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Publish traceable conversation explorer and standalone Python decoder
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---
language:
- en
tags:
- conversation-trees
- aac
- symbolic
- traceability
- python
size_categories:
- 1K<n<10K
pretty_name: SML Conversation Trees and Python Decoder
configs:
- config_name: source_catalog
data_files: catalog.json
---
# SML Conversation Trees + Python Decoder
**[Try the live conversation explorer](https://huggingface.co/spaces/SMLBuilder/SML-Tree-Explorer)**
Explore recorded conversation trees while preserving every branch, the full
conversation history and the exact location of each turn in the source JSON.
This public release brings together **1,309 JSON source files** from SMLBuilder's
existing public corpora, a standalone Python decoder, and a live interactive demo.
The catalog includes branching conversations, linear chats and derived training
files. It is a source-file count, not a claim that every file is a conversation tree.
## Public source datasets
- [AAC Children Conversations](https://huggingface.co/datasets/SMLBuilder/AAC_Children_Conversations)
— pinned at `c060d83904330dd578167e29d7714becd7aaec17`.
- [TinkyBrain TrainingData](https://huggingface.co/datasets/SMLBuilder/TinkyBrain-TrainingData)
— pinned at `b7f5285c28d77359687c59f2b948b66aefe869c7`.
`catalog.json` points directly to these files at their immutable source revisions.
This avoids uploading another copy of the full training corpora. Fields include
`id`, `repo`, `revision`, `path`, `bytes`, `sha256` (LFS objects), `blob_id` (Git
objects), `category` and `title`. A null SHA-256 means the Git blob ID is the
integrity anchor; the live explorer additionally computes SHA-256 after download.
## Use the lightweight decoder
Download [tree_decoder.py](tree_decoder.py) and a tree JSON from either source
dataset. Python 3.9 or newer is enough; there are no package dependencies, model
weights, API credentials or GPU requirements.
```sh
python3 tree_decoder.py conversation.json --list
python3 tree_decoder.py conversation.json --conversation 0 --path 0,2,1
python3 tree_decoder.py conversation.json --all > conversation-traces.jsonl
```
`--all` emits every reachable state in every supported conversation root, including
all history, available next replies, terminal status, original tile metadata and
RFC 6901 JSON pointers. It never merges identical-looking responses from different
branches or invents a continuation. Paths and conversation indices are zero-based.
Linear `messages` files are explicitly labeled and have one recorded next message.
Training tables without conversation structure are not interpreted as dialogue.
Supported tree families: Q&A, AAC/continuation, doctor/patient, caller/dispatcher,
dispatcher/AAC. The original [AAC_cli.py](AAC_cli.py) is included unchanged alongside
the expanded path-preserving decoder.
## Trace format
A trace contains `conversation_index`, `conversation_id`, `root_pointer`,
`node_pointer`, `branch_path`, `depth`, `schema`, `history`, `options` and `terminal`.
Each turn carries its original `text`, `role` or schema role label, `pointer`,
`tile_id` when present and unmodified `metadata`. Web exports add the exact pinned
source URL, repository revision and verified content hash. CLI exports identify
the input filename and its SHA-256. Missing source roles use neutral schema labels,
not an inference about the real speaker.
The starter tree is a byte-identical public example bundled for immediate demo
startup. Its original path and hash are recorded in the catalog under
`25519a9782d5d013`.
## Scope and verification
The release contains public conversation data references and decoder code. Raw
recovery archives, absolute machine paths, credentials and personal runtime history
are not part of this public dataset. Existing upstream dataset terms remain
applicable; this release does not add a license to underlying material.
An initial audit walked **13,401 reachable states** in 22 representative public files,
including 11,159 terminal states. **111,118** displayed history/option pointers were
resolved against the original JSON and matched their recorded text. This is a
format/trace audit of those samples, not an accuracy or safety certification of
the full corpora. Unsupported files and malformed paths return explicit errors.
The live demo replays dataset content. It does not generate answers, assess their
truth, or provide professional advice through specialist examples. It uses public
read-only source files and does not save visitors' paths as personal conversations.