Case: 20260425T103325Z__gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked
TIMEOUT Trajectory: clean | Invoked: yes | Match: 0%
gptme --non-interactive --no-confirm --name gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-warmup --system short --workspace /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/workspace-warmup --tools shell --tool-format tool -m local/gemma4:e4b '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_gemma4_e4b__ollama__tool__web_search_json_ranked-measured --system short --workspace /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/workspace --tools shell --tool-format tool -m local/gemma4:e4b '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: strongly_diverged
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 66f1eccf709f checkbox", "selected_id": "doc-191417a1", "selected_score": 0.97, "selected_url": "https://kb.example/cd929097c2ba"}
REQUEST_HIT=/search/7rMsJqEQNrU1hJyAgl-_E-rm
MISSING_FILE
--- expected
+++ observed
@@ -1,2 +1 @@
-{"query": "weekly plan 66f1eccf709f checkbox", "selected_id": "doc-191417a1", "selected_score": 0.97, "selected_url": "https://kb.example/cd929097c2ba"}
-REQUEST_HIT=/search/7rMsJqEQNrU1hJyAgl-_E-rm
+MISSING_FILE
No structured session transcript found.
[gripprobe] process_started_at=2026-04-25T10:33:26+00:00
[10:33:27] Created config file at /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/home/.config/gptme/config.toml
[10:33:27] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/gemma4:e4b
Using logdir: /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/state/gptme-logs/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-warmup
Using workspace: /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__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://127.0.0.1:39265/search/oZXO3ZmyyZ2NzCB9WMvISNhq
````
````query.txt
warmup query 4f56f93c
````
````required-token.txt
1f8bd2f6d6599d41
````
Assistant: Thinking...
Assistant:
<think>
<think>
The user wants me to perform a multi-step task using the shell tool.
The goal is to:
1. Read three files: `search-url.txt`, `query.txt`, and `required-token.txt`.
2. Construct a search query using the content of `query.txt` and URL-encode it.
3. Call a search endpoint specified in `search-url.txt` with specific query parameters:
- `q=<urlencoded query from query.txt>`
- `format=json`
- `limit=8`
4. Process the JSON response:
- Select the result from the `results` array that has the highest `score`.
- This result must also have a `snippet` that contains the exact token read from `required-token.txt`.
5. Write the selected result's metadata (query, id, url, score) into a new file named `search-result.json` with specific keys.
6. If successful, reply only "DONE".
7. If any command execution fails, reply only "FAIL".
I need to use shell commands to achieve this. Since the task involves reading files, making an HTTP request, and processing JSON, I will likely need tools like `curl`, `jq`, and potentially some scripting logic (like `grep` or `awk` combined with shell variables).
Let's break down the steps for execution:
Step 1: Read file contents.
Step 2: URL-encode the query from `query.txt`.
Step 3: Construct and execute the `curl` command.
Step 4: Parse the JSON response using `jq` to find the best result.
Step 5: Write the final JSON structure to `search-result.json`.
I will start by listing the files to ensure they are present and then proceed with the logic.
The files are provided in the prompt, so I assume they exist in the current working directory.
**Step 1 & 2: Reading and Encoding Query**
The content of `query.txt` is `warmup query 4f56f93c`. I need to URL-encode this. I'll use a combination of shell tools or a dedicated encoding function if available, but since I'm limited to standard shell tools, I'll assume a mechanism or use a placeholder for encoding and focus on the structure. A common way to URL-encode in shell scripting is complex, but I'll try to use `jq` or `curl`'s capabilities if possible, or rely on a simple substitution if the query is simple enough. Given the complexity, I'll assume I can encode it first.
Let's assume the query encoding is handled by a function or tool that I can simulate or use `python` for if necessary, but I should stick to shell tools first.
If I use `curl`, I can pass the encoded query directly.
**Step 3: Constructing the API Call**
* Base URL: `search-url.txt` content: `http://127.0.0.1:39265/search/oZXO3ZmyyZ2NzCB9WMvISNhq`
* Query: `q=<encoded_query>`
* Parameters: `format=json`, `limit=8`
The full URL will be constructed using the base URL and query parameters.
**Step 4: Processing the Response**
I need to iterate through the `results` array, filter by `snippet` containing `required-token.txt` content, and then find the maximum `score` among the filtered results.
Let's start by reading the files and then constructing the command.
</think>
</think>
<thinking>
<thinking>
The user wants me to execute a complex task involving file reading, URL encoding, making an HTTP request, and JSON processing using shell tools.
**Plan:**
1. Read the content of `search-url.txt`, `query.txt`, and `required-token.txt`.
2. URL-encode the content of `query.txt`.
3. Construct the full `curl` command using the base URL, the encoded query, and the required parameters (`format=json`, `limit=8`).
4. Execute the `curl` command.
5. Process the JSON output using `jq` to find the best result based on the criteria:
* Snippet must contain the required token.
* Among those, select the one with the highest score.
6. Extract `query`, `selected_id`, `selected_url`, and `selected_score` and write them to `search-result.json`.
7. If successful, output "DONE". If any step fails, output "FAIL".
**Execution Details:**
* **File Contents:**
* `search-url.txt`: `http://127.0.0.1:39265/search/oZXO3ZmyyZ2NzCB9WMvISNhq`
* `query.txt`: `warmup query 4f56f93c`
* `required-token.txt`: `1f8bd2f6d6599d41`
* **URL Encoding:** I need to encode `warmup query 4f56f93c`. I will use `python -c` within the shell tool to handle URL encoding reliably, as standard shell tools might be insufficient.
* **API Call:** The endpoint is in `search-url.txt`. The parameters are `q=<encoded_query>&format=json&limit=8`.
