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.gitignore

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+__pycache__/
+.env
+*.pyc

LICENSE

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/

README.md

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----
-license: apache-2.0
----
+# IntentGraphLM 🚀
+
+**IntentGraphLM** is an open-source language model pipeline that converts raw natural language into **dynamic intent graphs**, enabling smarter agents, workflow orchestration, and multi-step task planning.
+
+This project is designed to be **lightweight, extensible, and Hugging Face–ready**, with strong future scope in agentic AI systems.
+
+---
+
+## 🔍 What Problem Does It Solve?
+
+Traditional intent detection returns flat labels.  
+IntentGraphLM goes further by:
+
+- Extracting **multiple intents**
+- Modeling **relationships between intents**
+- Representing them as a **directed graph**
+- Enabling **dynamic updates** during conversations
+
+---
+
+## ✨ Key Features
+
+- 🧠 Intent extraction from raw text  
+- 🕸️ Graph-based intent representation  
+- 🔁 Dynamic intent graph updates  
+- ⚡ Lightweight, dependency-minimal design  
+- 🤗 Hugging Face–compatible inference pipeline  
+- 🧩 Modular and extensible architecture  
+
+---
+
+## 📂 Project Structure
+
+```
+intentgraph-lm/
+├── config/
+├── data/
+├── src/
+├── training/
+├── scripts/
+├── tests/
+├── notebooks/
+├── README.md
+├── model_card.md
+├── requirements.txt
+└── LICENSE
+```
+
+---
+
+## ⚙️ Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## 🚀 Quick Usage
+
+```python
+from src import IntentGraphPipeline
+
+pipeline = IntentGraphPipeline()
+
+output = pipeline("Book a flight and then reserve a hotel")
+
+print(output)
+```
+
+---
+
+## 🧠 How It Works
+
+1. Tokenization  
+2. Intent Extraction  
+3. Graph Building  
+4. Dynamic Updates  
+
+---
+
+## 🧪 Testing
+
+```bash
+pytest tests/
+```
+
+---
+
+## 🔮 Future Scope
+
+- Transformer-based intent extraction
+- Hierarchical intent graphs
+- Agent tool planning
+- Multilingual support
+- Hugging Face pipeline release
+- Gradio demo space
+
+---
+
+## 📜 License
+
+Apache License 2.0

config/graph_schema.json

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+{
+  "node_types": [
+    "intent"
+  ],
+  "edge_types": [
+    "depends_on",
+    "follows"
+  ],
+  "weighted": true
+}

config/model_config.yaml

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+model_name: intentgraph-lm
+max_intents: 5
+confidence_threshold: 0.5

data/examples.json

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+[
+  {
+    "text": "Book a flight and then reserve a hotel",
+    "intents": [
+      "book_flight",
+      "reserve_hotel"
+    ]
+  }
+]

data/synthetic_intents.json

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+[
+  "book_flight",
+  "reserve_hotel",
+  "order_food",
+  "check_weather"
+]

model_card.md

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+---
+language: en
+license: apache-2.0
+pipeline_tag: text-to-graph
+---
+
+# IntentGraphLM

notebooks/intentgraph_demo.ipynb

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+{"cells": [], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

requirements.txt

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+networkx

scripts/export_model.py

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+def export():
+    print('Exporting model')
+
+if __name__ == '__main__':
+    export()

scripts/preprocess_data.py

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+def preprocess():
+    print('Preprocessing data')
+
+if __name__ == '__main__':
+    preprocess()

src/__init__.py

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+from .inference import IntentGraphPipeline

src/graph_builder.py

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+import networkx as nx
+
+class GraphBuilder:
+    def build(self, intents):
+        graph = nx.DiGraph()
+        for i, intent in enumerate(intents):
+            graph.add_node(intent, type='intent')
+            if i > 0:
+                graph.add_edge(intents[i-1], intent, weight=1.0)
+        return graph

src/graph_updater.py

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+class GraphUpdater:
+    def update(self, graph, new_intent):
+        if new_intent not in graph:
+            graph.add_node(new_intent, type='intent')
+        return graph

src/inference.py

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+from .tokenizer import SimpleTokenizer
+from .intent_extractor import IntentExtractor
+from .graph_builder import GraphBuilder
+
+class IntentGraphPipeline:
+    def __init__(self):
+        self.tokenizer = SimpleTokenizer()
+        self.extractor = IntentExtractor()
+        self.builder = GraphBuilder()
+
+    def __call__(self, text: str):
+        tokens = self.tokenizer.tokenize(text)
+        intents = self.extractor.extract(tokens)
+        graph = self.builder.build(intents)
+        return {'intents': intents, 'nodes': list(graph.nodes), 'edges': list(graph.edges)}

src/intent_extractor.py

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+class IntentExtractor:
+    def extract(self, tokens):
+        intents = []
+        for token in tokens:
+            if token in ['book','reserve','order','check']:
+                intents.append(token)
+        return list(set(intents))

src/tokenizer.py

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+class SimpleTokenizer:
+    def tokenize(self, text: str):
+        return text.lower().split()

tests/test_graph_building.py

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+from src.graph_builder import GraphBuilder
+
+def test_graph():
+    builder = GraphBuilder()
+    graph = builder.build(['a','b'])
+    assert len(graph.nodes) == 2

tests/test_intent_extraction.py

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+from src.intent_extractor import IntentExtractor
+
+def test_extract():
+    extractor = IntentExtractor()
+    intents = extractor.extract(['book','flight'])
+    assert 'book' in intents

training/evaluate.py

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+def evaluate():
+    print('Evaluation placeholder')
+
+if __name__ == '__main__':
+    evaluate()

training/loss.py

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+def loss_fn(pred, target):
+    return abs(len(pred) - len(target))

training/train.py

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+def train():
+    print('Training placeholder')
+
+if __name__ == '__main__':
+    train()