Case: 20260501T144517Z__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/20260501T144517Z/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/20260501T144517Z/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 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_score": 0.97, "selected_url": "https://kb.example/620f0062a759"}
REQUEST_HIT=/search/Xe4H9GyqkBSC5p_oLBI9eYFB
{"query": "weekly plan 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_score": 0.97, "selected_url": "https://kb.example/620f0062a759"}
REQUEST_HIT=yes
--- expected
+++ observed
@@ -1,2 +1,2 @@
{"query": "weekly plan 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_score": 0.97, "selected_url": "https://kb.example/620f0062a759"}
-REQUEST_HIT=/search/Xe4H9GyqkBSC5p_oLBI9eYFB
+REQUEST_HIT=yes
No structured session transcript found.
[gripprobe] process_started_at=2026-05-01T15:08:34+00:00
[15:08:35] Created config file at /work/results/runs/20260501T144517Z/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
[15:08:35] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/aravhawk/qwen3.5-opus-4.6-text:9b
[15:08:36] Using logdir:
/work/results/runs/20260501T144517Z/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/20260501T144517Z/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://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX
````
````query.txt
warmup query d4efd4b0
````
````required-token.txt
b06befc77fa0f38c
````
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_8cacypg9): {"command":"cat search-url.txt query.txt required-token.txt"}
http://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX
warmup query d4efd4b0
b06befc77fa0f38c
System:
Ran allowlisted command: `cat search-url.txt query.txt required-token.txt`
```stdout
http://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX
warmup query d4efd4b0
b06befc77fa0f38c
```
[15:08:55] 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...
[15:09:07] 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://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX
- Query: warmup query d4efd4b0
- Required token: b06befc77fa0f38c
I need to call the search endpoint with:
- q=warmup%20query%20d4efd4b0 (URL encoded)
- format=json
- limit=8
Let me make the API call.
</think>
</think>
@shell(call_zo40f7kt): {"command":"curl -s \"http://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX?q=warmup%20query%20d4efd4b0\u0026format=json\u0026limit=8\""}
Preview
curl -s "http://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX?q=warmup%20query%20d4efd4b0&format=json&limit=8"
{"query": "warmup query d4efd4b0", "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-e9f59bd3", "title": "Weekly planning with checkboxes", "url": "https://kb.example/1a9d225d21b3", "snippet": "Actionable template. Required token: 0bc8f7d6421f.", "score": 0.94, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions token 0bc8f7d6421f, 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://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX?q=warmup%20query%20d4efd4b0&format=json&limit=8"`
`{"query": "warmup query d4efd4b0", "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-e9f59bd3", "title": "Weekly planning with
checkboxes", "url": "https://kb.example/1a9d225d21b3", "snippet": "Actionable template. Required token: 0bc8f7d6421f.", "score": 0.94, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions
token 0bc8f7d6421f, 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 "b06befc77fa0f38c".
Looking at the results:
1. doc-intro: snippet "Basic markdown checklist examples." - no token
2. doc-e9f59bd3: snippet "Actionable template. Required token: 0bc8f7d6421f." - no token
3. doc-alt-token: snippet "Also mentions token 0bc8f7d6421f, 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 required token "b06befc77fa0f38c".
Wait, let me re-read the task. It says "snippet contains the exact required token". Let me check each snippet again:
- "Basic markdown checklist examples." - no
- "Actionable template. Required token: 0bc8f7d6421f." - no (has 0bc8f7d6421f, not b06befc77fa0f38c)
- "Also mentions token 0bc8f7d6421f, but less relevant." - no
- "Русскоязычный шаблон." - no
- "Deutsche Checklisten-Idee." - no
- "Time-blocking article." - no
- "Unrelated meeting summary." - no
- "Groceries list example." - no
None of them contain "b06befc77fa0f38c".
Hmm, this is strange. Let me re-read the problem...
Oh wait, I think I misread.
I'm carefully examining each snippet for the specific token "b06befc77fa0f38c". After a thorough check, I confirm that none of the snippets contain this exact token. The search results don't match the required criteria, which means I cannot select a result based on the given instructions.
The task requires finding a result with the highest score that includes the precise token, but no such result exists in the current dataset. This suggests either a data mismatch or a potential error in the search parameters.
I'll need to investigate further to understand why the token isn't appearing in any of the search results.
</think>
</think>
@shell(call_hd4jmzby): {"command":"curl -s \"http://127.0.0.1:42949/search/txfkyBdwfQ0ls_kUU5u7pnQX?q=warmup%20query%20d4efd4b0\u0026format=json\u0026limit=8\" | python3 -c \"import json,sys; d=json.load(sys.stdin); print(json.dumps(d, indent=2))\""}
System:
Command denied: `| python3 `
Piping to shell interpreters or script execution is blocked. This pattern can execute arbitrary code and is a security risk.
Skipped 1 hidden system messages, show with --show-hidden
Assistant: Thinking...
Assistant:
<think>
<think>
Let me try a different approach - I'll use python3 directly to parse the JSON.
