Server Logs.txt

===== Application Startup at 2026-02-19 18:09:03 =====

INFO:src.api.app:👥 Multi-user session isolation initialized (fast mode)
INFO:src.api.app:🔒 Workflow lock initialized for request queuing
INFO:src.api.app:✅ Frontend assets mounted from /home/user/app/FRRONTEEEND/dist
INFO:     Started server process [1]
INFO:     Waiting for application startup.
INFO:src.api.app:Initializing legacy global agent for health checks...
🤖 Initialized with Mistral provider - Model: mistral-large-latest
✅ Cache database initialized at /home/user/app/cache_db/cache.db
🧠 Loading SBERT model: all-MiniLM-L6-v2...
INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: cpu
INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2
✅ SBERT loaded on CPU
📊 Token Budget: 120,000 tokens available (128,000 - 8,000 reserve)
✅ Sessions database initialized at ./cache_db/sessions.db
✅ Created new session: 5081830f-32ee-4f0a-81c5-cf0fc6906e43
📁 Output directory: /tmp/data_science_agent/outputs
INFO:src.api.app:✅ Health check agent initialized with provider: mistral
INFO:src.api.app:👥 Per-session agents enabled - each user gets isolated instance
INFO:src.api.app:🤖 Multi-agent architecture enabled with 5 specialists
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:7860 (Press CTRL+C to quit)
INFO:     10.16.34.155:32798 - "GET / HTTP/1.1" 200 OK
INFO:     10.16.4.123:43387 - "GET /index.css HTTP/1.1" 200 OK
INFO:     10.16.34.155:32798 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
INFO:     10.16.4.123:54623 - "GET / HTTP/1.1" 200 OK
INFO:     10.16.4.123:54623 - "GET /index.css HTTP/1.1" 200 OK
INFO:     10.16.34.155:7477 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
INFO:     10.16.34.155:53091 - "GET /favicon.ico HTTP/1.1" 200 OK
[SSE] ENDPOINT: Client connected for session_id=d41f7d5d-57f2-4b66-8137-b91496eda810
[SSE] Queue registered, total subscribers: 1
INFO:     10.16.4.123:30718 - "GET /api/progress/stream/d41f7d5d-57f2-4b66-8137-b91496eda810 HTTP/1.1" 200 OK
[SSE] SENDING connection event to client
[SSE] No history to replay (fresh session)
[SSE] Starting event stream loop for session d41f7d5d-57f2-4b66-8137-b91496eda810
INFO:src.api.app:SSE stream cancelled for session d41f7d5d-57f2-4b66-8137-b91496eda810
INFO:src.api.app:SSE stream closed for session d41f7d5d-57f2-4b66-8137-b91496eda810
INFO:src.api.app:[ASYNC] Created new session: 2df5d6d1...
INFO:src.api.app:[ASYNC] File saved: uber_trips_dataset_50k.csv
INFO:     10.16.4.123:29159 - "POST /run-async HTTP/1.1" 200 OK
INFO:src.api.app:[BACKGROUND] Starting analysis for session 2df5d6d1...
[🧹] Clearing SSE history for 2df5d6d1...
INFO:src.api.app:[🆕] Creating lightweight session for 2df5d6d1...
INFO:src.api.app:✅ Session created for 2df5d6d1 (cache: 1/50) - <1s init
[DEBUG] Session fallback triggered - has_ambiguous_ref=True, resolved={}
[DEBUG] resolve_ambiguity returning: {}
[DEBUG] Orchestrator received resolved_params: {}
[DEBUG] Current file_path: '/tmp/data_science_agent/uber_trips_dataset_50k.csv', target_col: 'None'
📝 User provided new file: /tmp/data_science_agent/uber_trips_dataset_50k.csv (ignoring session file: none)
🔍 Extracting dataset schema locally (no LLM)...
[SSE] ENDPOINT: Client connected for session_id=2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Queue registered, total subscribers: 1
INFO:     10.16.34.155:32511 - "GET /api/progress/stream/2df5d6d1-f23c-48fb-b353-373e7693b75e HTTP/1.1" 200 OK
[SSE] SENDING connection event to client
[SSE] No history to replay (fresh session)
[SSE] Starting event stream loop for session 2df5d6d1-f23c-48fb-b353-373e7693b75e
🧠 Semantic layer: Embedded 14 columns
   Found 10 similar column pairs (potential duplicates)
🧠 Semantic layer enriched 14 columns
✅ Schema extracted: 50000 rows × 14 cols
   File size: 8.46 MB

🎯 Intent Classification:
   Mode: EXPLORATORY
   Confidence: 40%
   Reasoning: No strong pattern match, defaulting to exploratory analysis
   Sub-intent: default
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=intent_classified, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 1
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

🧠 Routing to REASONING LOOP (exploratory mode)
[SSE] GOT event from queue: intent_classified
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_mode, msg=🧠 Reasoning Loop activated (exploratory mode)
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 2
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

============================================================
🧠 REASONING LOOP (EXPLORATORY mode)
   Question: Explain the important features in this dataset
   Max iterations: 8
============================================================

🔬 Generating hypotheses from data profile...
