dialograph

Dialograph is a lightweight Python library for representing, evolving, and traversing dialogue memory as a temporal graph. It is designed for proactive dialogue agents, where reasoning over user preferences, beliefs, and strategies matters more than raw generation.

At its core, Dialograph wraps a dynamic graph structure around typed dialogue memories and provides clean hooks for retrieval, scoring, and long-term evolution.

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Core Concepts

Nodes

Nodes represent dialogue memory units such as:

• user preferences
• beliefs / facts
• dialogue strategies

Each node carries a state object (e.g. PreferenceState, BeliefState) with confidence and temporal metadata.

Edges

Edges represent relations between memory units:

• semantic relations (supports, contradicts, elicits)
• dialogue flow dependencies
• strategy activation paths

Edges are directional, weighted, and time-aware.

Time

Dialograph maintains an internal time counter that allows:

• decay of confidence
• forgetting
• recency-based retrieval

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Project Structure

dialograph/
├── core/               # graph primitives
│   ├── graph.py        # Dialograph wrapper
│   ├── node.py         # node state definitions
│   └── edge.py         # edge state definitions
│
├── memory/             # typed dialogue memory
│   ├── preference.py
│   ├── belief.py
│   └── strategy.py
│
├── traversal/          # retrieval and scoring
│   ├── retrieve.py
│   └── score.py
│
├── utils/              # persistence & visualization
│   ├── io.py
│   └── visualize.py

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Installation

For development:

git clone https://github.com/nabin2004/dialograph.git

cd dialograph

uv venv

source .venv/bin/activate

uv pip install -e .

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Quick Example

from dialograph import Node, Edge, Dialograph, draw

graph = Dialograph()

# Nabin Eats Rice.
n1 = Node(node_id='n1',node_type='personal_details',data={"value":"Nabin"})
n2 = Node(node_id='n2',node_type="object", data={"value":"Rice"})
n3 = Node(node_id='n3',node_type='personal_details',data={"value":"Football"})
n4 = Node(node_id='n4',node_type='person',data={"value":"R. Feynman"})
n5 = Node(node_id='n5',node_type='subject',data={"value":"Quantum Physics"})


e1 = Edge(edge_id='e1', source_node_id='n1', target_node_id='n2', relation='eats')
e2 = Edge(edge_id='e2', source_node_id='n1', target_node_id='n4', relation='knows')
e3 = Edge(edge_id='e3', source_node_id='n1', target_node_id='n3', relation='plays')
e4 = Edge(edge_id='e4', source_node_id='n1', target_node_id='n5', relation='interested')
e5 = Edge(edge_id='e5', source_node_id='n4', target_node_id='n5', relation='famousFor')


graph.add_node(n1)
graph.add_node(n2)
graph.add_node(n3)
graph.add_node(n4)
graph.add_node(n5)

graph.add_edge(e1)
graph.add_edge(e2)
graph.add_edge(e3)
graph.add_edge(e4)
graph.add_edge(e5)

draw(graph)

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Intended Use Cases

• Proactive dialogue systems
• Emotional support agents
• Preference elicitation
• Strategy planning and reuse
• Dialogue memory research

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Status

This project is under active development. APIs may evolve, but core abstractions are expected to remain stable.

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Roadmap

• Path-based retrieval
• Forgetting thresholds
• Graph serialization
• 3D / interactive visualization
• LLM-facing retrieval API

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License

MIT License.

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Citation

If you use Dialograph in academic work, please cite the corresponding paper (coming soon).

- How can an agent use past interactions to proactively adapt its dialog strategies over time, without relying on opaque embedding-only retrieval or heavy ontologies?

- We're not retrieving facts, we're retrieving how to act.

2. Scoring functions
3. Graph serialization (save/load)
4. Retrieval:
   1. Simple neighbor retrieval
   2. Smart path-based retrieval
   3. Context-aware retrieval

python real_run3.py