dialogue-memory-pipeline 0.1.0

LLM-driven dialogue segmentation and episodic memory construction pipeline.

Dialogue Memory Pipeline

dialogue_memory_pipeline turns a dialogue transcript into:

• candidate topic-shift boundaries
• per-utterance local discourse states
• finalized dialogue segments
• episode-style memory records for each segment

The package is built around an OpenAI-compatible JSON LLM client and is intended for dialogue understanding, segmentation, and memory construction workflows.

Project Status

This package is currently in active development and should be treated as an alpha release.

• APIs, behavior, and output formats may still change.
• The package is not yet production-ready.
• At the moment, the only provider setup that is tested and supported is Bailian (DashScope) using its OpenAI-compatible endpoint.

If you publish this package, it is best to assume early-adopter usage rather than stable general availability.

What It Does

Given a sequence of utterances, the pipeline runs four stages:

1. Candidate boundary generation Scores every possible boundary between adjacent utterances and keeps only the highest-confidence candidates.
2. Local state extraction Extracts a structured state for each utterance, including topic, intent, entities, cue markers, and obligation signals.
3. Transition judgment and segmentation Walks candidate boundaries in order and decides whether each one starts a new segment.
4. Episodic memory building Produces one memory record per final segment.

The top-level entrypoint is DialogueSegmentationPipeline.

Features

• End-to-end dialogue segmentation and memory generation
• OpenAI-compatible client with optional custom base_url
• Structured JSON outputs for every pipeline stage
• Configurable candidate selection thresholding
• Simple API for loading dialogues from JSON files

Installation

Install the package from source:

git clone https://github.com/Keyan0412/dialogue_memory_pipeline.git
cd dialogue_memory_pipeline
python -m venv .venv
source .venv/bin/activate
pip install .

Required dependencies:

• openai
• python-dotenv

Environment Variables

The pipeline currently expects a Bailian (DashScope) API key and endpoint through an OpenAI-compatible interface.

Supported environment variables:

• OPENAI_API_KEY
• OPENAI_BASE_URL (optional)
• OPENAI_MODEL (optional when using from_env)

Example .env:

OPENAI_API_KEY=YOUR_API_KEY
OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
OPENAI_MODEL=qwen3.5-plus

At this stage, other OpenAI-compatible providers may or may not work, but they are not yet officially supported by this package.

Quick Start

Use the pipeline with environment variables

from dialogue_memory_pipeline import (
    DialogueSegmentationPipeline,
    PipelineConfig,
    load_sample_dialogue,
)

dialogue = load_sample_dialogue()

config = PipelineConfig(
    top_p_candidates=0.30,
    min_candidate_score=0.20,
    right_preview_window=3,
    min_segment_len=2,
)

pipeline = DialogueSegmentationPipeline.from_env(config=config)
result = pipeline.run(dialogue)

print(result["segments"])
print(result["episodes"])

Use the pipeline with explicit credentials

from dialogue_memory_pipeline import DialogueSegmentationPipeline, load_sample_dialogue

dialogue = load_sample_dialogue()

pipeline = DialogueSegmentationPipeline.from_openai(
    model="qwen3.5-plus",
    api_key="YOUR_API_KEY",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

result = pipeline.run(dialogue)

Public API

The package exports:

from dialogue_memory_pipeline import (
    DialogueSegmentationPipeline,
    PipelineConfig,
    load_dialogue,
    load_sample_dialogue,
)

DialogueSegmentationPipeline

Constructors:

• DialogueSegmentationPipeline(llm, config=None)
• DialogueSegmentationPipeline.from_env(model=None, config=None)
• DialogueSegmentationPipeline.from_openai(model, api_key=None, base_url=None, config=None)

Main method:

• run(utterances) -> dict

PipelineConfig

Current configuration fields:

PipelineConfig(
    top_p_candidates=0.30,
    min_candidate_score=0.20,
    right_preview_window=3,
    min_segment_len=2,
)

Field meanings:

• top_p_candidates: Fraction of available boundaries to keep after scoring. If a dialogue has N utterances, there are N - 1 possible boundaries. The retained candidate count is ceil((N - 1) * top_p_candidates), with a minimum of 1 whenever any boundary exists.
• min_candidate_score: Minimum boundary score to keep before the top-p cap is applied.
• right_preview_window: Number of right-side local states shown to the transition judge when evaluating a candidate split.
• min_segment_len: Minimum allowed segment length used during segmentation and cleanup merge.

Input Format

load_sample_dialogue() loads the packaged example dialogue shipped in the wheel.

load_dialogue(...) expects a JSON file containing a list of utterances:

[
  {
    "turn_id": 0,
    "speaker": "user",
    "text": "I need to reschedule my flight."
  },
  {
    "turn_id": 1,
    "speaker": "assistant",
    "text": "Sure, what is your booking number?"
  }
]

Each item must contain:

• turn_id
• speaker
• text

Output Structure

pipeline.run(...) returns a dictionary with these top-level keys:

• candidates
• local_states
• decisions
• segments
• episodes
• timing

candidates

Each candidate includes:

• boundary_after_turn
• score
• left_turn_id
• right_turn_id
• left_text
• right_text
• reasoning
• source

local_states

Each local state includes:

• turn_id
• speaker
• summary_topic
• intent
• salient_entities
• cue_markers
• obligation.opens
• obligation.resolves

segments

Each finalized segment includes:

• segment_id
• utterance_span
• utterances
• local_states
• segment_state

segment_state contains:

• stable_topic
• discourse_goal
• focus_topics
• entity_core
• open_obligations
• dominant_relation

episodes

Each episodic memory record includes:

• episode_id
• utterance_span
• utterances
• retrieval_summary
• key_entities
• importance

Running the Included Scripts

Full pipeline demo

python scripts/demo.py

Optional flags:

python scripts/demo.py --output outputs/demo_output.json --model qwen3.5-plus

Candidate boundary generator test

python scripts/test_candidate_generator.py --top-p 0.30 --min-score 0.40

This writes a JSON report with:

• all scored boundaries
• filtered candidate boundaries
• the effective candidate-generation config

Repository Layout

• src/dialogue_memory_pipeline/ Importable package
• src/dialogue_memory_pipeline/clients/ LLM client adapters
• src/dialogue_memory_pipeline/core/ Shared dataclasses and schemas
• src/dialogue_memory_pipeline/modules/ Pipeline modules for boundary generation, state extraction, transition judgment, and memory building
• src/dialogue_memory_pipeline/data/ Packaged sample dialogue data included in the wheel
• examples/ Small usage examples
• scripts/ Runnable scripts for demos and module-level testing
• tests/ Local test coverage for packaging and defensive parsing behavior
• outputs/ Generated artifacts

Model and Provider Notes

• The current tested provider is Bailian (DashScope) via its OpenAI-compatible endpoint.
• OPENAI_BASE_URL should currently point to the Bailian compatible endpoint unless you are experimenting on your own.
• from_env() defaults to OPENAI_MODEL when set, otherwise it falls back to qwen3.5-plus.
• Support for additional providers is not finalized yet.

Current Limitations

• The project is still in alpha and may change in breaking ways.
• Only Bailian (DashScope) API credentials and endpoint configuration are currently supported.
• The implementation is fully LLM-driven; there is no local fallback model path in the package.
• Transition-judge behavior is model-dependent because split decisions are generated by the LLM.
• The implementation depends on an OpenAI-compatible JSON-capable model endpoint.

License

This project is released under the Apache License 2.0. See LICENSE.