   📋 Final parameters: ['file_path']
[SSE] GOT event from queue: reasoning_mode
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Generated 5 hypotheses:
   1. [0.9] The 'fare_amount' distribution exhibits unexpected spikes at round numbers (e.g., $10, $20, $50), suggesting potential fare manipulation, rounding errors, or psychological pricing effects by drivers. This could indicate data quality issues or behavioral patterns worth investigating.
   2. [0.9] 'distance_km' and 'fare_amount' show a non-linear relationship, with fares increasing disproportionately for very short or very long trips. This may reveal surge pricing effects, city-specific pricing models, or data entry errors in distance calculations.
   3. [0.8] Certain 'driver_id' or 'rider_id' values appear in an implausibly high number of trips (e.g., >100 trips/day), suggesting potential data duplication, bot activity, or fraudulent ride patterns that could skew demand/supply metrics.
   4. [0.8] The 'status' column contains a small percentage of 'cancelled' or 'failed' trips, but these are disproportionately concentrated in specific cities or time windows, indicating potential regional service issues or rider/driver behavior differences.
   5. [0.8] Geospatial anomalies exist where 'pickup_lat'/'pickup_lng' or 'drop_lat'/'drop_lng' fall outside expected city boundaries, suggesting GPS errors, incorrect city assignments, or fraudulent trip reporting.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=hypotheses_generated, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 3
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 1/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: hypotheses_generated
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: To explain the important features in the dataset, we first need a comprehensive understanding of the data, including distributions, correlations, and potential relationships between features. This will help identify which features are most relevant or influential.
   Tool: generate_ydata_profiling_report
   Hypothesis: The profiling report will reveal key statistics, distributions, correlations, and potential data quality issues, which will help identify the most important features in the dataset.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 4
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing generate_ydata_profiling_report...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: generate_ydata_profiling_report
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 5
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'output_path', 'minimal', 'title']
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
INFO:visions.backends:Pandas backend loaded 2.3.3
INFO:visions.backends:Numpy backend loaded 2.1.3
INFO:visions.backends:Pyspark backend NOT loaded
INFO:visions.backends:Python backend loaded
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INFO:     10.16.34.155:49568 - "GET / HTTP/1.1" 200 OK
INFO:     10.16.34.155:49568 - "GET /index.css HTTP/1.1" 200 OK
INFO:     10.16.4.123:38750 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
INFO:     10.16.34.155:49568 - "GET /favicon.ico HTTP/1.1" 200 OK
[SSE] ENDPOINT: Client connected for session_id=1963e777-4930-4200-9ada-ad742e5de6bc
[SSE] Queue registered, total subscribers: 1
INFO:     10.16.4.123:1392 - "GET /api/progress/stream/1963e777-4930-4200-9ada-ad742e5de6bc HTTP/1.1" 200 OK
[SSE] SENDING connection event to client
[SSE] No history to replay (fresh session)
[SSE] Starting event stream loop for session 1963e777-4930-4200-9ada-ad742e5de6bc
Summarize dataset:  74%|███████▎  | 14/19 [00:03<00:00,  7.05it/s, Get variable types]          
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Summarize dataset:  55%|█████▍    | 58/106 [00:46<00:22,  2.09it/s, scatter trip_id, pickup_lng]
