docs(cookbook): add Reasoning module notebook (#990)

* docs(cookbook): add Reasoning module notebook

Add cookbook/introduction/23_Reasoning.ipynb covering the reasoning
module with verified, executable examples:

- Reasoner facade: add_fact / add_rule / forward_chain
- one-shot infer_facts(facts, rules)
- backward_chain goal proving with premises
- re-run-safe rule deduplication (#732)
- DatalogReasoner: semi-naive fixpoint evaluation + variable queries
- ExplanationGenerator: Explanation / ReasoningPath records

The reasoning module currently has no cookbook coverage even though it
ships reasoning_usage.md in the package. All API calls and outputs were
verified against semantica/reasoning/reasoner.py,
datalog_reasoner.py, and explanation_generator.py.

Signed-off-by: LeonSGP43 <LeonSGP43@users.noreply.github.com>

* docs(cookbook): correct infer_facts semantics description (appends to instance state, no reset)

Signed-off-by: LeonSGP43 <leonsgp43@users.noreply.github.com>

* docs(cookbook): execute reasoning notebook in Jupyter (real kernel run, stream outputs, execution counts)

Signed-off-by: LeonSGP43 <cine.dreamer.one@gmail.com>

---------

Signed-off-by: LeonSGP43 <LeonSGP43@users.noreply.github.com>
Signed-off-by: LeonSGP43 <leonsgp43@users.noreply.github.com>
Signed-off-by: LeonSGP43 <cine.dreamer.one@gmail.com>
Co-authored-by: LeonSGP43 <LeonSGP43@users.noreply.github.com>
This commit is contained in:
LeonSGP
2026-08-27 12:55:02 +05:00
committed by GitHub
co-authored by LeonSGP43
parent f187d4b5da
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{
"cells": [
{
"cell_type": "markdown",
"id": "b76a5997",
"metadata": {},
"source": [
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/semantica-agi/semantica/blob/main/cookbook/introduction/23_Reasoning.ipynb)\n",
"\n",
"# Reasoning Module — Practical Guide\n",
"\n",
"Semantica's `reasoning` module derives new knowledge from existing facts and knowledge graphs. It ships several strategies behind one facade:\n",
"\n",
"- **`Reasoner`** — unified facade with forward chaining, backward chaining, and one-shot `infer_facts`\n",
"- **`DatalogReasoner`** — semi-naive Datalog fixpoint evaluation with variable queries\n",
"- **`ExplanationGenerator`** — human-readable explanations and reasoning paths for inferred conclusions\n",
"- Plus lower-level engines: `ReteEngine`, `SPARQLReasoner`, `GraphReasoner`, temporal reasoning\n",
"\n",
"This notebook walks through the facade, the Datalog engine, and explanations. All APIs are verified against `semantica/reasoning/`."
]
},
{
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"outputs": [],
"source": [
"!pip install -q semantica"
]
},
{
"cell_type": "markdown",
"id": "06deb916",
"metadata": {},
"source": [
"## 1) Forward chaining with the `Reasoner` facade\n",
"\n",
"Facts are simple `Predicate(args)` strings. Rules use `IF <conditions> THEN <conclusion>` with `?x`-style variables. `forward_chain()` derives everything possible and returns a list of `InferenceResult` objects."
]
},
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{
"data": {
"text/html": [
"<div style='font-family: monospace;'><h4>🧠 Semantica - 📊 Current Progress</h4><table style='width: 100%; border-collapse: collapse;'><tr><th>Status</th><th>Action</th><th>Module</th><th>Submodule</th><th>Progress</th><th>ETA</th><th>Rate</th><th>Time</th><th>Extracted</th></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>Reasoner</td><td>100.0%</td><td>-</td><td>-</td><td>0.00s</td><td>-</td></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>DatalogReasoner</td><td>100.0%</td><td>-</td><td>-</td><td>0.00s</td><td>-</td></tr><tr><td>✅</td><td>Semantica is reasoning</td><td>🤔 reasoning</td><td>ExplanationGenerator</td><td>100.0%</td><td>-</td><td>-</td><td>0.00s</td><td>-</td></tr></table></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔄 Semantica is reasoning: Performing forward chaining 🤔 reasoning Reasoner |░░░░░░░░░░░░░░░| 0.0% ETA: - Rate: - Time: 0.00s Extracted: -"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inferred 2 new facts\n",
" Human(Jane) (rule: Rule 1, confidence: 1.0)\n",
" Human(John) (rule: Rule 1, confidence: 1.0)\n"
]
}
],
"source": [
"from semantica.reasoning import Reasoner\n",
"\n",
"reasoner = Reasoner()\n",
"\n",
"reasoner.add_fact(\"Person(John)\")\n",
"reasoner.add_fact(\"Person(Jane)\")\n",
"reasoner.add_rule(\"IF Person(?x) THEN Human(?x)\")\n",
"\n",
"results = reasoner.forward_chain()\n",
"print(f\"Inferred {len(results)} new facts\")\n",
"for res in results:\n",
" print(f\" {res.conclusion} (rule: {res.rule_used.name}, confidence: {res.confidence})\")"
]
},
{
"cell_type": "markdown",
"id": "c1131c45",
"metadata": {},
"source": [
"## 2) One-shot inference with `infer_facts`\n",
"\n",
"`infer_facts(facts, rules)` **adds** the given facts and rules to this `Reasoner` instance, runs forward chaining to fixpoint, and returns the derived facts as strings. It does not reset the instance's existing state — create a fresh `Reasoner()` first if you need isolation between runs."
]
},
{
"cell_type": "code",
