Case: 20260426T071749Z__gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked
TIMEOUT artifact reached Trajectory: recovered | Invoked: yes | Match: 100%
The expected workspace artifact was present before the harness timeout elapsed.
gptme --non-interactive --no-confirm --name gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked-warmup --system short --workspace /work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/workspace-warmup --tools shell --tool-format tool -m local/aravhawk/qwen3.5-opus-4.6-text:9b 'Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL. '
gptme --non-interactive --no-confirm --name gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked-measured --system short --workspace /work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/workspace --tools shell --tool-format tool -m local/aravhawk/qwen3.5-opus-4.6-text:9b 'Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL. '
Consistency: consistent
Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL.
{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_score": 0.97, "selected_url": "https://kb.example/e3706401c503"}
REQUEST_HIT=/search/y12LkluZz_R7lfhqNt4Lf0ZX
{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_score": 0.97, "selected_url": "https://kb.example/e3706401c503"}
REQUEST_HIT=yes
--- expected
+++ observed
@@ -1,2 +1,2 @@
{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_score": 0.97, "selected_url": "https://kb.example/e3706401c503"}
-REQUEST_HIT=/search/y12LkluZz_R7lfhqNt4Lf0ZX
+REQUEST_HIT=yes
No structured session transcript found.
[gripprobe] process_started_at=2026-04-26T07:23:59+00:00
[07:24:00] Created config file at /work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/home/.config/gptme/config.toml
[07:24:00] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/aravhawk/qwen3.5-opus-4.6-text:9b
Using logdir:
/work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/state/gptme-logs/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked-warmup
Using workspace: /work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/workspace-warmup
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-url.txt, query.txt, and required-token.txt from the current workspace.
Call the search endpoint with query parameters:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
The endpoint returns JSON with a results array.
Select the result with the highest score among results whose snippet contains the exact required token.
Write search-result.json in the current workspace as JSON with exactly these keys:
- query
- selected_id
- selected_url
- selected_score
If successful, reply only DONE.
If command execution fails, reply only FAIL.
````search-url.txt
http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP
````
````query.txt
warmup query c531f334
````
````required-token.txt
2fee9dee2fd24900
````
Assistant: Thinking...
Assistant:
<think>
<think>
Let me break down this task:
1. Read the search URL from search-url.txt
2. Read the query from query.txt
3. Read the required token from required-token.txt
4. Call the search endpoint with:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
5. Find the result with the highest score whose snippet contains the exact required token
6. Write search-result.json with:
- query
- selected_id
- selected_url
- selected_score
Let me start by reading the files and then making the API call.
</think>
</think>
@shell(call_myohlura): {"command":"cat search-url.txt query.txt required-token.txt"}
http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP
warmup query c531f334
2fee9dee2fd24900
System:
Ran allowlisted command: `cat search-url.txt query.txt required-token.txt`
```stdout
http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP
warmup query c531f334
2fee9dee2fd24900
```
[07:24:18] ERROR exception during auto-name auto_naming.py:100
Traceback (most recent call last):
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/util/auto_naming.py", line 97, in _generate_llm_name
summary_model_name = get_summary_model(current_model.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/llm/models.py", line 576, in get_summary_model
raise ValueError(f"Provider {provider} did not have a summary model")
ValueError: Provider local did not have a summary model
Assistant: Thinking...
[07:24:31] INFO Reasoning content: Let me analyze this conversation to create an appropriate title. llm_openai.py:321
The conversation involves:
1. Using a shell tool
2. Reading multiple text files (search-url.txt, query.txt, required-token.txt)
3. Making an API call to a search endpoint
4. Using URL-encoded query parameters
This is a technical task involving shell scripting, file I/O, and API integration.
Possible titles:
- "Shell script API call"
- "Search endpoint integration"
- "File reading and API call"
- "Shell tool API usage"
- "Search API integration"
The most concise and accurate title would capture the main topic: using a shell tool to make an API call to a search endpoint.
INFO Auto-generated conversation name: Shell script API call chat.py:332
Assistant:
<think>
<think>
Now I have:
- URL: http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP
- Query: warmup query c531f334
- Required token: 2fee9dee2fd24900
I need to URL-encode the query and call the search endpoint.