Summarize dataset:  55%|█████▍    | 58/106 [00:46<00:22,  2.09it/s, scatter driver_id, pickup_lng]
Summarize dataset:  56%|█████▌    | 59/106 [00:46<00:17,  2.73it/s, scatter driver_id, pickup_lng]
Summarize dataset:  56%|█████▌    | 59/106 [00:46<00:17,  2.73it/s, scatter rider_id, pickup_lng] 
Summarize dataset:  57%|█████▋    | 60/106 [00:46<00:13,  3.46it/s, scatter rider_id, pickup_lng]
Summarize dataset:  57%|█████▋    | 60/106 [00:46<00:13,  3.46it/s, scatter pickup_lat, pickup_lng]
Summarize dataset:  58%|█████▊    | 61/106 [00:46<00:10,  4.17it/s, scatter pickup_lat, pickup_lng]
Summarize dataset:  58%|█████▊    | 61/106 [00:46<00:10,  4.17it/s, scatter pickup_lng, pickup_lng]
Summarize dataset:  58%|█████▊    | 62/106 [00:46<00:08,  4.97it/s, scatter pickup_lng, pickup_lng]
Summarize dataset:  58%|█████▊    | 62/106 [00:46<00:08,  4.97it/s, scatter drop_lat, pickup_lng]  
Summarize dataset:  59%|█████▉    | 63/106 [00:46<00:08,  4.97it/s, scatter drop_lng, pickup_lng]
Summarize dataset:  60%|██████    | 64/106 [00:47<00:06,  6.46it/s, scatter drop_lng, pickup_lng]
Summarize dataset:  60%|██████    | 64/106 [00:47<00:06,  6.46it/s, scatter distance_km, pickup_lng]
Summarize dataset:  61%|██████▏   | 65/106 [00:47<00:05,  6.95it/s, scatter distance_km, pickup_lng]
Summarize dataset:  61%|██████▏   | 65/106 [00:47<00:05,  6.95it/s, scatter fare_amount, pickup_lng]
Summarize dataset:  62%|██████▏   | 66/106 [00:47<00:05,  7.55it/s, scatter fare_amount, pickup_lng]
Summarize dataset:  62%|██████▏   | 66/106 [00:47<00:05,  7.55it/s, scatter trip_id, drop_lat]      
Summarize dataset:  63%|██████▎   | 67/106 [00:47<00:04,  7.85it/s, scatter trip_id, drop_lat]
Summarize dataset:  63%|██████▎   | 67/106 [00:47<00:04,  7.85it/s, scatter driver_id, drop_lat]
Summarize dataset:  64%|██████▍   | 68/106 [00:47<00:04,  7.85it/s, scatter rider_id, drop_lat] 
Summarize dataset:  65%|██████▌   | 69/106 [00:47<00:04,  8.34it/s, scatter rider_id, drop_lat]
Summarize dataset:  65%|██████▌   | 69/106 [00:47<00:04,  8.34it/s, scatter pickup_lat, drop_lat]
Summarize dataset:  66%|██████▌   | 70/106 [00:47<00:04,  8.54it/s, scatter pickup_lat, drop_lat]
Summarize dataset:  66%|██████▌   | 70/106 [00:47<00:04,  8.54it/s, scatter pickup_lng, drop_lat]
Summarize dataset:  67%|██████▋   | 71/106 [00:47<00:04,  8.08it/s, scatter pickup_lng, drop_lat]
Summarize dataset:  67%|██████▋   | 71/106 [00:47<00:04,  8.08it/s, scatter drop_lat, drop_lat]  
Summarize dataset:  68%|██████▊   | 72/106 [00:47<00:04,  8.09it/s, scatter drop_lat, drop_lat]
Summarize dataset:  68%|██████▊   | 72/106 [00:47<00:04,  8.09it/s, scatter drop_lng, drop_lat]
Summarize dataset:  69%|██████▉   | 73/106 [00:48<00:03,  8.43it/s, scatter drop_lng, drop_lat]
Summarize dataset:  69%|██████▉   | 73/106 [00:48<00:03,  8.43it/s, scatter distance_km, drop_lat]
Summarize dataset:  70%|██████▉   | 74/106 [00:48<00:04,  7.70it/s, scatter distance_km, drop_lat]
Summarize dataset:  70%|██████▉   | 74/106 [00:48<00:04,  7.70it/s, scatter fare_amount, drop_lat]
Summarize dataset:  71%|███████   | 75/106 [00:48<00:03,  8.05it/s, scatter fare_amount, drop_lat]
Summarize dataset:  71%|███████   | 75/106 [00:48<00:03,  8.05it/s, scatter trip_id, drop_lng]    
Summarize dataset:  72%|███████▏  | 76/106 [00:48<00:03,  8.24it/s, scatter trip_id, drop_lng]
Summarize dataset:  72%|███████▏  | 76/106 [00:48<00:03,  8.24it/s, scatter driver_id, drop_lng]
Summarize dataset:  73%|███████▎  | 77/106 [00:48<00:03,  8.24it/s, scatter rider_id, drop_lng] 
Summarize dataset:  74%|███████▎  | 78/106 [00:48<00:03,  8.81it/s, scatter rider_id, drop_lng]
Summarize dataset:  74%|███████▎  | 78/106 [00:48<00:03,  8.81it/s, scatter pickup_lat, drop_lng]
Summarize dataset:  75%|███████▍  | 79/106 [00:48<00:03,  8.96it/s, scatter pickup_lat, drop_lng]
