diff --git a/CHANGELOG.md b/CHANGELOG.md index a504e2b8..7bde29b8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,11 +7,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -## [0.12.3] - 20.11.2025 +### Added +- Support for predefined nodesets in smc simulation config. ### Changed - Use `within_brain_region_direction=ascendants_and_descendants` in every applicable tool. +## [0.12.3] - 20.11.2025 + ### Fixed - Properly wait for python subprocess to finish before stdout parsing. diff --git a/backend/eval/output/detailed.json b/backend/eval/output/detailed.json index f7a93481..aacb7532 100644 --- a/backend/eval/output/detailed.json +++ b/backend/eval/output/detailed.json @@ -27,14 +27,8 @@ "circuit" ] }, - "ai_response": "## Analysis \\u2014 Inhibitory \\u2192 Excitatory connectivity\\n**Circuit ID:** **2eb602ea-9ffc-42a2-9fb7-5234921343d0** \\nPerformed: connectivity metrics for chemical edges, pre_node_set = **Inhibitory**, post_node_set = **Excitatory**, grouped by **mtype**.\\n\\n### Results (grouped by mtype)\\n| **Pre (inhibitory mtype)** | **Post (excitatory mtype)** | **Connection probability (%)** | **Mean number of synapses** |\\n|----------------------------|------------------------------|-------------------------------:|----------------------------:|\\n| **L23_CHC** | **L2_TPC:B** | 100.0 | 7.333333333333333 |\\n| **L23_LBC** | **L2_TPC:B** | 0.0 | 0.0 |\\n| **L23_NBC** | **L2_TPC:B** | 16.666666666666668 | 33.0 |\\n| **L23_CHC** | **L3_TPC:A** | 100.0 | 5.0 |\\n| **L23_LBC** | **L3_TPC:A** | 100.0 | 33.0 |\\n| **L23_NBC** | **L3_TPC:A** | 100.0 | 36.0 |\\n\\n### Summary / Key observations\\n- Number of reported inhibitory\\u2192excitatory mtype pairs in this query: **6**. \\n- Several pairs show **100% connection probability** (L23_CHC\\u2192L2_TPC:B, L23_CHC\\u2192L3_TPC:A, L23_LBC\\u2192L3_TPC:A, L23_NBC\\u2192L3_TPC:A). \\n- One pair shows **0% connection probability**: **L23_LBC \\u2192 L2_TPC:B** (and mean synapses = 0). \\n- Highest mean number of synapses in the reported set: **36.0** for **L23_NBC \\u2192 L3_TPC:A**. \\n- Notable high mean synapse counts also observed for L23_NBC\\u2192L2_TPC:B (33.0) and L23_LBC\\u2192L3_TPC:A (33.0).\\n\\n### Notes and next steps\\n- This analysis used the circuit chemical edge population **S1nonbarrel_neurons__S1nonbarrel_neurons__chemical** with pre_node_set = **Inhibitory** and post_node_set = **Excitatory**, grouped by **mtype**.\\n- If you want, I can:\\n - Filter further by layer (e.g., only layer 2 or layer 3 postsynaptic targets).\\n - Group by a different property (e.g., layer or synapse_class).\\n - Return connection counts, STD or distributions (if available).\\n - Run the same analysis for other edge populations in this circuit (e.g., thalamic inputs or external sources).\\n\\nWhich follow-up would you like?", + "ai_response": "## Analysis of inhibitory \\u2192 excitatory connectivity (grouped by **mtype**)\\n\\n**Circuit ID:** **2eb602ea-9ffc-42a2-9fb7-5234921343d0** \\n**Edge population:** **S1nonbarrel_neurons__S1nonbarrel_neurons__chemical** \\n**Pre node set:** **Inhibitory** \\n**Post node set:** **Excitatory** \\n**Grouping used:** **mtype**\\n\\nData source: output of obione-circuitconnectivitymetrics-getone (group_by = \\mtype\\\").\\n\\n### Key results (per pre \\u2192 post mtype)\\n| Pre (inhibitory mtype) | Post (excitatory mtype) | Connection probability (%) | Mean number of synapses (per connection) |\\n|-------------------------|--------------------------:|---------------------------:|-----------------------------------------:|\\n| **L23_CHC** | **L2_TPC:B** | 100.0 | 7.3333 |\\n| **L23_LBC** | **L2_TPC:B** | 0.0 | 0.0 |\\n| **L23_NBC** | **L2_TPC:B** | 16.6667 | 33.0 |\\n| **L23_CHC** | **L3_TPC:A** | 100.0 | 5.0 |\\n| **L23_LBC** | **L3_TPC:A** | 100.0 | 33.0 |\\n| **L23_NBC** | **L3_TPC:A** | 100.0 | 36.0 |\\n\\n### Quick interpretation / highlights\\n- **L23_CHC \\u2192 (L2_TPC:B, L3_TPC:A)**: very high connection probability (100%) to both tested excitatory mtypes, though mean synapse counts differ (\\u22487.33 to L2_TPC:B vs 5.0 to L3_TPC:A).\\n- **L23_LBC \\u2192 L2_TPC:B**: no detected connections (0% probability, 0 mean synapses) in this result, while **L23_LBC \\u2192 L3_TPC:A** is strong (100% prob, mean \\u224833 synapses).\\n- **L23_NBC**: low probability to **L2_TPC:B** (~16.7%) but high mean synapses when present (33 synapses); by contrast **L23_NBC \\u2192 L3_TPC:A** shows 100% probability with mean \\u224836 synapses.\\n- The combination of probability and mean synapse count matters: some pre\\u2192post pairs show lower probability but very large synapse counts when connections exist (e.g., **L23_NBC \\u2192 L2_TPC:B**).\\n\\n### Suggested next steps\\n- If you want, I can:\\n - Expand grouping to additional properties (e.g., group_by = layer or synapse_class).\\n - Inspect other edge populations (POm__S1nonbarrel_neurons__chemical, VPM__..., external_...) to see whether thalamic or external sources alter inhibitory \\u2192 excitatory patterns.\\n - Run the same inhibitory\\u2192excitatory query but restrict pre/post selections to particular layers or specific node sets (e.g., Layer23Inhibitory \\u2192 Layer2Excitatory).\\n - Provide plots (connection probability and mean synapse counts) grouped by mtype.\\n\\nWhich of those would you like me to run next?", "actual_tool_calls": [ - { - "name": "obione-circuitmetrics-getone", - "arguments": { - "circuit_id": "2eb602ea-9ffc-42a2-9fb7-5234921343d0" - } - }, { "name": "obione-circuitmetrics-getone", "arguments": { @@ -56,31 +50,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.7988542312874786, + "score": 0.8150026799242844, "success": true, "threshold": 0.5, - "reason": "The actual output is relevant to the input, focusing on the analysis of inhibitory-to-excitatory connections in the specified circuit. It includes all relevant sections such as metadata, connectivity results, and follow-up options. However, the structure differs slightly from the expected output, particularly in the presentation of results and the summary format, which affects the overall alignment with the expected output." + "reason": "The actual output effectively addresses the input by analyzing the inhibitory-to-excitatory connectivity patterns as requested, and it maintains the appropriate context related to neuroscience. It includes relevant details such as the circuit ID and the grouping by mtype. However, while the output is comprehensive, it lacks a clear introductory summary that matches the expected output's structure, which slightly detracts from its alignment with the evaluation steps." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", - "score": 0.3333333333333333, - "success": false, + "score": 0.5, + "success": true, "threshold": 0.5, - "reason": "The score is 0.33 because the argument lacks input parameters for both ToolCalls, which limits the ability to analyze the requested connectivity patterns effectively. However, the focus on inhibitory-to-excitatory connections and the request to group by mtype shows a clear intent for a specific analysis, which is a positive aspect." + "reason": "The score is 0.50 because there was no input parameter provided for the first tool call to analyze connectivity patterns, which limited the analysis. However, the request was clear and specific about the focus on inhibitory-to-excitatory connections, which is a positive aspect." }, { "name": "Deterministic Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -90,7 +84,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523639" + "created_at": "2025-11-24 17:08:32.832197" } }, "literature_search": { @@ -110,21 +104,13 @@ "literature_search" ] }, - "ai_response": "## Clarifying question before I search\\n\\nI can find recent research papers (reviews and primary studies) on **synaptic plasticity in the hippocampus**, but before I run a literature search I want to confirm your preferences so I return the most useful results.\\n\\nPlease tell me:\\n- Do you want **reviews**, **experimental primary research**, or **both**?\\n- Any specific hippocampal subregion or cell type of interest? (e.g., **CA1**, **CA3**, **dentate gyrus**, **interneurons**, **pyramidal cells**)\\n- Any focus on particular mechanisms or signals? (e.g., **LTP/LTD**, **metaplasticity**, **synaptic tagging & capture**, **NMDA/AMPA receptor trafficking**, **structural plasticity**, **behavioral correlates**)\\n- Do you prefer a date range? (e.g., **2020\\u20132025**, or \\u201clast 3 years\\u201d)\\n- Do you want only open-access papers or any papers (including paywalled)?\\n\\nOnce you confirm, I will run a targeted literature search and return:\\n- A short list (5\\u201310) of the most relevant recent papers with citation details\\n- One-line summary of each paper\\u2019s key finding or focus\\n- Links/DOIs and thumbnails if available\\n- Optionally, I can fetch full-text PDFs for open-access articles or read specific papers in full.", + "ai_response": "## Recent papers on synaptic plasticity in the hippocampus\\n\\nBelow are recent, relevant papers returned by a literature search (2022\\u20132025). I include title, authors, publication date, link (DOI/URL returned by the search tool), a short excerpt/summary from the tool output, and images when available.\\n\\n### 1) Synaptic basis of feature selectivity in hippocampal neurons \\n- **Authors:** Losonczy, Attila (senior author shown in result) \\n- **Published:** 2024-12-18 \\n- **Link:** [Synaptic basis of feature selectivity in hippocampal neurons](https://www.nature.com/articles/s41586-024-08325-9?error=cookies_not_supported&code=99fed048-9696-4c0c-81ab-d1c517d6a190) \\n- **Summary (excerpt):** All-optical approach to monitor spatiotemporal tuning and synaptic weight changes of dendritic spines before and after place-field induction in CA1 pyramidal neurons during navigation. Identifies a temporally asymmetric plasticity kernel and compartment-specific differences (basal vs oblique dendrites). \\n- **Figure:** \\n ![Figure from paper](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41586-024-08325-9/MediaObjects/41586_2024_8325_Fig1_HTML.png)\\n\\n---\\n\\n### 2) Formation of an expanding memory representation in the hippocampus \\n- **Authors:** Magee, Jeffrey C. (result lists Magee) \\n- **Published:** 2025-06-04 \\n- **Link:** [Formation of an expanding memory representation in the hippocampus](https://www.nature.com/articles/s41593-025-01986-3) \\n- **Summary (excerpt):** Longitudinal tracking of CA1 place cells over 7 days during learning. Evidence that both the number of stable place cells and their stability increase across days; behavioral-timescale synaptic plasticity (BTSP) accompanies place-cell formation and retrieval. \\n- **Figure:** \\n ![Figure from paper](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41593-025-01986-3/MediaObjects/41593_2025_1986_Fig1_HTML.png)\\n\\n---\\n\\n### 3) Synaptic plasticity and mental health: methods, challenges and opportunities \\n- **Authors:** Lawrence G. Appelbaum, Mohammad Ali Shenasa, Louise Stolz, Zafiris Daskalakis \\n- **Published:** 2022-10-17 (Neuropsychopharmacology) \\n- **Link:** [Synaptic plasticity and mental health: methods, challenges and opportunities](https://doi.org/10.1038/s41386-022-01370-w) (PDF link returned) \\n- **Summary (excerpt):** Review of mechanisms of plasticity across scales and a survey of measurement techniques (structural imaging, hemodynamic measures, neuromodulation) relevant to synaptic plasticity and psychiatric disorders.\\n\\n---\\n\\n### 4) The Intriguing Contribution of Hippocampal Long-Term Depression to Spatial Learning and Long-Term Memory \\n- **Authors:** Martin Stacho & Denise Manahan-Vaughan \\n- **Published:** 2022-04-25 \\n- **Link:** [The Intriguing Contribution of Hippocampal Long-Term Depression to Spatial Learning and Long-Term Memory](https://www.frontiersin.org/articles/10.3389/fnbeh.2022.806356/pdf) \\n- **Summary (excerpt):** Review focused on hippocampal LTD (long-term depression), its roles in spatial learning, memory retention of content details, spine pruning, and interaction with LTP for pattern separation.\\n\\n---\\n\\n### 5) Editorial: Cellular and molecular mechanisms of synaptic plasticity at hippocampal and cortical synapses \\n- **Authors:** Nathalia Vitureira, Roberto De Pasquale, Ricardo M. Le\\u00e3o, Francesco Mattia Rossi (editorial) \\n- **Published:** 2022-07-15 \\n- **Link:** [Editorial: Cellular and molecular mechanisms of synaptic plasticity at hippocampal and cortical synapses](https://www.frontiersin.org/articles/10.3389/fncel.2022.980623/pdf) \\n- **Summary (excerpt):** Editorial for a Research Topic that highlights current directions and open questions in cellular and molecular mechanisms underlying hippocampal and cortical plasticity, including LTP, LTD, STDP, homeostatic plasticity.\\n\\n---\\n\\n### 6) Modulation of hippocampal plasticity in learning and memory (PubMed entry) \\n- **Authors:** Tanja Fuchsberger, Ole Paulsen \\n- **Published:** 2022 (PubMed entry) \\n- **Link:** [PubMed: Modulation of hippocampal plasticity in learning and memory](https://pubmed.ncbi.nlm.nih.gov/35660989/) \\n- **Summary (excerpt):** Review discussing modulatory mechanisms of hippocampal plasticity relevant to learning and memory (PubMed page returned by the search tool).\\n\\n---\\n\\n## Next steps / what I can do for you\\n- I can fetch the full text (PDF) for any of the above articles that are publicly accessible using the read-paper tool \\u2014 tell me which paper(s) you want the full text for and I\\u2019ll try to retrieve them. \\n- I can provide a focused summary, extract methods, or pull key figures/experimental details from any of these papers. Which paper should I analyze next?", "actual_tool_calls": [ { "name": "literature-search-tool", "arguments": { - "query": "synaptic plasticity hippocampus review 2020..2025 OR 2021..2025", - "start_publish_date": "2020-01-01T00:00:00Z", - "end_publish_date": "2025-11-24T00:00:00Z", - "num_results": 6 - } - }, - { - "name": "web-search-tool", - "arguments": { - "query": "recent papers synaptic plasticity hippocampus 2020 2021 2022 2023 2024 review research article", + "query": "synaptic plasticity hippocampus review OR paper", + "start_publish_date": "2022-01-01T00:00:00Z", "num_results": 6 } } @@ -133,17 +119,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6203063398313884, + "score": 0.9294276453877884, "success": true, "threshold": 0.5, - "reason": "The actual output effectively engages with the input by confirming the user's preferences before conducting a literature search, which is a proactive approach. However, it does not provide the expected list of recent research papers, which is a significant shortcoming. The response is relevant to the topic of synaptic plasticity in the hippocampus, but it lacks the structured output format that was anticipated." + "reason": "The actual output effectively addresses the input by providing a comprehensive list of recent research papers on synaptic plasticity in the hippocampus, including titles, authors, publication dates, links, and summaries. It maintains the appropriate context related to neuroscience and aligns closely with the expected output format. The only minor shortcoming is that it includes additional details such as figures and next steps, which, while informative, were not explicitly requested in the expected output." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['literature-search-tool'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['literature-search-tool'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -157,7 +143,7 @@ "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"literature-search-tool\",\n input_parameters={\n \"query\": \"synaptic plasticity hippocampus\",\n \"num_results\": 5\n }\n)]; expected ['literature-search-tool'], called ['literature-search-tool', 'web-search-tool']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"literature-search-tool\",\n input_parameters={\n \"query\": \"synaptic plasticity hippocampus\",\n \"num_results\": 5\n }\n)]; expected ['literature-search-tool'], called ['literature-search-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -167,7 +153,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523686" + "created_at": "2025-11-24 17:08:32.832226" } }, "platform_news": { @@ -186,7 +172,7 @@ "platform_description" ] }, - "ai_response": "## Platform News \\u2014 Latest Items (page 1)\\n\\nTotal news items available: **58**\\n\\nBelow are the most recent 5 news entries (page 1, newest first). Let me know if you want me to fetch more pages.\\n\\n### 1) **Visit Us at SfN 2025 and Experience Our Virtual Labs in Action!**\\n- **ID:** c87adacc-cc18-41b7-80b7-fbe54bd5e8da \\n- **Category:** Event \\n- **Created:** 2025-11-17T14:37:02Z \\n- **Updated:** 2025-11-17T14:51:59Z \\n- **Summary:** Announcement of the Open Brain Institute attending SfN 2025 with virtual lab demos. (No additional content provided in this entry.)\\n\\n---\\n\\n### 2) **Join us for hands-on tutorials and live demos**\\n- **ID:** 1c4fe446-c83c-4764-a341-d0cc870d252e \\n- **Category:** Event \\n- **Created:** 2025-06-16T06:26:01Z \\n- **Updated:** 2025-07-04T14:46:43Z \\n- **Summary:** Detailed program for the Open Brain Institute presence at CNS 2025 in Florence. Hands-on sessions and live demos covering:\\n - Biophysical modeling, connectomics, and morphological analysis\\n - Interactive demos (single cell biophysics, dendritic inhibition, atlas-based cortical & hippocampal circuits)\\n - Speakers include Lida Kanari, Darshan Mandge, Idan Segev, Sapir Shapira, James Isbister, Armando Romani, Alexandre Guet-McCreight\\n - Practical details: internet connection and laptop required\\n\\n---\\n\\n### 3) **Breakdown and repair of metabolism in the aging brain**\\n- **ID:** 783a9989-ae2f-405f-92c0-77e2cebf4dce \\n- **Category:** Paper release \\n- **Created:** 2025-03-25T16:03:08Z \\n- **Updated:** 2025-04-14T02:42:13Z \\n- **Summary:** Announcement of a comprehensive open-source model of brain metabolism published in Frontiers in Science. \\n - Integrates the neuro-glia-vascular system and links electrical behavior, energy metabolism, and blood flow across ~16,800 biochemical interactions. \\n - The full model is available through the Open Brain Institute resources.\\n\\n---\\n\\n### 4) **The Open Brain Institute Announces the Dawn of a New Frontier in Neuroscience**\\n- **ID:** 86bd2ea7-993e-4306-9c86-7aa565c3ba94 \\n- **Category:** Milestone \\n- **Created:** 2025-03-17T11:52:16Z \\n- **Updated:** 2025-04-14T02:42:13Z \\n- **Summary:** Milestone announcement (no further content provided in this entry).\\n\\n---\\n\\n### 5) **Neurons warp space and time**\\n- **ID:** ff67b304-b633-402a-b414-390da4c8c8a8 \\n- **Category:** BBP news \\n- **Created:** 2025-02-19T09:28:08Z \\n- **Updated:** 2025-04-14T02:42:13Z \\n- **Summary:** Long-form news item describing research showing that population spike sequences are compressed or stretched depending on cortical excitability \\u2014 a proposed \\u201ctime-warping\\u201d mechanism for neural coding. \\n - Discusses implications for in vivo / in silico model validation and references work by James Isbister and collaborators. \\n\\n---\\n\\nWould you like:\\n- More news items (next page)? \\n- The full text/content of any of the above items? \\n- Links or downloadable resources for the paper release or model mentioned in item 3?", + "ai_response": "## Latest platform news \\u2014 summary (most recent first)\\n\\n**Total items available:** **58**\\n\\n### 1) **Visit Us at SfN 2025 and Experience Our Virtual Labs in Action!**\\n- **ID:** c87adacc-cc18-41b7-80b7-fbe54bd5e8da \\n- **Category:** Event \\n- **Created:** 2025-11-17T14:37:02Z \\n- **Updated:** 2025-11-17T14:51:59Z \\n- **Content:** Not provided in the record.\\n\\n---\\n\\n### 2) **Join us for hands-on tutorials and live demos**\\n- **ID:** 1c4fe446-c83c-4764-a341-d0cc870d252e \\n- **Category:** Event \\n- **Created:** 2025-06-16T06:26:01Z \\n- **Updated:** 2025-07-04T14:46:43Z \\n- **Content (excerpt):** \\n Open Brain Institute at CNS 2025 \\u2014 hands-on tutorials and live demos showcasing the Open Brain Platform. \\n Program highlights include sessions on:\\n - Interactive topological analysis of neuronal morphologies\\n - Single cell biophysical modeling demos\\n - Analysis of the Harvard EM connectome\\n - Atlas-based cortical and hippocampal circuit simulation and analysis \\n (Includes instructor list and practical details; full program and contact emails included in the item.)\\n\\n---\\n\\n### 3) **Breakdown and repair of metabolism in the aging brain**\\n- **ID:** 783a9989-ae2f-405f-92c0-77e2cebf4dce \\n- **Category:** Paper release \\n- **Created:** 2025-03-25T16:03:08Z \\n- **Updated:** 2025-04-14T02:42:13Z \\n- **Content (excerpt):** \\n Announcement of an open-source comprehensive model of brain metabolism (neuro-glia-vascular system) integrating neuronal electrical behavior, energy metabolism, and blood flow with ~16,800 biochemical interactions. Model available through the Open Brain Institute resources.\\n\\n---\\n\\n### 4) **The Open Brain Institute Announces the Dawn of a New Frontier in Neuroscience**\\n- **ID:** 86bd2ea7-993e-4306-9c86-7aa565c3ba94 \\n- **Category:** Milestone \\n- **Created:** 2025-03-17T11:52:16Z \\n- **Updated:** 2025-04-14T02:42:13Z \\n- **Content:** Not provided in the record.\\n\\n---\\n\\n### 5) **Neurons warp space and time**\\n- **ID:** ff67b304-b633-402a-b414-390da4c8c8a8 \\n- **Category:** BBP news \\n- **Created:** 2025-02-19T09:28:08Z \\n- **Updated:** 2025-04-14T02:42:13Z \\n- **Content (excerpt):** \\n Summary of a Nature Scientific Reports study describing \\u201ctime-warping\\u201d of spike sequences \\u2014 cortical excitability compresses or stretches spike-timing sequences, with implications for neural coding. Discusses in vivo findings, theoretical implications, and in silico validation efforts. Mentions lead researchers and links between in vivo and in silico modeling.\\n\\n---\\n\\n## Options \\u2014 what would you like next?\\n- View more news items (there are **58** total). I can fetch the next page(s).\\n- Show the full content of any of the above items (I can retrieve full article text where available).\\n- Search news by category (Events, Paper release, Milestone, BBP news, etc.).\\n- Get contact or event details extracted (e.g., instructors, emails) from a specific item.\\n\\nWhich would you like me to do?", "actual_tool_calls": [ { "name": "obi-expert", @@ -199,17 +185,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.72430801616656, + "score": 0.7870604116716843, "success": true, "threshold": 0.5, - "reason": "The actual output provides relevant news about the Open Brain Platform, aligning with the input request. It includes a structured list of recent news items, which is a strength. However, it lacks the specific formatting and phrasing of the expected output, such as the introductory sentence and the placeholders for news titles and descriptions. While the content is informative, it does not fully match the expected structure, leading to a slightly lower score." + "reason": "The actual output provides relevant news about the Open Brain Platform, aligning well with the input request. It includes a structured list of news items with details such as IDs, categories, and content excerpts, which is consistent with the expected output's format. However, it lacks the introductory phrase and the specific mention of 'highlights' as in the expected output, which slightly detracts from its alignment." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['obi-expert'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['obi-expert'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -223,7 +209,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['obi-expert'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['obi-expert'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -233,7 +219,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523717" + "created_at": "2025-11-24 17:08:32.832243" } }, "web_search": { @@ -253,20 +239,12 @@ "web_search" ] }, - "ai_response": "## Overview of **NEURON** simulation software\\n\\n**NEURON** is a widely used simulation environment for modeling individual neurons and networks of neurons with detailed biophysical properties. It supports multicompartmental neuron models, Hodgkin\\u2013Huxley\\u2013style ion channels, synapses, and network connectivity, and is commonly used in cellular- and systems-level computational neuroscience.\\n\\n### Key features\\n- **Multicompartmental modeling**: Represent complex dendritic and axonal morphologies with compartmental electrical equations.\\n- **Biophysical mechanisms**: Implement voltage-gated ion channels, passive properties, synaptic mechanisms, and intracellular mechanisms.\\n- **Flexible scripting**: Models can be built and driven with the original HOC language or with **Python** (recommended for new work).\\n- **Integration with morphology formats**: Import/export neuronal morphologies (SWC, Hoc morphology files, NeuroMorpho formats).\\n- **Network simulations**: Build networks of cells with detailed connectivity and synaptic dynamics.\\n- **Variable time-step and fixed time-step integrators**: Handle stiff and non-stiff problems; CVODE support for variable-step integration.\\n- Parallelization:\\n - Support for MPI-based distributed simulations for large networks.\\n - Multi-threading options for shared-memory parallelism (depending on build).\\n- **Extensibility**: Write custom mechanisms in NMODL (NEURON Modeling Description Language) which are compiled into efficient C code.\\n- **GUI & visualization**: GUI tools for model construction, parameter exploration, and basic visualization (though many users prefer Python-based workflows and plotting).\\n\\n### Typical workflows / interfaces\\n- HOC: Original embedded language for NEURON (still supported).\\n- Python interface: Most common modern interface; wraps NEURON objects and allows integration with scientific Python stack (NumPy, SciPy, Matplotlib, pandas).\\n- NMODL: Define ion channels and other mechanisms; compile to make them available to simulators.\\n- Import/Export: Morphologies in SWC/ASC/HOC; parameter sets and generated data can be saved for analysis.\\n\\n### Strengths\\n- Mature and widely adopted in academic computational neuroscience.\\n- Strong support for detailed, biophysically realistic single-neuron and small-to-moderate network models.\\n- Large body of existing models and example code (many model publications provide NEURON code).\\n- Active development and community support with tutorials and workshops.\\n\\n### Limitations / considerations\\n- Learning curve: understanding compartmental modeling, HOC/NMODL, and the Python API takes time.\\n- Performance: for very large-scale network simulations, specialized network simulators (or optimized NEURON builds with MPI) may be required; performance depends on model detail and parallelization strategy.\\n- GUI is less central in modern workflows; many users employ Python-based scripts and external plotting/analysis tools.\\n\\n### Installation & platforms\\n- Runs on Linux, macOS, and Windows.\\n- Install options:\\n - Prebuilt binaries / installers for common OSes.\\n - pip-installable Python wheel (for many platforms): pip install neuron (note: some features, e.g., MPI builds or NMODL compilation, may require additional system dependencies).\\n - Build from source to enable custom options (MPI, CoreNEURON, GPU support if included).\\n- Related projects / accelerators:\\n - CoreNEURON: optimized compute engine for NEURON to speed large-scale simulations.\\n - nrnunit / validation tools: tools for testing and validating models.\\n\\n### Typical commands and example (Python)\\n- Creating a section and running a simulation (conceptual example):\\n - Create sections (soma, dendrites) and set geometry and biophysics.\\n - Insert mechanisms (e.g., hh).\\n - Set up stimuli (IClamp) and recordings (Vector.record).\\n - Run simulation with h.run() and retrieve recorded vectors into NumPy arrays for plotting.\\n\\n### Resources to learn NEURON\\n- Official NEURON documentation and tutorials (walkthroughs for HOC and Python).\\n- NMODL documentation and examples for custom mechanism writing.\\n- Community tutorials, online courses, and workshop materials (many universities and research labs provide notebooks and examples).\\n- Model repositories containing NEURON implementations (useful to study published models).\\n\\n### Recommended next steps (if you want me to continue)\\n- I can search for and return specific official NEURON resources (documentation pages, tutorials, GitHub repo) and embed images from search results.\\n- I can fetch recent review papers or tutorial articles about NEURON and its ecosystem (CoreNEURON, NMODL, Python interface).\\n- I can provide a short example Python script that demonstrates a simple single-compartment HH model and plotting (and execute it with Plotly if you want a runnable figure).\\n\\nWhich of the above would you like next?", + "ai_response": "## Summary \\u2014 NEURON simulation software\\n\\n**NEURON** is a widely used, open\\u2011source simulation environment for modeling individual neurons and neural networks. It supports building biologically detailed single\\u2011cell and network models, offers both a graphical interface and scripting (Python, HOC), and provides extensible biophysical mechanisms (NMODL / ChannelBuilder). It runs on local machines, HPC and cloud platforms.\\n\\n---\\n\\n## Key resources (official docs & guides)\\n\\n- **NEURON documentation** \\u2014 full docs, guides, installation, programmer reference, videos, and tutorials: [The NEURON Simulator](https://nrn.readthedocs.io/) \\n- **What is NEURON** (overview & design goals): [What is NEURON](https://nrn.readthedocs.io/en/latest/guide/what_is_neuron.html) \\n- **Getting started** (installation, basic usage, tutorials): [How to get started with NEURON](https://nrn.readthedocs.io/en/latest/guide/how_to_get_started.html) \\n- **NEURON Python reference & examples**: see the Python docs inside the main documentation (links from the docs index). \\n- **Training videos & courses**: linked from the docs (NEURON and NetPyNE recorded courses). \\n- **Forum / community support**: NEURON Forum \\u2014 [NEURON Forum](https://www.neuron.yale.edu/phpBB/) \\n- **Model sharing / example models**: Many NEURON models are hosted on ModelDB \\u2014 [ModelDB modellist for NEURON](https://modeldb.science/modellist/1882?all_simu=true) \\n- **Foundational paper**: The NEURON simulation environment \\u2014 [Hines & Carnevale 1997](https://doi.org/10.1162/neco.1997.9.6.1179)\\n\\n---\\n\\n## Installation (quick commands)\\n\\n- Typical (Linux / macOS / Cloud notebooks):\\n - pip: pip3 install neuron\\n - On Google Colab: !pip install neuron\\n- Windows:\\n - Use the Windows installer available on NEURON GitHub releases (or install via WSL and pip).