perf think improvements

benchmarks/locomo/benchmark_results_think.json

@@ -0,0 +1,356 @@
+{
+  "overall_accuracy": 100.0,
+  "total_correct": 1,
+  "total_questions": 1,
+  "num_items": 1,
+  "item_results": [
+    {
+      "item_id": "conv-26",
+      "metrics": {
+        "accuracy": 100.0,
+        "correct": 1,
+        "total": 1,
+        "category_stats": {
+          "2": {
+            "correct": 1,
+            "total": 1
+          }
+        },
+        "detailed_results": [
+          {
+            "question": "When did Caroline go to the LGBTQ support group?",
+            "correct_answer": "7 May 2023",
+            "predicted_answer": "Caroline attended the LGBTQ support group on May\u202f7\u202f2023 (event recorded at 2023\u201105\u201107T13:56:00\u202fUTC).",
+            "reasoning": "Think API: 20 world facts, 0 agent facts, 20 opinions",
+            "category": 2,
+            "retrieved_memories": [
+              {
+                "id": "5ddabf72-967c-4bd0-a0a8-1545324e48f2",
+                "text": "Caroline attended an LGBTQ support group on 2023-05-07 and found it powerful.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_1)",
+                "event_date": "2023-05-07T13:56:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "7b85f829-743e-4827-8349-ca6d01dba5dc",
+                "text": "Caroline volunteered at an LGBTQ+ youth center",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_15)",
+                "event_date": "2023-08-28T15:19:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "6794d0e8-5e4d-44bb-8238-21249021d101",
+                "text": "Caroline joined the LGBTQ activist group \"Connected LGBTQ Activists\" on 2023-07-18.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_10)",
+                "event_date": "2023-07-18T20:56:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "eb8952a1-9b7d-45b3-8f63-2411d6421725",
+                "text": "Caroline tried to apologize to the people she encountered during the hike.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_14)",
+                "event_date": "2023-08-18T13:33:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "2decc740-9674-4b25-acf1-1da8ba8f61dd",
+                "text": "Caroline owns a guinea pig named Oscar.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_13)",
+                "event_date": "2023-08-23T15:31:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "1aad5731-397b-4c3f-b78d-58768d376133",
+                "text": "Caroline told Melanie that she is lucky to have such an awesome family.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_10)",
+                "event_date": "2023-07-20T20:56:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "5b3e87cc-6892-4fb2-945e-5921a32c3453",
+                "text": "Caroline attended a poetry reading on Friday, October 6, 2023.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_17)",
+                "event_date": "2023-10-06T10:31:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "3fd7f709-6b78-443c-8768-abeee8f28e31",
+                "text": "Caroline created a self-portrait last week and posted a photo of it.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_13)",
+                "event_date": "2023-08-16T15:31:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "640e4293-755b-4634-b570-1f15128eb1e6",
+                "text": "The event room was electric with energy and support, and the posters displayed pride and strength, which inspired Caroline to create new artwork.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_17)",
+                "event_date": "2023-10-06T10:31:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "a9331948-bf5f-4791-86e9-0bf62342ed88",
+                "text": "Caroline visited the beach.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_14)",
+                "event_date": "2023-08-18T13:33:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "c48b29f0-bcb4-4fde-b616-4e08d9166146",
+                "text": "Caroline attended an adoption advice and assistance group, receiving a lot of help.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_13)",
+                "event_date": "2023-08-23T15:31:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "eba11741-e2ae-4ccf-976a-4df864b0cace",
+                "text": "Caroline heard transgender stories at the LGBTQ support group, which she found inspiring.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_1)",
+                "event_date": "2023-05-07T13:56:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "5968eee3-380d-4780-afec-9576fbafe422",
+                "text": "Caroline is interested in a career in counseling or mental health to support people with similar issues.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_1)",
+                "event_date": "2023-05-08T13:56:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "aee0214f-e49d-47a4-83f5-5f97304a3a51",
+                "text": "Caroline feels supported by people around her, which makes her feel okay.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_12)",
+                "event_date": "2023-08-17T13:50:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "149347b4-b7cb-4bf9-a8be-f743700a9783",
+                "text": "The city held a pride parade on 2023-07-15, where many people marched, waved flags, held signs, and celebrated love and diversity; Caroline missed the parade.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_10)",
+                "event_date": "2023-07-15T20:56:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "21ac438b-ce0b-4efd-8c6e-d424295ec3cc",
+                "text": "Caroline created a recent painting that represents inclusivity and diversity and uses it to speak up for the LGBTQ+ community and push for acceptance.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_11)",
+                "event_date": "2023-08-14T14:24:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "442b4482-f31b-4ef5-b545-eb0befab7cfe",
+                "text": "Caroline mentioned an advocacy event that was a cool experience with love and support.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_11)",
+                "event_date": "2023-08-14T14:24:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "a100f54b-7112-4616-8f7c-9a7347587597",
+                "text": "Caroline had a not-so-great experience on a hike where she ran into a group of religious conservatives who said something that upset her, leading her to reflect on the need for more work on LGBTQ rights.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_12)",
+                "event_date": "2023-08-17T13:50:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "3e2e6418-29db-44e4-963f-ea2089d33dd5",
+                "text": "Caroline has a lifelong love for horses.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_13)",
+                "event_date": "2023-08-23T15:31:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "0dda577c-9ea3-4a47-835a-63cade830838",
+                "text": "Caroline gave a talk at her school event last week about her transgender journey, encouraging students to get involved in the LGBTQ community and observed positive reactions from the audience.",