Summarize dataset:  75%|███████▍  | 79/106 [00:48<00:03,  8.96it/s, scatter pickup_lng, drop_lng]
Summarize dataset:  75%|███████▌  | 80/106 [00:48<00:02,  8.96it/s, scatter drop_lat, drop_lng]  
Summarize dataset:  76%|███████▋  | 81/106 [00:48<00:02,  9.44it/s, scatter drop_lat, drop_lng]
Summarize dataset:  76%|███████▋  | 81/106 [00:48<00:02,  9.44it/s, scatter drop_lng, drop_lng]
Summarize dataset:  77%|███████▋  | 82/106 [00:49<00:02,  9.44it/s, scatter distance_km, drop_lng]
Summarize dataset:  78%|███████▊  | 83/106 [00:49<00:02,  8.99it/s, scatter distance_km, drop_lng]
Summarize dataset:  78%|███████▊  | 83/106 [00:49<00:02,  8.99it/s, scatter fare_amount, drop_lng]
Summarize dataset:  79%|███████▉  | 84/106 [00:49<00:02,  8.99it/s, scatter trip_id, distance_km] 
Summarize dataset:  80%|████████  | 85/106 [00:49<00:02,  8.51it/s, scatter trip_id, distance_km]
Summarize dataset:  80%|████████  | 85/106 [00:49<00:02,  8.51it/s, scatter driver_id, distance_km]
Summarize dataset:  81%|████████  | 86/106 [00:49<00:02,  8.51it/s, scatter driver_id, distance_km]
Summarize dataset:  81%|████████  | 86/106 [00:49<00:02,  8.51it/s, scatter rider_id, distance_km] 
Summarize dataset:  82%|████████▏ | 87/106 [00:49<00:02,  8.20it/s, scatter rider_id, distance_km]
Summarize dataset:  82%|████████▏ | 87/106 [00:49<00:02,  8.20it/s, scatter pickup_lat, distance_km]
Summarize dataset:  83%|████████▎ | 88/106 [00:49<00:02,  7.70it/s, scatter pickup_lat, distance_km]
Summarize dataset:  83%|████████▎ | 88/106 [00:49<00:02,  7.70it/s, scatter pickup_lng, distance_km]
Summarize dataset:  84%|████████▍ | 89/106 [00:50<00:02,  7.61it/s, scatter pickup_lng, distance_km]
Summarize dataset:  84%|████████▍ | 89/106 [00:50<00:02,  7.61it/s, scatter drop_lat, distance_km]  
Summarize dataset:  85%|████████▍ | 90/106 [00:50<00:02,  7.95it/s, scatter drop_lat, distance_km]
Summarize dataset:  85%|████████▍ | 90/106 [00:50<00:02,  7.95it/s, scatter drop_lng, distance_km]
Summarize dataset:  86%|████████▌ | 91/106 [00:50<00:01,  7.81it/s, scatter drop_lng, distance_km]
Summarize dataset:  86%|████████▌ | 91/106 [00:50<00:01,  7.81it/s, scatter distance_km, distance_km]
Summarize dataset:  87%|████████▋ | 92/106 [00:50<00:01,  7.84it/s, scatter distance_km, distance_km]
Summarize dataset:  87%|████████▋ | 92/106 [00:50<00:01,  7.84it/s, scatter fare_amount, distance_km]
Summarize dataset:  88%|████████▊ | 93/106 [00:50<00:01,  7.86it/s, scatter fare_amount, distance_km]
Summarize dataset:  88%|████████▊ | 93/106 [00:50<00:01,  7.86it/s, scatter trip_id, fare_amount]    
Summarize dataset:  89%|████████▊ | 94/106 [00:50<00:01,  8.37it/s, scatter trip_id, fare_amount]
Summarize dataset:  89%|████████▊ | 94/106 [00:50<00:01,  8.37it/s, scatter driver_id, fare_amount]
Summarize dataset:  90%|████████▉ | 95/106 [00:50<00:01,  8.60it/s, scatter driver_id, fare_amount]
Summarize dataset:  90%|████████▉ | 95/106 [00:50<00:01,  8.60it/s, scatter rider_id, fare_amount] 
Summarize dataset:  91%|█████████ | 96/106 [00:50<00:01,  8.64it/s, scatter rider_id, fare_amount]
Summarize dataset:  91%|█████████ | 96/106 [00:50<00:01,  8.64it/s, scatter pickup_lat, fare_amount]
Summarize dataset:  92%|█████████▏| 97/106 [00:50<00:01,  8.70it/s, scatter pickup_lat, fare_amount]
Summarize dataset:  92%|█████████▏| 97/106 [00:50<00:01,  8.70it/s, scatter pickup_lng, fare_amount]
Summarize dataset:  92%|█████████▏| 98/106 [00:51<00:00,  8.89it/s, scatter pickup_lng, fare_amount]
Summarize dataset:  92%|█████████▏| 98/106 [00:51<00:00,  8.89it/s, scatter drop_lat, fare_amount]  
Summarize dataset:  93%|█████████▎| 99/106 [00:51<00:00,  7.61it/s, scatter drop_lat, fare_amount]
Summarize dataset:  93%|█████████▎| 99/106 [00:51<00:00,  7.61it/s, scatter drop_lng, fare_amount]
Summarize dataset:  94%|█████████▍| 100/106 [00:51<00:00,  8.13it/s, scatter drop_lng, fare_amount]