</think>
</think>
@shell(call_i0091wll): {"command":"curl -s \"http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP?q=warmup%20query%20c531f334\u0026format=json\u0026limit=8\""}
Preview
curl -s "http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP?q=warmup%20query%20c531f334&format=json&limit=8"
{"query": "warmup query c531f334", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-d918f506", "title": "Weekly planning with checkboxes", "url": "https://kb.example/d5fe65297684", "snippet": "Actionable template. Required token: d8763828f50a.", "score": 0.94, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions token d8763828f50a, but less relevant.", "score": 0.8099999999999999, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}System:
Ran command: `curl -s "http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP?q=warmup%20query%20c531f334&format=json&limit=8"`
`{"query": "warmup query c531f334", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-d918f506", "title": "Weekly planning with
checkboxes", "url": "https://kb.example/d5fe65297684", "snippet": "Actionable template. Required token: d8763828f50a.", "score": 0.94, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions
token d8763828f50a, but less relevant.", "score": 0.8099999999999999, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan
Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang":
"en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries
list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}`
Assistant: Thinking...
Assistant:
<think>
<think>
Now I need to find the result with the highest score whose snippet contains the exact required token "2fee9dee2fd24900".
Looking at the results:
1. doc-intro: snippet "Basic markdown checklist examples." - no token
2. doc-d918f506: snippet "Actionable template. Required token: d8763828f50a." - no token
3. doc-alt-token: snippet "Also mentions token d8763828f50a, but less relevant." - no token
4. doc-ru: snippet "Русскоязычный шаблон." - no token
5. doc-de: snippet "Deutsche Checklisten-Idee." - no token
6. doc-noise-1: snippet "Time-blocking article." - no token
7. doc-noise-2: snippet "Unrelated meeting summary." - no token
8. doc-noise-3: snippet "Groceries list example." - no token
None of the snippets contain the exact required token "2fee9dee2fd24900".
Wait, let me re-read the task. It says "snippet contains the exact required token". The required token is "2fee9dee2fd24900".
Looking at all snippets again:
- "Basic markdown checklist examples."
- "Actionable template. Required token: d8763828f50a."
- "Also mentions token d8763828f50a, but less relevant."
- "Русскоязычный шаблон."
- "Deutsche Checklisten-Idee."
- "Time-blocking article."
- "Unrelated meeting summary."
- "Groceries list example."
None contain "2fee9dee2fd24900".
This is strange. Let me double-check by searching for the token in the response.
</think>
</think>
@shell(call_qjy4hbka): {"command":"echo \"2fee9dee2fd24900\" | grep -o \"2fee9dee2fd24900\""}
2fee9dee2fd24900
System:
Ran allowlisted command: `echo "2fee9dee2fd24900" | grep -o "2fee9dee2fd24900"`
`2fee9dee2fd24900`
Skipped 1 hidden system messages, show with --show-hidden
Assistant: Thinking...
Assistant:
<think>
<think>
The token "2fee9dee2fd24900" is confirmed. Now let me search for it in the API response.
</think>
</think>
@shell(call_6bbpf0jb): {"command":"curl -s \"http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP?q=warmup%20query%20c531f334\u0026format=json\u0026limit=8\" | grep -o \"2fee9dee2fd24900\""}
Preview
curl -s "http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP?q=warmup%20query%20c531f334&format=json&limit=8" | grep -o "2fee9dee2fd24900"
System:
Ran command: `curl -s "http://127.0.0.1:43931/search/kgnvKhUoaznpaLmpIzpPS0RP?q=warmup%20query%20c531f334&format=json&limit=8" | grep -o "2fee9dee2fd24900"`
No output
Return code: 1
Assistant: Thinking...
Assistant:
<think>
<think>
The token "2fee9dee2fd24900" is not found in the API response. This means no result has a snippet containing the exact required token.
According to the task: "Select the result with the highest score among results whose snippet contains the exact required token."
If no results match, there's no valid selection. However, the task expects me to write search-result.json with selected_id, selected_url, and selected_score.
Let me re-read the task more carefully...
"Select the result with the highest score among results whose snippet contains the exact required token."
If no results contain the token, then there's no valid result to select. But the output format requires selected_id, selected_url, and selected_score.
Wait, maybe I should interpret this differently. Perhaps the task expects me to handle the case where no match is found. But the instructions say "If successful, reply only DONE. If command execution fails, reply only FAIL."
