Add chat mode: use the node as a general VLM, not just a judge

New mode='chat' with system_prompt + user_prompt inputs runs your own prompt over
the image(s) and returns raw text in 'analysis' — reusing the same model dropdown,
quant, auto-download and backend. Makes it a one-node abliterated VLM for captioning,
tagging, Q&A, prompt-from-image, etc. agent_bridge gains --mode chat /
--system-prompt / --user-prompt (no receptor needed). Writes a chat report
(latest.json) for the agent. Docs updated.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

README.md

@@ -31,7 +31,7 @@ can act on it.
 | name | type | default | notes |
 |---|---|---|---|
 | `reference_image` | IMAGE | — | the target |
-| `mode` | compare / describe | compare | `describe` = first pass over the reference only → caption + target spec (seeds the prompt). `compare` = score ref vs generated |
+| `mode` | compare / describe / chat | compare | `compare` = score ref vs generated. `describe` = first pass over the reference → caption + target spec. `chat` = **general VLM**: your `system_prompt` + `user_prompt` over the image(s) → raw text |
 | `profile` | general / oral / penetration / handjob / solo | general | **analysis profile** — act-specialized axis set; the act-critical axes are distance/proximity-aware (e.g. `mouth_genital_distance`) so magnitude isn't hidden behind a coarse label |
 | `generated_image` | IMAGE (optional) | — | the candidate to score (required for `compare`, ignored for `describe`) |
 | `model_select` | dropdown (model name) | 4B local | **which judge** (transformers/safetensors, auto-downloaded): Qwen3-VL 4B/8B/30B-A3B, **Qwen3.5-9B**, **Qwen3.6-27B/35B-A3B** (newer, natively multimodal). Param size shown in the label |
@@ -43,19 +43,27 @@ can act on it.
 | `swap_eval` | BOOL | true | run twice with images swapped, average → cuts position bias |
 | `keep_loaded` | BOOL | true | cache weights across loop iterations |
 | `auto_download` | BOOL | true | if `model_path` is a repo id/alias and not local, fetch it from HF into `models/prompt_generator/` |
+| `system_prompt` | STRING | "" | **chat mode**: your system prompt |
+| `user_prompt` | STRING | "Describe this image." | **chat mode**: your instruction over the image(s) |
 
 **Auto-download:** set `model_path` to `30b-a3b` (alias) or any `org/name` repo id and leave
 `auto_download` on — the node snapshot-downloads it on first run (into ComfyUI's
 `models/prompt_generator/<name>`) and reuses the local copy afterward. Local paths and the
 default skip download entirely.
 
+**General VLM (chat mode):** set `mode=chat` and the node becomes a plain vision-language
+node — feed an image (and optionally a second), write your own `system_prompt`/`user_prompt`,
+and read the model's text from the `analysis` output. Reuses the same model dropdown, quant,
+and auto-download as the judge, so it's a one-node abliterated VLM for captioning, tagging,
+Q&A, prompt-from-image, etc. (CLI: `agent_bridge.py --mode chat --user-prompt "..."`).
+
 **Outputs**
 
 | name | type | use |
 |---|---|---|
 | `overall_score` | FLOAT 0..1 | compare: mean verdict (computed here, not by the model). describe: `1.0` placeholder |
 | `axis_scores_json` | STRING (JSON) | compare: per-axis `{verdict, ref, gen}` (verdict = match/partial/mismatch). describe: `{axis: value}` |
-| `analysis` | STRING | compare: header (`overall, N mismatches`) + axes worst-first (`VERDICT ref:[…] gen:[…]`). describe: the `caption` |
+| `analysis` | STRING | compare: header (`overall, N mismatches`) + axes worst-first (`VERDICT ref:[…] gen:[…]`). describe: the `caption`. chat: the model's response |
 | `raw` | STRING | raw model output (both passes if `swap_eval`) |
 | `report_path` | STRING | path to the written `calib_<tag>.json` (carries `mismatch_count`) |