Summarize dataset:  94%|█████████▍| 100/106 [00:51<00:00,  8.13it/s, scatter distance_km, fare_amount]
Summarize dataset:  95%|█████████▌| 101/106 [00:51<00:00,  8.37it/s, scatter distance_km, fare_amount]
Summarize dataset:  95%|█████████▌| 101/106 [00:51<00:00,  8.37it/s, scatter fare_amount, fare_amount]
Summarize dataset:  96%|█████████▌| 102/106 [00:51<00:00,  8.70it/s, scatter fare_amount, fare_amount]
Summarize dataset:  94%|█████████▍| 102/108 [00:51<00:00,  8.70it/s, Missing diagram bar]             
Summarize dataset:  95%|█████████▌| 103/108 [00:52<00:01,  4.24it/s, Missing diagram bar]
Summarize dataset:  95%|█████████▌| 103/108 [00:52<00:01,  4.24it/s, Missing diagram matrix]
Summarize dataset:  96%|█████████▋| 104/108 [00:52<00:01,  3.10it/s, Missing diagram matrix]
Summarize dataset:  96%|█████████▋| 104/108 [00:52<00:01,  3.10it/s, Take sample]           
Summarize dataset:  97%|█████████▋| 105/108 [00:52<00:00,  3.10it/s, Detecting duplicates]
Summarize dataset:  98%|█████████▊| 106/108 [00:52<00:00,  4.15it/s, Detecting duplicates]
Summarize dataset:  98%|█████████▊| 106/108 [00:52<00:00,  4.15it/s, Get alerts]          
Summarize dataset:  99%|█████████▉| 107/108 [00:52<00:00,  4.15it/s, Get reproduction details]
Summarize dataset: 100%|██████████| 108/108 [00:52<00:00,  4.15it/s, Completed]               
Summarize dataset: 100%|██████████| 108/108 [00:52<00:00,  2.04it/s, Completed]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]INFO:src.api.app:SSE stream cancelled for session 1963e777-4930-4200-9ada-ad742e5de6bc
INFO:src.api.app:SSE stream closed for session 1963e777-4930-4200-9ada-ad742e5de6bc
INFO:     10.16.34.155:42907 - "GET / HTTP/1.1" 200 OK
INFO:     10.16.34.155:42907 - "GET /index.css HTTP/1.1" 200 OK
INFO:     10.16.34.155:42907 - "GET /favicon.ico HTTP/1.1" 200 OK
Generate report structure: 100%|██████████| 1/1 [00:48<00:00, 48.28s/it]
Generate report structure: 100%|██████████| 1/1 [00:48<00:00, 48.29s/it]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.94s/it]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.96s/it]
Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]
Export report to file: 100%|██████████| 1/1 [00:00<00:00, 51.99it/s]
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: generate_ydata_profiling_report
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 6
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   ✓ Tool completed successfully
[SSE] GOT event from queue: tool_completed
💾 Checkpoint saved: iteration 1, last tool: generate_ydata_profiling_report
📊 EVALUATE: Interpreting results...
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The result indicates that a comprehensive profiling report has been generated, which will detail the statistical properties, distributions, correlations, and missing values of features in the Uber trips dataset. This report is essential for identifying important features but does not itself interpret them—manual review is required to extract actionable insights.
   Answered: False (confidence: 40%)
   Should stop: False
   Next questions: ['Which features show the strongest correlation with trip duration or fare amount?', 'Are there unexpected patterns or outliers in key features (e.g., pickup/dropoff times, locations)?', 'How do missing values or data quality issues impact feature importance?', 'What are the most surprising or counterintuitive relationships between features?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 7
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 2/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The profiling report has been generated but not yet analyzed for feature importance. The next step is to analyze correlations between features, particularly focusing on 'fare_amount' as the target, to identify the most influential features driving fare pricing.
