needs.