\\n- Source / developer builds:\\n - Source and release packages available on the NEURON GitHub: [neuronsimulator/nrn](https://github.com/neuronsimulator/nrn)\\n\\nFor detailed, OS\\u2011specific instructions and troubleshooting see: [NEURON install instructions](https://nrn.readthedocs.io/install/install_instructions.html)\\n\\n---\\n\\n## Notable features\\n\\n- Scripting with **Python** and **HOC**; GUI tools for cell building and visualization \\n- User\\u2011extendable mechanisms via **NMODL** and the GUI **ChannelBuilder** (ChannelBuilder recommended for many channel models) \\n- Support for parallel/HPC simulations (MPI) \\n- Rich ecosystem: example models (ModelDB), training materials, optimization tools, and active forum support\\n\\n---\\n\\n## Useful next steps I can do for you\\n\\n- Walk through a short example (single compartment or multicompartment) in Python for NEURON and provide runnable code. \\n- Retrieve installation troubleshooting steps for a specific OS (Linux/macOS/Windows/WSL). \\n- Fetch specific tutorial(s) or recorded course video links from the NEURON docs. \\n- Find NEURON models on ModelDB for a particular neuron type or brain region.\\n\\nWhich of these would you like next?", "actual_tool_calls": [ { "name": "web-search-tool", "arguments": { - "query": "NEURON simulation software NEURON simulator hoc Python guide review", - "num_results": 8 - } - }, - { - "name": "literature-search-tool", - "arguments": { - "query": "NEURON simulation software review NEURON simulator Python HOC documentation NEURON 2020 2010 2018", - "num_results": 6 + "query": "NEURON simulation software NEURON simulator neuroscience official website tutorial documentation" } } ], @@ -274,17 +252,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.5953510197804116, + "score": 0.5509989256748369, "success": true, "threshold": 0.5, - "reason": "The actual output provides a comprehensive overview of NEURON simulation software, detailing its features, strengths, limitations, and installation options, which aligns well with the input request. However, it deviates from the expected output format, which was structured as a list of results with titles and excerpts, rather than a detailed narrative. This structural misalignment affects the overall score despite the high quality of the content." + "reason": "The actual output provides a comprehensive overview of NEURON simulation software, including its features, installation instructions, and key resources, which aligns with the context of the input. However, it does not follow the expected output structure, which includes placeholders for titles and content excerpts. The response is informative but lacks the specific format requested in the expected output." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['web-search-tool'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['web-search-tool'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -298,7 +276,7 @@ "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"web-search-tool\",\n input_parameters={\n \"query\": \"NEURON simulation software\",\n \"num_results\": 5\n }\n)]; expected ['web-search-tool'], called ['web-search-tool', 'literature-search-tool']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"web-search-tool\",\n input_parameters={\n \"query\": \"NEURON simulation software\",\n \"num_results\": 5\n }\n)]; expected ['web-search-tool'], called ['web-search-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -308,7 +286,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523748" + "created_at": "2025-11-24 17:08:32.832259" } }, "software_docs_entitysdk": { @@ -331,15 +309,14 @@ "mcp" ] }, - "ai_response": "## List all cell morphologies with the entitysdk (Python)\\n\\nBelow is a concise recipe and example code to list CellMorphology entities using the Open Brain Platform Python SDK **entitysdk**.\\n\\n### Key steps\\n- Authenticate and create a client instance.\\n- Use `client.search_entity(entity_type=CellMorphology)` to build the query.\\n- Use `.all()` to fetch all pages (or use `limit` / pagination as needed).\\n- Inspect results (length, first entries, or iterate).\\n\\n### Example code\\n```python\\n# Install and import (if not already installed)\\n# pip install entitysdk\\n\\nfrom entitysdk import Client\\nfrom entitysdk.models import CellMorphology\\n\\n# 1) Create authenticated client (replace with your auth method)\\nclient = Client.from_env() # or Client(api_key=\\...\\\") depending on your setup\\n\\n# 2) Search for all CellMorphology entities\\nmorph_search = client.search_entity(entity_type=CellMorphology)\\n\\n# 3) Fetch all results (handles pagination for you)\\nmorphs = morph_search.all()\\n\\n# 4) Inspect results\\nprint(f\\Total morphologies retrieved: {len(morphs)}\\\")\\nprint(\\First two morphologies:\\\")\\nfor m in morphs[:2]:\\n print(f\\ - id: {m.id}, name: {m.name}, brain_region: {getattr(m, 'brain_region', None)}\\\")\\n```\\n\\n### Limiting results or filtered searches\\n- Limit to the first N results:\\n```python\\nmorphs = client.search_entity(entity_type=CellMorphology, limit=10).all()\\n```\\n- Filter by MType pref_label (example):\\n```python\\nmorphs = client.search_entity(\\n entity_type=CellMorphology,\\n query={\\mtype__pref_label\\\": \\SR_PC\\\"}\\n).all()\\n```\\n\\n### Notes and tips\\n- Use `.all()` to automatically page through results. If you expect very large result sets and want to avoid fetching everything, use `limit` or iterate pages.\\n- Each returned object is a model instance (e.g., has `.id`, `.name`, `.description`, `.mtypes`, `.assets`, etc.).\\n- To fetch full details or assets for a specific morphology, retrieve by ID or use the higher-level entity methods in the SDK.\\n\\nIf you want, I can:\\n- Provide an example showing how to list morphologies within a specific brain region (need the region UUID), or\\n- Show how to download morphology assets (SWC/HDF5) for each morphology.", + "ai_response": "## Overview\\n\\nBelow are concise, practical examples to list all **CellMorphology** entities using the **entitysdk** (Open Brain Institute Entity SDK). These examples assume you already have an authenticated SDK client object named **client** (see your SDK auth docs / examples for creating the client).\\n\\n---\\n\\n### 1) Minimal: retrieve all morphologies (simple)\\n\\n```python\\n# Assumes an authenticated `client` exists\\n# Import names (CellMorphology is the entity class used in queries)\\nfrom entitysdk.entities import CellMorphology\\n\\n# Retrieve all CellMorphology entities (will page internally)\\nmorphs = client.search_entity(entity_type=CellMorphology).all()\\n\\nprint(f\\Total morphologies retrieved: {len(morphs)}\\\")\\nfor m in morphs:\\n print(m.id, m.name)\\n```\\n\\nNotes:\\n- `.all()` will fetch all pages for you.\\n- Each item `m` is a CellMorphology entity object; fields available depend on the entity representation returned by the SDK.\\n\\n---\\n\\n### 2) Previewing when many items exist (limit and sample)\\n\\n```python\\nfrom entitysdk.entities import CellMorphology\\n\\n# Fetch only 10 items (useful for quick previews)\\nmorphs_page = client.search_entity(entity_type=CellMorphology, limit=10).all()\\n\\nprint(\\Preview (first 10):\\\", len(morphs_page))\\nfor m in morphs_page:\\n print(m.id, m.name, \\\"-\\\", getattr(m, \\description\\\", \\\"\\\")[:120])\\n```\\n\\n---\\n\\n### 3) Example: print useful metadata for each morphology\\n\\n```python\\nfrom entitysdk.entities import CellMorphology\\n\\nmorphs = client.search_entity(entity_type=CellMorphology).all()\\n\\nfor m in morphs:\\n # Example fields commonly present on returned objects\\n print(\\ID: \\\", m.id)\\n print(\\Name: \\\", m.name)\\n print(\\Desc: \\\", getattr(m, \\description\\\", None))\\n print(\\Species:\\\", getattr(getattr(m, \\subject\\\", None), \\species\\\", None))\\n # If brain_region is nested object:\\n br = getattr(m, \\brain_region\\\", None)\\n if br:\\n print(\\Brain region:\\\", getattr(br, \\pref_label\\\", getattr(br, \\name\\\", None)))\\n print(\\\"-\\ * 40)\\n```\\n\\n---\\n\\n### 4) Filtering examples (species, mtype, brain region)\\n\\n```python\\nfrom entitysdk.entities import CellMorphology\\n\\n# By species name (scientific name)\\nmorphs_mouse = client.search_entity(entity_type=CellMorphology, query={\\subject__species__name\\\":\\Mus musculus\\\"}).all()\\n\\n# By mtype preferred label\\nmorphs_mtype = client.search_entity(entity_type=CellMorphology, query={\\mtype__pref_label\\\":\\L5_PC\\\"}).all()\\n\\n# By brain region (use the brain region's field name expected by the API)\\nmorphs_region = client.search_entity(entity_type=CellMorphology, query={\\brain_region__pref_label\\\":\\primary visual area\\\"}).all()\\n```\\n\\n---\\n\\n## Tips / Best practices\\n\\n- If you expect very large result sets, prefer paginated calls (use `limit`/`offset` or rely on `.all()` if memory is sufficient).\\n- Use `query` with entity field names to filter server-side and reduce data transfer.\\n- To inspect what fields are available on returned objects, print `dir(m)` or `m.__dict__` for a single returned entity.\\n- The SDK examples in the official repo show full workflows (registering, uploading assets, adding mtype classifications) \\u2014 refer to those for client initialization and advanced usage.\\n\\n---\\n\\nIf you want, I can:\\n- Provide a full example that includes authenticating/creating the `client` object for your environment (I\\u2019ll need to know which auth method you use), or\\n- Show a paginated loop example that streams results and writes a CSV with a selected subset of fields. Which would you prefer?", "actual_tool_calls": [ { "name": "get-obi-software-docs", "arguments": { "context7CompatibleLibraryID": "openbraininstitute/entitysdk", "mode": "code", - "topic": "list cell morphologies", - "page": 1 + "topic": "cell morphology list" } } ], @@ -347,31 +324,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.733165273979345, + "score": 0.6764777066300486, "success": true, "threshold": 0.5, - "reason": "The actual output provides relevant information on listing cell morphologies using the entitysdk, aligning with the input request. It includes key steps and example code, which are essential components. However, it lacks a clear overview section and does not explicitly mention pagination and total_items, which are critical aspects highlighted in the expected output. While the content is mostly complete, it could better match the structure and detail of the expected output." + "reason": "The actual output provides practical examples for listing CellMorphology entities using the entitysdk, which aligns with the input request. However, it lacks some clarity and structure compared to the expected output, particularly in the organization of examples and the inclusion of pagination details. While it addresses the core functionality, it does not fully match the expected output's emphasis on pagination and total items, which are important for robust usage." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['get-obi-software-docs'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['get-obi-software-docs'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "The score is 1.00 because the input is clear and correctly asks for a method to list cell morphologies using the entitysdk in Python, with no incorrect tool calls." + "reason": "The score is 1.00 because there are no incorrect tool calls or issues with the input." }, { "name": "Deterministic Argument Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"get-obi-software-docs\",\n input_parameters={\n \"context7CompatibleLibraryID\": \"/openbraininstitute/entitysdk\",\n \"topic\": \"get all cell morphologies\",\n \"tokens\": 2000\n }\n)]; expected ['get-obi-software-docs'], called ['get-obi-software-docs']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"get-obi-software-docs\",\n input_parameters={\n \"context7CompatibleLibraryID\": \"/openbraininstitute/entitysdk\",\n \"topic\": \"get all cell morphologies\",\n \"tokens\": 2000\n }\n)]; expected ['get-obi-software-docs'], called ['get-obi-software-docs']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -381,7 +358,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523776" + "created_at": "2025-11-24 17:08:32.832274" } }, "connectivity_metrics_extra_filters": { @@ -419,7 +396,7 @@ "layer-filter" ] }, - "ai_response": "## Analysis request\\nYou asked for inhibitory \\u2192 excitatory connectivity in layer **2** for circuit **2eb602ea-9ffc-42a2-9fb7-5234921343d0**, grouped by *mtype*, with **layer = 2** applied to both pre- and post-synaptic selections.\\n\\n## Circuit and populations used\\n- **Circuit ID:** **2eb602ea-9ffc-42a2-9fb7-5234921343d0** \\n- **Edge population used:** **S1nonbarrel_neurons__S1nonbarrel_neurons__chemical** \\n- **Pre node set:** **Inhibitory** (filtered to *layer = 2*) \\n- **Post node set:** **Excitatory** (filtered to *layer = 2*) \\n- **Grouping:** **mtype**\\n\\n## Results (grouped by pre-synaptic mtype)\\n| Pre mtype (inhibitory, layer 2) | Post mtype (excitatory, layer 2) | Connection probability (%) | Mean number of synapses per connected pair |\\n|---------------------------------|----------------------------------:|---------------------------:|-------------------------------------------:|\\n| **L23_CHC** | **L2_TPC:B** | 100.0 | 7.3333 |\\n| **L23_LBC** | **L2_TPC:B** | 0.0 | 0.0 |\\n| **L23_NBC** | **L2_TPC:B** | 16.6667 | 33.0 |\\n\\n## Brief interpretation\\n- **L23_CHC \\u2192 L2_TPC:B**: Every tested L23_CHC inhibitory neuron (within layer 2 filter) connects to L2_TPC:B excitatory targets (100% probability), with a modest mean synapse count (~7.33 synapses per connection).\\n- **L23_LBC \\u2192 L2_TPC:B**: No observed connections under the applied filters (0% probability).\\n- **L23_NBC \\u2192 L2_TPC:B**: Sparse connections (\\u224816.7% probability) but, when present, these connections have a high mean synapse count (~33 synapses per connected pair), suggesting strong but infrequent contacts.\\n\\n## Notes and provenance\\n- Metrics were computed from the circuit's chemical edge population **S1nonbarrel_neurons__S1nonbarrel_neurons__chemical**, with both pre- and post-synaptic selections filtered to **layer = \\2\\** and pre/post node sets set to **Inhibitory** / **Excitatory**, respectively.\\n- Grouping by **mtype** produced the three pre-synaptic mtypes reported above; all results come directly from the platform connectivity metrics tool.\\n\\nIf you'd like, I can:\\n- Expand the grouping to include additional properties (e.g., layer of post-synaptic cells, synapse_class), \\n- Run the same analysis for other layers, or \\n- Compute summary statistics across all inhibitory mtypes in layer 2 (e.g., weighted average connection probability or total expected synapses per postsynaptic neuron). Which would you prefer?", + "ai_response": "## Analysis of inhibitory \\u2192 excitatory connectivity (Layer 2)\\n**Circuit ID:** **2eb602ea-9ffc-42a2-9fb7-5234921343d0** \\n**Edge population used:** **S1nonbarrel_neurons__S1nonbarrel_neurons__chemical** \\n**Filters applied:** **pre_node_set = Inhibitory**, **post_node_set = Excitatory**, **pre_selection = {layer: \\2\\\"}**, **post_selection = {layer: \\2\\\"}** \\n**Group by:** **mtype**\\n\\n### Summary\\nI queried the circuit metrics (level_of_detail_nodes=1) to discover available node/edge populations, then computed connectivity metrics for chemical connections from inhibitory to excitatory neurons restricted to layer 2, grouped by morphological type (**mtype**). The tool returned connection probability and mean number of synapses per connected pair for the mtypes present in the filtered populations.\\n\\n### Results (grouped by mtype)\\n\\n| Pre mtype (Inhibitory, L2) | Post mtype (Excitatory, L2) | Connection probability (%) | Mean number of synapses |\\n|----------------------------|-----------------------------:|---------------------------:|------------------------:|\\n| **L23_CHC** | **L2_TPC:B** | 100.0 | 7.333333333333333 |\\n| **L23_LBC** | **L2_TPC:B** | 0.0 | 0.0 |\\n| **L23_NBC** | **L2_TPC:B** | 16.666666666666668 | 33.0 |\\n\\n### Notes and brief interpretation\\n- All reported post-synaptic targets in layer 2 (under these filters) are of mtype **L2_TPC:B**.\\n- **L23_CHC \\u2192 L2_TPC:B**: very high connection probability (100%), with ~7.33 synapses on average per connected pair.\\n- **L23_LBC \\u2192 L2_TPC:B**: no observed connections under these filters (0% probability).\\n- **L23_NBC \\u2192 L2_TPC:B**: low-to-moderate connection probability (~16.7%), but when present these connections show a high mean synapse count (33.0 synapses per connection).\\n- The pre-synaptic mtypes reported are labeled with \\L23_\\ (e.g., L23_CHC) despite the layer filter being \\2\\\". This reflects the mtype naming in the circuit (the grouping property returned by the tool) \\u2014 the numeric layer filter applied to the neurons themselves was layer = \\2\\ for both pre and post selections.\\n\\nIf you want, I can:\\n- Expand the analysis to other edge populations (e.g., include thalamic inputs) or\\n- Change grouping (e.g., group by synapse_class or layer instead of mtype), or\\n- Provide the raw numerical output in JSON/CSV form for downstream use. Which would you like next?", "actual_tool_calls": [ { "name": "obione-circuitmetrics-getone", @@ -448,31 +425,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.848027598936177, + "score": 0.8142110364455851, "success": true, "threshold": 0.5, - "reason": "The actual output is relevant to the input, focusing on inhibitory-to-excitatory connections in layer 2, as requested. It includes the correct circuit ID and applies the specified filters. However, while the structure is mostly aligned with the expected output, it lacks clear section headers for results and interpretation, which slightly detracts from clarity. The content aligns well with the expected intent, but the presentation could be improved for better readability." + "reason": "The actual output effectively addresses the input by analyzing the specified inhibitory-to-excitatory connections in layer 2, and it includes relevant sections such as the circuit ID, edge population, filters, and results grouped by mtype. However, there are minor discrepancies in formatting and phrasing compared to the expected output, particularly in the summary and results sections, which could be more aligned with the expected structure. Overall, the response is comprehensive and maintains the necessary context related to neuroscience." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 0.5, "success": true, "threshold": 0.5, - "reason": "The score is 0.50 because the first tool call lacked an input parameter, which is essential for accurate analysis. This limitation prevents a higher score, as it indicates incomplete execution of the request. However, the request itself is clear and well-structured, demonstrating a good understanding of the analysis needed." + "reason": "The score is 0.50 because the first tool call does not address the specific analysis of inhibitory-to-excitatory connections in layer 2, which is crucial for the requested analysis." }, { "name": "Deterministic Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['obione-circuitmetrics-getone', 'obione-circuitconnectivitymetrics-getone'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -482,7 +459,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523805" + "created_at": "2025-11-24 17:08:32.832289" } }, "cortex_morphologies": { @@ -507,19 +484,12 @@ "entitycore" ] }, - "ai_response": "## Retrieved morphologies \\u2014 Cortex (5 items)\\n\\nI located 5 cell morphologies within the **Cerebral cortex (CTX)** subtree. Summary and key metadata are below.\\n\\n### Summary\\n- Brain region searched: **Cerebral cortex (CTX)** \\u2014 ID: **abe63c70-1eb0-4b42-9421-d2c914ecb493**\\n- Returned items: **5** morphologies (page 1, page_size 5)\\n- Species: *Mus musculus* (Generic Mus musculus)\\n\\n---\\n\\n### Quick comparison table\\n\\n| Name | ID | Brain region | Mtype (label) | Created |\\n|---|---:|---|---|---:|\\n| **dend-jy180118_B_idB_axon-05311-05329-X10166-Y11730_final_-_Scale_x1.000_y1.025_z1.000_-_Clone_9** | **7e69531a-c976-4b4c-be44-881e5e3defdd** | **Frontal pole, layer 1 (FRP1)** | *L23_PC* (Layer 2/3 Pyramidal Cell) | 2025-11-12T13:23:17.324607Z |\\n| **dend-jy180118_B_idB_axon-05311-05329-X10166-Y11730_final_-_Scale_x1.000_y1.025_z1.000_-_Clone_9** (ASC variant) | **9f4c2eda-1d41-4d8b-88fe-f8db27efb3b7** | **Frontal pole, layer 1 (FRP1)** | *L23_PC* | 2025-11-12T13:19:57.974966Z |\\n| **dend-jy180118_B_idB_axon-05311-05329-X10166-Y11730_final_-_Scale_x1.000_y1.025_z1.000_-_Clone_9** | **1e3c212b-aa17-4baa-8390-9d229d4c192b** | **Frontal pole, layer 1 (FRP1)** | *L23_PC* | 2025-11-11T19:29:37.112470Z |\\n| **dend-jy180118_B_idB_axon-05311-05329-X10166-Y11730_final_-_Scale_x1.000_y1.025_z1.000_-_Clone_9** | **cb705953-a653-4e3d-8d04-b2b2c53dff2a** | **Frontal pole, layer 1 (FRP1)** | *L23_PC* | 2025-11-11T19:05:43.471593Z |\\n| **dend-jy180118_B_idB_axon-05311-05329-X10166-Y11730_final_-_Scale_x1.000_y1.025_z1.000_-_Clone_9** | **1d4c8bc1-099e-4151-a886-1c7d4ae27f30** | **Frontal pole, layer 1 (FRP1)** | *L23_PC* | 2025-11-11T15:31:35.149068Z |\\n\\n---\\n\\n### Individual entries (detailed)\\n\\n#### 1) **Name:** dend-jy180118_B_idB_axon-05311-05329-X10166-Y11730_final_-_Scale_x1.000_y1.025_z1.000_-_Clone_9 \\n- **ID:** **7e69531a-c976-4b4c-be44-881e5e3defdd** \\n- **Brain region:** **Frontal pole, layer 1 (FRP1)** \\n- **Mtype:** *L23_PC* (Layer 2/3 Pyramidal Cell) \\n- **Generation:** computationally_synthesized (protocol id **884d5832-7734-45b1-9795-b8fb12efb604**) \\n- **Assets:** SWC id **3120df98-8e78-461a-acb8-55494c2888ab**, HDF5 id **ae9f6260-8d79-4d78-b07a-9b50f937a267** \\n- **License:** CC BY-NC 4.0 (id **1283454d-b5ad-488f-acb7-d00b9f02873d**) \\n- **Created:** 2025-11-12T13:23:17.324607Z \\n- [View Details](https://openbraininstitute.org/app/entity/7e69531a-c976-4b4c-be44-881e5e3defdd)\\n\\n#### 2) **Name:** dend-jy180118_B_idB_axon-..._Clone_9 (ASC variant) \\n- **ID:** **9f4c2eda-1d41-4d8b-88fe-f8db27efb3b7** \\n- **Brain region:** **Frontal pole, layer 1 (FRP1)** \\n- **Mtype:** *L23_PC* \\n- **Assets:** SWC id **b38d37c4-5a6f-43b7-ad92-e59f6a5a28e9**, HDF5 id **5925fced-b5d1-4802-838d-2ef46641dd4a** \\n- **Created:** 2025-11-12T13:19:57.974966Z \\n- [View Details](https://openbraininstitute.org/app/entity/9f4c2eda-1d41-4d8b-88fe-f8db27efb3b7)\\n\\n#### 3) **Name:** dend-jy180118_B_idB_axon-..._Clone_9 \\n- **ID:** **1e3c212b-aa17-4baa-8390-9d229d4c192b** \\n- **Brain region:** **Frontal pole, layer 1 (FRP1)** \\n- **Mtype:** *L23_PC* \\n- **Assets:** SWC id **c1d26738-c836-4b8f-8653-82929ba773a6**, HDF5 id **91aa269b-557c-46dc-9742-9157c23c9e6f** \\n- **Created:** 2025-11-11T19:29:37.112470Z \\n- [View Details](https://openbraininstitute.org/app/entity/1e3c212b-aa17-4baa-8390-9d229d4c192b)\\n\\n#### 4) **Name:** dend-jy180118_B_idB_axon-..._Clone_9 \\n- **ID:** **cb705953-a653-4e3d-8d04-b2b2c53dff2a** \\n- **Brain region:** **Frontal pole, layer 1 (FRP1)** \\n- **Mtype:** *L23_PC* \\n- **Assets:** SWC id **169f9927-853c-44e2-8065-a3e9bcfe3150**, HDF5 id **163d3282-8f24-4dfd-aa63-7cd9fadcccbc** \\n- **Created:** 2025-11-11T19:05:43.471593Z \\n- [View Details](https://openbraininstitute.org/app/entity/cb705953-a653-4e3d-8d04-b2b2c53dff2a)\\n\\n#### 5) **Name:** dend-jy180118_B_idB_axon-..._Clone_9 \\n- **ID:** **1d4c8bc1-099e-4151-a886-1c7d4ae27f30** \\n- **Brain region:** **Frontal pole, layer 1 (FRP1)** \\n- **Mtype:** *L23_PC* \\n- **Assets:** SWC id **16a788cc-17ca-4646-b78f-9da1d0897623**, HDF5 id **455ae0dd-567b-46a8-9ecd-58063f13207d** \\n- **Created:** 2025-11-11T15:31:35.149068Z \\n- [View Details](https://openbraininstitute.org/app/entity/1d4c8bc1-099e-4151-a886-1c7d4ae27f30)\\n\\n---\\n\\nIf you want:\\n- The raw asset file IDs or download links extracted for any specific morphology,\\n- More results (next page) \\u2014 note total_items = **4824** for cortex morphologies,\\n- Or filtering by mtype, layer, or license,\\n\\ntell me which and I\\u2019ll retrieve them.", + "ai_response": "## Retrieved morphologies \\u2014 Cortex (5 items)\\n\\nTotal matches for your search: **219** (returned first 5). \\nBelow are the five morphologies retrieved (metadata and primary assets). Each entry includes the entity link.\\n\\n---\\n\\n### 1) **NMO_159989** \\n**ID:** **8ad9fa02-d206-4578-95c5-abf8fe930a91** \\n**Name:** **NMO_159989** \\n**Description:** Morphological reconstruction of a pyramidal cell from the human temporal cortex. Part of Moradi et al., 2021 (https://doi.org/10.1038/s41467-021-22741-9) \\n**Brain region:** **Temporal association areas, layer 5 (TEa5)** \\n- **Brain region ID:** **e182532d-9864-4016-99a9-94a05c8f8d9a** \\n**Subject:** *Homo sapiens* (Generic) \\n**Mtype:** **Pyramidal Neuron** \\n**License:** **CC BY 4.0** \\n**Assets (primary morphology files):**\\n- label: **morphology** \\u2014 NMO_159989.swc (size: 204,645 bytes) \\u2014 asset id: **5bb8dd5c-3009-40d0-86d9-9420f8fa7ba5** \\n- NMO_159989.asc (id: **a02ccc6a-ffb8-4177-8129-4e1830699917**) \\n- NMO_159989.h5 (id: **ee0895f6-4fd3-42e6-b536-4a6ae64b8519**) \\n**URL:** [View Morphology](https://openbraininstitute.org/app/entity/8ad9fa02-d206-4578-95c5-abf8fe930a91)\\n\\n---\\n\\n### 2) **NMO_194161** \\n**ID:** **d9b6bcb6-e14c-490a-b7ab-4b513805016c** \\n**Name:** **NMO_194161** \\n**Description:** Morphological reconstruction of a pyramidal cell from the human temporal cortex. Part of Howard et al., 2022 (https://doi.org/10.1093/gigascience/giac108) \\n**Brain region:** **Temporal association areas, layer 2/3 (TEa2/3)** \\n- **Brain region ID:** **fe3aeb11-da80-43bb-a6fc-c59ac962efe0** \\n**Subject:** *Homo sapiens* (Generic) \\n**Mtype:** **Pyramidal Neuron** \\n**License:** **CC0 1.0** \\n**Assets (primary morphology files):**\\n- label: **morphology** \\u2014 NMO_194161.CNG.swc (size: 194,749 bytes) \\u2014 asset id: **90ac48c5-ebb3-4b2a-a63d-0938d8280302** \\n- NMO_194161.CNG.asc (id: **89dd6e1d-1cd3-48db-9a68-9344b967b3f4**) \\n- NMO_194161.CNG.h5 (id: **e2605eed-8109-4744-84ed-889208cc03e3**) \\n**URL:** [View Morphology](https://openbraininstitute.org/app/entity/d9b6bcb6-e14c-490a-b7ab-4b513805016c)\\n\\n---\\n\\n### 3) **NMO_159995** \\n**ID:** **c8b57a6d-5c81-4f4f-9079-0b09b0581678** \\n**Name:** **NMO_159995** \\n**Description:** Morphological reconstruction of a pyramidal cell from the human temporal cortex. Part of Moradi et al., 2021 (https://doi.org/10.1038/s41467-021-22741-9) \\n**Brain region:** **Temporal association areas, layer 2/3 (TEa2/3)** \\n- **Brain region ID:** **fe3aeb11-da80-43bb-a6fc-c59ac962efe0** \\n**Subject:** *Homo sapiens* (Generic) \\n**Mtype:** **Pyramidal Neuron** \\n**License:** none specified (null) \\n**Assets (primary morphology files):**\\n- label: **morphology** \\u2014 NMO_159995.CNG.swc (size: 277,039 bytes) \\u2014 asset id: **dcdbb46e-a33b-4ae2-b725-3c16f84e6f67** \\n- NMO_159995.CNG.asc (id: **e1b4b786-d231-41cb-a386-13a78f989edf**) \\n- NMO_159995.CNG.h5 (id: **0837f2bb-3e92-4ed2-95d2-b70a3d5100f2**) \\n**URL:** [View Morphology](https://openbraininstitute.org/app/entity/c8b57a6d-5c81-4f4f-9079-0b09b0581678)\\n\\n---\\n\\n### 4) **NMO_159994** \\n**ID:** **ebc3049a-0ca6-43a7-a7cb-1d205e26c01c** \\n**Name:** **NMO_159994** \\n**Description:** Morphological reconstruction of a pyramidal cell from the human temporal cortex. Part of Moradi et al., 2021 (https://doi.org/10.1038/s41467-021-22741-9) \\n**Brain region:** **Temporal association areas, layer 3 (TEa3)** \\n- **Brain region ID:** **50440b2d-397f-4974-a837-148649e22959** \\n**Subject:** *Homo sapiens* (Generic) \\n**Mtype:** **Pyramidal Neuron** \\n**License:** none specified (null) \\n**Assets (primary morphology files):**\\n- label: **morphology** \\u2014 NMO_159994.CNG.swc (size: 350,177 bytes) \\u2014 asset id: **ff0984f3-dcf0-43d2-b8da-9e0e92987989** \\n- NMO_159994.CNG.asc (id: **5d214041-daae-4420-9140-abe1fdbf0a9b**) \\n- NMO_159994.CNG.h5 (id: **54ac25c9-3bf3-42e9-92ad-3cf98b028b97**) \\n**URL:** [View Morphology](https://openbraininstitute.org/app/entity/ebc3049a-0ca6-43a7-a7cb-1d205e26c01c)\\n\\n---\\n\\n### 5) **NMO_159993** \\n**ID:** **bd57dd40-e14f-4940-bfb9-335bcfba06d0** \\n**Name:** **NMO_159993** \\n**Description:** Morphological reconstruction of a pyramidal cell from the human temporal cortex. Part of Moradi et al., 2021 (https://doi.org/10.1038/s41467-021-22741-9) \\n**Brain region:** **Temporal association areas, layer 5 (TEa5)** \\n- **Brain region ID:** **e182532d-9864-4016-99a9-94a05c8f8d9a** \\n**Subject:** *Homo sapiens* (Generic) \\n**Mtype:** **Pyramidal Neuron** \\n**License:** none specified (null) \\n**Assets (primary morphology files):**\\n- label: **morphology** \\u2014 NMO_159993.CNG.swc (size: 919,944 bytes) \\u2014 asset id: **0de1bf0e-e612-4abe-ba1f-c60b6cfcebc5** \\n- NMO_159993.CNG.asc (id: **b72d177c-e6ae-448f-a759-62897e7473a3**) \\n- NMO_159993.CNG.h5 (id: **aaf6e119-bb49-4b19-818d-20cea1bbab05**) \\n**URL:** [View Morphology](https://openbraininstitute.org/app/entity/bd57dd40-e14f-4940-bfb9-335bcfba06d0)\\n\\n---\\n\\nIf you want any of these morphologies visualized (thumbnail plot), or specific morphometrics computed (total length, soma radius, max radial distance, etc.), tell me which morphology ID(s) and which metrics to compute and I will generate them.", "actual_tool_calls": [ - { - "name": "entitycore-brainregion-getall", - "arguments": { - "semantic_search": "cortex" - } - }, { "name": "entitycore-cellmorphology-getall", "arguments": { - "within_brain_region_brain_region_id": "abe63c70-1eb0-4b42-9421-d2c914ecb493", - "within_brain_region_direction": "descendants" + "search": "cortex" } } ], @@ -527,41 +497,41 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.62348770330439, - "success": true, + "score": 0.3186384511202486, + "success": false, "threshold": 0.5, - "reason": "The actual output provides detailed information about the retrieved cortex morphologies, including IDs, brain regions, and morphological types, which aligns with the topic of neuroscience. However, it deviates from the expected output format, lacking the specified structure and placeholders for descriptions, contributors, and licenses. While it maintains coherence and logical flow, it does not fully meet the expected output's requirements." + "reason": "The actual output provides detailed information about five cortex morphologies, including descriptions, IDs, and links, which partially addresses the input request. However, it does not align with the expected output format, which specifies a focus on rodent morphologies and includes specific fields like 'Morphological Type' and 'Contributors' that are missing in the actual output. Additionally, the response includes an offer for further assistance, which is not present in the expected output." }, { "name": "Tool Correctness", - "score": 1.0, + "score": 