   Tool: analyze_correlations
   Hypothesis: We expect to identify strong correlations between 'fare_amount' and features like 'distance_km', 'city', and temporal features (e.g., 'pickup_time'), which will highlight the most important features in the dataset.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 8
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing analyze_correlations...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: analyze_correlations
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 9
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'target']
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: analyze_correlations
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 10
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   ✓ Tool completed successfully
💾 Checkpoint saved: iteration 2, last tool: analyze_correlations
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: tool_completed
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The `analyze_correlations` tool completed execution, meaning it has computed the statistical relationships between 'fare_amount' and other features in the dataset. However, the raw result does not provide interpretable insights—it only confirms the correlation analysis ran successfully. The actual correlation values and their significance are missing, so we cannot yet determine which features are important drivers of fare amounts.
   Answered: False (confidence: 20%)
   Should stop: False
   Next questions: ["What are the exact correlation coefficients and p-values between 'fare_amount' and other features (e.g., 'distance_km', 'trip_duration', 'surge_multiplier')?", 'Are the observed correlations statistically significant, or could they be artifacts of data quality issues (e.g., outliers, duplicates)?', "How do non-linear relationships (e.g., between 'distance_km' and 'fare_amount') affect feature importance rankings?"]
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 11
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 3/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The `analyze_correlations` tool was executed but the actual correlation values and their significance are missing. To identify the most important features, we need to visualize the correlation results to interpret the relationships between 'fare_amount' and other features.
   Tool: generate_interactive_correlation_heatmap
   Hypothesis: We expect to visually identify strong correlations between 'fare_amount' and features like 'distance_km', 'city', and temporal features (e.g., 'pickup_time'), which will highlight the most important features in the dataset.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 12
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing generate_interactive_correlation_heatmap...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: generate_interactive_correlation_heat
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 13
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path']
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: generate_interactive_correlation_heat
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 14
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   ✓ Tool completed successfully
💾 Checkpoint saved: iteration 3, last tool: generate_interactive_correlation_heatmap
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: tool_completed
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The interactive correlation heatmap generation means we now have a visual tool to explore and quantify the strength and direction of relationships between features, particularly how they relate to 'fare_amount'. This is a critical step toward identifying important features, but the raw output alone does not provide direct answers—it requires manual interpretation to extract actionable insights.
   Answered: False (confidence: 40%)
   Should stop: False
   Next questions: ["Which features show statistically significant correlations with 'fare_amount', and what is the magnitude of these relationships?", "Are there any unexpected or counterintuitive correlations (e.g., weak correlation between 'distance_km' and 'fare_amount') that warrant further investigation?", "How do temporal features (e.g., 'pickup_time', 'day_of_week') interact with other variables, and do they reveal patterns like surge pricing or demand fluctuations?", 'Are there multicollinearity issues among features that could distort feature importance in predictive models?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 15
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 4/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The interactive correlation heatmap has been generated but not yet interpreted to identify the most important features driving 'fare_amount'. We need to extract and quantify feature importance to answer the user's question confidently.
   Tool: auto_feature_selection
   Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features for further analysis.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 16
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing auto_feature_selection...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 17
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
⚠️ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed (attempt 1/2): Unsupported file format: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
   Retrying in 1.0s...
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
❌ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed after 2 attempts
💡 Suggested fallback tools: []
   ❌ Tool failed: Unsupported file format: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 18
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: tool_failed
INFO:     10.16.34.155:21041 - "GET / HTTP/1.1" 200 OK
INFO:     10.16.4.123:60395 - "GET /index.css HTTP/1.1" 200 OK
INFO:     10.16.34.155:21041 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 304 Not Modified
INFO:     10.16.34.155:21041 - "GET /favicon.ico HTTP/1.1" 200 OK
INFO:     10.16.34.155:21041 - "GET /favicon.ico HTTP/1.1" 200 OK
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The feature importance analysis failed due to an unsupported file format (HTML heatmap instead of raw data), meaning we cannot yet directly quantify which features most influence 'fare_amount'. However, prior steps (correlation analysis and profiling) provide indirect evidence about potential important features, though manual interpretation is still required.
   Answered: False (confidence: 30%)
   Should stop: False
   Next questions: ["What are the exact correlation values between 'fare_amount' and other features (e.g., 'distance_km', 'pickup_time') from the raw correlation analysis?", 'Can we extract the numerical correlation matrix from the interactive heatmap to perform feature importance ranking?', 'Are there non-linear relationships or interaction effects (e.g., distance × city) that simple correlation misses?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 19
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 5/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The `auto_feature_selection` tool failed due to an unsupported file format, and prior steps (correlation heatmap and profiling) provide indirect evidence but no direct quantification of feature importance. Running `train_with_autogluon` will train a model and explicitly rank features by their importance to 'fare_amount', addressing the user's question directly.