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"shell.execute_reply": "2026-08-26T18:46:00.002873Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['Employee(Jane, Acme)', 'Employee(John, Acme)']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from semantica.reasoning import Reasoner\n",
"\n",
"derived = Reasoner().infer_facts(\n",
" facts=[\"WorksFor(John, Acme)\", \"WorksFor(Jane, Acme)\"],\n",
" rules=[\"IF WorksFor(?x, ?y) THEN Employee(?x, ?y)\"],\n",
")\n",
"derived"
]
},
{
"cell_type": "markdown",
"id": "d5504a38",
"metadata": {},
"source": [
"## 3) Backward chaining: proving a goal\n",
"\n",
"`backward_chain(goal)` works backwards from a conclusion through the rules. It returns the `InferenceResult` that proves the goal, or `None`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c4ef85dd",
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Human(John)\n",
"premises: ['Person(John)']\n"
]
}
],
"source": [
"from semantica.reasoning import Reasoner\n",
"\n",
"reasoner = Reasoner()\n",
"reasoner.add_fact(\"Person(John)\")\n",
"reasoner.add_rule(\"IF Person(?x) THEN Human(?x)\")\n",
"\n",
"proof = reasoner.backward_chain(\"Human(John)\")\n",
"print(proof.conclusion if proof else \"not provable\")\n",
"print(\"premises:\", proof.premises if proof else None)"
]
},
{
"cell_type": "markdown",
"id": "b245581d",
"metadata": {},
"source": [
"## 4) Re-run safety\n",
"\n",
"`add_rule` deduplicates rules with identical conditions and conclusion, so re-executing a setup cell (the common Jupyter re-run) does not duplicate rules — see issue #732."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fb2aeb39",
"metadata": {
"execution": {
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"shell.execute_reply": "2026-08-26T18:46:00.022836Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Skipping duplicate rule (same conditions/conclusion as 'rule_1'): IF Person(?x) THEN Human(?x)\n"
]
},
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from semantica.reasoning import Reasoner\n",
"\n",
"reasoner = Reasoner()\n",
"reasoner.add_fact(\"Person(John)\")\n",
"\n",
"# Simulate a Jupyter cell re-run: add the same rule twice\n",
"r1 = reasoner.add_rule(\"IF Person(?x) THEN Human(?x)\")\n",
"r2 = reasoner.add_rule(\"IF Person(?x) THEN Human(?x)\")\n",
"\n",
"len(reasoner.rules)"
]
},
{
"cell_type": "markdown",
"id": "ba2e5c4a",
"metadata": {},
"source": [
"## 5) Datalog reasoning\n",
"\n",
"`DatalogReasoner` uses classic Datalog syntax (`head :- body.`) and semi-naive fixpoint evaluation. Queries return variable bindings as a list of dicts — use uppercase variables to ask *which* facts hold."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9ec5c0c4",
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"execution": {
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"shell.execute_reply": "2026-08-26T18:46:00.032672Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[{'X': 'tom', 'Z': 'ann'}]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from semantica.reasoning import DatalogReasoner\n",
"\n",
"datalog = DatalogReasoner()\n",
"datalog.add_fact(\"parent(tom, mary)\")\n",
"datalog.add_fact(\"parent(mary, ann)\")\n",
"datalog.add_rule(\"grandparent(X, Z) :- parent(X, Y), parent(Y, Z)\")\n",
"\n",
"datalog.derive_all()\n",
"datalog.query(\"grandparent(X, Z)\")"
]
},
{
"cell_type": "markdown",
"id": "d4f0689b",
"metadata": {},
"source": [
"## 6) Explanations for inferred conclusions\n",
"\n",
"`ExplanationGenerator` turns `InferenceResult` objects into structured `Explanation` and `ReasoningPath` records, so agents can show *why* they believe a derived fact."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "19dcd3a7",
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"execution": {
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"shell.execute_reply": "2026-08-26T18:46:00.057805Z"
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"outputs": [
{
"data": {
"text/plain": [
"('Explanation', 'ReasoningPath')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from semantica.reasoning import Reasoner, ExplanationGenerator\n",
"\n",
"reasoner = Reasoner()\n",
"reasoner.add_fact(\"Person(John)\")\n",
"reasoner.add_rule(\"IF Person(?x) THEN Human(?x)\")\n",
"results = reasoner.forward_chain()\n",
"\n",
"gen = ExplanationGenerator()\n",
"explanation = gen.generate_explanation(results[0])\n",
"path = gen.show_reasoning_path(results[0])\n",
"\n",
"type(explanation).__name__, type(path).__name__"
]
},
{
"cell_type": "markdown",
"id": "fb882ee4",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"| Task | API |\n",
"|---|---|\n",
"| Derive all new facts | `Reasoner.forward_chain()` |\n",
"| One-shot inference | `Reasoner.infer_facts(facts, rules)` |\n",
"| Prove a goal | `Reasoner.backward_chain(goal)` |\n",
"| Datalog fixpoint | `DatalogReasoner.derive_all()` + `query(\"p(X, Y)\")` |\n",
"| Explain a conclusion | `ExplanationGenerator.generate_explanation(result)` |\n",
"\n",
"See also `semantica/reasoning/reasoning_usage.md` and the module docstrings for `ReteEngine`, `SPARQLReasoner`, and temporal reasoning."
]
}
],
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