* **JSON Processing:** This is the hardest part. I need to use `jq` to:
1. Filter `results[]` where `.snippet` contains the required token.
2. Sort the filtered results by `score` descending.
3. Take the first result.
4. Extract the required fields.
Let's start by reading the files and encoding the query.
<channel|>
[gripprobe] process_finished_at=2026-04-25T10:35:26+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-25T10:33:26+00:00 [gripprobe] process_finished_at=2026-04-25T10:35:26+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-25T10:35:26+00:00
[10:35:27] Created config file at /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/home/.config/gptme/config.toml
[10:35:27] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/gemma4:e4b
Using logdir: /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/state/gptme-logs/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-measured
Using workspace: /work/results/runs/20260425T103325Z/cases/gptme__local_gemma4_e4b__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://127.0.0.1:39265/search/7rMsJqEQNrU1hJyAgl-_E-rm
````
````query.txt
weekly plan 66f1eccf709f checkbox
````
````required-token.txt
1f8bd2f6d6599d41
````
Assistant: Thinking...
Assistant:
<think>
<think>
The user wants me to perform a multi-step task using the shell tool.
The goal is to:
1. Read three files: `search-url.txt`, `query.txt`, and `required-token.txt`.
2. Construct a search request using the content of these files.
3. Call a search endpoint (provided in `search-url.txt`) with specific query parameters:
- `q=<urlencoded query from query.txt>`
- `format=json`
- `limit=8`
4. Process the JSON response:
- Select the result from the `results` array that has the highest score.
- This result must also have a snippet containing the exact required token (from `required-token.txt`).
5. Write the selected result's metadata (query, selected_id, selected_url, selected_score) into a new file named `search-result.json`.
6. If successful, reply only "DONE".
7. If any command execution fails, reply only "FAIL".
I need to use shell commands to achieve this. Since the search endpoint is likely an API call, I will use `curl` or similar tools.
Step 1: Read the files.
Step 2: URL-encode the query from `query.txt`.
Step 3: Construct the full URL and execute the search.
Step 4: Parse the JSON response using tools like `jq` (assuming it's available, which is standard for shell scripting tasks involving JSON).
Step 5: Filter and select the best result.
Step 6: Write the output to `search-result.json`.
Let's start by listing the files to confirm they are present and readable.
</think>
</think>
<thinking>
<thinking>
The user wants me to execute a complex workflow involving reading multiple files, making an API call, parsing JSON, filtering results based on a score and a token match, and finally writing a specific JSON structure to a new file.
I will break this down into steps:
1. **Read files:** I need the content of `search-url.txt`, `query.txt`, and `required-token.txt`.
2. **URL Encoding:** The query from `query.txt` must be URL-encoded.
3. **API Call:** Use `curl` to call the endpoint defined in `search-url.txt` with the parameters: `q=<encoded_query>&format=json&limit=8`.
4. **JSON Processing:** Use `jq` to process the returned JSON.
* Filter `results` array: Keep only results where `snippet` contains the required token.
* Find the best result: Among the filtered results, select the one with the maximum `score`.
* Extract required fields: `query`, `id`, `url`, `score`.
5. **Output:** Write the final JSON structure to `search-result.json`.
I will start by checking the current directory contents and then proceed with the execution.
</thinking>
</thinking>
@shell(call_8p9qgriz): {"command":"ls -l"}
total 12
-rw-r--r-- 1 1000 1000 34 Apr 25 10:33 query.txt
-rw-r--r-- 1 1000 1000 17 Apr 25 10:33 required-token.txt
-rw-r--r-- 1 1000 1000 55 Apr 25 10:33 search-url.txt
System:
Ran allowlisted command: `ls -l`
```stdout
total 12
-rw-r--r-- 1 1000 1000 34 Apr 25 10:33 query.txt
-rw-r--r-- 1 1000 1000 17 Apr 25 10:33 required-token.txt
-rw-r--r-- 1 1000 1000 55 Apr 25 10:33 search-url.txt
```
[10:36:12] 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...
[10:36:29] INFO Reasoning content: Thinking Process: llm_openai.py:321
1. **Analyze the Request:** The user is instructing the assistant to use a "shell tool" to perform a specific, multi-step task.
2. **Identify Key Actions/Topics:**
* Using the shell tool.
* Reading multiple files (`search-url.txt`, `query.txt`, `required-token.txt`).
* Calling a search endpoint/API.
* The overall goal is executing a search query programmatically.
3. **Determine the Core Theme:** The conversation is about automating a search function using system tools and files.
4. **Draft Potential Titles (Aiming for 2-4 words):**
* Shell search execution
* Automated search query
* Using shell tool
* Search API workflow
5. **Select the Most Concise and Accurate Title:** "Search API workflow" or "Shell search execution" best captures the technical nature and the process involved. "Search API workflow" is slightly broader and more descriptive of the
*process* the user is defining.
6. **Final Check:** (2-4 words, ONLY the title, captures the main topic). -> "Search API workflow" (3 words).
INFO Auto-generated conversation name: Search API workflow chat.py:332
[gripprobe] process_finished_at=2026-04-25T10:37:26+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-25T10:35:26+00:00 [gripprobe] process_finished_at=2026-04-25T10:37:26+00:00 exit_code=124 timeout=true
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM gemma4:e4b
FROM /usr/share/ollama/.ollama/models/blobs/sha256-4c27e0f5b5adf02ac956c7322bd2ee7636fe3f45a8512c9aba5385242cb6e09a
TEMPLATE {{ .Prompt }}
RENDERER gemma4
PARSER gemma4
PARAMETER temperature 1
PARAMETER top_k 64
PARAMETER top_p 0.95
LICENSE """ Apache License
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