   Tool: train_with_autogluon
   Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 20
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing train_with_autogluon...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: train_with_autogluon
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 21
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'time_limit', 'presets']
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
[SSE] ENDPOINT: Client connected for session_id=1963e777-4930-4200-9ada-ad742e5de6bc
[SSE] Queue registered, total subscribers: 1
INFO:     10.16.4.123:23205 - "GET /api/progress/stream/1963e777-4930-4200-9ada-ad742e5de6bc HTTP/1.1" 200 OK
[SSE] SENDING connection event to client
[SSE] No history to replay (fresh session)
[SSE] Starting event stream loop for session 1963e777-4930-4200-9ada-ad742e5de6bc

🚀 AutoGluon Training Starting...
   📁 Dataset: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
   🎯 Target: fare_amount
   ⏱️  Time limit: 1800s
   📊 Presets: medium_quality
⚠️ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed (attempt 1/2): Failed to load data: Error tokenizing data. C error: Expected 1 fields in line 7, saw 3

   Retrying in 1.0s...
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'time_limit', 'presets']

🚀 AutoGluon Training Starting...
   📁 Dataset: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
   🎯 Target: fare_amount
   ⏱️  Time limit: 1800s
   📊 Presets: medium_quality
❌ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed after 2 attempts
💡 Suggested fallback tools: []
   ❌ Tool failed: Failed to load data: Error tokenizing data. C error: Expected 1 fields in line 7, saw 3

[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: train_with_autogluon
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 22
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: tool_failed
INFO:src.api.app:SSE stream cancelled for session 1963e777-4930-4200-9ada-ad742e5de6bc
INFO:src.api.app:SSE stream closed for session 1963e777-4930-4200-9ada-ad742e5de6bc
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The AutoGluon training failed due to a data tokenization error, likely caused by malformed input data (e.g., inconsistent delimiters or corrupted rows). This means we cannot yet directly quantify feature importance for 'fare_amount' using automated methods, and prior indirect evidence (e.g., correlations) remains unvalidated.
   Answered: False (confidence: 20%)
   Should stop: False
   Next questions: ['What is the root cause of the tokenization error (e.g., delimiter mismatch, corrupted rows), and how can the data be cleaned to resolve it?', 'Can we manually inspect the raw data (e.g., line 7) to identify and fix formatting issues before retrying automated feature importance analysis?', 'Are there alternative methods (e.g., manual feature engineering, simpler models) to estimate feature importance given the current data limitations?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 23
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 6/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The previous `auto_feature_selection` and `train_with_autogluon` steps failed due to incorrect input formats, but we have an interactive correlation heatmap that visually suggests relationships. To quantify feature importance, we need to extract the raw correlation data or re-run feature selection on the original dataset (not the HTML file).
   Tool: auto_feature_selection
   Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 24
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing auto_feature_selection...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 25
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
   ❌ Tool failed: File not found: /tmp/data_science_agent/data/processed_data.parquet
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 26
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
[SSE] GOT event from queue: tool_failed
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The failure of the `auto_feature_selection` tool due to a missing file means we cannot directly quantify feature importance for 'fare_amount' at this stage. This blocks our ability to answer the original question about important features, though prior indirect evidence (e.g., correlations) suggests potential candidates like 'distance_km' and 'city'. The root cause appears to be a file path or data pipeline issue, not a methodological limitation.
   Answered: False (confidence: 20%)
   Should stop: False
   Next questions: ['What is the correct file path or format for the processed data to enable feature importance analysis?', 'Can we rerun prior correlation analyses on the raw data to extract quantifiable feature importance metrics (e.g., correlation coefficients) as a temporary workaround?', 'Are there data quality issues (e.g., missing values, incorrect formats) in the raw data that caused the AutoGluon tokenization error and need to be addressed first?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 27
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 7/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The previous attempts to quantify feature importance failed due to file format issues or data errors. The most direct next step is to re-run `auto_feature_selection` on the raw dataset (not the HTML heatmap) to obtain a ranked list of important features for 'fare_amount'.
   Tool: auto_feature_selection
   Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 28
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing auto_feature_selection...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 29
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
   ❌ Tool failed: File not found: /tmp/data_science_agent/data/raw_dataset.parquet
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 30
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
[SSE] GOT event from queue: tool_failed
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The failure due to a missing file means we cannot directly quantify feature importance for 'fare_amount' at this stage, blocking the original question. This is likely a data pipeline or file path issue, not a methodological limitation, and requires immediate resolution before proceeding with feature analysis.