0.5, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Incorrect tool usage: missing tools ['entitycore-brainregion-getall'] and out-of-order tools ['entitycore-brainregion-getall']; expected ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'], called ['entitycore-cellmorphology-getall']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "The score is 0.00 because the tool calls failed to specify the number of cortex morphologies to retrieve and did not address the request to retrieve 5 cortex morphologies." + "reason": "The score is 0.00 because there was no input parameter provided to specify the number of cortex morphologies to retrieve, which is essential for the tool to function correctly." }, { "name": "Deterministic Argument Correctness", - "score": 0.75, - "success": true, + "score": 0.0, + "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-cellmorphology-getall\",\n input_parameters={\n \"within_brain_region_brain_region_id\": \"abe63c70-1eb0-4b42-9421-d2c914ecb493\"\n }\n)]; expected ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'], called ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-brainregion-getall\",\n input_parameters={\n \"semantic_search\": \"cortex\"\n }\n), ToolCall(\n name=\"entitycore-cellmorphology-getall\",\n input_parameters={\n \"within_brain_region_brain_region_id\": \"abe63c70-1eb0-4b42-9421-d2c914ecb493\"\n }\n)]; expected ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'], called ['entitycore-cellmorphology-getall']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", - "score": 0.75, - "success": true, + "score": 0.0, + "success": false, "threshold": 0.5, "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523837" + "created_at": "2025-11-24 17:08:32.832305" } }, "get_specific_circuit": { @@ -581,7 +551,7 @@ "circuit" ] }, - "ai_response": "## Circuit summary\\n\\n**Name:** **nbS1-O1-vSub-nCN-HEX0-L2-01** \\n**ID:** **2eb602ea-9ffc-42a2-9fb7-5234921343d0** \\n**Type:** **circuit** (scale: **small**) \\n**Public:** **Yes** \\n**Link:** [View Details](https://openbraininstitute.org/app/entity/2eb602ea-9ffc-42a2-9fb7-5234921343d0)\\n\\n---\\n\\n## Key metadata\\n\\n| Field | Value |\\n|-------|-------|\\n| **Description** | A volumetric subcircuit (vSub) extracted from the nbS1-O1 circuit, centered on a high-centrality neuron in layer 2 of subcolumn HEX0. Contains the central neuron and its nine nearest neighbors; suffix \\u201c01\\u201d = first instance. |\\n| **Creation date** | 2025-07-01T09:02:39.094768Z |\\n| **Update date** | 2025-07-01T09:02:39.094768Z |\\n| **Experiment date** | 2024-11-01T00:00:00Z |\\n| **Contact email** | michael.reimann@openbraininstitute.org |\\n| **Published in** | Reimann et al and Isbister et al |\\n| **Build category** | **computational_model** |\\n| **Root circuit id** | **0f25396a-1c16-43d7-9943-43e78f84e1ed** |\\n| **Has morphologies** | **true** |\\n| **Has point neurons** | **false** |\\n| **Has electrical cell models** | **true** |\\n| **Has spines** | **false** |\\n| **Number neurons** | **10** |\\n| **Number synapses** | **186** |\\n| **Number connections** | **15** |\\n\\n---\\n\\n## Brain region & subject\\n\\n- **Brain region:** **Primary somatosensory area** (acronym: **SSp**) \\n - **ID:** **61d89b07-dfa0-439a-9187-7ebfe60e212b** \\n - **Annotation value:** **322**\\n\\n- **Subject:** **Average rat P14** \\n - **ID:** **e5ecb660-504f-4840-b674-f31f0eada439** \\n - **Species:** *Rattus norvegicus* (ID: **3b1c2a25-b4fb-468d-98d2-d2d431ac8b4a**, taxonomy: NCBITaxon:10116) \\n - **Age value:** **1209600.0** (postnatal seconds \\u2192 P14 representation)\\n\\n---\\n\\n## License\\n\\n**CC BY 4.0** \\nLabel: **https://creativecommons.org/licenses/by/4.0/** \\n(ID: **ad8686db-3cdd-4e3f-bcbd-812380a9eba7**)\\n\\n---\\n\\n## Assets (selected)\\n\\n| Label | Path (asset id) | Content type | Size (bytes) |\\n|-------|-----------------|--------------|--------------|\\n| **sonata_circuit** | public/.../assets/circuit/.../sonata_circuit (id: **a6cec94a-0013-475e-b5ab-89d7cdfb5e92**) | directory | - |\\n| **compressed_sonata_circuit (circuit.gz)** | public/.../circuit.gz (id: **736b961f-ef7a-4441-81d1-56c4f82bedab**) | application/gzip | 4,965,720 |\\n| **simulation_designer_image (main.png)** | public/.../main.png (id: **1dce066a-679d-4228-a25a-711c4694060a**) | image/png | 8,359,219 |\\n| **circuit_visualization.webp** | (id: **91dd7f45-3af4-4f54-b27d-4214cffa16bf**) | image/webp | 1,074,360 |\\n| **node_stats.webp** | (id: **e9098491-a598-4c47-87bf-f803fae4d362**) | image/webp | 44,318 |\\n| **network_stats_a.webp** | (id: **f791535d-24e5-49a5-b42b-fb7b80c3ae69**) | image/webp | 72,008 |\\n| **network_stats_b.webp** | (id: **f2b6b574-9a0c-4afa-88fa-c6855b33cd43**) | image/webp | 235,628 |\\n| **circuit_connectivity_matrices** | directory (id: **22678058-e968-430e-8d5e-8d090272e025**) | directory | - |\\n\\n(Full asset paths are available in the entity record; I listed labels, IDs and sizes for quick reference.)\\n\\n---\\n\\n## Contributors / provenance\\n\\n- Created/Updated by: **Christoph Pokorny** (agent id: **38fc8a62-5823-4129-a09e-3037d1af4834**) \\n- Contribution roster includes many investigators and organizations (e.g., Henry Markram, Michael W. Reimann, Blue Brain Project / EPFL, Open Brain Institute, Dept. of Neurosciences at Universit\\u00e9 de Montr\\u00e9al, and numerous others). Total contributions listed: 70+ entries (people and organizations). \\n\\n---\\n\\nIf you'd like, I can:\\n- Retrieve the circuit structure and filterable properties (use obione-circuitmetrics-getone) so you can run connectivity analyses, or\\n- Download and summarize specific asset contents (e.g., list files inside the sonata_circuit directory), or\\n- Run a connectivity metrics query (e.g., connection probabilities grouped by mtype) \\u2014 tell me which analysis you want and I will proceed.", + "ai_response": "## Circuit details \\u2014 **ID: 2eb602ea-9ffc-42a2-9fb7-5234921343d0**\\n\\n### Summary\\n- **Name:** nbS1-O1-vSub-nCN-HEX0-L2-01 \\n- **Type:** **circuit** \\n- **Scale:** **small** \\n- **Build category:** **computational_model** \\n- **Root circuit id:** **0f25396a-1c16-43d7-9943-43e78f84e1ed** \\n- **Public access:** **authorized_public: true** \\n- **URL (EntityCore):** [View Details](https://openbraininstitute.org/app/entity/2eb602ea-9ffc-42a2-9fb7-5234921343d0)\\n\\n### Description\\nA volumetric subcircuit (vSub) extracted from the nbS1-O1 circuit, centered on a neuron with high centrality in the connectivity graph and located in layer 2 of subcolumn HEX0. The subcircuit contains the central neuron and its nine nearest neighbors (total 10 neurons), capturing local connectivity. The suffix \\u201c01\\u201d denotes the first instance of this subnetwork.\\n\\n### Identifiers & Dates\\n- **ID:** **2eb602ea-9ffc-42a2-9fb7-5234921343d0** \\n- **Experiment date:** 2024-11-01T00:00:00Z \\n- **Creation date:** 2025-07-01T09:02:39.094768Z \\n- **Update date:** 2025-07-01T09:02:39.094768Z \\n- **Contact email:** michael.reimann@openbraininstitute.org \\n- **Published in:** Reimann et al and Isbister et al\\n\\n### Counts & Features\\n- **Number of neurons:** **10** \\n- **Number of synapses:** **186** \\n- **Number of connections:** **15** \\n- **Has morphologies:** **true** \\n- **Has point neurons:** **false** \\n- **Has electrical cell models:** **true** \\n- **Has spines:** **false**\\n\\n### Spatial / Subject / Brain region\\n- **Brain region:** \\n - **Name:** Primary somatosensory area \\n - **Acronym:** **SSp** \\n - **ID:** **61d89b07-dfa0-439a-9187-7ebfe60e212b** \\n - **Annotation value:** **322** \\n - **Hierarchy id:** **e3e70682-c209-4cac-a29f-6fbed82c07cd**\\n- **Subject (model):** \\n - **Name:** Average rat P14 \\n - **ID:** **e5ecb660-504f-4840-b674-f31f0eada439** \\n - **Species:** *Rattus norvegicus* (**ID:** 3b1c2a25-b4fb-468d-98d2-d2d431ac8b4a, taxonomy: NCBITaxon:10116) \\n - **Age value:** 1209600.0 (seconds \\u2014 postnatal P14 representation) \\n - **Sex:** unknown \\n - **Description:** Abstract representation of a P14 rat model built from average statistical metrics across individual rats\\n\\n### License\\n- **License:** CC BY 4.0 \\n - **ID:** **ad8686db-3cdd-4e3f-bcbd-812380a9eba7** \\n - **Label / URL:** https://creativecommons.org/licenses/by/4.0/ \\n - **Description:** Creative Commons Attribution 4.0 (text provided in metadata)\\n\\n### Assets (selected)\\nThe circuit includes multiple assets. Key items:\\n\\n| Label | Storage id | Content type | Size (bytes) | Note |\\n|-------|------------|--------------:|-------------:|------|\\n| **sonata_circuit** | **a6cec94a-0013-475e-b5ab-89d7cdfb5e92** | application/vnd.directory | -1 | Directory containing the sonata circuit files |\\n| **simulation_designer_image (main.png)** | **1dce066a-679d-4228-a25a-711c4694060a** | image/png | 8,359,219 | Visualization / designer image |\\n| **compressed_sonata_circuit (circuit.gz)** | **736b961f-ef7a-4441-81d1-56c4f82bedab** | application/gzip | 4,965,720 | Compressed sonata circuit archive |\\n| **node_stats (node_stats.webp)** | **e9098491-a598-4c47-87bf-f803fae4d362** | image/webp | 44,318 | Node statistics image |\\n| **circuit_connectivity_matrices (dir)** | **22678058-e968-430e-8d5e-8d090272e025** | application/vnd.directory | -1 | Directory with connectivity matrices |\\n| **circuit_visualization (circuit_visualization.webp)** | **91dd7f45-3af4-4f54-b27d-4214cffa16bf** | image/webp | 1,074,360 | Circuit visualization |\\n| **network_stats_a.webp** | **f791535d-24e5-49a5-b42b-fb7b80c3ae69** | image/webp | 72,008 | Network statistics |\\n| **network_stats_b.webp** | **f2b6b574-9a0c-4afa-88fa-c6855b33cd43** | image/webp | 235,628 | Network statistics |\\n\\n(IDs above are the asset storage IDs returned by the system. The frontend may display these assets directly.)\\n\\n### Contributors / Contributions\\nThe circuit metadata lists many contributions (authors, institutions). A non-exhaustive selection of contributing agents includes: **Alexis Arnaudon**, **Omar Awile**, **Natali Barros-Zulaica**, **Jorge Blanco Alonso**, **Elvis Boci**, **Sirio Bola\\u00f1os-Puchet**, **Giuseppe Chindemi**, **Beno\\u00eet Coste**, **Jean-Denis Courcol**, **Tanguy Damart**, **Fabien Delalondre**, **Thomas Delemontex**, **Adrien Devresse**, and organizational contributors including **Blue Brain Project, \\u00c9cole polytechnique f\\u00e9d\\u00e9rale de Lausanne (EPFL)** and **Open Brain Institute**. (Full contributions list is present in the entity metadata.)\\n\\n### Created / Updated by\\n- **Created by / Updated by:** Christoph Pokorny (**ID:** 38fc8a62-5823-4129-a09e-3037d1af4834; sub_id: e41b43dc-5078-40d5-a081-12bc031f3b17)\\n\\n---\\n\\nIf you want, I can:\\n- Retrieve the full list of contributors expanded into a table (there are many entries). \\n- Fetch the sonata circuit contents (the sonata_circuit directory / compressed circuit.gz) or download links for specific assets. \\n- Run obione circuit metrics (structure, node/edge populations) for this circuit (useful before connectivity analyses). Which would you like next?", "actual_tool_calls": [ { "name": "entitycore-circuit-getone", @@ -594,31 +564,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.7778544923990117, + "score": 0.8693521101681745, "success": true, "threshold": 0.5, - "reason": "The actual output provides relevant details about the circuit ID, including its name, type, and metadata, which aligns well with the input request. However, while it covers most sections expected in the output, it lacks a clear 'Core identifiers' section header and does not explicitly mention the 'Summary / Description' as a separate section, which slightly detracts from its structural alignment with the expected output." + "reason": "The actual output effectively addresses the input by providing comprehensive details about the specified circuit ID, including identifiers, summary, counts, and contributors. It maintains the appropriate context related to neuroscience and the Open Brain Platform. However, the structure slightly differs from the expected output, particularly in the section headings and the order of information, which prevents a perfect score." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-circuit-getone'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-circuit-getone'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "The score is 1.00 because there were no incorrect tool calls and the input was clear and specific." + "reason": "The score is 1.00 because there were no incorrect tool calls, indicating that the input was handled perfectly." }, { "name": "Deterministic Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['entitycore-circuit-getone'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['entitycore-circuit-getone'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -628,7 +598,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523864" + "created_at": "2025-11-24 17:08:32.832320" } }, "species_list": { @@ -647,7 +617,7 @@ "entitycore" ] }, - "ai_response": "## Species (first page)\\n\\nTotal items: **9**\\n\\n| # | **Species Name** | **ID** | **Taxonomy ID** | **Creation Date (UTC)** |\\n|---:|------------------|--------|-----------------|-------------------------|\\n| 1 | **Aquarana catesbeiana** | **d322a735-3ead-4efe-840e-2b4fda9c025a** | **NCBITaxon:8400** | 2025-06-27T11:10:28.715311Z |\\n| 2 | **Cricetulus griseus** | **9779ac1f-45e9-4d32-8c85-5fdfa7251af0** | **NCBITaxon:10029** | 2025-06-27T11:10:28.715307Z |\\n| 3 | **Felis catus** | **ea748db9-e020-407a-aeb6-db2c3a038a70** | **NCBITaxon:9685** | 2025-06-27T11:10:28.715308Z |\\n| 4 | **Homo sapiens** | **ff3fe32a-30ff-44ee-90a7-bd04e85bfcdd** | **NCBITaxon:9606** | 2025-06-27T11:10:28.715304Z |\\n| 5 | **Hybrid human-mouse** | **0f756132-e16d-4e72-b18e-859835e1f291** | **NA** | 2025-06-27T11:10:28.715312Z |\\n| 6 | **Loligo pealeii** | **22f1a831-85b9-4f5f-811e-60de1b343f52** | **NCBITaxon:6619** | 2025-06-27T11:10:28.715310Z |\\n| 7 | **Mus musculus** (*house mouse*) | **b7ad4cca-4ac2-4095-9781-37fb68fe9ca1** | **NCBITaxon:10090** | 2025-06-27T11:10:53.183929Z |\\n| 8 | **Rattus norvegicus** | **3b1c2a25-b4fb-468d-98d2-d2d431ac8b4a** | **NCBITaxon:10116** | 2025-06-27T11:11:57.148444Z |\\n| 9 | **Xenopus laevis** | **930cdbd3-0f0a-42a8-9b2d-19a2beaa14a7** | **NCBITaxon:8355** | 2025-06-27T11:10:28.715306Z |\\n\\nSummary:\\n- You requested the first 10 species; the database returned 9 total items on page 1.\\n- Want me to fetch additional pages (if any) or retrieve details for any specific species ID?", + "ai_response": "## Species \\u2014 first page (requested 10; platform returned 9)\\n\\nTotal items available: **9**\\n\\n| # | **Species Name** | **ID** | **Taxonomy ID** | **Created** | **Updated** |\\n|---:|------------------|--------|-----------------|-------------|-------------|\\n| 1 | **Aquarana catesbeiana** (*Aquarana catesbeiana*) | **d322a735-3ead-4efe-840e-2b4fda9c025a** | **NCBITaxon:8400** | 2025-06-27T11:10:28.715311Z | 2025-06-27T11:10:28.715312Z |\\n| 2 | **Cricetulus griseus** (*Cricetulus griseus*) | **9779ac1f-45e9-4d32-8c85-5fdfa7251af0** | **NCBITaxon:10029** | 2025-06-27T11:10:28.715307Z | 2025-06-27T11:10:28.715308Z |\\n| 3 | **Felis catus** (*Felis catus*) | **ea748db9-e020-407a-aeb6-db2c3a038a70** | **NCBITaxon:9685** | 2025-06-27T11:10:28.715308Z | 2025-06-27T11:10:28.715309Z |\\n| 4 | **Homo sapiens** (*Homo sapiens*) | **ff3fe32a-30ff-44ee-90a7-bd04e85bfcdd** | **NCBITaxon:9606** | 2025-06-27T11:10:28.715304Z | 2025-06-27T11:10:28.715305Z |\\n| 5 | **Hybrid human-mouse** (*Hybrid human-mouse*) | **0f756132-e16d-4e72-b18e-859835e1f291** | **NA** | 2025-06-27T11:10:28.715312Z | 2025-06-27T11:10:28.715313Z |\\n| 6 | **Loligo pealeii** (*Loligo pealeii*) | **22f1a831-85b9-4f5f-811e-60de1b343f52** | **NCBITaxon:6619** | 2025-06-27T11:10:28.715310Z | 2025-06-27T11:10:28.715310Z |\\n| 7 | **Mus musculus** (*Mus musculus*) | **b7ad4cca-4ac2-4095-9781-37fb68fe9ca1** | **NCBITaxon:10090** | 2025-06-27T11:10:53.183929Z | 2025-06-27T11:10:53.183929Z |\\n| 8 | **Rattus norvegicus** (*Rattus norvegicus*) | **3b1c2a25-b4fb-468d-98d2-d2d431ac8b4a** | **NCBITaxon:10116** | 2025-06-27T11:11:57.148444Z | 2025-06-27T11:11:57.148444Z |\\n| 9 | **Xenopus laevis** (*Xenopus laevis*) | **930cdbd3-0f0a-42a8-9b2d-19a2beaa14a7** | **NCBITaxon:8355** | 2025-06-27T11:10:28.715306Z | 2025-06-27T11:10:28.715306Z |\\n\\nSummary:\\n- You requested the first 10 species; the API returned **9** total species.\\n- Each entry shows the platform ID, species name, taxonomy identifier, and timestamps.\\n\\nWould you like me to:\\n- Fetch additional species if more exist (use pagination), or\\n- Retrieve detailed information for any of these species (by providing the species **ID**)?", "actual_tool_calls": [ { "name": "entitycore-species-getall", @@ -660,17 +630,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6397351056900727, + "score": 0.64719334646425, "success": true, "threshold": 0.5, - "reason": "The actual output provides relevant information about the species, including their names, IDs, and taxonomy IDs, which aligns with the input request. However, it does not fully match the expected output's structure, as it presents the data in a table format rather than a list format, and lacks the additional context and invitation for further queries found in the expected output. The summary at the end is a good addition but does not compensate for the structural differences." + "reason": "The actual output provides a detailed list of species, including their names, IDs, and taxonomy IDs, which aligns with the request for available species. However, it deviates from the expected output format by including a table structure instead of a simple list, and it does not present the species names in a straightforward manner as requested. Additionally, while it summarizes the total number of species returned, it lacks the clear introductory statement found in the expected output. Overall, it addresses the input but does not fully match the expected format and context." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-species-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-species-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -684,7 +654,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['entitycore-species-getall'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['entitycore-species-getall'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -694,7 +664,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523890" + "created_at": "2025-11-24 17:08:32.832336" } }, "cerebellum_morphologies": { @@ -719,7 +689,7 @@ "entitycore" ] }, - "ai_response": "## Cerebellum \\u2014 5 morphologies\\n\\nBelow are five cell morphologies located in the **Cerebellum**. For each entry I include the **Name**, **ID**, short **description**, **mtype(s)**, any available **location** coordinates, available asset files, license, and a link to view the entity.\\n\\n| Name | ID | Short description | mtype(s) | Location (coordinates) | Main assets | License | View |\\n|------|----:|-------------------|---------|------------------------:|-------------|--------:|------|\\n| **17302_00064** | **12811d3a-9705-4eec-867c-65053d646d5b** | Neuronal morphology reconstructed by SEU and curated by BBP; contains whole-brain axonal reconstruction. | *Interneuron* | **(x: 7242.7007, y: 2798.82495, z: 3697.52466)** | SWC: **17302_00064.swc** (id: 6fe3b673-...); ASC, H5 also available | CC BY 4.0 | [View Details](https://openbraininstitute.org/app/entity/12811d3a-9705-4eec-867c-65053d646d5b) |\\n| **soma_10c** | **e892fa95-c724-457a-8dc6-176f5d6cc5d9** | Purkinje cell morphology: flask-like soma in Purkinje cell layer; large, highly branched dendritic tree in molecular layer; single long axon to white matter. | *CBXpu_PuC (Cerebellum Purkinje Cell)* | location: none listed | SWC: **soma_10c.swc** (id: 85f98df0-...); ASC, H5 available | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/e892fa95-c724-457a-8dc6-176f5d6cc5d9) |\\n| **01bc** | **d083a0bd-00a4-4594-b52f-950a5dd3ec70** | Basket cell: soma pyramidal/oval in inner molecular layer; dendrites extend to Pia; axon runs parallel to Purkinje layer with collaterals. | *CBXmo_BC (Cerebellum Basket Cell)* | location: none listed | SWC: **01bc.swc** (id: 34723c67-...); ASC, H5 available | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/d083a0bd-00a4-4594-b52f-950a5dd3ec70) |\\n| **stellate** | **00ba95d3-13e4-4703-ad85-3df816febf53** | Stellate cell: small fusiform soma in outer molecular layer; thin, contorted dendrites branching in all directions; short axon collaterals. | *CBXmo_StC (Cerebellum Stellate Cell)* | location: none listed | SWC: **stellate.swc** (id: 4503498e-...); ASC, H5 available | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/00ba95d3-13e4-4703-ad85-3df816febf53) |\\n| **pair-140514-C2-1_split_1** | **cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c** | Golgi cell morphology: soma in granular layer; multiple dendrites with fan-shaped apical arbor into molecular layer; extensive axon plexus in granular layer. | *CBXgr_GoC (Cerebellum Golgi Cell)* | location: none listed | SWC: **pair-140514-C2-1_split_1.swc** (id: e0546c06-...); ASC, H5 available | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c) |\\n\\nSummary: total available cerebellum morphologies in the query = **78** (I retrieved the first two pages and returned five items). \\nIf you want any of these downloaded (SWC/H5), visualized, filtered by mtype, or if you want more items (next page), tell me which action and I\\u2019ll proceed.", + "ai_response": "## Cerebellum \\u2014 5 morphologies\\n\\nBelow are five cell morphologies located in the **Cerebellum** (brain region **ID: 062ebc92-cebb-498a-a76d-b5ef1baf02cf**). For each entity I list key metadata, available asset files, mtype(s) and the entity link.\\n\\n| Name | **ID** | Type / mtype(s) | Location (when available) | Assets (label : file / asset id) | License | Link |\\n|---|---:|---|---|---|---|---|\\n| **17302_00064** | **12811d3a-9705-4eec-867c-65053d646d5b** | Type: cell_morphology \\u00b7 mtype: *Interneuron* | **(x: 7242.70068359375, y: 2798.824951171875, z: 3697.524658203125)** | morphology : 17302_00064.swc (asset id: **6fe3b673-371b-46f8-83dd-3a78a2cb2025**); 17302_00064.asc (id: **5c21481d-aab5-4309-8796-6156bea87ada**); 17302_00064.h5 (id: **95177b6b-ec18-4b79-a27f-93cc8585287d**) | CC BY 4.0 | [View Details](https://openbraininstitute.org/app/entity/12811d3a-9705-4eec-867c-65053d646d5b) |\\n| **soma_10c** | **e892fa95-c724-457a-8dc6-176f5d6cc5d9** | Type: cell_morphology \\u00b7 mtype: *CBXpu_PuC* (Purkinje cell) | location: *not specified* | morphology : soma_10c.swc (asset id: **85f98df0-c8a1-4d37-b9af-dca010d4a8fe**); soma_10c.asc (id: **0cc31dd9-2ab2-4f8b-946e-7335c65631cb**); soma_10c.h5 (id: **e8008d5c-2c1a-417b-9d08-233cbdedb15e**) | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/e892fa95-c724-457a-8dc6-176f5d6cc5d9) |\\n| **01bc** | **d083a0bd-00a4-4594-b52f-950a5dd3ec70** | Type: cell_morphology \\u00b7 mtype: *CBXmo_BC* (Basket cell) | location: *not specified* | morphology : 01bc.swc (asset id: **34723c67-7ead-41c9-bf43-f196299f1b97**); 01bc.asc (id: **4636bd08-0907-44af-bfea-9b563f75318b**); 01bc.h5 (id: **743d29ba-ce48-4a85-aacd-beb9225146f4**) | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/d083a0bd-00a4-4594-b52f-950a5dd3ec70) |\\n| **stellate** | **00ba95d3-13e4-4703-ad85-3df816febf53** | Type: cell_morphology \\u00b7 mtype: *CBXmo_StC* (Stellate cell) | location: *not specified* | morphology : stellate.swc (asset id: **4503498e-f4cb-4c97-92fa-f111d457d8df**); stellate.asc (id: **27067b0b-15a6-47a1-a67a-8839170b75f4**); stellate.h5 (id: **f4c3a51a-4604-4b88-b405-ec5b2fb6e40a**) | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/00ba95d3-13e4-4703-ad85-3df816febf53) |\\n| **pair-140514-C2-1_split_1** | **cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c** | Type: cell_morphology \\u00b7 mtype: *CBXgr_GoC* (Golgi cell) | location: *not specified* | morphology : pair-140514-C2-1_split_1.swc (asset id: **e0546c06-9ceb-424d-b0ed-9664ba5e1f8b**); .asc (id: **8061bc5d-1dbd-4e89-b4b1-56bba967f077**); .h5 (id: **e5eafb9f-f87a-43eb-be77-91f32941c255**) | CC BY-NC-SA 4.0 | [View Details](https://openbraininstitute.org/app/entity/cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c) |\\n\\n### Notes\\n- total cerebellum morphologies available in the database: **78** (I retrieved the first 2 pages at page_size=5).\\n- Asset files listed include SWC / ASC / HDF5 variants when present; use the entity link to download or preview assets in the platform.\\n- If you want thumbnail plots for any of these morphologies, I can generate them (I will need the entity_id + asset id to plot). Which morphology(ies) would you like plotted?", "actual_tool_calls": [ { "name": "entitycore-brainregion-getall", @@ -745,31 +715,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.5898276636506745, + "score": 0.5622758377449195, "success": true, "threshold": 0.5, - "reason": "The actual output provides relevant information about five cerebellum morphologies, which aligns with the input request. However, it deviates from the expected output structure by including additional details such as IDs, location coordinates, and asset files, which were not requested. The expected output format is more concise and focuses on descriptions, morphological types, contributors, and licenses without extra details. While the content is informative, it lacks the expected structure and completeness in terms of contributor information." + "reason": "The actual output provides five morphologies from the cerebellum, which aligns with the request. However, it lacks the structured format and specific details outlined in the expected output, such as descriptions, contributors, and a clear separation of each morphology. While it includes relevant metadata and links, it does not fully meet the expected structure or context, leading to a lower score." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "The score is 0.00 because there were no input parameters provided for any of the tool calls to fetch morphologies, which is essential for generating a valid output." + "reason": "The score is 0.00 because there were no input parameters provided for any of the tool calls to fetch morphologies, which is essential for generating a valid response." }, { "name": "Deterministic Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -779,7 +749,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523919" + "created_at": "2025-11-24 17:08:32.832352" } }, "off_topic_weather": { @@ -792,23 +762,23 @@ "rejection" ] }, - "ai_response": "I'm specialized in neuroscience and the Open Brain Platform. I can't help with that topic, but I'd be happy to assist with brain research or platform-related questions. If you'd like, tell me what brain data or platform feature you want to explore today.", + "ai_response": "I'm specialized in neuroscience and the Open Brain Platform. I can't help with that topic, but I'd be happy to assist with brain research or platform-related questions.", "actual_tool_calls": [], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.8970620529657471, + "score": 1.0, "success": true, "threshold": 0.5, - "reason": "The actual output closely aligns with the expected output by politely declining to address the weather question and redirecting the conversation to neuroscience and the Open Brain Platform. It maintains the overall structure and intent of the expected response, with only a slight addition of an offer to explore specific brain data or platform features, which does not detract from the main message." + "reason": "The actual output perfectly matches the expected output in both content and structure, confirming that it directly addresses the input while maintaining a focus on neuroscience and the Open Brain Platform. It appropriately declines to provide weather information, aligning with the context of the input." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools [] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools [] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -822,7 +792,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools [] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools [] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -832,7 +802,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523946" + "created_at": "2025-11-24 17:08:32.832366" } }, "morphology_studies": { @@ -853,40 +823,33 @@ "literature_search" ] }, - "ai_response": "## Clarifying question\\n\\nDo you want a literature list (specific recent papers and DOIs), or a concise summary of recent themes and findings in studies of neuron morphology in the cerebrum? \\nIf you want papers, tell me the species (mouse/rat/human), brain area (e.g., neocortex, hippocampus), and time range (e.g., 2020\\u20132025) so I can target the search.\\n\\n---\\n\\n## Quick summary \\u2014 recent directions in neuron morphology research (cerebrum)\\n\\nBelow are major themes and representative types of findings from recent (roughly past 5 years) work on neuron morphology in the cerebrum.\\n\\n### 1) Large-scale morphological atlases and cell-type classification\\n- Efforts to combine high-throughput morphological reconstructions with electrophysiology, transcriptomics, and connectivity to create integrated cell-type taxonomies for cortical and hippocampal neurons.\\n- Key outcomes: improved morphological features that separate excitatory subtypes (e.g., layer-specific pyramidal cell classes) and inhibitory interneuron classes (PV, SST, VIP), and standardized metrics/feature sets for comparisons.\\n\\n### 2) Single-cell multimodal integration (morphology + transcriptomics + electrophysiology)\\n- Studies combine single-cell RNA-seq with 3D reconstructions to link gene expression profiles to dendritic/axonal patterns and intrinsic electrical properties.\\n- Result: identification of molecular markers associated with distinct dendritic arbor shapes, spine densities, and axonal projection motifs.\\n\\n### 3) Developmental and experience-dependent morphological plasticity\\n- Work characterizing how dendritic branching, spine formation/elimination, and axonal remodeling change during development or after sensory experience, learning, or injury.\\n- Findings include critical-period\\u2013dependent dendritic growth, region- and cell-type\\u2013specific spine dynamics, and structural correlates of synaptic plasticity.\\n\\n### 4) Advances in imaging, reconstruction, and computational morphometrics\\n- Adoption of automated or semi-automated 3D imaging (clearing + volume imaging, array tomography, serial-section EM, light-sheet) and automated tracing tools.\\n- Improved morphometric descriptors (e.g., Sholl-derived measures, fractal dimension, branching topology analyses) and machine learning methods for classifying morphologies.