[gripprobe] process_finished_at=2026-05-01T15:10:34+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-05-01T15:08:34+00:00 [gripprobe] process_finished_at=2026-05-01T15:10:34+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-05-01T15:10:34+00:00
[15:10:35] Created config file at /work/results/runs/20260501T144517Z/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
[15:10:35] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/aravhawk/qwen3.5-opus-4.6-text:9b
[15:10:36] Using logdir:
/work/results/runs/20260501T144517Z/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/20260501T144517Z/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://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB
````
````query.txt
weekly plan 2f060e82f744 checkbox
````
````required-token.txt
b06befc77fa0f38c
````
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_uqd1lvv9): {"command":"cat search-url.txt query.txt required-token.txt"}
http://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB
weekly plan 2f060e82f744 checkbox
b06befc77fa0f38c
System:
Ran allowlisted command: `cat search-url.txt query.txt required-token.txt`
```stdout
http://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB
weekly plan 2f060e82f744 checkbox
b06befc77fa0f38c
```
[15:10:55] 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...
[15:11:08] 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://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB
- Query: weekly plan 2f060e82f744 checkbox
- Required token: b06befc77fa0f38c
I need to call the search endpoint with:
- q=weekly+plan+2f060e82f744+checkbox (URL encoded)
- format=json
- limit=8
Let me make the API call.
</think>
</think>
@shell(call_zwrhz2sg): {"command":"curl -s \"http://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB?q=weekly+plan+2f060e82f744+checkbox\u0026format=json\u0026limit=8\""}
Preview
curl -s "http://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB?q=weekly+plan+2f060e82f744+checkbox&format=json&limit=8"
{"query": "weekly plan 2f060e82f744 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-e0feabaf", "title": "Weekly planning with checkboxes", "url": "https://kb.example/620f0062a759", "snippet": "Actionable template. Required token: b06befc77fa0f38c.", "score": 0.97, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions token b06befc77fa0f38c, 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://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB?q=weekly+plan+2f060e82f744+checkbox&format=json&limit=8"`
`{"query": "weekly plan 2f060e82f744 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-e0feabaf", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/620f0062a759", "snippet": "Actionable template. Required token: b06befc77fa0f38c.", "score": 0.97, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet":
"Also mentions token b06befc77fa0f38c, 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: `b06befc77fa0f38c`
Let me check each result:
- doc-intro: snippet "Basic markdown checklist examples." - NO
- doc-e0feabaf: snippet "Actionable template. Required token: b06befc77fa0f38c." - YES, contains the token, score 0.97
- doc-alt-token: snippet "Also mentions token b06befc77fa0f38c, but less relevant." - YES, contains the token, score 0.84
- 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
Results with the required token:
- doc-e0feabaf: score 0.97
- doc-alt-token: score 0.84
The highest score is 0.97 for doc-e0feabaf.
So the selected result is:
- selected_id: doc-e0feabaf
- selected_url: https://kb.example/620f0062a759
- selected_score: 0.97
Now I need to write search-result.json with:
- query: "weekly plan 2f060e82f744 checkbox"
- selected_id: "doc-e0feabaf"
- selected_url: "https://kb.example/620f0062a759"
- selected_score: 0.97
</think>
</think>
@shell(call_hvh4l1zb): {"command":"echo '{\"query\": \"weekly plan 2f060e82f744 checkbox\", \"selected_id\": \"doc-e0feabaf\", \"selected_url\": \"https://kb.example/620f0062a759\", \"selected_score\": 0.97}' \u003e search-result.json"}
Preview
echo '{"query": "weekly plan 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_url": "https://kb.example/620f0062a759", "selected_score": 0.97}' > search-result.json
System:
Ran command: `echo '{"query": "weekly plan 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_url": "https://kb.example/620f0062a759", "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 the content to make sure it's correct.
</think>
</think>
@shell(call_34w0nmyl): {"command":"cat search-result.json"}
{"query": "weekly plan 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_url": "https://kb.example/620f0062a759", "selected_score": 0.97}
System:
Ran allowlisted command: `cat search-result.json`
`{"query": "weekly plan 2f060e82f744 checkbox", "selected_id": "doc-e0feabaf", "selected_url": "https://kb.example/620f0062a759", "selected_score": 0.97}`
Assistant: Thinking...
Assistant:
<think>
<think>
The search-result.json file has been created correctly with all the required fields:
- query: "weekly plan 2f060e82f744 checkbox"
- selected_id: "doc-e0feabaf"
- selected_url: "https://kb.example/620f0062a759"
- selected_score: 0.97
The task is complete.
</think>
</think>
DONE
[15:12:26] INFO Auto-reply: Assistant message had no tools. Asking for confirmation... complete.py:138
[15:12:26] [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...
Assistant:
<think>
<think>
Let me verify my work:
1. I read the three input files:
- search-url.txt: http://127.0.0.1:42949/search/Xe4H9GyqkBSC5p_oLBI9eYFB
- query.txt: weekly plan 2f
[gripprobe] process_finished_at=2026-05-01T15:12:34+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-05-01T15:10:34+00:00 [gripprobe] process_finished_at=2026-05-01T15:12:34+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