   Answered: False (confidence: 10%)
   Should stop: False
   Next questions: ['What is the correct file path for the raw dataset, and does the file exist in the expected location?', 'Are there alternative data sources or formats (e.g., CSV, JSON) available for the same dataset to bypass the file path issue?', 'Can we validate the integrity of the data pipeline to ensure no corruption or misalignment occurred during prior steps?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 31
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

── Iteration 8/8 ──
🤔 REASON: Deciding next action...
[SSE] GOT event from queue: finding_discovered
INFO:src.api.app:[ASYNC] Created new session: f0b5e746...
INFO:src.api.app:[ASYNC] File saved: intraday_tips.csv
INFO:     10.16.4.123:13810 - "POST /run-async HTTP/1.1" 200 OK
[SSE] ENDPOINT: Client connected for session_id=f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
[SSE] Queue registered, total subscribers: 1
INFO:     10.16.34.155:48122 - "GET /api/progress/stream/f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77 HTTP/1.1" 200 OK
[SSE] SENDING connection event to client
[SSE] No history to replay (fresh session)
[SSE] Starting event stream loop for session f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Status: investigating
   Reasoning: The previous attempts to quantify feature importance failed due to file format issues (HTML instead of raw data). We need to ensure the raw data is used for feature selection to directly answer the user's question about important features.
   Tool: auto_feature_selection
   Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 32
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
⚡ ACT: Executing auto_feature_selection...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 33
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
   📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
   ❌ Tool failed: File not found: /tmp/data_science_agent/data/raw_data.parquet
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 34
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
📊 EVALUATE: Interpreting results...
[SSE] GOT event from queue: reasoning_step
[SSE] GOT event from queue: tool_executing
[SSE] GOT event from queue: tool_failed
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
   Interpretation: The result indicates a critical data pipeline failure—specifically, the input file cannot be located—blocking any direct analysis of feature importance for 'fare_amount'. This means the original question remains unanswered, and no new insights about feature importance can be derived until the file path or data access issue is resolved.
   Answered: False (confidence: 10%)
   Should stop: False
   Next questions: ['What is the correct file path for the dataset, and does the file exist in the expected location?', 'Are there alternative datasets or backups available to proceed with feature importance analysis?', 'Can we validate the data pipeline to ensure no other files are missing or corrupted before retrying?', 'Should we prioritize debugging the file access issue or switch to indirect methods (e.g., correlation analysis) to infer feature importance temporarily?']
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 35
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1

============================================================
📝 SYNTHESIZE: Building final answer from 8 findings...
============================================================
[SSE] GOT event from queue: finding_discovered
INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
[DEBUG] Report tool detected: generate_ydata_profiling_report
[DEBUG] nested_result keys: ['success', 'report_path', 'message', 'statistics']
[DEBUG] Report path found: /tmp/data_science_agent/uber_trips_profiling_report.html
[DEBUG] Added to artifacts[reports], total reports: 1
[DEBUG] Extracting plot from tool: generate_interactive_correlation_heatmap
[DEBUG]   result keys: ['success', 'tool', 'arguments', 'result']
[DEBUG]   nested_result keys: ['status', 'plot_type', 'output_path', 'num_features']
[DEBUG]   output_path in nested_result: True
[DEBUG]   output_path value: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
[DEBUG] Added plot to array:
[DEBUG]   title: Correlation Heatmap
[DEBUG]   url: /outputs/plots/interactive/correlation_heatmap.html
[DEBUG]   type: html
[DEBUG] Merging 1 reports into plots array
[DEBUG] Added report to plots array: title='Generate Ydata Profiling Report', url='/outputs//tmp/data_science_agent/uber_trips_profiling_report.html'
[DEBUG] Final plots array length: 2

✅ Reasoning loop completed in 233.75s
   Iterations: 8
   Tools used: generate_ydata_profiling_report, analyze_correlations, generate_interactive_correlation_heatmap, auto_feature_selection, train_with_autogluon
   API calls: 18
INFO:src.api.app:[BACKGROUND] Analysis completed for session 2df5d6d1...
[SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=analysis_complete, msg=✅ Analysis completed successfully!
[SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 36
[SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
[SSE] Successfully queued event to subscriber 1
[SSE] GOT event from queue: analysis_complete
INFO:src.api.app:SSE stream closed for session 2df5d6d1-f23c-48fb-b353-373e7693b75e
INFO:src.api.app:Found file at: /tmp/data_science_agent/uber_trips_profiling_report.html
INFO:     10.16.4.123:27264 - "GET /outputs//tmp/data_science_agent/uber_trips_profiling_report.html HTTP/1.1" 200 OK
INFO:src.api.app:Found file at: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
INFO:     10.16.4.123:46507 - "GET /outputs/plots/interactive/correlation_heatmap.html HTTP/1.1" 200 OK