The command execution didn't fail - the API
[gripprobe] process_finished_at=2026-04-26T07:25:59+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T07:23:59+00:00 [gripprobe] process_finished_at=2026-04-26T07:25:59+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T07:25:59+00:00
[07:26:00] Created config file at /work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/home/.config/gptme/config.toml
[07:26:00] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/aravhawk/qwen3.5-opus-4.6-text:9b
Using logdir:
/work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/state/gptme-logs/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked-measured
Using workspace: /work/results/runs/20260426T071749Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__web_search_json_ranked/workspace
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-url.txt, query.txt, and required-token.txt from the current workspace.
Call the search endpoint with query parameters:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
The endpoint returns JSON with a results array.
Select the result with the highest score among results whose snippet contains the exact required token.
Write search-result.json in the current workspace as JSON with exactly these keys:
- query
- selected_id
- selected_url
- selected_score
If successful, reply only DONE.
If command execution fails, reply only FAIL.
````search-url.txt
http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX
````
````query.txt
weekly plan 8ccf286f8dfc checkbox
````
````required-token.txt
2fee9dee2fd24900
````
Assistant: Thinking...
Assistant:
<think>
<think>
Let me break down this task:
1. Read the search URL from search-url.txt
2. Read the query from query.txt
3. Read the required token from required-token.txt
4. Call the search endpoint with:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
5. Find the result with the highest score whose snippet contains the exact required token
6. Write search-result.json with:
- query
- selected_id
- selected_url
- selected_score
Let me start by reading the files and then making the API call.
</think>
</think>
@shell(call_irz8ivnz): {"command":"cat search-url.txt query.txt required-token.txt"}
http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX
weekly plan 8ccf286f8dfc checkbox
2fee9dee2fd24900
System:
Ran allowlisted command: `cat search-url.txt query.txt required-token.txt`
```stdout
http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX
weekly plan 8ccf286f8dfc checkbox
2fee9dee2fd24900
```
[07:26:20] ERROR exception during auto-name auto_naming.py:100
Traceback (most recent call last):
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/util/auto_naming.py", line 97, in _generate_llm_name
summary_model_name = get_summary_model(current_model.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/llm/models.py", line 576, in get_summary_model
raise ValueError(f"Provider {provider} did not have a summary model")
ValueError: Provider local did not have a summary model
Assistant: Thinking...
[07:26:33] INFO Reasoning content: Let me analyze this conversation to create an appropriate title. llm_openai.py:321
The conversation involves:
1. Using a shell tool
2. Reading multiple text files (search-url.txt, query.txt, required-token.txt)
3. Making an API call to a search endpoint
4. Using URL-encoded query parameters
This is a technical task involving shell scripting, file I/O, and API integration.
Possible titles:
- "Shell script API call"
- "Search endpoint integration"
- "File reading and API call"
- "Shell tool API usage"
- "Search API integration"
The most concise and accurate title would capture the main topic: using a shell tool to make an API call to a search endpoint.
INFO Auto-generated conversation name: Shell script API call chat.py:332
Assistant:
<think>
<think>
Now I have:
- URL: http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX
- Query: weekly plan 8ccf286f8dfc checkbox
- Required token: 2fee9dee2fd24900
I need to call the search endpoint with:
- q=<urlencoded query>
- format=json
- limit=8
The query is "weekly plan 8ccf286f8dfc checkbox". URL encoding this would be:
- "weekly" -> "weekly"
- " " -> "%20"
- "plan" -> "plan"
- " " -> "%20"
- "8ccf286f8dfc" -> "8ccf286f8dfc"
- " " -> "%20"
- "checkbox" -> "checkbox"
So: weekly%20plan%208ccf286f8dfc%20checkbox
Let me make the API call.
</think>
</think>
@shell(call_fhz7868n): {"command":"curl -s \"http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX?q=weekly%20plan%208ccf286f8dfc%20checkbox\u0026format=json\u0026limit=8\""}
Preview
curl -s "http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX?q=weekly%20plan%208ccf286f8dfc%20checkbox&format=json&limit=8"
{"query": "weekly plan 8ccf286f8dfc checkbox", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-106ef23d", "title": "Weekly planning with checkboxes", "url": "https://kb.example/e3706401c503", "snippet": "Actionable template. Required token: 2fee9dee2fd24900.", "score": 0.97, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions token 2fee9dee2fd24900, but less relevant.", "score": 0.84, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}System:
Ran command: `curl -s "http://127.0.0.1:43931/search/y12LkluZz_R7lfhqNt4Lf0ZX?q=weekly%20plan%208ccf286f8dfc%20checkbox&format=json&limit=8"`
`{"query": "weekly plan 8ccf286f8dfc checkbox", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-106ef23d", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/e3706401c503", "snippet": "Actionable template. Required token: 2fee9dee2fd24900.", "score": 0.97, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet":
"Also mentions token 2fee9dee2fd24900, but less relevant.", "score": 0.84, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan
Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang":
"en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries
list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}`
Assistant: Thinking...