\\n\\n### 5) Human cortical neuron morphology and disease\\n- Growing datasets of human cortical neuron reconstructions (post-mortem, surgical tissue) revealing differences from rodent neurons: larger dendritic trees, higher spine numbers in some layers.\\n- Comparative studies linking morphological changes to neurodevelopmental or neurodegenerative disorders (e.g., altered dendritic complexity or spine loss in disease).\\n\\n### 6) Connectivity-informed morphology: axonal projections and synaptic distributions\\n- Detailed mapping of axonal arborizations and bouton distributions to infer projection patterns and local vs. long-range connectivity roles.\\n- Integration of bouton and spine density mapping with morphology to predict synapse numbers and input/output balance for specific neuron classes.\\n\\n---\\n\\n## If you want a targeted literature list\\nTell me:\\n- Species (mouse / rat / human / comparative)\\n- Brain region (e.g., neocortex, prefrontal cortex, hippocampus)\\n- Years of interest (e.g., 2020\\u20132024)\\n- Paper type preference (reviews, primary articles, datasets/atlases)\\n\\nI can then run a focused literature search and return a set of recent papers with citations and links (and can read full texts where available).", + "ai_response": "## Recent notable studies on neuron morphology in the cerebrum\\n\\nBelow are recent high-impact papers and resources (2023\\u20132025) focused on neuronal morphologies in the cerebrum / neocortex. I summarize each, give citation details and links, and embed available figures returned by the literature search.\\n\\n### 1) Neuronal diversity and stereotypy at multiple scales through whole brain morphometry \\n- **Authors:** Peng, Hanchuan et al. \\n- **Journal / Date:** Nature Communications \\u2014 26 Nov 2024 \\n- **Summary:** Large-scale whole\\u2011brain morphometry across hundreds of mouse brains. Annotated ~182k cell bodies, modeled >15k dendritic microenvironments and ~1,876 full neuron morphologies, plus ~2.63M axonal varicosities (putative boutons). Provides multi-scale datasets (cell, dendritic microenvironment, full morphology, axonal motifs) registered to the Allen CCF. Emphasizes diversity vs. stereotypy across scales. \\n- **Link:** https://www.nature.com/articles/s41467-024-54745-6 \\n- **Key resource:** PDF available from article page. \\n![Fig from Peng et al. 2024](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41467-024-54745-6/MediaObjects/41467_2024_54745_Fig1_HTML.png)\\n\\n---\\n\\n### 2) A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images \\n- **Authors:** Peng, Hanchuan et al. \\n- **Journal / Date:** Scientific Data \\u2014 23 Apr 2025 \\n- **Summary:** Dataset paper accompanying peta\\u2011voxel whole\\u2011brain imaging and morphometry workflows. Describes production pipeline, data formats, and access to the large multi-scale morphometry dataset (registered to Allen CCF). Valuable for cross-scale morphological analyses and method re-use. \\n- **Link:** https://www.nature.com/articles/s41597-025-04379-0 \\n![Fig from Peng et al. 2025 dataset paper](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41597-025-04379-0/MediaObjects/41597_2025_4379_Fig1_HTML.png)\\n\\n---\\n\\n### 3) An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex \\n- **Authors:** Ecker, Alexander S. et al. \\n- **Journal / Date:** Nature Communications \\u2014 9 Apr 2025 \\n- **Summary:** Data\\u2011driven, unsupervised mapping of dendritic morphologies of >30,000 excitatory neurons from MICrONS serial EM data in mouse visual cortex. Suggests cortical excitatory dendritic morphologies form a continuum (axes of variation) rather than discrete m\\u2011types, with layer\\u2011 and area\\u2011specific trends. Important for rethinking morphological classification. \\n- **Link:** https://www.nature.com/articles/s41467-025-58763-w \\n![Fig from Ecker et al. 2025](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41467-025-58763-w/MediaObjects/41467_2025_58763_Fig1_HTML.png)\\n\\n---\\n\\n### 4) Variation and convergence in the morpho-functional properties of the mammalian neocortex (Review) \\n- **Author:** S\\u00e9verine Mahon \\n- **Journal / Date:** Frontiers in Systems Neuroscience \\u2014 20 Jun 2024 \\n- **Summary:** Comparative review of neocortical neuron structure and function across mammals. Discusses how morphological features (dendritic length, spine density, arborization) scale with brain size and highlights conserved versus species\\u2011specific adaptations. Useful for evolutionary / comparative perspective. \\n- **Link / DOI:** https://doi.org/10.3389/fnsys.2024.1413780 \\n![Fig from Mahon 2024 review](https://images-provider.frontiersin.org/api/ipx/w=1200&f=png/https://www.frontiersin.org/files/Articles/1413780/fnsys-18-1413780-HTML/image_m/fnsys-18-1413780-g001.jpg)\\n\\n---\\n\\n### 5) Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy (preprint / model resource) \\n- **Authors:** Abdellah M., Hernando J., Eilemann S., Lapere S., Antille N., Markram H., Sch\\u00fcrmann F. \\n- **Date:** Aug 2024 (eLife reviewed preprint) \\n- **Summary:** Large-scale data\\u2011driven anatomical model of juvenile rat somatosensory cortex with ~4.2M morphologically and biophysically detailed neurons placed in an atlas and connected by ~14.2B synapses. Focuses on anatomical realism and limits of deducing connectivity from anatomy. Includes openly available subvolumes for simulation. Relevant for how morphology is used in large\\u2011scale circuit models. \\n- **Link:** https://elifesciences.org/reviewed-preprints/99688v1/pdf\\n\\n---\\n\\n### 6) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices (preprint) \\n- **Authors:** Aronoff R., Petersen C.C.H. \\n- **Date:** May 2024 (eLife reviewed preprint) \\n- **Summary:** Brain\\u2011wide light\\u2011sheet imaging of genetically labeled projection neurons; quantifies layer/subclass-specific long-range axonal outputs from whisker somatosensory cortex. Demonstrates how morphological projection patterns map onto functional connectivity. Useful for linking soma/layer location and axonal morphology to circuit function. \\n- **Link:** https://elifesciences.org/reviewed-preprints/97602v1/pdf\\n\\n---\\n\\n## Which of these would you like next?\\n- I can fetch the full text / PDFs for any of the open articles using the read\\u2011paper tool (if publicly available). \\n- I can extract figures, methods, or specific morphological measurements (e.g., dendritic length, branching statistics, bouton counts) from a chosen paper. \\n- I can search specifically for human cerebrum morphology studies, or for morphological analyses of particular cell types (pyramidal cells, interneurons, L5 vs L2/3) or modalities (EM reconstructions, light-sheet, tracing).\\n\\nIf you want full\\u2011text extraction for any of the above, tell me which paper(s) and I\\u2019ll attempt to retrieve them.", "actual_tool_calls": [ { "name": "literature-search-tool", "arguments": { - "query": "neuron morphology cerebrum recent studies 2020..2025", + "query": "neuron morphology cerebrum recent studies review 2020..2025", "start_publish_date": "2020-01-01T00:00:00Z", "end_publish_date": "2025-11-24T00:00:00Z", "num_results": 6 } - }, - { - "name": "web-search-tool", - "arguments": { - "query": "recent studies neuron morphology cerebrum 2020 2021 2022 2023 2024 2025 review pyramidal neurons dendritic morphology cortex", - "num_results": 6 - } } ], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.500242960279023, + "score": 0.8942312072094533, "success": true, "threshold": 0.5, - "reason": "The actual output provides a detailed summary of recent themes and findings in neuron morphology research, which is relevant to the input question. However, it does not directly list specific studies or papers as requested in the expected output, which diminishes its alignment with the evaluation steps. While the content is accurate and informative, it lacks the structured format of specific studies, authors, and links that the expected output outlines." + "reason": "The actual output provides a comprehensive list of recent studies on neuron morphology in the cerebrum, aligning well with the input request. It includes relevant details such as authors, publication dates, summaries, and links, which are all present and appropriately addressed. The structure closely mirrors the expected output format, although it contains specific content rather than placeholders. The only minor shortcoming is that it does not explicitly label the studies with titles as requested in the expected output, but the information is still highly relevant and accurate." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['literature-search-tool'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['literature-search-tool'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -900,7 +863,7 @@ "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"literature-search-tool\",\n input_parameters={\n \"query\": \"neuron morphology cerebrum\",\n \"numResults\": 5,\n \"maxCharacters\": 1000\n }\n)]; expected ['literature-search-tool'], called ['literature-search-tool', 'web-search-tool']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"literature-search-tool\",\n input_parameters={\n \"query\": \"neuron morphology cerebrum\",\n \"numResults\": 5,\n \"maxCharacters\": 1000\n }\n)]; expected ['literature-search-tool'], called ['literature-search-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -910,7 +873,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.523974" + "created_at": "2025-11-24 17:08:32.832380" } }, "ion_channel": { @@ -930,20 +893,13 @@ "ion_channel" ] }, - "ai_response": "## Ion channels related to the gene **Asic3**\\n\\n### Summary\\nFound **1** ion channel entity associated with gene **Asic3** (total_items: **1**).\\n\\n| **ID** | **Name** | **Label** | **Gene** | **Synonyms** | **Created** | **Updated** |\\n|--------|----------|-----------|----------|--------------|-------------|-------------|\\n| **1bdf731a-df80-48ad-9f06-ed92906221d4** | **ASIC3** | **ASIC3** | **Asic3** | *DRASIC, TNaC1, ACCN3, amiloride-sensitive cation channel 3, testis, testis sodium channel 1, acid sensing ion channel 3, dorsal root ASIC, acid-sensing (proton-gated) ion channel 3, acid sensing (proton gated) ion channel 3* | 2025-09-19T07:49:01.188929Z | 2025-09-19T07:49:01.188929Z |\\n\\n### Notes\\n- The entity record contains basic identifiers and synonyms for **ASIC3**.\\n- If you want detailed ion-channel models (measurements, mtypes, or model assets) or to search within a brain region or species, I can run the ion channel model search next. Which would you like?", + "ai_response": "## Ion channels matching gene **Asic3**\\n\\n### Summary\\n- **Total items found:** **1**\\n\\n---\\n\\n### Detailed entry\\n\\n| Field | Value |\\n|-------|-------|\\n| **Name** | **ASIC3** |\\n| **ID** | **1bdf731a-df80-48ad-9f06-ed92906221d4** |\\n| **Label** | **ASIC3** |\\n| **Gene** | **Asic3** |\\n| **Description** | (empty in record) |\\n| **Synonyms** | DRASIC; TNaC1; ACCN3; amiloride-sensitive cation channel 3, testis; testis sodium channel 1; acid sensing ion channel 3; dorsal root ASIC; acid-sensing (proton-gated) ion channel 3; acid sensing (proton gated) ion channel 3 |\\n| **Created by** | Eleftherios Zisis (ID: 6654bb3e-a44b-4fbd-bcd4-44d2ba2eba01) |\\n| **Creation date** | 2025-09-19T07:49:01.188929Z |\\n| **Last updated** | 2025-09-19T07:49:01.188929Z |\\n\\n---\\n\\nIf you want more detailed metadata or the full ion-channel record (measurements, properties, related models, assets, contributions), I can fetch the full entry using the ion-channel GET ONE tool for **ion_channel_id = 1bdf731a-df80-48ad-9f06-ed92906221d4**. Would you like me to retrieve that?", "actual_tool_calls": [ { "name": "entitycore-ionchannel-getall", "arguments": { "page_size": 10, - "gene": "ASIC3" - } - }, - { - "name": "entitycore-ionchannel-getall", - "arguments": { - "page_size": 10, - "search": "Asic3" + "gene": "Asic3" } } ], @@ -951,31 +907,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6181389519578939, + "score": 0.723258668553424, "success": true, "threshold": 0.5, - "reason": "The actual output is relevant to the input, as it lists the ion channel associated with the gene Asic3. However, the structure differs significantly from the expected output, lacking clear labels for each section and including unnecessary additional notes. While it provides the necessary information, it does not fully align with the expected format, which affects clarity and completeness." + "reason": "The actual output addresses the input by listing the ion channel related to the gene Asic3 and includes relevant details such as the name, ID, label, gene, synonyms, and creator information. However, it deviates from the expected output format by including a summary section and an offer to fetch more details, which were not present in the expected output. Additionally, the 'Updated by' and 'Update date' fields are missing, which are critical components outlined in the expected output." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-ionchannel-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-ionchannel-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "The score is 1.00 because there were no incorrect tool calls and the input was clear and specific." + "reason": "The score is 1.00 because the input was clear and directly related to the request for information about ion channels associated with the Asic3 gene, with no incorrect tool calls." }, { "name": "Deterministic Argument Correctness", - "score": 0.0, - "success": false, + "score": 0.5, + "success": true, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-ionchannel-getall\",\n input_parameters={\n \"gene\": \"Asic3\"\n }\n)]; expected ['entitycore-ionchannel-getall'], called ['entitycore-ionchannel-getall', 'entitycore-ionchannel-getall']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-ionchannel-getall\",\n input_parameters={\n \"gene\": \"Asic3\"\n }\n)]; expected ['entitycore-ionchannel-getall'], called ['entitycore-ionchannel-getall']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -985,7 +941,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524003" + "created_at": "2025-11-24 17:08:32.832394" } }, "platform_viewing": { @@ -1002,42 +958,48 @@ "platform_ui" ] }, - "ai_response": "## Summary \\u2014 what you're looking at\\n\\nYou are viewing the platform page for **cell morphologies** in a specific brain region.\\n\\n- **Observed entity type:** *cell-morphology* \\n- **Brain region ID:** **2a156e47-0842-4a40-bd1e-2afffb4dbafd** \\n- **Query parameter (atlas version?):** **br_av=477**\\n\\nIn plain terms: this view lists neuronal reconstructions (morphologies) that are associated with the selected brain region. Each item on that page typically represents a single reconstructed neuron and will include metadata such as cell type, species, dataset/source, links to the morphology file (e.g., SWC), and visualization/preview controls.\\n\\n## What you can do from here\\n- View individual neuron details (soma, dendrites, axon, metadata). \\n- Open/download reconstruction files (e.g., SWC/JSON) if available. \\n- Filter or search by cell type, species, dataset, or quality metrics. \\n- Visualize morphologies in 2D/3D previews provided on the page. \\n- Ask me to retrieve the list of morphologies in this region (I can fetch the items and show the totals and links).\\n\\nDo you want me to:\\n- Fetch the list of morphologies for this brain region and show the first page? \\n- Find a specific neuron or cell type within this region?", + "ai_response": "## Quick answer \\u2014 what you're looking at\\n\\nYou are viewing the platform page for **cell morphologies** filtered to the brain region **Striatum (STR)** \\u2014 the neuronal morphology catalog for annotation value **477**.\\n\\n---\\n\\n## What this page contains\\n- **Entity type:** *Cell Morphology* (digital reconstructions / morphologies of single neurons). \\n- **Brain region:** **Striatum** \\u2014 indicated by **ID: 2a156e47-0842-4a40-bd1e-2afffb4dbafd**, name **Striatum**, acronym **STR**. \\n- **Purpose:** lists individual neuron reconstructions and their metadata (IDs, contributor, species, measurements, mtypes, descriptions, and links to the full data).\\n\\n---\\n\\n## Typical fields you can expect for each morphology\\n- **Cell morphology ID** and **name** (clickable link to view details). \\n- **Subject species** (e.g., *Mus musculus*). \\n- **Brain region annotation / location** (here: Striatum). \\n- **Measurements & morphological metrics** (e.g., total dendritic length, soma size). \\n- **mtype** (morphological cell-type label). \\n- **Availability / authorization** (public vs project-restricted). \\n- **Creation / update dates** and contributor information.\\n\\n---\\n\\n## What you can do next\\n- Click an individual morphology entry to open detailed view (full reconstruction, visualizations, downloadable SWC/format files). \\n- Filter or search within the Striatum list (e.g., by species, mtype, measurement ranges). \\n- Ask me to pull the first page of morphologies here (I can list the items and their key metadata). \\n- If you want a specific cell ID explained, copy it here and I\\u2019ll fetch the entity details.\\n\\n---\\n\\nIf you'd like, I can list the first few cell morphologies in this Striatum view now \\u2014 would you like me to do that?", "actual_tool_calls": [ { "name": "context-analyzer-tool", "arguments": {} + }, + { + "name": "entitycore-brainregion-getall", + "arguments": { + "id": "2a156e47-0842-4a40-bd1e-2afffb4dbafd" + } } ], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6778050964436126, + "score": 0.7556301076899657, "success": true, "threshold": 0.5, - "reason": "The actual output is relevant to the input, providing a clear summary of the platform page for cell morphologies. It includes key details such as the observed entity type and brain region ID, which align with the expected output. However, it lacks some specific content from the expected output, such as the mention of species information and quantitative measurements, which affects its completeness. The structure is mostly appropriate, but the absence of certain details prevents a perfect alignment." + "reason": "The actual output effectively addresses the input by providing a detailed overview of the cell morphologies related to the Striatum brain region, which aligns with the expected output's context. It includes relevant sections such as a summary, typical fields, and next steps, demonstrating a strong structure. However, it lacks some specific phrasing and clarity found in the expected output, particularly in the summary and the explanation of the brain region ID's significance, which slightly detracts from its completeness." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['context-analyzer-tool'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['context-analyzer-tool'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", - "score": 0.0, - "success": false, + "score": 0.5, + "success": true, "threshold": 0.5, - "reason": "The score is 0.00 because there was no input parameter provided, which is essential for evaluating the argument correctly." + "reason": "The score is 0.50 because the lack of an input parameter limited the effectiveness of the tool calls. However, the initial question shows curiosity and engagement, which is a great starting point!" }, { "name": "Deterministic Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['context-analyzer-tool'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['context-analyzer-tool'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1047,7 +1009,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524029" + "created_at": "2025-11-24 17:08:32.832409" } }, "ion_channel_recording": { @@ -1073,13 +1035,12 @@ "ion_channel" ] }, - "ai_response": "## Summary\\n\\nI searched for ion channel recordings **within the Thalamus (ID: 75e3944e-8dcd-4310-a3a2-258f93de63d6)**. The query returned many recordings \\u2014 the tool reports **total_items: 33198** ion-channel recordings matching the search (page 1 returned 10 items). Below I list the first page of results and key metadata for each recording. If you want, I can page through more results or filter by ion channel, species, recording type, date, or other fields.\\n\\n---\\n\\n### Query details\\n- Brain region searched: **Thalamus** \\n - **ID:** 75e3944e-8dcd-4310-a3a2-258f93de63d6\\n- Endpoint: **ion-channel-recordings GET ALL**\\n- Page size returned: 10\\n- Total items (matching search): **33198**\\n\\n---\\n\\n## First-page results (10 recordings) \\u2014 key fields\\n\\n| # | Name (ID) | Ion channel | Type / Origin | Temperature (\\u00b0C) | Subject species | Published in | Link |\\n|---|-----------|-------------|---------------|------------------:|-----------------|--------------|------|\\n| 1 | **rCell25437** (**38586d7f-6d11-4a3e-91f5-fe41b788697c**) | **Kv6.2** (id: 426165d8-f96f-4f95-9e5e-eac6fb081993) | intracellular / in_vitro | 25.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/38586d7f-6d11-4a3e-91f5-fe41b788697c) |\\n| 2 | **rCell25609** (**562138e9-eb4e-4dc3-a3c6-018096aedd5a**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/562138e9-eb4e-4dc3-a3c6-018096aedd5a) |\\n| 3 | **rCell25635** (**6bf9d4be-ab56-4c24-9b41-d9c1599b5eb8**) | **Kv6.2** | intracellular / in_vitro | 35.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/6bf9d4be-ab56-4c24-9b41-d9c1599b5eb8) |\\n| 4 | **rCell25621** (**a070082f-5fd2-4487-a742-72f007167cf9**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/a070082f-5fd2-4487-a742-72f007167cf9) |\\n| 5 | **rCell25580** (**a9eeebd2-dac9-4235-a7b9-bb1a659efdb8**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/a9eeebd2-dac9-4235-a7b9-bb1a659efdb8) |\\n| 6 | **rCell25594** (**04e04a19-e430-49d5-8ff2-a5439a546ffb**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/04e04a19-e430-49d5-8ff2-a5439a546ffb) |\\n| 7 | **rCell25595** (**2c0809a5-51be-4e9f-a2bf-0151258b8f67**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/2c0809a5-51be-4e9f-a2bf-0151258b8f67) |\\n| 8 | **rCell25620** (**060e99b3-ebf4-49db-bfa4-55230f11b713**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/060e99b3-ebf4-49db-bfa4-55230f11b713) |\\n| 9 | **rCell25634** (**b34a2d1b-0b79-4029-805c-d95beedff0c4**) | **Kv6.2** | intracellular / in_vitro | 35.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/b34a2d1b-0b79-4029-805c-d95beedff0c4) |\\n| 10 | **rCell25581** (**329b1662-199b-40df-847c-e1cfa9b54306**) | **Kv6.2** | intracellular / in_vitro | 15.0 | Generic *Rattus norvegicus* | Ranjan et al. | [View recording](https://openbraininstitute.org/app/entity/329b1662-199b-40df-847c-e1cfa9b54306) |\\n\\nNotes for these items:\\n- All listed recordings on page 1 are labeled as recordings of **Kv6.2** (gene: Kcng2).\\n- Recording assets are provided as NWB files (asset labels and storage IDs are present in the tool output).\\n- Many entries include details on stimuli (e.g., Activation, Inactivation, Deactivation, Ramp, AP, VRest, Recovery) and number of runs / comments (e.g., \\5 runs with CHO rKv6.2 at 15c.\\\").\\n- All items on this page are marked **authorized_public: true** and have a platform URL via the **url_link** field.\\n\\n---\\n\\n## What would you like to do next?\\n\\nOptions I can run now (pick one):\\n- Page through more results (next page or specific page number).\\n- Filter results (examples: ion channel name, gene, cell line, temperature range, recording_type, date range, subject species).\\n- Download / list asset IDs or file paths for specific recordings (I can provide storage IDs and NWB full paths).\\n- Retrieve full details for any specific recording (by ID).\\n\\nTell me which option you prefer and I\\u2019ll proceed.", + "ai_response": "## Search summary \\u2014 Ion channel recordings in the Thalamus\\n\\n### Brain region used for search\\n- **Name:** **Thalamus** \\n- **ID:** **75e3944e-8dcd-4310-a3a2-258f93de63d6**\\n\\nI searched the EntityCore ion-channel-recording collection for recordings within the Thalamus. The query returned a total of **33,198** ion channel recording items (pagination: page 1 shown). Below I list the first page (10 items) returned with key metadata and links.\\n\\n---\\n\\n### Results (page 1 \\u2014 10 items)\\n\\n| Name | ID | Ion channel | Recording type / origin | Temperature (\\u00b0C) | Experiment date | Link |\\n|------|----:|-------------|-------------------------|------------------:|-----------------|------|\\n| **rCell25437** | **38586d7f-6d11-4a3e-91f5-fe41b788697c** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 25.0 | 2018-03-20 | [View Details](https://openbraininstitute.org/app/entity/38586d7f-6d11-4a3e-91f5-fe41b788697c) |\\n| **rCell25609** | **562138e9-eb4e-4dc3-a3c6-018096aedd5a** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/562138e9-eb4e-4dc3-a3c6-018096aedd5a) |\\n| **rCell25635** | **6bf9d4be-ab56-4c24-9b41-d9c1599b5eb8** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 35.0 | 2018-03-22 | [View Details](https://openbraininstitute.org/app/entity/6bf9d4be-ab56-4c24-9b41-d9c1599b5eb8) |\\n| **rCell25621** | **a070082f-5fd2-4487-a742-72f007167cf9** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/a070082f-5fd2-4487-a742-72f007167cf9) |\\n| **rCell25580** | **a9eeebd2-dac9-4235-a7b9-bb1a659efdb8** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/a9eeebd2-dac9-4235-a7b9-bb1a659efdb8) |\\n| **rCell25594** | **04e04a19-e430-49d5-8ff2-a5439a546ffb** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/04e04a19-e430-49d5-8ff2-a5439a546ffb) |\\n| **rCell25595** | **2c0809a5-51be-4e9f-a2bf-0151258b8f67** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/2c0809a5-51be-4e9f-a2bf-0151258b8f67) |\\n| **rCell25620** | **060e99b3-ebf4-49db-bfa4-55230f11b713** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/060e99b3-ebf4-49db-bfa4-55230f11b713) |\\n| **rCell25634** | **b34a2d1b-0b79-4029-805c-d95beedff0c4** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 35.0 | 2018-03-22 | [View Details](https://openbraininstitute.org/app/entity/b34a2d1b-0b79-4029-805c-d95beedff0c4) |\\n| **rCell25581** | **329b1662-199b-40df-847c-e1cfa9b54306** | **Kv6.2** (Kcng2) | intracellular / in_vitro | 15.0 | 2018-03-21 | [View Details](https://openbraininstitute.org/app/entity/329b1662-199b-40df-847c-e1cfa9b54306) |\\n\\nNotes:\\n- All items on page 1 are associated with the same ion channel (**Kv6.2**, gene *Kcng2*) and were published as part of the same contribution (Ranjan et al.). \\n- Each item includes an NWB asset (path shown in tool output) and is publicly accessible (license CC BY 4.0 where present).\\n- The tool returned **total_items = 33,198** \\u2014 many more recordings are available beyond this first page.\\n\\n---\\n\\nWould you like me to:\\n- Fetch the next page(s) of results (page 2, 3, \\u2026)? \\n- Filter results by ion channel, species, recording type (intracellular/extracellular), or date range? \\n- Download or preview one of the NWB assets listed above (I can open specific entity details if you pick an ID)?", "actual_tool_calls": [ { "name": "entitycore-brainregion-getall", "arguments": { - "semantic_search": "thalamus", - "page_size": 10 + "semantic_search": "thalamus" } }, { @@ -1094,17 +1055,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.8153318403827823, + "score": 0.7822989515826755, "success": true, "threshold": 0.5, - "reason": "The actual output effectively addresses the input by providing detailed information about ion channel recordings in the thalamus, including the total number of recordings and the first page of results. It maintains a clear structure and includes relevant metadata for each recording. However, it deviates from the expected output format by including specific details rather than using placeholders for certain fields, which slightly reduces alignment with the expected output's intent." + "reason": "The actual output effectively addresses the input by providing a detailed summary of ion channel recordings in the thalamus, including relevant metadata and links. It maintains the context of neuroscience and the Open Brain Platform. However, it lacks some specific details from the expected output, such as the explicit mention of 'cell line' and 'description/comment' for each recording, which were not included in the actual output. Overall, it is comprehensive but could be improved by aligning more closely with the expected structure." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-ionchannelrecording-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-ionchannelrecording-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1115,10 +1076,10 @@ }, { "name": "Deterministic Argument Correctness", - "score": 0.5, + "score": 0.75, "success": true, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-ionchannelrecording-getall\",\n input_parameters={\n \"within_brain_region_brain_region_id\": \"75e3944e-8dcd-4310-a3a2-258f93de63d6\"\n }\n), ToolCall(\n name=\"entitycore-brainregion-getall\",\n input_parameters={\n \"semantic_search\": \"thalamus\"\n }\n)]; expected ['entitycore-brainregion-getall', 'entitycore-ionchannelrecording-getall'], called ['entitycore-brainregion-getall', 'entitycore-ionchannelrecording-getall']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-ionchannelrecording-getall\",\n input_parameters={\n \"within_brain_region_brain_region_id\": \"75e3944e-8dcd-4310-a3a2-258f93de63d6\"\n }\n)]; expected ['entitycore-brainregion-getall', 'entitycore-ionchannelrecording-getall'], called ['entitycore-brainregion-getall', 'entitycore-ionchannelrecording-getall']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1128,7 +1089,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524056" + "created_at": "2025-11-24 17:08:32.832424" } }, "off_topic_cooking": { @@ -1147,17 +1108,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.8043740261928456, + "score": 0.7936238992992612, "success": true, "threshold": 0.5, - "reason": "The actual output effectively communicates the limitation of expertise regarding the input topic of chocolate chip cookies, aligning with the evaluation steps. It politely declines to assist with the unrelated topic while offering help on neuroscience and the Open Brain Platform. However, it could have more closely mirrored the expected output's phrasing and structure for a perfect alignment." + "reason": "The actual output effectively declines the request for cookie-making advice, aligning with the input's unrelated topic to neuroscience. It maintains a clear focus on the areas of expertise, similar to the expected output. However, the phrasing is slightly less inviting than the expected response, which could have improved engagement." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools [] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools [] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1171,7 +1132,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools [] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools [] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1181,7 +1142,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524081" + "created_at": "2025-11-24 17:08:32.832437" } }, "read_paper": { @@ -1202,47 +1163,52 @@ "read_paper" ] }, - "ai_response": "## I can help \\u2014 I need a specific article\\n\\nThe link you provided (https://pubmed.ncbi.nlm.nih.gov/) is the PubMed homepage. I can read and summarize a specific PubMed entry, but I need one of the following to proceed:\\n\\n- A direct PubMed article URL (e.g., https://pubmed.ncbi.nlm.nih.gov/PMID), \\n- The PMID number, or \\n- The article title / authors / DOI.\\n\\nWhat would you like me to read from PubMed? If you provide one of the items above I will fetch the paper and:\\n\\n- extract the abstract and metadata (authors, journal, DOI, publication date),\\n- summarize key findings and methods,\\n- list figures/tables available (and embed images if a valid image URL is returned),\\n- and offer to read the full text (if publicly available) for a deeper summary.\\n\\nPlease paste the PMID or the full PubMed article URL.", - "actual_tool_calls": [], + "ai_response": "## I can help \\u2014 but which page exactly?\\n\\nI can't read the generic PubMed homepage without a specific article. Please tell me one of the following so I can fetch and read the paper:\\n\\n- A PubMed article link (full URL to a specific article, e.g. https://pubmed.ncbi.nlm.nih.gov/XXXXXXXX/)\\n- A PMID number\\n- Article title, authors, or DOI\\n- A short topic or search query (I can search PubMed / the literature for relevant papers)\\n\\nWhat would you like me to read or search for?", + "actual_tool_calls": [ + { + "name": "context-analyzer-tool", + "arguments": {} + } + ], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.3886326021513766, + "score": 0.22227001388253087, "success": false, "threshold": 0.5, - "reason": "The actual output acknowledges the input link to PubMed but fails to provide a summary of the homepage content as expected. While it offers to assist with specific articles, it does not address the general information about PubMed, which is crucial for alignment with the expected output. The response is somewhat relevant to neuroscience but lacks the necessary details and structure outlined in the expected output." + "reason": "The actual output does not provide a summary of the PubMed homepage as requested in the input. Instead, it asks for specific articles or topics, which is not relevant to the task of summarizing the page. While it acknowledges the need for specific information, it fails to address the expected output's structure and content requirements, leading to a significant misalignment." }, { "name": "Tool Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incorrect tool usage: missing tools ['read-paper'] and out-of-order tools ['read-paper']; expected ['read-paper'], called []. See more details above." + "reason": "[\n\t Tool Calling Reason: Incorrect tool usage: missing tools ['read-paper'] and out-of-order tools ['read-paper']; expected ['read-paper'], called ['context-analyzer-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", - "score": 1.0, - "success": true, + "score": 0.0, + "success": false, "threshold": 0.5, - "reason": "No tool calls provided" + "reason": "The score is 0.00 because there was no input parameter provided, which is essential for processing the request." }, { "name": "Deterministic Argument Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"read-paper\",\n input_parameters={\n \"urls\": [\n \"https://pubmed.ncbi.nlm.nih.gov/\"\n ]\n }\n)]; expected ['read-paper'], called []. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"read-paper\",\n input_parameters={\n \"urls\": [\n \"https://pubmed.ncbi.nlm.nih.gov/\"\n ]\n }\n)]; expected ['read-paper'], called ['context-analyzer-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", - "score": 1.0, - "success": true, + "score": 0.0, + "success": false, "threshold": 0.5, "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524104" + "created_at": "2025-11-24 17:08:32.832450" } }, "platform_explore": { @@ -1254,23 +1220,23 @@ "platform_ui" ] }, - "ai_response": "I'm specialized in neuroscience and the Open Brain Platform. I can't help with that topic, but I'd be happy to assist with brain research or platform-related questions.", + "ai_response": "I don't have access to the platform's UI and can't guide you through the interface. I can't see where the \\Explore\\ button is located.\\n\\nYou can try these alternatives:\\n- Check the platform documentation or help center for navigation guidance.\\n- Use the platform's search or keyboard shortcuts (if available) to locate \\Explore\\\".\\n- Contact the support team or your project administrator and ask them to point out the Explore feature or provide a screenshot.\\n\\nWould you like me to search the platform documentation or news for any mention of an \\Explore\\ feature?", "actual_tool_calls": [], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.4218221236053461, - "success": false, + "score": 0.6318898161612772, + "success": true, "threshold": 0.5, - "reason": "The actual output acknowledges its specialization in neuroscience and the Open Brain Platform, which aligns with the input's context. However, it fails to provide specific guidance on locating the Explore button, which is a key aspect of the expected output. While it politely declines to assist with the specific query, it does not offer the detailed recommendations or alternative actions that the expected output includes, resulting in a lack of completeness and relevance." + "reason": "The actual output addresses the input by acknowledging the inability to access the platform's UI, which aligns with the expected output's initial statement. However, it lacks the specific structure and detailed recommendations found in the expected output, such as the mention of a main navigation bar and specific keywords to search for. While it provides alternative suggestions, the overall message is less organized and comprehensive compared to the expected response." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools [] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools [] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1284,7 +1250,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools [] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools [] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1294,7 +1260,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524128" + "created_at": "2025-11-24 17:08:32.832463" } }, "cerebellum_morphologies_descendants": { @@ -1320,7 +1286,7 @@ "entitycore" ] }, - "ai_response": "## Results \\u2014 5 morphologies in **Cerebellum** (searching **descendants**)\\n- Total matches in this search (within cerebellum descendants): **50** \\n (I returned the first 5 items \\u2014 tell me if you want more or a different page.)\\n\\n### Summary table\\n| Name | ID | Primary mtype | Short description | Link |\\n|------|----:|---------------|-------------------|------|\\n| **soma_10c** | **e892fa95-c724-457a-8dc6-176f5d6cc5d9** | *CBXpu_PuC* (Cerebellum Purkinje Cell) | Soma flask-like (~7.5 \\u00b5m), located in Purkinje cell layer; large flat, highly branched dendritic tree in molecular layer; single long axon to white matter. | [View Details](https://openbraininstitute.org/app/entity/e892fa95-c724-457a-8dc6-176f5d6cc5d9) |\\n| **01bc** | **d083a0bd-00a4-4594-b52f-950a5dd3ec70** | *CBXmo_BC* (Cerebellum Basket Cell) | Soma pyramidal/oval (~6 \\u00b5m), inner 1/3 molecular layer; long straight dendrites upward; axon runs parallel to Purkinje layer with collaterals. | [View Details](https://openbraininstitute.org/app/entity/d083a0bd-00a4-4594-b52f-950a5dd3ec70) |\\n| **stellate** | **00ba95d3-13e4-4703-ad85-3df816febf53** | *CBXmo_StC* (Cerebellum Stellate Cell) | Small fusiform soma (~4 \\u00b5m), outer 2/3 molecular layer; long thin contorted aspinous dendrites; axon branches immediately into short collaterals. | [View Details](https://openbraininstitute.org/app/entity/00ba95d3-13e4-4703-ad85-3df816febf53) |\\n| **pair-140514-C2-1_split_1** | **cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c** | *CBXgr_GoC* (Cerebellum Golgi Cell) | Soma round/polygonal (~8 \\u00b5m), granular layer; basal dendrites remain in granular layer, apical dendrites ascend to molecular layer forming fan-shaped tree; axon forms dense granule-layer plexus. | [View Details](https://openbraininstitute.org/app/entity/cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c) |\\n| **GrC2018** | **b03ceac9-d657-488c-a43c-01be275d0861** | *CBXgr_GrC* (Cerebellum Granule Cell) | Very small soma (~2.5 \\u00b5m), granular layer; 4\\u20135 dendrites ending in claw-like appendages; axon ascends and bifurcates into parallel fibers in molecular layer. | [View Details](https://openbraininstitute.org/app/entity/b03ceac9-d657-488c-a43c-01be275d0861) |\\n\\n---\\n\\n### Notes & assets\\n- Each morphology entry includes downloadable assets (SWC / ASC / H5) and a license (CC BY-NC-SA 4.0) as shown in the entity records.\\n- If you want:\\n - The raw asset IDs or direct asset filenames for any item, I can list them.\\n - More results (next page) \\u2014 say \\next page\\ or specify a page number.\\n - A filtered subset (e.g., only Purkinje cells or only granule cells), tell me the mtype or keyword.\\n\\nWould you like the next 5 results (page 2), or asset file IDs for any of these five?", + "ai_response": "## Cerebellum \\u2014 Descendant morphologies (5 results)\\n\\nFound 5 cell morphologies within the *Cerebellum* (search restricted to descendants). Below are the entries returned (one per morphology) with key metadata and links to view each entity.\\n\\n---\\n\\n### 1) **soma_10c** \\n- **ID:** **e892fa95-c724-457a-8dc6-176f5d6cc5d9** \\n- **Name:** **soma_10c** \\n- **Brain region:** **Cerebellum** (CB) \\u2014 **ID:** **062ebc92-cebb-498a-a76d-b5ef1baf02cf** \\n- **Species:** *Mus musculus* \\n- **Mtype(s):** **CBXpu_PuC** (Cerebellum Purkinje Cell) \\n- **Short description:** Soma flask-like (~7.5 \\u00b5m radius). Located in Purkinje cell layer. Large, flat, highly branched dendritic tree in the molecular layer. Single long axon projecting to white matter \\u2014 principal output neuron. \\n- **Assets (selected):** soma_10c.swc, soma_10c.h5, soma_10c.asc \\n- **License:** CC BY-NC-SA 4.0 \\n- [View Details](https://openbraininstitute.org/app/entity/e892fa95-c724-457a-8dc6-176f5d6cc5d9)\\n\\n---\\n\\n### 2) **01bc** \\n- **ID:** **d083a0bd-00a4-4594-b52f-950a5dd3ec70** \\n- **Name:** **01bc** \\n- **Brain region:** **Cerebellum** (CB) \\u2014 **ID:** **062ebc92-cebb-498a-a76d-b5ef1baf02cf** \\n- **Species:** *Mus musculus* \\n- **Mtype(s):** **CBXmo_BC** (Cerebellum Basket Cell) \\n- **Short description:** Pyramidal/oval soma (~6 \\u00b5m radius) in inner 1/3 of molecular layer. Dendrites extend upward with few branches. Axon runs parallel to Purkinje cell layer with ascending and descending collaterals. \\n- **Assets (selected):** 01bc.swc, 01bc.h5, 01bc.asc \\n- **License:** CC BY-NC-SA 4.0 \\n- [View Details](https://openbraininstitute.org/app/entity/d083a0bd-00a4-4594-b52f-950a5dd3ec70)\\n\\n---\\n\\n### 3) **stellate** \\n- **ID:** **00ba95d3-13e4-4703-ad85-3df816febf53** \\n- **Name:** **stellate** \\n- **Brain region:** **Cerebellum** (CB) \\u2014 **ID:** **062ebc92-cebb-498a-a76d-b5ef1baf02cf** \\n- **Species:** *Mus musculus* \\n- **Mtype(s):** **CBXmo_StC** (Cerebellum Stellate Cell) \\n- **Short description:** Small fusiform soma (~4 \\u00b5m radius) in outer 2/3 of molecular layer. Dendrites long, thin, contorted, aspinous; axon branches immediately into short circumscribed collaterals. \\n- **Assets (selected):** stellate.swc, stellate.h5, stellate.asc \\n- **License:** CC BY-NC-SA 4.0 \\n- [View Details](https://openbraininstitute.org/app/entity/00ba95d3-13e4-4703-ad85-3df816febf53)\\n\\n---\\n\\n### 4) **pair-140514-C2-1_split_1** \\n- **ID:** **cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c** \\n- **Name:** **pair-140514-C2-1_split_1** \\n- **Brain region:** **Cerebellum** (CB) \\u2014 **ID:** **062ebc92-cebb-498a-a76d-b5ef1baf02cf** \\n- **Species:** *Mus musculus* \\n- **Mtype(s):** **CBXgr_GoC** (Cerebellum Golgi Cell) \\n- **Short description:** Soma round/polygonal (~8 \\u00b5m radius) in granular layer. 4\\u201310 dendrites, basal dendrites remain in granular layer, apical dendrites ascend into molecular layer forming a fan. Axon forms dense plexus in granular layer. \\n- **Assets (selected):** pair-140514-C2-1_split_1.swc, .h5, .asc \\n- **License:** CC BY-NC-SA 4.0 \\n- [View Details](https://openbraininstitute.org/app/entity/cb635d11-f5f4-4ec5-8c6e-3e5af6d4eb6c)\\n\\n---\\n\\n### 5) **GrC2018** \\n- **ID:** **b03ceac9-d657-488c-a43c-01be275d0861** \\n- **Name:** **GrC2018** \\n- **Brain region:** **Cerebellum** (CB) \\u2014 **ID:** **062ebc92-cebb-498a-a76d-b5ef1baf02cf** \\n- **Species:** *Mus musculus* \\n- **Mtype(s):** **CBXgr_GrC** (Cerebellum Granule Cell) \\n- **Short description:** Very small round soma (~2.5 \\u00b5m radius) in granular layer. 4\\u20135 dendrites ending in claw-like appendages. Ascending axon bifurcates in molecular layer into parallel fibers. Extremely numerous and densely packed. \\n- **Assets (selected):** GrC2018.swc, GrC2018.h5, GrC2018.asc \\n- **License:** CC BY-NC-SA 4.0 \\n- [View Details](https://openbraininstitute.org/app/entity/b03ceac9-d657-488c-a43c-01be275d0861)\\n\\n---\\n\\nSummary: 5 morphologies returned (page 1, page_size 5). Total available descendant morphologies for Cerebellum: **50** (from the query pagination). \\nWould you like me to (choose one): \\n- retrieve more morphologies (next page), or \\n- download the SWC/H5 asset IDs for any of these entries, or \\n- filter by mtype (e.g., only Purkinje cells)?", "actual_tool_calls": [ { "name": "entitycore-brainregion-getall", @@ -1340,31 +1306,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.696975831164821, + "score": 0.7863307857283821, "success": true, "threshold": 0.5, - "reason": "The actual output is relevant to the input, providing five morphologies from the cerebellum and focusing on descendant regions as requested. However, it deviates from the expected output structure, lacking the specific sections for contributors and licenses, which are present in the expected format. While the content is accurate and coherent, the absence of these details affects the overall alignment with the expected output." + "reason": "The actual output effectively addresses the input by providing five morphologies from the cerebellum, specifically focusing on descendant regions as requested. It includes detailed metadata for each morphology, such as ID, name, brain region, species, morphological type, and a short description, which aligns well with the expected output's structure. However, it lacks explicit mention of contributors, which is a critical component in the expected output, leading to a slight deduction in the score." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 0.5, "success": true, "threshold": 0.5, - "reason": "The score is 0.50 because the first tool call does not specify the descendant regions of the cerebellum, which is required to address the input correctly. This omission prevents a higher score, as the input specifically requests information about descendant regions." + "reason": "The score is 0.50 because the first tool call lacks specificity regarding the number of morphologies to display and does not filter for descendant regions as requested." }, { "name": "Deterministic Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1374,7 +1340,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524154" + "created_at": "2025-11-24 17:08:32.832477" } }, "plotting": { @@ -1404,7 +1370,7 @@ "obi-one" ] }, - "ai_response": "## Morphology section_length analysis \\u2014 **ID: c9ab5cef-e01f-4d60-bbbb-d3b103f4b950**\\n\\n### Summary statistics\\n- **Count:** 289 sections \\n- **Mean:** 122.8281 \\n- **Median:** 66.4375 \\n- **Min:** 0.72845 \\n- **Max:** 6697.1475 \\n- **Q1 (25th percentile):** 24.8907 \\n- **Q3 (75th percentile):** 132.4731\\n\\n### Plot\\nThe distribution (histogram + boxplot) has been generated. View the plot below.\\n\\n![Section length distribution](storage:f9a1f0b6-8fcf-411e-9e4e-d56957296f81)\\n\\nIf you want:\\n- A different binning or log-scale histogram (recommended because of the large outlier at 6697) \\u2014 I can regenerate it.\\n- The raw section_length values as a CSV or downloadable file.\\n- Additional morphometrics (total_length, number_of_neurites, bifurcation angles, etc.).", + "ai_response": "## Summary \\u2014 section_lengths for **morphology c9ab5cef-e01f-4d60-bbbb-d3b103f4b950**\\n\\n**Requested metric:** *section_lengths*\\n\\n- **Count:** 289 sections \\n- **Mean:** 122.8280521701364 \\u03bcm \\n- **Median:** 66.43753814697266 \\u03bcm \\n- **Min:** 0.7284518480300903 \\u03bcm \\n- **Max:** 6697.1474609375 \\u03bcm\\n\\n### Distribution plot\\nBelow is a combined boxplot + histogram of the section_length distribution for this morphology.\\n\\n![Section length distribution](storage_id://089e7705-a35d-4a29-acdb-3613ae297b3d)\\n\\nIf you want:\\n- A log-scaled histogram or kernel density estimate (KDE)\\n- Summary percentiles (e.g., 10th/90th)\\n- The raw section_lengths as a downloadable file\\n\\ntell me which and I\\u2019ll produce it.", "actual_tool_calls": [ { "name": "obione-morphometrics-getone", @@ -1418,7 +1384,7 @@ { "name": "run-python", "arguments": { - "python_script": "import json\nfrom statistics import mean, median\nimport plotly.express as px\nimport plotly.graph_objects as go\n\nsection_lengths = [6697.1474609375,573.10009765625,55.9705810546875,140.81536865234375,36.473636627197266,333.4425354003906,32.24296569824219,85.4410171508789,164.65457153320312,90.48748779296875,38.77936935424805,431.8247375488281,124.64474487304688,313.6686096191406,154.44198608398438,245.48257446289062,157.74118041992188,28.805633544921875,1075.1058349609375,197.75595092773438,164.18865966796875,77.16008758544922,18.217559814453125,39.608062744140625,36.798221588134766,33.65286636352539,102.1196060180664,329.9515380859375,56.81739807128906,64.16143035888672,227.23036193847656,44.476715087890625,239.92428588867188,8.58110237121582,34.01866149902344,124.19037628173828,62.699119567871094,232.33050537109375,97.47404479980469,27.800334930419922,9.682034492492676,268.062255859375,185.7609100341797,17.808170318603516,53.5123291015625,316.735595703125,21.915584564208984,24.26759910583496,6.120256423950195,117.0146484375,130.61920166015625,70.36585235595703,20.952255249023438,21.830989837646484,392.17352294921875,4.727529525756836,44.566471099853516,77.8578109741211,7.712652683258057,145.4837646484375,0.7284518480300903,86.63726806640625,57.58983612060547,17.12718391418457,121.6731948852539,13.140891075134277,150.819580078125,210.67970275878906,66.58440399169922,21.894250869750977,33.85200881958008,58.51990509033203,19.31520652770996,60.08998489379883,39.81935119628906,229.39385986328125,85.65229797363281,20.848783493041992,103.49125671386719,175.70407104492188,81.50428009033203,26.977664947509766,271.2418212890625,119.87942504882812,178.37741088867188,141.38162231445312,105.13993072509766,12.260920524597168,418.6307373046875,8.473603248596191,178.32553100585938,227.80740356445312,84.23662567138672,30.383647918701172,122.328125,106.88761138916016,15.465989112854004,12.980880737304688,59.18584442138672,95.90320587158203,271.0279541015625,26.603588104248047,257.04205322265625,36.66156005859375,572.6156616210938,3.7158889770507812,15.10144329071045,8.836631774902344,154.59872436523438,235.0324249267578,276.84698486328125,312.54852294921875,43.94004440307617,57.29133224487305,449.3951416015625,54.9459114074707,133.6972198486328,297.5891418457031,46.98112869262695,4.657102108001709,355.1236877441406,23.339155197143555,17.566104888916016,31.922155380249023,4.4416046142578125,42.76015090942383,64.76997375488281,138.0548553466797,92.97811889648438,84.63085174560547,27.42153549194336,14.138972282409668,307.19390869140625,299.5655212402344,181.8392333984375,91.97017669677734,240.2989501953125,254.39871215820312,57.275596618652344,54.78379821777344,28.957874298095703,206.90635681152344,86.7742919921875,31.245250701904297,68.78656005859375,56.15248107910156,143.3094482421875,30.037090301513672,293.792724609375,23.340255737304688,99.94407653808594,21.89372444152832,10.40378189086914,99.30420684814453,87.48847198486328,58.964988708496094,36.856178283691406,162.45303344726562,32.429443359375,63.95743942260742,94.7013168334961,16.93044090270996,33.11799240112305,18.503421783447266,67.18305206298828,52.03638458251953,274.91131591796875,81.41813659667969,42.515342712402344,171.6693878173828,32.38789367675781,369.61810302734375,201.2120361328125,112.14703369140625,149.54388427734375,197.25094604492188,47.632179260253906,34.79545593261719,52.7253532409668,363.64447021484375,33.711273193359375,21.57476806640625,25.26631736755371,211.69512939453125,51.54615783691406,215.5805206298828,81.154541015625,39.663307189941406,97.93379211425781,118.50657653808594,128.47711181640625,72.29827117919922,46.21430587768555,202.44485473632812,203.90780639648438,126.46947479248047,140.21160888671875,20.90896224975586,132.56808471679688,2.621845245361328,9.437539100646973,13.171874046325684,139.26409912109375,141.21392822265625,143.427001953125,114.22775268554688,20.170995712280273,46.56403732299805,79.65634155273438,94.17543029785156,27.157724380493164,3.573380947113037,70.72016906738281,66.09829711914062,51.29396438598633,126.02339935302734,8.224746704101562,56.858665466308594,89.28418731689453,15.09274959564209,5.068365097045898,3.9129891395568848,19.94595718383789,53.08961486816406,100.0195083618164,90.3989028930664,14.247901916503906,21.650705337524414,48.33447265625,76.72879028320312,8.34957504272461,79.95813751220703,88.83263397216797,11.095174789428711,11.781899452209473,65.68526458740234,4.227246284484863,79.37328338623047,68.57018280029297,31.184030532836914,71.63761138916016,37.759700775146484,19.896329879760742,126.56001281738281,10.061229705810547,104.44141387939453,6.036439895629883,133.40872192382812,5.418631553649902,73.36334228515625,3.4293947219848633,115.03713989257812,22.21061134338379,69.6187973022461,54.073333740234375,20.459684371948242,23.330360412597656,5.568202018737793,23.197193145751953,43.05461502075195,93.19111633300781,52.248931884765625,81.2469711303711,110.28421783447266,24.515010833740234,1.7868305444717407,5.230838775634766,76.94654083251953,104.12735748291016,13.086082458496094,132.37808227539062,12.34406852722168,2.8383755683898926,110.71104431152344,66.43753814697266,51.17279052734375,152.34329223632812,116.47325897216797,19.002477645874023,5.458802223205566,95.04762268066406,3.064882755279541,29.80864906311035,54.85593032836914,86.56462097167969,139.5838165283203,16.046419143676758,67.78485107421875,125.24442291259766]\n\nimport statistics\ncount = len(section_lengths)\nmean_val = statistics.mean(section_lengths)\nmedian_val = statistics.median(section_lengths)\nmin_val = min(section_lengths)\nmax_val = max(section_lengths)\nq1 = statistics.quantiles(section_lengths, n=4)[0]\nq3 = statistics.quantiles(section_lengths, n=4)[2]\n\n# Create histogram and boxplot\nfig = go.Figure()\nfig.add_trace(go.Histogram(x=section_lengths, nbinsx=80, name='Histogram'))\nfig.update_layout(title='Section length distribution (morphology c9ab5cef-e01f-4d60-bbbb-d3b103f4b950)',\n xaxis_title='Section length (units: same as source)',\n yaxis_title='Count')\n\n# Add boxplot as inset via secondary y axis overlay\nbox = go.Box(x=section_lengths, name='Boxplot', boxpoints='outliers', marker_color='orange', yaxis='y2')\nfig.add_trace(box)\n# add secondary y-axis\nfig.update_layout(yaxis2=dict(overlaying='y', side='right', showgrid=False, zeroline=False))\n\n# Show figure\nfig.show()\n\n# Print summary stats as JSON so assistant can parse\nprint(json.dumps({\n 'morphology_id':'c9ab5cef-e01f-4d60-bbbb-d3b103f4b950',\n 'count':count,\n 'mean':mean_val,\n 'median':median_val,\n 'min':min_val,\n 'max':max_val,\n 'q1':q1,\n 'q3':q3\n}))\n" + "python_script": "import json\nfrom statistics import mean, median\nimport plotly.graph_objects as go\n\nsection_lengths = [6697.1474609375,573.10009765625,55.9705810546875,140.81536865234375,36.473636627197266,333.4425354003906,32.24296569824219,85.4410171508789,164.65457153320312,90.48748779296875,38.77936935424805,431.8247375488281,124.64474487304688,313.6686096191406,154.44198608398438,245.48257446289062,157.74118041992188,28.805633544921875,1075.1058349609375,197.75595092773438,164.18865966796875,77.16008758544922,18.217559814453125,39.608062744140625,36.798221588134766,33.65286636352539,102.1196060180664,329.9515380859375,56.81739807128906,64.16143035888672,227.23036193847656,44.476715087890625,239.92428588867188,8.58110237121582,34.01866149902344,124.19037628173828,62.699119567871094,232.33050537109375,97.47404479980469,27.800334930419922,9.682034492492676,268.062255859375,185.7609100341797,17.808170318603516,53.5123291015625,316.735595703125,21.915584564208984,24.26759910583496,6.120256423950195,117.0146484375,130.61920166015625,70.36585235595703,20.952255249023438,21.830989837646484,392.17352294921875,4.727529525756836,44.566471099853516,77.8578109741211,7.712652683258057,145.4837646484375,0.7284518480300903,86.63726806640625,57.58983612060547,17.12718391418457,121.6731948852539,13.140891075134277,150.819580078125,210.67970275878906,66.58440399169922,21.894250869750977,33.85200881958008,58.51990509033203,19.31520652770996,60.08998489379883,39.81935119628906,229.39385986328125,85.65229797363281,20.848783493041992,103.49125671386719,175.70407104492188,81.50428009033203,26.977664947509766,271.2418212890625,119.87942504882812,178.37741088867188,141.38162231445312,105.13993072509766,12.260920524597168,418.6307373046875,8.473603248596191,178.32553100585938,227.80740356445312,84.23662567138672,30.383647918701172,122.328125,106.88761138916016,15.465989112854004,12.980880737304688,59.18584442138672,95.90320587158203,271.0279541015625,26.603588104248047,257.04205322265625,36.66156005859375,572.6156616210938,3.7158889770507812,15.10144329071045,8.836631774902344,154.59872436523438,235.0324249267578,276.84698486328125,312.54852294921875,43.94004440307617,57.29133224487305,449.3951416015625,54.9459114074707,133.6972198486328,297.5891418457031,46.98112869262695,4.657102108001709,355.1236877441406,23.339155197143555,17.566104888916016,31.922155380249023,4.4416046142578125,42.76015090942383,64.76997375488281,138.0548553466797,92.97811889648438,84.63085174560547,27.42153549194336,14.138972282409668,307.19390869140625,299.5655212402344,181.8392333984375,91.97017669677734,240.2989501953125,254.39871215820312,57.275596618652344,54.78379821777344,28.957874298095703,206.90635681152344,86.7742919921875,31.245250701904297,68.78656005859375,56.15248107910156,143.3094482421875,30.037090301513672,293.792724609375,23.340255737304688,99.94407653808594,21.89372444152832,10.40378189086914,99.30420684814453,87.48847198486328,58.964988708496094,36.856178283691406,162.45303344726562,32.429443359375,63.95743942260742,94.7013168334961,16.93044090270996,33.11799240112305,18.503421783447266,67.18305206298828,52.03638458251953,274.91131591796875,81.41813659667969,42.515342712402344,171.6693878173828,32.38789367675781,369.61810302734375,201.2120361328125,112.14703369140625,149.54388427734375,197.25094604492188,47.632179260253906,34.79545593261719,52.7253532409668,363.64447021484375,33.711273193359375,21.57476806640625,25.26631736755371,211.69512939453125,51.54615783691406,215.5805206298828,81.154541015625,39.663307189941406,97.93379211425781,118.50657653808594,128.47711181640625,72.29827117919922,46.21430587768555,202.44485473632812,203.90780639648438,126.46947479248047,140.21160888671875,20.90896224975586,132.56808471679688,2.621845245361328,9.437539100646973,13.171874046325684,139.26409912109375,141.21392822265625,143.427001953125,114.22775268554688,20.170995712280273,46.56403732299805,79.65634155273438,94.17543029785156,27.157724380493164,3.573380947113037,70.72016906738281,66.09829711914062,51.29396438598633,126.02339935302734,8.224746704101562,56.858665466308594,89.28418731689453,15.09274959564209,5.068365097045898,3.9129891395568848,19.94595718383789,53.08961486816406,100.0195083618164,90.3989028930664,14.247901916503906,21.650705337524414,48.33447265625,76.72879028320312,8.34957504272461,79.95813751220703,88.83263397216797,11.095174789428711,11.781899452209473,65.68526458740234,4.227246284484863,79.37328338623047,68.57018280029297,31.184030532836914,71.63761138916016,37.759700775146484,19.896329879760742,126.56001281738281,10.061229705810547,104.44141387939453,6.036439895629883,133.40872192382812,5.418631553649902,73.36334228515625,3.4293947219848633,115.03713989257812,22.21061134338379,69.6187973022461,54.073333740234375,20.459684371948242,23.330360412597656,5.568202018737793,23.197193145751953,43.05461502075195,93.19111633300781,52.248931884765625,81.2469711303711,110.28421783447266,24.515010833740234,1.7868305444717407,5.230838775634766,76.94654083251953,104.12735748291016,13.086082458496094,132.37808227539062,12.34406852722168,2.8383755683898926,110.71104431152344,66.43753814697266,51.17279052734375,152.34329223632812,116.47325897216797,19.002477645874023,5.458802223205566,95.04762268066406,3.064882755279541,29.80864906311035,54.85593032836914,86.56462097167969,139.5838165283203,16.046419143676758,67.78485107421875,125.24442291259766]\n\n# basic stats\ncount = len(section_lengths)\nmean_val = mean(section_lengths)\nmedian_val = median(section_lengths)\nmin_val = min(section_lengths)\nmax_val = max(section_lengths)\n\n# create histogram with log x for visibility but keep linear as well\nfig = go.Figure()\nfig.add_trace(go.Histogram(x=section_lengths, nbinsx=80, name='Section lengths'))\nfig.update_layout(title='Distribution of section_lengths for morphology c9ab5cef-e01f-4d60-bbbb-d3b103f4b950',\n xaxis_title='section_length (micrometers)', yaxis_title='Count', bargap=0.1)\n\n# add boxplot below as a separate subplot\nfrom plotly.subplots import make_subplots\nfig2 = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.2,0.8], vertical_spacing=0.02)\nfig2.add_trace(go.Box(x=section_lengths, name='Box', boxpoints='suspectedoutliers'), row=1, col=1)\nfig2.add_trace(go.Histogram(x=section_lengths, nbinsx=80, name='Histogram'), row=2, col=1)\nfig2.update_layout(title='Section length distribution with boxplot (morphology c9ab5cef...)', xaxis2_title='section_length (micrometers)', yaxis2_title='Count')\n\n# show fig2\nfig2.show()\n\n# print stats as JSON for the chat\nprint(json.dumps({'morphology_id':'c9ab5cef-e01f-4d60-bbbb-d3b103f4b950','count':count,'mean':mean_val,'median':median_val,'min':min_val,'max':max_val}))\n" } } ], @@ -1426,17 +1392,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.563480439810853, + "score": 0.7336164979041554, "success": true, "threshold": 0.5, - "reason": "The actual output provides relevant statistics and a plot for the morphology section_length, addressing the input request. However, it lacks the specific structure and phrasing of the expected output, such as the explicit mention of 'Metric computed: section_lengths' and the detailed options for next steps. While it includes summary statistics and a plot, it does not fully align with the expected format and additional details requested." + "reason": "The actual output effectively addresses the input by providing section lengths for the specified morphology and includes a distribution plot, which aligns with the request. However, it lacks some details present in the expected output, such as the specific mention of a logarithmic histogram and the storage IDs for the figures. Additionally, while it offers next steps, it does not fully match the structure and content of the expected output." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['obione-morphometrics-getone', 'run-python'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['obione-morphometrics-getone', 'run-python'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1450,7 +1416,7 @@ "score": 0.5, "success": true, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"run-python\",\n input_parameters={\n \"python_script\": \"import plotly.graph_objects as go\\nimport numpy as np\\nsection_lengths = [6697.1474609375,573.10009765625,55.9705810546875,140.81536865234375,36.473636627197266,333.4425354003906,32.24296569824219,85.4410171508789,164.65457153320312,90.48748779296875,38.77936935424805,431.8247375488281,124.64474487304688,313.6686096191406,154.44198608398438,245.48257446289062,157.74118041992188,28.805633544921875,1075.1058349609375,197.75595092773438,164.18865966796875,77.16008758544922,18.217559814453125,39.608062744140625,36.798221588134766,33.65286636352539,102.1196060180664,329.9515380859375,56.81739807128906,64.16143035888672,227.23036193847656,44.476715087890625,239.92428588867188,8.58110237121582,34.01866149902344,124.19037628173828,62.699119567871094,232.33050537109375,97.47404479980469,27.800334930419922,9.682034492492676,268.062255859375,185.7609100341797,17.808170318603516,53.5123291015625,316.735595703125,21.915584564208984,24.26759910583496,6.120256423950195,117.0146484375,130.61920166015625,70.36585235595703,20.952255249023438,21.830989837646484,392.17352294921875,4.727529525756836,44.566471099853516,77.8578109741211,7.712652683258057,145.4837646484375,0.7284518480300903,86.63726806640625,57.58983612060547,17.12718391418457,121.6731948852539,13.140891075134277,150.819580078125,210.67970275878906,66.58440399169922,21.894250869750977,33.85200881958008,58.51990509033203,19.31520652770996,60.08998489379883,39.81935119628906,229.39385986328125,85.65229797363281,20.848783493041992,103.49125671386719,175.70407104492188,81.50428009033203,26.977664947509766,271.2418212890625,119.87942504882812,178.37741088867188,141.38162231445312,105.13993072509766,12.260920524597168,418.6307373046875,8.473603248596191,178.32553100585938,227.80740356445312,84.23662567138672,30.383647918701172,122.328125,106.88761138916016,15.465989112854004,12.980880737304688,59.18584442138672,95.90320587158203,271.0279541015625,26.603588104248047,257.04205322265625,36.66156005859375,572.6156616210938,3.7158889770507812,15.10144329071045,8.836631774902344,154.59872436523438,235.0324249267578,276.84698486328125,312.54852294921875,43.94004440307617,57.29133224487305,449.3951416015625,54.9459114074707,133.6972198486328,297.5891418457031,46.98112869262695,4.657102108001709,355.1236877441406,23.339155197143555,17.566104888916016,31.922155380249023,4.4416046142578125,42.76015090942383,64.76997375488281,138.0548553466797,92.97811889648438,84.63085174560547,27.42153549194336,14.138972282409668,307.19390869140625,299.5655212402344,181.8392333984375,91.97017669677734,240.2989501953125,254.39871215820312,57.275596618652344,54.78379821777344,28.957874298095703,206.90635681152344,86.7742919921875,31.245250701904297,68.78656005859375,56.15248107910156,143.3094482421875,30.037090301513672,293.792724609375,23.340255737304688,99.94407653808594,21.89372444152832,10.40378189086914,99.30420684814453,87.48847198486328,58.964988708496094,36.856178283691406,162.45303344726562,32.429443359375,63.95743942260742,94.7013168334961,16.93044090270996,33.11799240112305,18.503421783447266,67.18305206298828,52.03638458251953,274.91131591796875,81.41813659667969,42.515342712402344,171.6693878173828,32.38789367675781,369.61810302734375,201.2120361328125,112.14703369140625,149.54388427734375,197.25094604492188,47.632179260253906,34.79545593261719,52.7253532409668,363.64447021484375,33.711273193359375,21.57476806640625,25.26631736755371,211.69512939453125,51.54615783691406,215.5805206298828,81.154541015625,39.663307189941406,97.93379211425781,118.50657653808594,128.47711181640625,72.29827117919922,46.21430587768555,202.44485473632812,203.90780639648438,126.46947479248047,140.21160888671875,20.90896224975586,132.56808471679688,2.621845245361328,9.437539100646973,13.171874046325684,139.26409912109375,141.21392822265625,143.427001953125,114.22775268554688,20.170995712280273,46.56403732299805,79.65634155273438,94.17543029785156,27.157724380493164,3.573380947113037,70.72016906738281,66.09829711914062,51.29396438598633,126.02339935302734,8.224746704101562,56.858665466308594,89.28418731689453,15.09274959564209,5.068365097045898,3.9129891395568848,19.94595718383789,53.08961486816406,100.0195083618164,90.3989028930664,14.247901916503906,21.650705337524414,48.33447265625,76.72879028320312,8.34957504272461,79.95813751220703,88.83263397216797,11.095174789428711,11.781899452209473,65.68526458740234,4.227246284484863,79.37328338623047,68.57018280029297,31.184030532836914,71.63761138916016,37.759700775146484,19.896329879760742,126.56001281738281,10.061229705810547,104.44141387939453,6.036439895629883,133.40872192382812,5.418631553649902,73.36334228515625,3.4293947219848633,115.03713989257812,22.21061134338379,69.6187973022461,54.073333740234375,20.459684371948242,23.330360412597656,5.568202018737793,23.197193145751953,43.05461502075195,93.19111633300781,52.248931884765625,81.2469711303711,110.28421783447266,24.515010833740234,1.7868305444717407,5.230838775634766,76.94654083251953,104.12735748291016,13.086082458496094,132.37808227539062,12.34406852722168,2.8383755683898926,110.71104431152344,66.43753814697266,51.17279052734375,152.34329223632812,116.47325897216797,19.002477645874023,5.458802223205566,95.04762268066406,3.064882755279541,29.80864906311035,54.85593032836914,86.56462097167969,139.5838165283203,16.046419143676758,67.78485107421875,125.24442291259766]\\narr = np.array(section_lengths)\\nmean = arr.mean()\\nmedian = np.median(arr)\\nstd = arr.std()\\ncount = arr.size\\nminv = arr.min()\\nmaxv = arr.max()\\n\\n# Create histogram with log-scaled x-axis bins for better visualization due to long tail\\nbins = np.logspace(np.log10(max(minv,1e-3)), np.log10(maxv), 80)\\nfig = go.Figure()\\nfig.add_trace(go.Histogram(x=arr, xbins=dict(start=bins[0], end=bins[-1]), nbinsx=80))\\nfig.update_xaxes(type='log', title_text='Section length (\\u03bcm, log scale)')\\nfig.update_yaxes(title_text='Count')\\nfig.update_layout(title_text='Distribution of section_lengths for morphology c9ab5cef-e01f-4d60-bbbb-d3b103f4b950', bargap=0.05)\\n\\n# Also create a kernel density estimate on linear axis inset\\nimport plotly.express as px\\nfig2 = px.histogram(arr, nbins=100, marginal='rug')\\nfig2.update_xaxes(title_text='Section length (\\u03bcm)')\\nfig2.update_yaxes(title_text='Count')\\nfig2.update_layout(title_text='Linear histogram (for reference)')\\n\\n# Show primary figure\\nfig.show()\\n\\n# Print summary stats\\nprint(f\\\"count: {count}\\\")\\nprint(f\\\"mean: {mean:.3f}\\\")\\nprint(f\\\"median: {median:.3f}\\\")\\nprint(f\\\"std: {std:.3f}\\\")\\nprint(f\\\"min: {minv:.6f}\\\")\\nprint(f\\\"max: {maxv:.3f}\\\")\\n\\n# Also display secondary linear histogram\\nfig2.show()\\n\\n# Return nothing explicit\\n\"\n }\n)]; expected ['obione-morphometrics-getone', 'run-python'], called ['obione-morphometrics-getone', 'run-python']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"run-python\",\n input_parameters={\n \"python_script\": \"import plotly.graph_objects as go\\nimport numpy as np\\nsection_lengths = [6697.1474609375,573.10009765625,55.9705810546875,140.81536865234375,36.473636627197266,333.4425354003906,32.24296569824219,85.4410171508789,164.65457153320312,90.48748779296875,38.77936935424805,431.8247375488281,124.64474487304688,313.6686096191406,154.44198608398438,245.48257446289062,157.74118041992188,28.805633544921875,1075.1058349609375,197.75595092773438,164.18865966796875,77.16008758544922,18.217559814453125,39.608062744140625,36.798221588134766,33.65286636352539,102.1196060180664,329.9515380859375,56.81739807128906,64.16143035888672,227.23036193847656,44.476715087890625,239.92428588867188,8.58110237121582,34.01866149902344,124.19037628173828,62.699119567871094,232.33050537109375,97.47404479980469,27.800334930419922,9.682034492492676,268.062255859375,185.7609100341797,17.808170318603516,53.5123291015625,316.735595703125,21.915584564208984,24.26759910583496,6.120256423950195,117.0146484375,130.61920166015625,70.36585235595703,20.952255249023438,21.830989837646484,392.17352294921875,4.727529525756836,44.566471099853516,77.8578109741211,7.712652683258057,145.4837646484375,0.7284518480300903,86.63726806640625,57.58983612060547,17.12718391418457,121.6731948852539,13.140891075134277,150.819580078125,210.67970275878906,66.58440399169922,21.894250869750977,33.85200881958008,58.51990509033203,19.31520652770996,60.08998489379883,39.81935119628906,229.39385986328125,85.65229797363281,20.848783493041992,103.49125671386719,175.70407104492188,81.50428009033203,26.977664947509766,271.2418212890625,119.87942504882812,178.37741088867188,141.38162231445312,105.13993072509766,12.260920524597168,418.6307373046875,8.473603248596191,178.32553100585938,227.80740356445312,84.23662567138672,30.383647918701172,122.328125,106.88761138916016,15.465989112854004,12.980880737304688,59.18584442138672,95.90320587158203,271.0279541015625,26.603588104248047,257.04205322265625,36.66156005859375,572.6156616210938,3.7158889770507812,15.10144329071045,8.836631774902344,154.59872436523438,235.0324249267578,276.84698486328125,312.54852294921875,43.94004440307617,57.29133224487305,449.3951416015625,54.9459114074707,133.6972198486328,297.5891418457031,46.98112869262695,4.657102108001709,355.1236877441406,23.339155197143555,17.566104888916016,31.922155380249023,4.4416046142578125,42.76015090942383,64.76997375488281,138.0548553466797,92.97811889648438,84.63085174560547,27.42153549194336,14.138972282409668,307.19390869140625,299.5655212402344,181.8392333984375,91.97017669677734,240.2989501953125,254.39871215820312,57.275596618652344,54.78379821777344,28.957874298095703,206.90635681152344,86.7742919921875,31.245250701904297,68.78656005859375,56.15248107910156,143.3094482421875,30.037090301513672,293.792724609375,23.340255737304688,99.94407653808594,21.89372444152832,10.40378189086914,99.30420684814453,87.48847198486328,58.964988708496094,36.856178283691406,162.45303344726562,32.429443359375,63.95743942260742,94.7013168334961,16.93044090270996,33.11799240112305,18.503421783447266,67.18305206298828,52.03638458251953,274.91131591796875,81.41813659667969,42.515342712402344,171.6693878173828,32.38789367675781,369.61810302734375,201.2120361328125,112.14703369140625,149.54388427734375,197.25094604492188,47.632179260253906,34.79545593261719,52.7253532409668,363.64447021484375,33.711273193359375,21.57476806640625,25.26631736755371,211.69512939453125,51.54615783691406,215.5805206298828,81.154541015625,39.663307189941406,97.93379211425781,118.50657653808594,128.47711181640625,72.29827117919922,46.21430587768555,202.44485473632812,203.90780639648438,126.46947479248047,140.21160888671875,20.90896224975586,132.56808471679688,2.621845245361328,9.437539100646973,13.171874046325684,139.26409912109375,141.21392822265625,143.427001953125,114.22775268554688,20.170995712280273,46.56403732299805,79.65634155273438,94.17543029785156,27.157724380493164,3.573380947113037,70.72016906738281,66.09829711914062,51.29396438598633,126.02339935302734,8.224746704101562,56.858665466308594,89.28418731689453,15.09274959564209,5.068365097045898,3.9129891395568848,19.94595718383789,53.08961486816406,100.0195083618164,90.3989028930664,14.247901916503906,21.650705337524414,48.33447265625,76.72879028320312,8.34957504272461,79.95813751220703,88.83263397216797,11.095174789428711,11.781899452209473,65.68526458740234,4.227246284484863,79.37328338623047,68.57018280029297,31.184030532836914,71.63761138916016,37.759700775146484,19.896329879760742,126.56001281738281,10.061229705810547,104.44141387939453,6.036439895629883,133.40872192382812,5.418631553649902,73.36334228515625,3.4293947219848633,115.03713989257812,22.21061134338379,69.6187973022461,54.073333740234375,20.459684371948242,23.330360412597656,5.568202018737793,23.197193145751953,43.05461502075195,93.19111633300781,52.248931884765625,81.2469711303711,110.28421783447266,24.515010833740234,1.7868305444717407,5.230838775634766,76.94654083251953,104.12735748291016,13.086082458496094,132.37808227539062,12.34406852722168,2.8383755683898926,110.71104431152344,66.43753814697266,51.17279052734375,152.34329223632812,116.47325897216797,19.002477645874023,5.458802223205566,95.04762268066406,3.064882755279541,29.80864906311035,54.85593032836914,86.56462097167969,139.5838165283203,16.046419143676758,67.78485107421875,125.24442291259766]\\narr = np.array(section_lengths)\\nmean = arr.mean()\\nmedian = np.median(arr)\\nstd = arr.std()\\ncount = arr.size\\nminv = arr.min()\\nmaxv = arr.max()\\n\\n# Create histogram with log-scaled x-axis bins for better visualization due to long tail\\nbins = np.logspace(np.log10(max(minv,1e-3)), np.log10(maxv), 80)\\nfig = go.Figure()\\nfig.add_trace(go.Histogram(x=arr, xbins=dict(start=bins[0], end=bins[-1]), nbinsx=80))\\nfig.update_xaxes(type='log', title_text='Section length (\u03bcm, log scale)')\\nfig.update_yaxes(title_text='Count')\\nfig.update_layout(title_text='Distribution of section_lengths for morphology c9ab5cef-e01f-4d60-bbbb-d3b103f4b950', bargap=0.05)\\n\\n# Also create a kernel density estimate on linear axis inset\\nimport plotly.express as px\\nfig2 = px.histogram(arr, nbins=100, marginal='rug')\\nfig2.update_xaxes(title_text='Section length (\u03bcm)')\\nfig2.update_yaxes(title_text='Count')\\nfig2.update_layout(title_text='Linear histogram (for reference)')\\n\\n# Show primary figure\\nfig.show()\\n\\n# Print summary stats\\nprint(f\\\"count: {count}\\\")\\nprint(f\\\"mean: {mean:.3f}\\\")\\nprint(f\\\"median: {median:.3f}\\\")\\nprint(f\\\"std: {std:.3f}\\\")\\nprint(f\\\"min: {minv:.6f}\\\")\\nprint(f\\\"max: {maxv:.3f}\\\")\\n\\n# Also display secondary linear histogram\\nfig2.show()\\n\\n# Return nothing explicit\\n\"\n }\n)]; expected ['obione-morphometrics-getone', 'run-python'], called ['obione-morphometrics-getone', 'run-python']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1460,7 +1426,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524182" + "created_at": "2025-11-24 17:08:32.832492" } }, "off_topic_programming": { @@ -1473,23 +1439,23 @@ "rejection" ] }, - "ai_response": "I\\u2019m specialized in neuroscience and the Open Brain Platform. I can\\u2019t help with that topic, but I\\u2019d be happy to assist with brain research or platform-related questions.\\n\\nIf you want, I can help with:\\n- Building a React component that displays neural data in a cart-like UI (e.g., a \\u201ccart\\u201d of selected brain regions, experiments, or datasets).\\n- Designing UI for selecting and filtering brain-related items from your Open Brain Platform data.\\n- Integrating a frontend component with the Open Brain Platform APIs (for example, displaying items from an entitycore query as a cart).\\n\\nTell me which brain-related items you want to manage in the cart (e.g., circuits, neuron morphologies, datasets), and what actions you need (add, remove, annotate, persist to a session). If you have example data shapes (JSON structure), share them and I\\u2019ll draft a React component tailored to that.", + "ai_response": "I'm specialized in neuroscience and the Open Brain Platform. I can't help with that topic, but I'd be happy to assist with brain research or platform-related questions.", "actual_tool_calls": [], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6408833572487395, + "score": 0.751516893687972, "success": true, "threshold": 0.5, - "reason": "The actual output correctly identifies that the input is outside the evaluator's expertise in neuroscience and the Open Brain Platform, which aligns with the evaluation steps. However, it provides an extensive response that, while creative, diverges from the expected output's more concise refusal. The additional suggestions, while relevant to neuroscience, may not directly address the user's request for a shopping cart component, leading to a partial misalignment with the expected response." + "reason": "The actual output is relevant to the input in that it clearly states the responder's specialization in neuroscience and the Open Brain Platform, aligning with the expected output's focus. However, it lacks the specific phrasing of being outside the area of expertise, which is present in the expected output. Overall, the response is coherent and appropriately addresses the user's request, but it could be improved by more closely mirroring the expected structure." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools [] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools [] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1503,7 +1469,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools [] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools [] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1513,7 +1479,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524208" + "created_at": "2025-11-24 17:08:32.832506" } }, "platform_ui_simulate": { @@ -1526,23 +1492,23 @@ "rejection" ] }, - "ai_response": "## Navigation help \\u2014 platform UI\\n\\nI don't have access to the platform's UI and can't guide you through the interface.\\n\\n### Alternatives I can offer\\n- Check the platform documentation or the \\Help\\ / \\Docs\\ pages \\u2014 they usually include a \\Simulations\\ or \\Simulation section\\ guide.\\n- Look for tutorials on the platform (video or written) \\u2014 many show where simulations live and how to run them.\\n- If you can describe what you currently see (menu labels, sidebar items, or a screenshot), I can suggest likely places or interpret the labels \\u2014 but I still can't tell you exactly where UI elements are located.\\n- Contact platform support or your account administrator for direct UI guidance.\\n\\nWould you like me to search the platform documentation or tutorials for instructions on running or managing simulations?", + "ai_response": "## Navigation help \\u2014 I can't access the UI\\n\\n**I don't have access to the platform's UI and can't guide you through the interface.** I can't see the layout or where features are located, so I can't tell you where the Simulation section is in the platform.\\n\\n### Alternatives I can offer\\n- **Check the platform documentation or Help center** \\u2014 they usually have a \\Simulations\\ or \\Run simulations\\ section with screenshots and steps.\\n- **Contact platform support** or your project admin for UI-specific guidance.\\n- **If you want, I can analyze the page you're currently viewing** (to extract which entity or section is open). If that would help, tell me and I will run a page analysis to provide context-aware suggestions.", "actual_tool_calls": [], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6125907335968761, + "score": 0.6225318568915615, "success": true, "threshold": 0.5, - "reason": "The actual output is relevant to the input about finding the simulation section on the platform, as it provides alternatives for guidance. However, it lacks a direct answer to the user's question and does not fully align with the expected output structure, which is more concise. While it offers helpful suggestions, the completeness and appropriateness of the information could be improved by more directly addressing the user's request." + "reason": "The actual output addresses the user's question about finding the simulation section, but it diverges from the expected output by providing excessive alternatives and suggestions that were not part of the expected response. While it maintains coherence and relevance to the topic of the Open Brain Platform, the additional content may confuse the user rather than directly answering the query." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools [] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools [] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1556,7 +1522,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools [] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools [] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1566,7 +1532,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524232" + "created_at": "2025-11-24 17:08:32.832519" } }, "off_topic_sports": { @@ -1585,17 +1551,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.995933528886245, + "score": 1.0, "success": true, "threshold": 0.5, - "reason": "The actual output is fully relevant to the input, as it clearly states the specialization in neuroscience and the Open Brain Platform, which aligns with the expected output. The structure is identical to the expected output, and the content matches perfectly, addressing the input's request while maintaining focus on the specified field." + "reason": "The actual output perfectly matches the expected output in both content and structure, directly addressing the input while maintaining the context of neuroscience and the Open Brain Platform. It appropriately declines to answer the unrelated question about the World Cup, aligning with the evaluation steps." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools [] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools [] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1609,7 +1575,7 @@ "score": 1.0, "success": true, "threshold": 0.5, - "reason": "All expected tools [] were called (order not considered)." + "reason": "[\n\t Tool Calling Reason: All expected tools [] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1619,7 +1585,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524256" + "created_at": "2025-11-24 17:08:32.832531" } }, "sin_plot": { @@ -1639,37 +1605,44 @@ "plotting" ] }, - "ai_response": "I'm specialized in neuroscience and the Open Brain Platform. I can't help with that topic, but I'd be happy to assist with brain research or platform-related questions.", - "actual_tool_calls": [], + "ai_response": "## Plot: y = sin(x) (8 evenly spaced points from 0 to 2\\u03c0)\\n\\nBelow is the plot showing **y = sin(x)** evaluated at 8 evenly spaced points between 0 and 2\\u03c0. The numeric coordinates used are listed after the figure.\\n\\n**Points used (x in radians):**\\n\\n| Index | x (rad) | y = sin(x) |\\n|-------:|---------:|-----------:|\\n| 1 | **0.000000** | **0.000000** |\\n| 2 | **0.897598** | **0.781831** |\\n| 3 | **1.795196** | **0.974928** |\\n| 4 | **2.692794** | **0.433884** |\\n| 5 | **3.590392** | **-0.433884** |\\n| 6 | **4.487990** | **-0.974928** |\\n| 7 | **5.385587** | **-0.781831** |\\n| 8 | **6.283185** | **-0.000000** |\\n\\nThe interactive Plotly figure is displayed above. You can hover points to see values, zoom, and download the image using the Plotly toolbar. If you'd like a denser sampling, a different style (bars, markers only, stems), or the data exported (CSV), tell me which format you prefer.", + "actual_tool_calls": [ + { + "name": "run-python", + "arguments": { + "python_script": "import numpy as np\nimport plotly.graph_objects as go\n\n# generate 8 evenly spaced points from 0 to 2pi\nx = np.linspace(0, 2 * np.pi, 8)\ny = np.sin(x)\n\nfig = go.Figure()\nfig.add_trace(go.Scatter(x=x, y=y, mode='markers+lines', marker=dict(size=8), name='sin(x)'))\nfig.update_layout(title='y = sin(x) (8 evenly spaced points from 0 to 2\u03c0)',\n xaxis_title='x', yaxis_title='y',\n xaxis=dict(tickmode='array', tickvals=x, ticktext=[f'{val:.2f}' for val in x]))\n\n# Print the numeric values for clarity\nprint('\\n'.join([f'x = {xi:.6f}, y = {yi:.6f}' for xi, yi in zip(x, y)]))\n\nfig" + } + } + ], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.002931223117533942, - "success": false, + "score": 0.704552344483199, + "success": true, "threshold": 0.5, - "reason": "The actual output does not address the input request to plot y=sin(x) with 8 evenly spaced points. Instead, it diverts to a topic unrelated to the input, failing to provide any relevant information or structure that aligns with the expected output." + "reason": "The actual output is relevant to the input, as it correctly plots y = sin(x) at 8 evenly spaced points from 0 to 2\u03c0. However, while it includes a table of points and an interactive Plotly figure, the structure deviates slightly from the expected output format, particularly in the presentation of the points and the additional interactive features. The numeric values are mostly accurate but do not match the expected output's precision and formatting, which affects the overall alignment with the evaluation steps." }, { "name": "Tool Correctness", - "score": 0.0, - "success": false, + "score": 1.0, + "success": true, "threshold": 0.5, - "reason": "Incorrect tool usage: missing tools ['run-python'] and out-of-order tools ['run-python']; expected ['run-python'], called []. See more details above." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['run-python'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "No tool calls provided" + "reason": "The score is 1.00 because the input is clear and correctly specifies the task of plotting y=sin(x) with 8 evenly spaced points from 0 to 2pi." }, { "name": "Deterministic Argument Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"run-python\",\n input_parameters={\n \"python_script\": \"import numpy as np\\nimport plotly.graph_objects as go\\n\\n# generate 8 evenly spaced points from 0 to 2pi\\nx = np.linspace(0, 2*np.pi, 8)\\ny = np.sin(x)\\n\\nfig = go.Figure()\\nfig.add_trace(go.Scatter(x=x, y=y, mode='markers+lines', name='sin(x)'))\\nfig.update_layout(title='y = sin(x) (8 evenly spaced points from 0 to 2\\u03c0)',\\n xaxis_title='x', yaxis_title='y',\\n xaxis = dict(tickmode='array', tickvals=x, ticktext=[f'{val:.2f}' for val in x]))\\n\\nfig.show()\\n\\n# return numeric lists for reference\\n{\\\"x\\\": x.tolist(), \\\"y\\\": y.tolist()}\"\n }\n)]; expected ['run-python'], called []. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"run-python\",\n input_parameters={\n \"python_script\": \"import numpy as np\\nimport plotly.graph_objects as go\\n\\n# generate 8 evenly spaced points from 0 to 2pi\\nx = np.linspace(0, 2*np.pi, 8)\\ny = np.sin(x)\\n\\nfig = go.Figure()\\nfig.add_trace(go.Scatter(x=x, y=y, mode='markers+lines', name='sin(x)'))\\nfig.update_layout(title='y = sin(x) (8 evenly spaced points from 0 to 2\u03c0)',\\n xaxis_title='x', yaxis_title='y',\\n xaxis = dict(tickmode='array', tickvals=x, ticktext=[f'{val:.2f}' for val in x]))\\n\\nfig.show()\\n\\n# return numeric lists for reference\\n{\\\"x\\\": x.tolist(), \\\"y\\\": y.tolist()}\"\n }\n)]; expected ['run-python'], called ['run-python']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1679,7 +1652,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524280" + "created_at": "2025-11-24 17:08:32.832544" } }, "thalamus_id": { @@ -1698,7 +1671,7 @@ "entitycore" ] }, - "ai_response": "## Thalamus \\u2014 ID\\n\\n- **Name:** **Thalamus** \\n- **ID:** **75e3944e-8dcd-4310-a3a2-258f93de63d6** \\n- **Acronym:** **TH** \\n- **Annotation value:** **549**\\n\\nSummary: The query returned 1,798 brain-region items in total (pagination total_items = **1798**). Would you like me to list related thalamic subregions (e.g., nuclei) from the results or expand the search to additional pages?", + "ai_response": "## Thalamus \\u2014 ID\\n\\n- **Name:** **Thalamus** \\n- **Acronym:** **TH** \\n- **Annotation value:** **549** \\n- **ID:** **75e3944e-8dcd-4310-a3a2-258f93de63d6**\\n\\nSummary: I searched the brain-region ontology and found the main **Thalamus** entry above. The search returned a total of **1798** matching brain-region items (including subnuclei and related entries). Would you like me to list thalamic subregions or retrieve details for any specific nuclei?", "actual_tool_calls": [ { "name": "entitycore-brainregion-getall", @@ -1712,31 +1685,31 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.6997452237760101, + "score": 0.7087950392960021, "success": true, "threshold": 0.5, - "reason": "The actual output provides the ID of the thalamus and includes relevant details such as its name and acronym, which aligns with the expected output's intent. However, the structure is more elaborate than necessary, including additional information about related thalamic subregions and pagination, which detracts from the simplicity of the expected response." + "reason": "The actual output provides relevant information about the thalamus, including its ID, which aligns with the input request. However, the structure is more elaborate than necessary, including additional details like the acronym and a summary that diverges from the simplicity of the expected output. While the content is accurate and informative, it does not match the expected output's straightforward format." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-brainregion-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-brainregion-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "The score is 1.00 because the input is clear and correctly asks for the id of the thalamus, with no incorrect tool calls present." + "reason": "The score is 1.00 because there were no incorrect tool calls and the input was clear and specific." }, { "name": "Deterministic Argument Correctness", "score": 0.5, "success": true, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-brainregion-getall\",\n input_parameters={\n \"semantic_search\": \"thalamus\"\n }\n)]; expected ['entitycore-brainregion-getall'], called ['entitycore-brainregion-getall']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-brainregion-getall\",\n input_parameters={\n \"semantic_search\": \"thalamus\"\n }\n)]; expected ['entitycore-brainregion-getall'], called ['entitycore-brainregion-getall']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1746,7 +1719,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524306" + "created_at": "2025-11-24 17:08:32.832558" } }, "neuroscientists_search": { @@ -1766,20 +1739,12 @@ "web_search" ] }, - "ai_response": "## Overview\\n\\nI can\\u2019t run web searches from here right now, but I can give you a concise, curated list of widely recognized and highly impactful figures in computational neuroscience (historical and contemporary), with one-line notes on their major contributions. If you want, I can then attempt an online lookup and return citations and links \\u2014 tell me if you want that and I\\u2019ll try to fetch sources.