---
license: odc-by
language:
- en
tags:
- embeddings
- swe-chat
size_categories:
- 1K<n<10K
---
# SWE-Chat Coding-Agent Session Embeddings

![swe-chat — topic-cluster density map](topic_map.png)

Embeddings of [SALT-NLP/SWE-chat](https://huggingface.co/datasets/SALT-NLP/SWE-chat) coding-agent session transcripts, produced with [amkdg/Qwen3-Embedding-8B-NVFP4](https://huggingface.co/amkdg/Qwen3-Embedding-8B-NVFP4) — 4096-d,
L2-normalized `float16` (cosine = dot product).

- **5,830** conversations → **8,663** vectors
- `emb.npy` — `float16 [8663, 4096]`
- `meta.parquet` — one row per vector, aligned with `emb.npy`: `id, uuid, tag, chunk, n_chunks, count, source_ref`
- `manifest.json` — counts and provenance

## Usage

```python
import numpy as np, pyarrow.parquet as pq

emb  = np.load("emb.npy", mmap_mode="r")           # [8663, 4096] float16
meta = pq.read_table("meta.parquet").to_pandas()   # one row per vector, aligned with emb

# A conversation = consecutive rows sharing one `uuid` (`chunk == 0` marks its start);
# conversations longer than 8192 tokens span several chunk-rows.
starts = meta.index[meta.chunk == 0]               # first row of each conversation
```

## Source mapping

Each row carries `source_ref`, the locator back into [SALT-NLP/SWE-chat](https://huggingface.co/datasets/SALT-NLP/SWE-chat) — source_ref is the `session_id`.

```python
ref = meta.iloc[0].source_ref      # -> the matching conversation in the source dataset
```

## Notes

One embedding per agent session: a short grounding header (repo · domain · language · agent · files · success) followed by **only the conversational user/assistant turns**. Tool calls, progress events, file snapshots and commits are dropped as noise (only ~3.8% of raw turns are conversational). `tag` is the repo domain (application / devtools / library). The ~26 sessions without dialogue fall back to their prompt summary.