Assistant:
<think>
<think>
Now I need to analyze the results:
1. I need to find results whose snippet contains the exact required token: "2fee9dee2fd24900"
Let me check each result:
- doc-intro: snippet "Basic markdown checklist examples." - NO
- doc-106ef23d: snippet "Actionable template. Required token: 2fee9dee2fd24900." - YES, contains "2fee9dee2fd24900"
- doc-alt-token: snippet "Also mentions token 2fee9dee2fd24900, but less relevant." - YES, contains "2fee9dee2fd24900"
- doc-ru: snippet "Русскоязычный шаблон." - NO
- doc-de: snippet "Deutsche Checklisten-Idee." - NO
- doc-noise-1: snippet "Time-blocking article." - NO
- doc-noise-2: snippet "Unrelated meeting summary." - NO
- doc-noise-3: snippet "Groceries list example." - NO
So the matching results are:
- doc-106ef23d with score 0.97
- doc-alt-token with score 0.84
The one with the highest score is doc-106ef23d with score 0.97.
Now I need to write search-result.json with:
- query: "weekly plan 8ccf286f8dfc checkbox"
- selected_id: "doc-106ef23d"
- selected_url: "https://kb.example/e3706401c503"
- selected_score: 0.97
</think>
</think>
@shell(call_dny95avc): {"command":"echo '{\"query\": \"weekly plan 8ccf286f8dfc checkbox\", \"selected_id\": \"doc-106ef23d\", \"selected_url\": \"https://kb.example/e3706401c503\", \"selected_score\": 0.97}' \u003e search-result.json"}
Preview
echo '{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_url": "https://kb.example/e3706401c503", "selected_score": 0.97}' > search-result.json
System:
Ran command: `echo '{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_url": "https://kb.example/e3706401c503", "selected_score": 0.97}' > search-result.json`
No output
Skipped 1 hidden system messages, show with --show-hidden
Assistant: Thinking...
Assistant:
<think>
<think>
The file has been written successfully. Let me verify it was written correctly.
</think>
</think>
@shell(call_mdg7t74x): {"command":"cat search-result.json"}
{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_url": "https://kb.example/e3706401c503", "selected_score": 0.97}
System:
Ran allowlisted command: `cat search-result.json`
`{"query": "weekly plan 8ccf286f8dfc checkbox", "selected_id": "doc-106ef23d", "selected_url": "https://kb.example/e3706401c503", "selected_score": 0.97}`
Assistant: Thinking...
Assistant:
<think>
<think>
The file has been written correctly with all the required fields:
- query: "weekly plan 8ccf286f8dfc checkbox"
- selected_id: "doc-106ef23d"
- selected_url: "https://kb.example/e3706401c503"
- selected_score: 0.97
The task is complete.
</think>
</think>
DONE
[07:27:57] INFO Auto-reply: Assistant message had no tools. Asking for confirmation... complete.py:138
[07:27:57] [Loop control] <system>No tool call detected in last message. Did you mean to finish? If so, make sure you are comp...
User: <system>No tool call detected in last message. Did you mean to finish? If so, make sure you are completely done and then use the `complete` tool to end the session.</system>
Assistant: Thinking...
[gripprobe] process_finished_at=2026-04-26T07:27:59+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T07:25:59+00:00 [gripprobe] process_finished_at=2026-04-26T07:27:59+00:00 exit_code=124 timeout=true
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM aravhawk/qwen3.5-opus-4.6-text:9b
FROM /usr/share/ollama/.ollama/models/blobs/sha256-d0ecd80b0e45b0d9e49c8cd1527b7f7d52d8d3bde2c569ab36aac59bb78f53f7
TEMPLATE {{ .Prompt }}
RENDERER qwen3.5
PARSER qwen3.5
PARAMETER num_ctx 32768
PARAMETER presence_penalty 1.5
PARAMETER stop <|im_end|>
PARAMETER stop <|im_start|>
PARAMETER temperature 1
PARAMETER top_k 20
PARAMETER top_p 0.95