\\n\\n## Selected impactful computational neuroscientists\\n\\n- **David Marr** \\u2014 foundational theoretical framework for levels of analysis in neuroscience; influential work on vision and computational theories of the brain. \\n- **Tomaso Poggio** \\u2014 computational vision, learning theory applied to biological vision; bridge between neuroscience and machine learning. \\n- **Terrence (Terry) Sejnowski** \\u2014 neural network models, statistical approaches to neural data, founder of computational neuroscience initiatives. \\n- **Karl Friston** \\u2014 predictive coding and the free-energy principle; influential modeling framework for brain function and neuroimaging analysis. \\n- **Larry F. Abbott** \\u2014 theoretical neurobiology, models of synaptic plasticity and network dynamics; influential in balanced network theory. \\n- **Haim Sompolinsky** \\u2014 theoretical neural networks and chaotic dynamics in large neural systems; influential mathematical approaches to population coding. \\n- **Wulfram Gerstner** \\u2014 spiking neuron models, synaptic plasticity rules (STDP), computational learning in spiking networks. \\n- **Peter Dayan** \\u2014 computational reinforcement learning in neuroscience; connections to dopamine and decision-making. \\n- **Bruno Olshausen** \\u2014 sparse coding models of sensory representations, influential in vision and efficient coding theories. \\n- **Olaf Sporns** \\u2014 network neuroscience and connectomics; graph-theoretic approaches to brain structure and function. \\n- **Christof Koch** \\u2014 theoretical and computational approaches to consciousness and neural correlates; influential in linking biophysics and systems-level hypotheses. \\n- **Eve Marder** \\u2014 computational and experimental work on neural circuits and neuromodulation; small-circuit models with strong theoretical insight. \\n- **Giacomo (Gio) Buzs\\u00e1ki** \\u2014 influential for oscillations and network dynamics; theoretical contributions to how rhythms coordinate computation. \\n- **Geoffrey Hinton** \\u2014 while primarily a machine learning pioneer, his work on deep learning has strongly influenced computational models used in neuroscience (representational and learning theories). \\n- **Matthias (Mattia) Rigotti** \\u2014 contributions to high-dimensional representations and mixed selectivity in cortical networks (representative of recent influential lab leads). \\n\\n## How I can help next\\n- I can attempt online searches to gather recent rankings, citation metrics, and primary sources for any subset of these names \\u2014 say which 5\\u201310 you want detailed and I\\u2019ll fetch links and short bios. \\n- I can produce a short bibliography-style summary (key papers) from my internal knowledge for any of the above researchers. \\n- I can filter by subfield (e.g., vision, learning, network dynamics, spiking models, connectomics) or time period (historical vs contemporary).\\n\\nWhich option would you like?", + "ai_response": "## Summary \\u2014 notable computational neuroscientists found online\\n\\nBelow are influential names that appear in recent literature and perspective pieces about NeuroAI / computational neuroscience. These names were taken from the search results I retrieved (links included). This is not an exhaustive ranking \\u2014 it\\u2019s a curated list of researchers who frequently appear as authors or signatories in community-wide papers about NeuroAI and large-scale computational approaches.\\n\\n![arXiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png)\\n\\n### Key names (from \\Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution\\ and related papers)\\n- **Anthony Zador** \\u2014 lead author of the NeuroAI perspective \\n [View paper (arXiv)](https://arxiv.org/abs/2210.08340) / [PDF](https://arxiv.org/pdf/2210.08340)\\n- **Sean Escola**\\n- **Blake Richards**\\n- **Bence \\u00d6lveczky**\\n- **Yoshua Bengio**\\n- **Kwabena Boahen**\\n- **Matthew Botvinick**\\n- **Dmitri Chklovskii**\\n- **Anne Churchland**\\n- **Claudia Clopath**\\n- **James DiCarlo**\\n- **Surya Ganguli**\\n- **Jeff Hawkins**\\n- **Konrad K\\u00f6rding**\\n- **Alexei Koulakov**\\n- **Yann LeCun**\\n- **Timothy Lillicrap**\\n- **Adam Marblestone**\\n- **Bruno Olshausen**\\n- **Alexandre Pouget**\\n- **Cristina Savin**\\n- **Terrence Sejnowski**\\n- **Eero Simoncelli**\\n- **Sara Solla**\\n- **David Sussillo**\\n- **Andreas S. Tolias**\\n- **Doris Tsao**\\n\\n### Additional names appearing in related literature search results\\n- **Edward S. Boyden** (appears on an arXiv article about reverse engineering nervous systems)\\n- **Kanaka Rajan**\\n- **Hanspeter Pfister**\\n- (Other co-authors and contributors found in those search results; many groups publish multi-author community papers.)\\n\\n## Links to the sources I used\\n- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution (arXiv)](https://arxiv.org/abs/2210.08340) \\u2014 list of contributors shown above. \\n PDF: https://arxiv.org/pdf/2210.08340\\n- [Catalyzing next-generation Artificial Intelligence through NeuroAI (Nature Communications perspective)](https://www.nature.com/articles/s41467-023-37180-x.pdf?error=cookies_not_supported&code=a5168282-072e-40b2-aff1-d2a68e6d665f)\\n- [The time is ripe to reverse engineer an entire nervous system (arXiv)](https://arxiv.org/abs/2308.06578)\\n\\n## Next steps / help I can offer\\n- I can read any of the papers above in full (if publicly available) and extract short bios, major contributions, or citation/contextual evidence for impact \\u2014 tell me which paper and I\\u2019ll fetch it using the read-paper tool.\\n- I can produce a categorized list (e.g., pioneers vs recent leaders; theory vs NeuroAI vs experimental computationalists) if you want a structured overview.\\n- I can expand the list (return up to 10\\u201320 more names), or build short one-paragraph summaries for specific individuals from the papers.\\n\\nWhich of those would you like me to do next?", "actual_tool_calls": [ - { - "name": "web-search-tool", - "arguments": { - "query": "most influential computational neuroscientists list influential computational neuroscience researchers names", - "num_results": 6 - } - }, { "name": "literature-search-tool", "arguments": { - "query": "most influential computational neuroscientists list influential computational neuroscience researchers", - "num_results": 8 + "query": "most influential computational neuroscientists list influential computational neuroscience pioneers prominent researchers" } } ], @@ -1787,17 +1752,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.5321950523084212, + "score": 0.6262135497223733, "success": true, "threshold": 0.5, - "reason": "The actual output provides a detailed list of impactful computational neuroscientists, which aligns with the topic of neuroscience. However, it diverges significantly from the expected output structure, which includes specific placeholders for names, institutions, and achievements, rather than a narrative format. While the content is relevant and accurate, it does not match the expected format, leading to a lower score." + "reason": "The actual output provides a comprehensive list of influential computational neuroscientists, which aligns with the input request. However, it diverges significantly from the expected output format, which specifies a structured presentation of award winners and their achievements. While the content is relevant and accurate, it lacks the specific structure and details outlined in the expected output, such as the inclusion of awards and institutions." }, { "name": "Tool Correctness", - "score": 1.0, - "success": true, + "score": 0.0, + "success": false, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['web-search-tool'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Incorrect tool usage: missing tools ['web-search-tool'] and out-of-order tools ['web-search-tool']; expected ['web-search-tool'], called ['literature-search-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1811,7 +1776,7 @@ "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"web-search-tool\",\n input_parameters={\n \"query\": \"most impactful computational neuroscientists\",\n \"numResults\": 5\n }\n)]; expected ['web-search-tool'], called ['web-search-tool', 'literature-search-tool']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"web-search-tool\",\n input_parameters={\n \"query\": \"most impactful computational neuroscientists\",\n \"numResults\": 5\n }\n)]; expected ['web-search-tool'], called ['literature-search-tool']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1821,7 +1786,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524333" + "created_at": "2025-11-24 17:08:32.832572" } }, "simulation_tutorial": { @@ -1841,23 +1806,23 @@ "platform_description" ] }, - "ai_response": "## Overview \\u2014 How to run simulations on the Open Brain Platform\\n\\nBelow is a concise, platform-aligned workflow you can follow to run simulations, plus what I can do to help you at each step.\\n\\n### 1) Choose a simulation target\\n- **Single neuron (ME-model / E-model)** \\u2014 simulate electrophysiology of one reconstructed cell.\\n- **Circuit** \\u2014 simulate a microcircuit, region, or other circuit-level model.\\n- **Simulation campaign** \\u2014 run a set of related simulations (parameter sweeps, stimuli variants).\\n\\n*What I can help with:* find suitable ME-models, circuits, or existing simulations if you tell me what species / brain region / model you want.\\n\\n### 2) Prepare the model assets\\n- For single-neuron simulations you generally need an **ME-model** (morphology + electrical properties) or an **E-model** (electrical model).\\n- For circuit simulations you need the circuit definition (populations, connectivity, extrinsic inputs) and any per-population ME/E-model mappings.\\n\\n*Tip:* The platform provides curated ME-models and circuits (EntityCore). If you want, I can search for ME-models or circuits matching a brain region or cell type.\\n\\n### 3) Define the simulation configuration\\nA simulation configuration contains:\\n- Which entity to simulate (ME-model or circuit)\\n- Simulation duration, integration timestep\\n- Stimulation protocol (current injections, synaptic input, network drive)\\n- Recording targets (which variables to save: membrane potential, synaptic currents, spike times)\\n- Random seeds, parallelization / hardware preferences, and postprocessing expectations\\n\\n*What I can do for you:* I can generate a complete simulation JSON configuration for the platform. If you want that, provide the target circuit or ME-model ID and describe the protocol (duration, stimuli, recordings, and any parameter sweeps). I will call the platform helper that produces the exact JSON configuration.\\n\\n### 4) Launch the simulation\\n- Submit the configuration to the platform\\u2019s simulation runner (single run or campaign).\\n- Monitor job status and logs while it runs.\\n- Retrieve outputs when done.\\n\\n*Note about UI:* I don\\u2019t have access to the platform UI and can\\u2019t tell you where buttons are. Check the platform documentation or contact support if you need UI-specific guidance.\\n\\n### 5) Analyze results\\n- Download raw outputs (spike trains, traces, metrics).\\n- Run built-in notebooks or export data to your analysis code.\\n- For parameter sweeps, aggregate results and visualize dependencies.\\n\\nI can help craft analysis workflows or provide code snippets for common analyses (raster plots, PSTHs, firing-rate vs current curves).\\n\\n---\\n\\n## Quick checklist to get started (choose which you need me to do)\\n1. Find or confirm a model/circuit (ME-model or circuit ID)\\n2. Describe the stimulation protocol & recordings (duration, stimuli types, which populations to record)\\n3. Decide whether you need a single run or a campaign (parameter sweep)\\n4. Ask me to generate the full simulation config JSON for the platform\\n\\nIf you want me to generate a simulation configuration now, please provide:\\n- The circuit or ME-model ID (UUID) you want to simulate, and\\n- A complete description of the desired configuration (duration, timestep, stimuli, recordings, any parameter sweeps).\\n\\nI\\u2019ll produce a ready-to-submit JSON configuration for the platform.", + "ai_response": "## Overview \\u2014 how simulation works on the Open Brain Platform\\n\\nSimulations on the Open Brain Platform are model-driven experiments you create from a digital circuit (single neuron, microcircuit, region, etc.). The typical workflow is:\\n\\n1. Select or build a circuit (an existing circuit or a circuit you create from atlas / neuron data). \\n2. Create a simulation configuration describing initialization, stimuli, recordings, and any manipulations. \\n3. Launch the simulation (the platform schedules and runs it). \\n4. Monitor progress and inspect results (spike trains, voltages, population summaries, saved artifacts). \\n5. Iterate: modify parameters, stimuli, or circuit composition as needed.\\n\\nBelow I give a practical, step-by-step guide and notes on the options you\\u2019ll commonly use.\\n\\n---\\n\\n## What you need before running a simulation\\n\\n- **Circuit ID** (UUID) or choose/create a circuit on the platform. Circuits have scales such as *single*, *pair*, *microcircuit*, *region*, etc. \\n- An idea of what you want to measure (e.g., membrane potential traces, spikes, population firing rates, synaptic currents). \\n- Any experiment-specific manipulations: synaptic blockers, changes in extracellular calcium, optogenetic stimuli, changed connection probabilities, etc.\\n\\nIf you want, I can help construct the simulation configuration for a given circuit \\u2014 just provide the circuit UUID and what you want to do.\\n\\n---\\n\\n## Types of simulations you can run\\n\\n- **Single-neuron simulations**: intracellular current steps, channel perturbations, I\\u2013V curves. \\n- **Paired neuron / small circuits**: synaptic pairing, paired-pulse, STDP protocols. \\n- **Microcircuit simulations** (20+ cells): network dynamics, population responses, brief stimuli. \\n- **Region / system simulations**: large-scale network activity, LFP proxies, population statistics.\\n\\n---\\n\\n## Key configuration components (what to specify)\\n\\nWhen you create a simulation config you usually specify only the things that differ from defaults. Typical fields you will set:\\n\\n- **initialize** \\n - type (e.g., deterministic / randomized) \\n - v_init (resting membrane potential) \\n - extracellular calcium concentration (if relevant) \\n - random_seed (for reproducibility) \\n- **simulation length** (ms or seconds) \\n- **neuron_sets / node_sets** \\u2014 which neuron populations to include/record from (IDs or labels) \\n- **stimuli** \\n - current clamps, synaptic inputs, optogenetic drives \\n - timing, amplitude, waveform \\n- **recordings** \\n - what to record (spikes, somatic Vm, compartments, synaptic currents) \\n - sampling frequency / channels to store \\n- **synaptic_manipulations** (optional) \\n - block or scale particular synapse types, change weights, change delays \\n- **timestamps / metadata** \\n - campaign name, description, tags for provenance\\n\\nNote: The platform provides reasonable defaults for many parameters; you only need to override items important for your experiment.\\n\\n---\\n\\n## How to create a valid simulation configuration (programmatic / API)\\n\\n- The platform expects a structured simulation configuration (JSON). \\n- There is a helper tool on the platform that generates a valid config from a natural-language description \\u2014 it will fill defaults and ensure the JSON is valid. If you want, tell me: (a) the circuit UUID, and (b) a short description of the experiment (stimulus, recordings, duration, any manipulations). I can generate the config for you.\\n\\nIf you want me to do that now, please provide the circuit UUID and a brief description of exactly what you want to simulate.\\n\\n---\\n\\n## Launching, monitoring, and retrieving results\\n\\n- Once the config is submitted, the platform schedules the job and runs it. \\n- You can monitor status (queued, running, success/failure) and view logs/status messages (the platform exposes simulation metadata and timestamps). \\n- After completion, results are stored as simulation artifacts: time series, spike lists, summary files, and plots. You can download them or use built-in viewers and analysis notebooks.\\n\\nProgrammatic endpoints exist to list simulations, get one simulation\\u2019s details, and list single-neuron simulations; these let you fetch metadata and results programmatically.\\n\\n---\\n\\n## Best practices and tips\\n\\n- Use a fixed random seed for reproducibility if you need exact repeats. \\n- Start with short runs or fewer recorded channels to debug config and stimuli before launching a long, expensive run. \\n- Record only the variables you need (high sampling / many compartments increases storage and runtime). \\n- Use population-level recordings (mean firing rates, LFP proxies) when scaling up to large circuits to reduce storage. \\n- Keep a clear metadata/campaign description for provenance and sharing.\\n\\n---\\n\\n## Common pitfalls\\n\\n- Forgetting to set the correct node_set / neuron_set ids \\u2014 the simulation may run but record nothing. \\n- Overly long simulation time or too many recorded traces leads to large outputs and slow transfer. \\n- Not setting initial conditions (v_init) when you expect a particular baseline state. \\n- Not specifying the extracellular calcium or receptor-specific manipulations when testing synaptic scaling.\\n\\n---\\n\\n## What I can do for you right now\\n\\n- Generate a complete simulation configuration if you give me:\\n - **Circuit UUID** (required) \\n - Short description: simulation length, stimuli (type, timing, amplitude), recordings you want (spikes, Vm, specific populations), and any manipulations (synapse block, calcium change, random_seed). \\n- Review an existing simulation config and suggest optimizations. \\n- Explain how to interpret common outputs (raster plots, firing-rate histograms, Vm traces).\\n\\nI can\\u2019t see or interact with the platform UI itself, so I won\\u2019t be able to walk you through buttons or screens. If you want me to generate a simulation config now, paste the circuit UUID and describe the experiment you\\u2019d like to run.", "actual_tool_calls": [], "results": { "metrics": [ { "name": "Correctness [GEval]", - "score": 0.5615612029368113, + "score": 0.7131396064894693, "success": true, "threshold": 0.5, - "reason": "The actual output provides a detailed workflow for running simulations on the Open Brain Platform, which is relevant to the input question. However, it lacks a specific focus on simulating a single neuron model as outlined in the expected output. While it covers multiple aspects of simulation, it does not follow the expected structure closely, missing key steps like accessing the Virtual Lab and managing projects. The content is accurate and relevant but does not fully align with the expected output's intent and structure." + "reason": "The actual output provides a comprehensive overview of simulating on the Open Brain Platform, covering various aspects such as circuit selection, simulation configuration, and monitoring results. However, it lacks the specific step-by-step guidance for simulating a single neuron model as outlined in the expected output. While it addresses relevant topics, it does not follow the exact structure or focus on the single neuron model simulation, which is a key detail in the expected output." }, { "name": "Tool Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incorrect tool usage: missing tools ['obi-expert'] and out-of-order tools ['obi-expert']; expected ['obi-expert'], called []. See more details above." + "reason": "[\n\t Tool Calling Reason: Incorrect tool usage: missing tools ['obi-expert'] and out-of-order tools ['obi-expert']; expected ['obi-expert'], called []. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1871,7 +1836,7 @@ "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"obi-expert\",\n input_parameters={\n \"document_type\": \"tutorial\",\n \"query\": \"simulate\"\n }\n)]; expected ['obi-expert'], called []. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"obi-expert\",\n input_parameters={\n \"document_type\": \"tutorial\",\n \"query\": \"simulate\"\n }\n)]; expected ['obi-expert'], called []. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1881,7 +1846,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524358" + "created_at": "2025-11-24 17:08:32.832585" } }, "software_docs_obione": { @@ -1904,14 +1869,15 @@ "mcp" ] }, - "ai_response": "## Single-cell simulation example using **obi_one**\\n\\nBelow is a concise, runnable example (Python) that shows the typical flow to run a single-cell (MEModel) simulation using the **obi_one** package. It follows the canonical steps:\\n\\n- authenticate & initialize client \\n- select a **MEModel** (by ID or interactively) \\n- build a validated simulation configuration (timestamps, stimulus, recordings, initialize) \\n- generate/run the grid-scan and execute the simulation with the BlueCelluLab backend\\n\\nReplace placeholders (MEMODEL_ID, token/auth calls, output paths) with your project-specific values.\\n\\n```python\\n# Single-cell MEModel simulation example using obi_one\\n# Requires: obi_one, entitysdk, obi_auth, obi_notebook, and the BlueCelluLab simulator available in your environment.\\n\\nfrom pathlib import Path\\nimport obi_one as obi\\nfrom entitysdk import Client\\nfrom obi_auth import get_token\\nfrom obi_notebook import get_projects, get_entities\\n\\n# === 0. Authentication & DB client setup ===\\n# Obtain token and project context (adjust environment/auth_mode as needed)\\ntoken = get_token(environment=\\production\\\", auth_mode=\\daf\\\") # adapt to your auth\\nproject_context = get_projects.get_projects(token)\\n\\n# Initialize entitysdk Client to interact with platform (used by grid scan execution)\\ndb_client = Client(environment=\\production\\\", project_context=project_context, token_manager=token)\\n\\n# === 1. Select an existing MEModel entity ===\\n# Option A: use a known MEModel ID\\nMEMODEL_ID = \\\"\\ # <<< REPLACE with your MEModel ID\\n\\n# Option B (interactive helper): get_entities.get_entities(...) - omitted for brevity\\n\\n# === 2. Build MEModel simulation configuration ===\\nsim_duration = 3000.0 # ms\\n\\n# Create an empty form config for MEModel single-cell simulations\\nsim_conf = obi.MEModelSimulationScanConfig.empty_config()\\n\\n# Campaign info\\ninfo = obi.Info(campaign_name=\\Single-cell MEModel Simulation\\\",\\n campaign_description=\\Soma current-clamp on single MEModel\\\")\\nsim_conf.set(info, name=\\info\\\")\\n\\n# Regular timestamps (used by stimuli/recordings)\\nregular_timestamps = obi.RegularTimestamps(start_time=0.0, number_of_repetitions=1, interval=sim_duration)\\nsim_conf.add(regular_timestamps, name=\\RegularTimestamps\\\")\\n\\n# Example stimulus: somatic constant current clamp (duration and amplitude adjustable)\\nstimulus = obi.ConstantCurrentClampSomaticStimulus(timestamps=regular_timestamps.ref,\\n duration=2000.0,\\n amplitude=0.5) # nA (or units as expected by the model)\\nsim_conf.add(stimulus, name=\\CurrentClampInput\\\")\\n\\n# Recording: Soma voltage (and optionally time-windowed recording)\\nvoltage_recording = obi.SomaVoltageRecording()\\nsim_conf.add(voltage_recording, name=\\VoltageRecording\\\")\\n\\ntime_window_voltage = obi.TimeWindowSomaVoltageRecording(start_time=0.0, end_time=2000.0)\\nsim_conf.add(time_window_voltage, name=\\TimeWindowVoltageRecording\\\")\\n\\n# Initialization block: point to MEModel by ID and set simulation length\\ninitialize = obi.MEModelSimulationScanConfig.Initialize(circuit=obi.MEModelFromID(id_str=MEMODEL_ID),\\n simulation_length=sim_duration)\\nsim_conf.set(initialize, name=\\initialize\\\")\\n\\n# Validate config (constructs a fully validated simulation config object)\\nvalidated_sim_conf = sim_conf.validated_config()\\n\\n# === 3. Wrap into a GridScan and execute ===\\n# GridScan wraps the validated config into a set of run directories (even if only one)\\noutput_root = Path.cwd() / \\obi-output\\ / \\memodel_single_cell\\\"\\ngrid_scan = obi.GridScanGenerationTask(form=validated_sim_conf,\\n coordinate_directory_option=\\ZERO_INDEX\\\",\\n output_root=str(output_root))\\n\\n# Create the run directories and register tasks in DB\\ngrid_scan.execute(db_client=db_client)\\n\\n# Launch the generated simulation tasks (uses the platform execution machinery)\\nobi.run_tasks_for_generated_scan(grid_scan, db_client=db_client)\\n\\n# === 4. Run the simulation using the BlueCelluLab backend (alternative direct run) ===\\n# If you prefer to call the execution helper with the simulation_config path produced by the grid scan:\\nsimulation_config_path = grid_scan.single_configs[0].coordinate_output_root / \\simulation_config.json\\\"\\n\\nfrom obi_one.scientific.library.simulation_execution import run\\nrun(simulation_config=simulation_config_path,\\n simulator=\\bluecellulab\\\", # or \\neurodamus\\ if available\\n save_nwb=False)\\n\\n# === 5. Inspect outputs ===\\n# The simulation outputs (reports, sonata folder, logs) will be under:\\nprint(\\Outputs in:\\\", grid_scan.single_configs[0].coordinate_output_root)\\n```\\n\\nNotes and tips\\n- **MEMODEL_ID**: you must provide a valid MEModel ID from the platform (or use the interactive get_entities helper to pick one).\\n- If you need synaptic inputs, use MEModelWithSynapsesCircuitSimulationScanConfig and add Poisson or spike stimuli (see obi_one examples).\\n- The example uses the BlueCelluLab backend via obi_one's run helper. Ensure BlueCelluLab is installed and accessible in your execution environment.\\n- The grid-scan machinery will create a folder per coordinate (even for a single coordinate). Use the produced simulation_config.json to re-run or inspect the run.\\n- For post-simulation analysis, the examples use bluepysnap to read spikes and reports (not shown above).\\n\\nReferences / example sources\\n- Example notebook with single-cell MEModel simulations in the obi-one repo: [obi-one examples - single cell simulations](https://github.com/openbraininstitute/obi-one/tree/main/examples/F_single_cell_simulations)\\n\\nIf you want, I can:\\n- produce a variant that uses a MEModelWithSynapses and Poisson input;\\n- show how to fetch MEModel IDs interactively with get_entities and filter by species/metadata;\\n- or provide an example of reading the NWB / bluepysnap outputs after the run. Which would you prefer?", + "ai_response": "## Single-cell simulation example using **obi-one**\\n\\nBelow is a concise, runnable-style example that shows the typical workflow for running a single-cell (MEModel) simulation with **obi-one**: authenticate & initialize the client, select an MEModel (by ID or interactively), build a simulation configuration (timestamps, stimulus, recordings), validate the config, generate a grid-scan (even if only a single coordinate), and execute the simulation tasks.\\n\\n> Prerequisites:\\n- Python environment with `obi_one` (obi-one), `entitysdk`, and `obi_notebook` packages installed.\\n- Valid credentials for the Open Brain Platform (token obtained via platform auth, here shown with the helper from examples).\\n\\n### Full example script\\n\\n```python\\n# Single-cell MEModel simulation with obi-one\\n# Adapted from obi-one examples (examples/F_single_cell_simulations/entitysdk_memodel_simulation.ipynb)\\n\\nfrom entitysdk import Client\\nfrom obi_auth import get_token\\nfrom obi_notebook import get_projects\\nimport obi_one as obi\\n\\n# --- 1) Authenticate and initialize DB client ---------------------------------\\n# Get token (production, DAF auth in example)\\ntoken = get_token(environment=\\production\\\", auth_mode=\\daf\\\")\\n\\n# Get project context (interactive helper from examples)\\nproject_context = get_projects.get_projects(token)\\n\\n# Initialize entitysdk client (db_client) used by obi-one utilities\\ndb_client = Client(environment=\\production\\\", project_context=project_context, token_manager=token)\\n\\n\\n# --- 2) Select MEModel to simulate -------------------------------------------\\n# Option A: Use a specific MEModel ID (replace with a real MEModel ID)\\nmemodel_id = \\\"\\ # <<< Replace with actual MEModel ID\\n\\n# Option B (interactive helper): list/select memodel entities (examples show get_entities helper)\\n# memodel_ids = get_entities.get_entities(\\memodel\\\", token, [], project_context=project_context, multi_select=False, default_scale=\\small\\\")\\n\\n\\n# --- 3) Build simulation configuration ---------------------------------------\\nsim_duration = 3000.0 # ms\\n\\n# Create an empty MEModelSimulationScanConfig\\nsim_conf = obi.MEModelSimulationScanConfig.empty_config()\\n\\n# Info\\ninfo = obi.Info(campaign_name=\\MEModel Single-Cell Simulation\\\",\\n campaign_description=\\Single-cell simulation with constant current clamp\\\")\\nsim_conf.set(info, name=\\info\\\")\\n\\n# Regular timestamps: here we create one repetition spanning the sim duration\\nregular_timestamps = obi.RegularTimestamps(start_time=0.0,\\n number_of_repetitions=1,\\n interval=sim_duration)\\nsim_conf.add(regular_timestamps, name=\\RegularTimestamps\\\")\\n\\n# Stimulus: Constant current clamp to soma (single amplitude)\\nstimulus = obi.ConstantCurrentClampSomaticStimulus(\\n timestamps=regular_timestamps.ref,\\n duration=2000.0, # ms (duration of injection)\\n amplitude=0.5, # nA\\n timestamp_offset=0.0\\n)\\nsim_conf.add(stimulus, name=\\CurrentClampInput\\\")\\n\\n# Record soma voltage for the time window of the stimulus\\nvoltage_recording = obi.SomaVoltageRecording()\\nsim_conf.add(voltage_recording, name=\\VoltageRecording\\\")\\n\\ntime_window_voltage_recording = obi.TimeWindowSomaVoltageRecording(start_time=0.0, end_time=2000.0)\\nsim_conf.add(time_window_voltage_recording, name=\\TimeWindowVoltageRecording\\\")\\n\\n# Initialization: point the simulation to the MEModel by ID\\ninitialize = obi.MEModelSimulationScanConfig.Initialize(\\n circuit=obi.MEModelFromID(id_str=memodel_id),\\n simulation_length=sim_duration\\n)\\nsim_conf.set(initialize, name=\\initialize\\\")\\n\\n\\n# --- 4) Validate the config --------------------------------------------------\\nvalidated_sim_conf = sim_conf.validated_config()\\n\\n\\n# --- 5) Generate and execute a grid scan (even for single coordinate) --------\\n# Create the GridScanGenerationTask. Output root can be any writeable path.\\ngrid_scan = obi.GridScanGenerationTask(\\n form=validated_sim_conf,\\n coordinate_directory_option=\\ZERO_INDEX\\\",\\n output_root=\\\"./obi-output/memodel_simulations/grid_scan\\\"\\n)\\n\\n# Optional: inspect parameters that will generate coordinates\\ngrid_scan.multiple_value_parameters(display=True)\\ngrid_scan.coordinate_parameters(display=True)\\n\\n# Execute generation (this writes scan/coordinate directories to output_root)\\ngrid_scan.execute(db_client=db_client)\\n\\n# Run tasks for the generated scan (this will schedule/execute simulations)\\nobi.run_tasks_for_generated_scan(grid_scan, db_client=db_client)\\n\\n\\n# --- 6) (Optional) Inspect outputs -------------------------------------------\\n# Output files (voltages, spikes, logs) are written into the grid scan output directories.\\n# Use the platform tools / artifacts viewer or file system to open generated HDF5 results.\\n```\\n\\n### Notes and tips\\n- Replace `` with the actual MEModel identifier you want to simulate. The example code in the obi-one repo also demonstrates interactive selection of available MEModels.\\n- If you want parameter scans (multiple amplitudes, durations, timestamps, or neuron selections), pass lists or `obi.FloatRange(...)` to the stimulus fields \\u2014 each distinct value multiplies coordinates in the grid scan.\\n- Use `grid_scan.multiple_value_parameters(display=True)` and `grid_scan.coordinate_parameters(display=True)` to preview how many coordinates will be generated before executing.\\n- The examples in the obi-one repository include notebooks demonstrating retrieving cell morphologies and other helper utilities; the script above is a minimal end-to-end workflow adapted from those examples.\\n\\nIf you want, I can:\\n- Provide a variant that runs a small amplitude-scan (e.g., amplitude = [0.1, 0.3, 0.5] nA) to produce multiple coordinates, or\\n- Show how to select a MEModel interactively from available entities (using the example get_entities helper shown in the examples).", "actual_tool_calls": [ { "name": "get-obi-software-docs", "arguments": { "context7CompatibleLibraryID": "openbraininstitute/obi-one", "mode": "code", - "topic": "single cell simulation example" + "topic": "single cell", + "page": 1 } } ], @@ -1919,17 +1885,17 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.8463566651362253, + "score": 0.7857044815425586, "success": true, "threshold": 0.5, - "reason": "The actual output provides a comprehensive and relevant code example for creating a single-cell simulation using the obi_one package, aligning well with the input request. It includes detailed steps for authentication, configuration, and execution, which are essential for users. However, the structure could be improved to match the expected output more closely, particularly in the organization of sections and the clarity of placeholder instructions. Some minor formatting issues and inconsistencies in naming conventions were also noted, which detracted slightly from the overall clarity." + "reason": "The actual output provides a detailed code example for creating a single-cell simulation using the obi-one package, closely aligning with the expected output's structure and content. It includes necessary steps such as selecting an MEModel, building a simulation configuration, and executing the simulation. However, it lacks some clarity in the organization of sections and minor details, such as explicitly mentioning the BlueCelluLab backend, which is present in the expected output. Overall, it effectively addresses the key topics related to the Open Brain Platform and neuroscience." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['get-obi-software-docs'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['get-obi-software-docs'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", @@ -1943,7 +1909,7 @@ "score": 0.0, "success": false, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"get-obi-software-docs\",\n input_parameters={\n \"context7CompatibleLibraryID\": \"/openbraininstitute/obi-one\",\n \"topic\": \"Single Cell Simulation Example\",\n \"tokens\": 2000\n }\n)]; expected ['get-obi-software-docs'], called ['get-obi-software-docs']. See more details above." + "reason": "[\n\t Tool Calling Reason: Incomplete tool usage: missing tools [ToolCall(\n name=\"get-obi-software-docs\",\n input_parameters={\n \"context7CompatibleLibraryID\": \"/openbraininstitute/obi-one\",\n \"topic\": \"Single Cell Simulation Example\",\n \"tokens\": 2000\n }\n)]; expected ['get-obi-software-docs'], called ['get-obi-software-docs']. See more details above.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", @@ -1953,7 +1919,7 @@ "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524385" + "created_at": "2025-11-24 17:08:32.832597" } }, "hippocampus_morphologies": { @@ -1978,7 +1944,7 @@ "entitycore" ] }, - "ai_response": "## Hippocampus \\u2014 5 cell morphologies\\n\\nBelow are five hippocampal morphologies retrieved for you.\\n\\n### 1) **Name:** 6-human-hcca1-idab1-porta5-sec1-cel2-soma-z-corr-1-7 \\n**ID:** **ff01b59a-77f5-46cc-b35f-e006c931326e** \\n**Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\u2014 **(annotation_value: 407)** \\n**Mtype:** **Pyramidal Neuron** \\n**Subject species:** *Homo sapiens* \\n**License:** CC BY 4.0 \\n**Assets (examples):**\\n- SWC: (id: **b86ad911-4c4a-4c8e-941b-7b95776cba64**) filename: 6-human-hcca1-... .swc \\n- ASC: (id: **6e29b47b-38f3-4a29-bb23-fe6e5e779aba**) filename: 6-human-hcca1-... .asc \\n- H5: (id: **e3bb2cc6-1fa0-4f67-bb9b-d950a8988488**) filename: 6-human-hcca1-... .h5 \\n**Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides-Piccione et al., 2020. \\n[View Details](https://openbraininstitute.org/app/entity/ff01b59a-77f5-46cc-b35f-e006c931326e)\\n\\n---\\n\\n### 2) **Name:** 54-human-idab2-hcca-porta4-cel4-S-soma-z-corr-1-7 \\n**ID:** **8593523b-55ef-4017-8c9d-927e4b534b5a** \\n**Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\u2014 **(annotation_value: 407)** \\n**Mtype:** **Pyramidal Neuron** \\n**Subject species:** *Homo sapiens* \\n**License:** CC BY 4.0 \\n**Assets (examples):**\\n- ASC: (id: **9d11dac5-e997-40ce-b555-33afe4033272**) filename: 54-human-... .asc \\n- SWC: (id: **e9884b36-6b22-4857-afb0-1d62d1d43a62**) filename: 54-human-... .swc \\n- H5: (id: **cf648a62-fd35-4f5a-a540-34c96d3fdd87**) filename: 54-human-... .h5 \\n**Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides-Piccione et al., 2020. \\n[View Details](https://openbraininstitute.org/app/entity/8593523b-55ef-4017-8c9d-927e4b534b5a)\\n\\n---\\n\\n### 3) **Name:** 50-human-hcca1-idab2-porta1-sec1-cel15-soma-z-corr-1-7 \\n**ID:** **9adaa03e-fb17-4336-ae07-b67b261625d2** \\n**Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\u2014 **(annotation_value: 407)** \\n**Mtype:** **Pyramidal Neuron** \\n**Subject species:** *Homo sapiens* \\n**License:** CC BY 4.0 \\n**Assets (examples):**\\n- H5: (id: **ee3af558-fd10-458f-accc-15c947731b45**) filename: 50-human-... .h5 \\n- ASC: (id: **c1636e46-ed4d-4f79-a617-55f84fda245a**) filename: 50-human-... .asc \\n- SWC: (id: **335e66d4-19c3-4c63-a3ab-e0358a841c1a**) filename: 50-human-... .swc \\n**Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides-Piccione et al., 2020. \\n[View Details](https://openbraininstitute.org/app/entity/9adaa03e-fb17-4336-ae07-b67b261625d2)\\n\\n---\\n\\n### 4) **Name:** 48-human-hcca1-idab2-porta1-sec1-cel12-soma-z-corr-1-7 \\n**ID:** **b5914f7e-65a8-426f-aa09-4cea01ed6395** \\n**Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\u2014 **(annotation_value: 407)** \\n**Mtype:** **Pyramidal Neuron** \\n**Subject species:** *Homo sapiens* \\n**License:** CC BY 4.0 \\n**Assets (examples):**\\n- ASC: (id: **a8e98103-abd5-4121-ba5d-9d3e76eae2d9**) filename: 48-human-... .asc \\n- SWC: (id: **8e0212b2-5ac8-4d99-b96e-6520870ba737**) filename: 48-human-... .swc \\n- H5: (id: **61bf76ea-7922-4df3-b2bf-e1af60031c11**) filename: 48-human-... .h5 \\n**Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides-Piccione et al., 2020. \\n[View Details](https://openbraininstitute.org/app/entity/b5914f7e-65a8-426f-aa09-4cea01ed6395)\\n\\n---\\n\\n### 5) **Name:** 46-human-hcca1-idab2-porta1-sec1-cel7-soma-z-corr-1-7 \\n**ID:** **33696a03-9e04-43bd-b893-fa65387e2e5f** \\n**Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\u2014 **(annotation_value: 407)** \\n**Mtype:** **Pyramidal Neuron** \\n**Subject species:** *Homo sapiens* \\n**License:** CC BY 4.0 \\n**Assets (examples):**\\n- H5: (id: **54ed991f-fdde-45e5-85e2-b1fd33b29748**) filename: 46-human-... .h5 \\n- ASC: (id: **94723b49-57e2-427e-b359-6bc339f1ced5**) filename: 46-human-... .asc \\n- SWC: (id: **c067c026-d1e0-4094-80a6-46756107bd8a**) filename: 46-human-... .swc \\n**Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides-Piccione et al., 2020. \\n[View Details](https://openbraininstitute.org/app/entity/33696a03-9e04-43bd-b893-fa65387e2e5f)\\n\\n---\\n\\nSummary:\\n- Source brain region used: **Hippocampal region** (descendants) \\u2014 total hippocampal morphologies available: **326** (first page returned 5 items). \\n- All five morphologies are human CA1 pyramidal neurons and publicly accessible (CC BY 4.0).\\n\\nWould you like me to:\\n- download the SWC files for these five morphologies, or \\n- compute morphometrics (e.g., total_length, soma_radius) for each?", + "ai_response": "## Hippocampus \\u2014 5 morphologies retrieved\\n\\nBelow are 5 hippocampal cell morphologies (no confirmations). Each entry includes key metadata and a link to the entity page.\\n\\n### 1) **6-human-hcca1-idab1-porta5-sec1-cel2-soma-z-corr-1-7**\\n- **ID:** **ff01b59a-77f5-46cc-b35f-e006c931326e** \\n- **Name:** 6-human-hcca1-idab1-porta5-sec1-cel2-soma-z-corr-1-7 \\n- **Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\n- **Subject / Species:** *Homo sapiens* (Generic Homo sapiens) \\n- **Mtype:** **Pyramidal Neuron** \\n- **Primary asset (morphology):** **b86ad911-4c4a-4c8e-941b-7b95776cba64** (path: 6-human\\u2011... .swc) \\n- **License:** CC BY 4.0 \\n- **Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides\\u2011Piccione et al., 2020. \\n- [View Details](https://openbraininstitute.org/app/entity/ff01b59a-77f5-46cc-b35f-e006c931326e)\\n\\n---\\n\\n### 2) **54-human-idab2-hcca-porta4-cel4-S-soma-z-corr-1-7**\\n- **ID:** **8593523b-55ef-4017-8c9d-927e4b534b5a** \\n- **Name:** 54-human-idab2-hcca-porta4-cel4-S-soma-z-corr-1-7 \\n- **Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\n- **Subject / Species:** *Homo sapiens* (Generic Homo sapiens) \\n- **Mtype:** **Pyramidal Neuron** \\n- **Primary asset (morphology):** **e9884b36-6b22-4857-afb0-1d62d1d43a62** (path: 54-human\\u2011... .swc) \\n- **License:** CC BY 4.0 \\n- **Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides\\u2011Piccione et al., 2020. \\n- [View Details](https://openbraininstitute.org/app/entity/8593523b-55ef-4017-8c9d-927e4b534b5a)\\n\\n---\\n\\n### 3) **50-human-hcca1-idab2-porta1-sec1-cel15-soma-z-corr-1-7**\\n- **ID:** **9adaa03e-fb17-4336-ae07-b67b261625d2** \\n- **Name:** 50-human-hcca1-idab2-porta1-sec1-cel15-soma-z-corr-1-7 \\n- **Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\n- **Subject / Species:** *Homo sapiens* (Generic Homo sapiens) \\n- **Mtype:** **Pyramidal Neuron** \\n- **Primary asset (morphology):** **335e66d4-19c3-4c63-a3ab-e0358a841c1a** (path: 50-human\\u2011... .swc) \\n- **License:** CC BY 4.0 \\n- **Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides\\u2011Piccione et al., 2020. \\n- [View Details](https://openbraininstitute.org/app/entity/9adaa03e-fb17-4336-ae07-b67b261625d2)\\n\\n---\\n\\n### 4) **48-human-hcca1-idab2-porta1-sec1-cel12-soma-z-corr-1-7**\\n- **ID:** **b5914f7e-65a8-426f-aa09-4cea01ed6395** \\n- **Name:** 48-human-hcca1-idab2-porta1-sec1-cel12-soma-z-corr-1-7 \\n- **Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\n- **Subject / Species:** *Homo sapiens* (Generic Homo sapiens) \\n- **Mtype:** **Pyramidal Neuron** \\n- **Primary asset (morphology):** **8e0212b2-5ac8-4d99-b96e-6520870ba737** (path: 48-human\\u2011... .swc) \\n- **License:** CC BY 4.0 \\n- **Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides\\u2011Piccione et al., 2020. \\n- [View Details](https://openbraininstitute.org/app/entity/b5914f7e-65a8-426f-aa09-4cea01ed6395)\\n\\n---\\n\\n### 5) **46-human-hcca1-idab2-porta1-sec1-cel7-soma-z-corr-1-7**\\n- **ID:** **33696a03-9e04-43bd-b893-fa65387e2e5f** \\n- **Name:** 46-human-hcca1-idab2-porta1-sec1-cel7-soma-z-corr-1-7 \\n- **Brain region:** Field CA1, pyramidal layer (**CA1sp**) \\n- **Subject / Species:** *Homo sapiens* (Generic Homo sapiens) \\n- **Mtype:** **Pyramidal Neuron** \\n- **Primary asset (morphology):** **c067c026-d1e0-4094-80a6-46756107bd8a** (path: 46-human\\u2011... .swc) \\n- **License:** CC BY 4.0 \\n- **Description:** Morphological reconstruction of a pyramidal neuron from human CA1. Part of Benavides\\u2011Piccione et al., 2020. \\n- [View Details](https://openbraininstitute.org/app/entity/33696a03-9e04-43bd-b893-fa65387e2e5f)\\n\\n---\\n\\nSummary: returned 5 hippocampal CA1 pyramidal neuron morphologies (public, CC BY 4.0). Would you like me to download any of the SWC/ASC/H5 assets or compute morphometrics (e.g., total_length, max_radial_distance) for any of these?", "actual_tool_calls": [ { "name": "entitycore-brainregion-getall", @@ -1989,8 +1955,7 @@ { "name": "entitycore-cellmorphology-getall", "arguments": { - "within_brain_region_brain_region_id": "08f3ff18-88db-48cc-bac5-d58c2fd16ec8", - "within_brain_region_direction": "descendants" + "within_brain_region_brain_region_id": "08f3ff18-88db-48cc-bac5-d58c2fd16ec8" } } ], @@ -1998,41 +1963,41 @@ "metrics": [ { "name": "Correctness [GEval]", - "score": 0.3474612115045808, + "score": 0.4102291016664522, "success": false, "threshold": 0.5, - "reason": "The actual output provides detailed information about five hippocampal morphologies, which is relevant to the input request. However, it fails to align with the expected output structure, which specifies a focus on rodent morphologies and includes placeholders for contributors and descriptions that are not present in the actual output. Additionally, the actual output lists human CA1 pyramidal neurons instead of the requested rodent morphologies, indicating a significant deviation from the expected content." + "reason": "The actual output provides five hippocampal morphologies, which is relevant to the input request. However, it deviates significantly from the expected output structure, lacking sections for contributors and morphological types, and it focuses on human data rather than the specified rodent morphologies. While the content is accurate and detailed, it does not align well with the expected format." }, { "name": "Tool Correctness", "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order." + "reason": "[\n\t Tool Calling Reason: Correct ordering: all expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called in the correct order.\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Argument Correctness", "score": 0.0, "success": false, "threshold": 0.5, - "reason": "The score is 0.00 because there were no input parameters provided for the tool calls to fetch morphologies, which is essential for the task. This lack of input directly impacts the ability to retrieve the necessary information." + "reason": "The score is 0.00 because the tool calls failed to specify and address the request for 5 morphologies from the hippocampus." }, { "name": "Deterministic Argument Correctness", - "score": 0.75, + "score": 1.0, "success": true, "threshold": 0.5, - "reason": "Incomplete tool usage: missing tools [ToolCall(\n name=\"entitycore-cellmorphology-getall\",\n input_parameters={\n \"within_brain_region_brain_region_id\": \"08f3ff18-88db-48cc-bac5-d58c2fd16ec8\"\n }\n)]; expected ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'], called ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall']. See more details above." + "reason": "[\n\t Tool Calling Reason: All expected tools ['entitycore-brainregion-getall', 'entitycore-cellmorphology-getall'] were called (order not considered).\n\t Tool Selection Reason: No available tools were provided to assess tool selection criteria\n]\n" }, { "name": "Overall Argument Correctness", - "score": 0.75, + "score": 1.0, "success": true, "threshold": 0.5, "reason": "" } ], - "created_at": "2025-11-24 15:22:22.524412" + "created_at": "2025-11-24 17:08:32.832610" } } -} \ No newline at end of file +} diff --git a/backend/eval/output/scores.json b/backend/eval/output/scores.json index 20bf2795..ae07700a 100644 --- a/backend/eval/output/scores.json +++ b/backend/eval/output/scores.json @@ -3,7 +3,7 @@ "metrics_df": [ { "test_name": "cerebellum_morphologies", - "Correctness [GEval]": 0.5898276636506745, + "Correctness [GEval]": 0.5622758377449195, "Tool Correctness": 1.0, "Argument Correctness": 0.0, "Deterministic Argument Correctness": 1.0, @@ -11,7 +11,7 @@ }, { "test_name": "cerebellum_morphologies_descendants", - "Correctness [GEval]": 0.696975831164821, + "Correctness [GEval]": 0.7863307857283821, "Tool Correctness": 1.0, "Argument Correctness": 0.5, "Deterministic Argument Correctness": 1.0, @@ -19,15 +19,15 @@ }, { "test_name": "connectivity_metrics", - "Correctness [GEval]": 0.7988542312874786, + "Correctness [GEval]": 0.8150026799242844, "Tool Correctness": 1.0, - "Argument Correctness": 0.3333333333333333, + "Argument Correctness": 0.5, "Deterministic Argument Correctness": 1.0, "Overall Argument Correctness": 1.0 }, { "test_name": "connectivity_metrics_extra_filters", - "Correctness [GEval]": 0.848027598936177, + "Correctness [GEval]": 0.8142110364455851, "Tool Correctness": 1.0, "Argument Correctness": 0.5, "Deterministic Argument Correctness": 1.0, @@ -35,15 +35,15 @@ }, { "test_name": "cortex_morphologies", - "Correctness [GEval]": 0.62348770330439, - "Tool Correctness": 1.0, + "Correctness [GEval]": 0.3186384511202486, + "Tool Correctness": 0.5, "Argument Correctness": 0.0, - "Deterministic Argument Correctness": 0.75, - "Overall Argument Correctness": 0.75 + "Deterministic Argument Correctness": 0.0, + "Overall Argument Correctness": 0.0 }, { "test_name": "get_specific_circuit", - "Correctness [GEval]": 0.7778544923990117, + "Correctness [GEval]": 0.8693521101681745, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -51,31 +51,31 @@ }, { "test_name": "hippocampus_morphologies", - "Correctness [GEval]": 0.3474612115045808, + "Correctness [GEval]": 0.4102291016664522, "Tool Correctness": 1.0, "Argument Correctness": 0.0, - "Deterministic Argument Correctness": 0.75, - "Overall Argument Correctness": 0.75 + "Deterministic Argument Correctness": 1.0, + "Overall Argument Correctness": 1.0 }, { "test_name": "ion_channel", - "Correctness [GEval]": 0.6181389519578939, + "Correctness [GEval]": 0.723258668553424, "Tool Correctness": 1.0, "Argument Correctness": 1.0, - "Deterministic Argument Correctness": 0.0, + "Deterministic Argument Correctness": 0.5, "Overall Argument Correctness": 1.0 }, { "test_name": "ion_channel_recording", - "Correctness [GEval]": 0.8153318403827823, + "Correctness [GEval]": 0.7822989515826755, "Tool Correctness": 1.0, "Argument Correctness": 1.0, - "Deterministic Argument Correctness": 0.5, + "Deterministic Argument Correctness": 0.75, "Overall Argument Correctness": 1.0 }, { "test_name": "literature_search", - "Correctness [GEval]": 0.6203063398313884, + "Correctness [GEval]": 0.9294276453877884, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, @@ -83,7 +83,7 @@ }, { "test_name": "morphology_studies", - "Correctness [GEval]": 0.500242960279023, + "Correctness [GEval]": 0.8942312072094533, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, @@ -91,15 +91,15 @@ }, { "test_name": "neuroscientists_search", - "Correctness [GEval]": 0.5321950523084212, - "Tool Correctness": 1.0, + "Correctness [GEval]": 0.6262135497223733, + "Tool Correctness": 0.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, "Overall Argument Correctness": 1.0 }, { "test_name": "off_topic_cooking", - "Correctness [GEval]": 0.8043740261928456, + "Correctness [GEval]": 0.7936238992992612, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -107,7 +107,7 @@ }, { "test_name": "off_topic_programming", - "Correctness [GEval]": 0.6408833572487395, + "Correctness [GEval]": 0.751516893687972, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -115,7 +115,7 @@ }, { "test_name": "off_topic_sports", - "Correctness [GEval]": 0.995933528886245, + "Correctness [GEval]": 1.0, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -123,7 +123,7 @@ }, { "test_name": "off_topic_weather", - "Correctness [GEval]": 0.8970620529657471, + "Correctness [GEval]": 1.0, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -131,7 +131,7 @@ }, { "test_name": "platform_explore", - "Correctness [GEval]": 0.4218221236053461, + "Correctness [GEval]": 0.6318898161612772, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -139,7 +139,7 @@ }, { "test_name": "platform_news", - "Correctness [GEval]": 0.72430801616656, + "Correctness [GEval]": 0.7870604116716843, "Tool Correctness": 1.0, "Argument Correctness": 0.0, "Deterministic Argument Correctness": 1.0, @@ -147,7 +147,7 @@ }, { "test_name": "platform_ui_simulate", - "Correctness [GEval]": 0.6125907335968761, + "Correctness [GEval]": 0.6225318568915615, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -155,15 +155,15 @@ }, { "test_name": "platform_viewing", - "Correctness [GEval]": 0.6778050964436126, + "Correctness [GEval]": 0.7556301076899657, "Tool Correctness": 1.0, - "Argument Correctness": 0.0, + "Argument Correctness": 0.5, "Deterministic Argument Correctness": 1.0, "Overall Argument Correctness": 1.0 }, { "test_name": "plotting", - "Correctness [GEval]": 0.563480439810853, + "Correctness [GEval]": 0.7336164979041554, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.5, @@ -171,15 +171,15 @@ }, { "test_name": "read_paper", - "Correctness [GEval]": 0.3886326021513766, + "Correctness [GEval]": 0.22227001388253087, "Tool Correctness": 0.0, - "Argument Correctness": 1.0, + "Argument Correctness": 0.0, "Deterministic Argument Correctness": 0.0, - "Overall Argument Correctness": 1.0 + "Overall Argument Correctness": 0.0 }, { "test_name": "simulation_tutorial", - "Correctness [GEval]": 0.5615612029368113, + "Correctness [GEval]": 0.7131396064894693, "Tool Correctness": 0.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, @@ -187,15 +187,15 @@ }, { "test_name": "sin_plot", - "Correctness [GEval]": 0.002931223117533942, - "Tool Correctness": 0.0, + "Correctness [GEval]": 0.704552344483199, + "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, "Overall Argument Correctness": 1.0 }, { "test_name": "software_docs_entitysdk", - "Correctness [GEval]": 0.733165273979345, + "Correctness [GEval]": 0.6764777066300486, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, @@ -203,7 +203,7 @@ }, { "test_name": "software_docs_obione", - "Correctness [GEval]": 0.8463566651362253, + "Correctness [GEval]": 0.7857044815425586, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, @@ -211,7 +211,7 @@ }, { "test_name": "species_list", - "Correctness [GEval]": 0.6397351056900727, + "Correctness [GEval]": 0.64719334646425, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 1.0, @@ -219,7 +219,7 @@ }, { "test_name": "thalamus_id", - "Correctness [GEval]": 0.6997452237760101, + "Correctness [GEval]": 0.7087950392960021, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.5, @@ -227,12 +227,12 @@ }, { "test_name": "web_search", - "Correctness [GEval]": 0.5953510197804116, + "Correctness [GEval]": 0.5509989256748369, "Tool Correctness": 1.0, "Argument Correctness": 1.0, "Deterministic Argument Correctness": 0.0, "Overall Argument Correctness": 1.0 } ], - "created_at": "2025-11-24 15:22:22.529089" -} \ No newline at end of file + "created_at": "2025-11-24 17:08:32.834182" +} diff --git a/backend/src/neuroagent/tools/obione_generatesimulationsconfig.py b/backend/src/neuroagent/tools/obione_generatesimulationsconfig.py index bba021f9..3c96b449 100644 --- a/backend/src/neuroagent/tools/obione_generatesimulationsconfig.py +++ b/backend/src/neuroagent/tools/obione_generatesimulationsconfig.py @@ -1,8 +1,9 @@ """Tool to generate an obi-one compatible simulation config.""" -from typing import ClassVar +from typing import Any, ClassVar from uuid import UUID +from httpx import AsyncClient from openai import AsyncOpenAI from pydantic import BaseModel, Field from pydantic.json_schema import SkipJsonSchema @@ -16,6 +17,20 @@ from neuroagent.utils import get_token_count +def extract_model_structure(model: type[BaseModel]) -> dict[str, Any]: + """Recursively extract nested field structure from a Pydantic model.""" + result = {} + for name, field_info in model.model_fields.items(): + field_type = field_info.annotation + + if isinstance(field_type, type) and issubclass(field_type, BaseModel): + result[name] = extract_model_structure(field_type) + else: + result[name] = {} + + return result + + class GenerateSimulationsConfigInput(BaseModel): """Inputs of the GenerateSimulationsConfig tool.""" @@ -23,7 +38,7 @@ class GenerateSimulationsConfigInput(BaseModel): description="UUID of the target circuit that has to be simulated." ) config_request: str = Field( - description="A complete description of the desired configuration that will be sent to an LLM for JSON generation. This should describe the ENTIRE configuration as it should exist after any requested changes, not just the modifications. For example, if a user says 'add X to my config' or 'remove Y from my config', translate this into a full description of what the complete configuration should contain, including all existing elements plus the requested additions or minus the requested removals." + description="A natural language description of what the user wants in their simulation configuration. Only specify what the user explicitly requested - defaults and standard parameters will be handled automatically by the downstream LLM. The output will always be valid JSON, so no need to request JSON format." ) @@ -31,6 +46,10 @@ class GenerateSimulationsConfigMetadata(BaseMetadata): """Metadata of the GenerateSimulationsConfig tool.""" openai_client: AsyncOpenAI + httpx_client: AsyncClient + obi_one_url: str + vlab_id: UUID | None + project_id: UUID | None token_consumption: dict[str, str | int | None] | None = None @@ -62,17 +81,19 @@ class GenerateSimulationsConfigTool(BaseTool): ] description: ClassVar[ str - ] = """This tool generates JSON configurations for simulations based on natural language descriptions. It takes a user's request for a configuration (including modifications to existing configs) and uses an LLM with structured output to produce the corresponding JSON. - Always use a circuit ID with this tool. If you can't see a reference to an existing circuit in the chat, ask for clarifications. -The tool is designed to handle both new configuration requests and modifications to existing configurations. When users request changes like "add feature X" or "remove setting Y", the tool requires a complete description of the desired final configuration state, not just the incremental changes. + ] = f"""This tool generates valid JSON configurations for simulations from natural language descriptions. + +Always use a circuit ID with this tool. If you can't see a reference to an existing circuit in the chat, ask for clarifications. + +IMPORTANT: In the config_request parameter, only describe what the user explicitly requested. Do NOT include default values or standard parameters - the downstream LLM will handle those automatically. The output will always be valid JSON. +The downstream LLM uses structured output. It has a lot of information about the structure and defaults of the simulations form. -Input: A comprehensive description of the desired configuration -Output: Structured JSON configuration generated by an LLM +Available configuration fields: {extract_model_structure(SimulationsForm)} Use this tool when users need to: -- Create new configurations from scratch +- Create new simulation configurations - Modify existing configurations -- Convert configuration requirements into structured JSON format +- Specify simulation parameters Always call this tool to generate a simulation config, never attempt to generate one yourself. Do not try to generate a simulation config yourself if the tool fails. @@ -86,20 +107,28 @@ class GenerateSimulationsConfigTool(BaseTool): async def arun(self) -> SimulationsForm: """Run the tool.""" - system_prompt = """# Simulation Configuration Generator + # Get the available nodesest in the circuit + headers: dict[str, str] = {} + if self.metadata.vlab_id is not None: + headers["virtual-lab-id"] = str(self.metadata.vlab_id) + if self.metadata.project_id is not None: + headers["project-id"] = str(self.metadata.project_id) + + circuit_node_set = await self.metadata.httpx_client.get( + self.metadata.obi_one_url.rstrip("/") + + f"/declared/circuit/{self.input_schema.circuit_id}/nodesets", + headers=headers, + ) + if circuit_node_set.status_code != 200: + raise ValueError( + f"The nodesets endpoint returned a non 200 response code. Error: {circuit_node_set.text}" + ) + system_prompt = f"""# Simulation Configuration Generator You are an expert at generating valid JSON simulation configurations following the SimulationsForm schema. The description of the required configuration is going to be the first `user` message you receive. To generate a meaningful simulation config, follow these critical rules: -## Neuron Sets and References -- Generate neuron sets in the "neuron_sets" dictionary with meaningful keys -- **Only create neuron sets that will actually be used** -- For references (NeuronSetReference, TimestampsReference): - - `block_name`: the dictionary key (e.g., "all_neurons") - - `block_dict_name`: the parent dictionary name (e.g., "neuron_sets", "timestamps") -- Avoid duplicate or unused neuron sets unless specifically requested - ## Timestamps and Stimuli Logic - **Only generate timestamps if stimuli will use them** - Choose stimulus type based on timing pattern: @@ -112,6 +141,16 @@ async def arun(self) -> SimulationsForm: - Lists indicate parameter sweeps - only use when scanning multiple values - For IDNeuronSet, include meaningful number of neurons (typically 50+ for populations) +## Neuron Sets and References +- Generate neuron sets in the "neuron_sets" dictionary with meaningful keys +- **Only create neuron sets that will actually be used** +- For references (NeuronSetReference, TimestampsReference): + - `block_name`: the dictionary key (e.g., "all_neurons") + - `block_dict_name`: the parent dictionary name (e.g., "neuron_sets", "timestamps") +- Avoid duplicate or unused neuron sets unless specifically requested +- Whenever you see a `circuit_property_type = CircuitNodeSet` in the schema of the neuron_sets you want to use, the string value within `node_set` must be one of the following: {circuit_node_set.json()} + + ## Validation Checklist Before outputting, verify: - [ ] All references use existing dictionary keys with correct block_dict_name