+                "context": "Conversation session between Caroline and Melanie (conversation conv-26 session session_3)",
+                "event_date": "2023-06-02T19:55:00+00:00",
+                "score": 0.0,
+                "fact_type": "world"
+              },
+              {
+                "id": "054d8d37-aa7d-4a21-b4bd-7968e29d74dd",
+                "text": "Caroline is a participant within the LGBTQ community",
+                "context": "formed during thinking about: In what ways is Caroline participating in the LGBTQ community?",
+                "event_date": "2025-11-04T18:24:51+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "9862a174-0e48-418c-b796-2cb1185b7f61",
+                "text": "Caroline's plan to build a personal library for her future children suggests she may consider formal study in library science or related fields.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:30:15+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "7318bff4-0702-44b9-8aa6-b50f5a59be86",
+                "text": "We cannot determine whether Caroline wants to move back to her home country soon",
+                "context": "formed during thinking about: Would Caroline want to move back to her home country soon?",
+                "event_date": "2025-11-04T18:05:51+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "cdb11bda-7c70-4b61-b36e-bdf21727a194",
+                "text": "Caroline is an event organizer within the LGBTQ community",
+                "context": "formed during thinking about: In what ways is Caroline participating in the LGBTQ community?",
+                "event_date": "2025-11-04T18:24:51+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "1d68207b-03e2-44ca-a79b-47ba427c1ba1",
+                "text": "Melanie and Caroline describe their journey as collaborative, supportive, and inspiring",
+                "context": "formed during thinking about: How do Melanie and Caroline describe their journey through life together?",
+                "event_date": "2025-11-04T18:24:08+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "8b6a13ff-7417-4458-a19c-cd101d24c53f",
+                "text": "Caroline has passed adoption agency interviews.",
+                "context": "formed during thinking about: What is Caroline's identity?",
+                "event_date": "2025-11-04T15:30:19+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "4e8a04ef-ea96-48ec-b016-c32a111bc819",
+                "text": "Caroline is an advocate within the LGBTQ community",
+                "context": "formed during thinking about: In what ways is Caroline participating in the LGBTQ community?",
+                "event_date": "2025-11-04T18:24:51+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "87c97473-6596-4e5c-bd0f-f989ab76e137",
+                "text": "The provided facts only describe Caroline's activities, goals, and interests in her current location",
+                "context": "formed during thinking about: Would Caroline want to move back to her home country soon?",
+                "event_date": "2025-11-04T18:05:51+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "6aa8c0fd-dffc-4d27-9559-b0687d263f59",
+                "text": "Caroline plays piano.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:27:06+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "04372c16-57ae-429b-8f9c-e3ab9ef3bacd",
+                "text": "Caroline could integrate art therapy classes into counseling work.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:27:06+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "d596524c-fdd6-4080-b7a7-f9da2cd3d6ed",
+                "text": "Caroline explicitly said she wants to continue her education.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T18:08:28+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "f5308bcf-a59e-4b1d-90e0-0e243b79b98d",
+                "text": "Caroline paints.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:27:06+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "f97a5f20-1e8d-45bc-93db-6626070ca5f1",
+                "text": "Caroline is interested in a career in mental-health services.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T18:08:28+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "fc805a93-3b1d-4f8a-a98e-3b7044bcb2d0",
+                "text": "Caroline feels a sense of empowerment from activism.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:27:06+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "31c26b9a-f4a3-4a08-bae8-410780b40414",
+                "text": "Previously I thought Caroline would likely focus on LGBTQ+ studies, gender studies, or social justice based on her activism. However, her explicit statements about pursuing a career in counseling and mental health, and her desire to support people with similar issues, indicate that her primary focus is now likely on a counseling or mental\u2011health career, potentially integrating her advocacy for marginalized communities.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:27:06+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "987325a5-6b67-42bf-8910-fd194987bd35",
+                "text": "Caroline is a future parent-to-be.",
+                "context": "formed during thinking about: What is Caroline's identity?",
+                "event_date": "2025-11-04T15:30:19+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "ba5ae772-2d00-4cbf-ac88-6988bca1be78",
+                "text": "Melanie and Caroline frame challenges as a shared roller-coaster",
+                "context": "formed during thinking about: How do Melanie and Caroline describe their journey through life together?",
+                "event_date": "2025-11-04T18:24:08+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "de3b98e6-f435-41b7-a38b-712a35cd0c54",
+                "text": "There is no information indicating Caroline identifies as religious.",
+                "context": "formed during thinking about: Would Caroline be considered religious?",
+                "event_date": "2025-11-04T18:05:26+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "314d9820-e75d-455f-bfad-55dcea90c044",
+                "text": "Caroline may choose a multidisciplinary program that combines counseling/mental\u2011health training, LGBTQ+ advocacy, and early childhood literacy.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:30:15+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              },
+              {
+                "id": "5ba2cc08-4998-4667-8b95-adb1f0986504",
+                "text": "Caroline is most likely to pursue LGBTQ+ Studies and Gender & Social Justice.",
+                "context": "formed during thinking about: What fields would Caroline be likely to pursue in her educaton?",
+                "event_date": "2025-11-04T15:30:15+00:00",
+                "score": 0.0,
+                "fact_type": "opinion"
+              }
+            ],
+            "is_correct": true,
+            "correctness_reasoning": "The generated answer states the same date (May\u202f7\u202f2023) as the gold answer, so it is correct."
+          }
+        ]
+      },
+      "num_sessions": -1
+    }
+  ]
+}

benchmarks/locomo/results_table_think.md

@@ -0,0 +1,7 @@
+# LoComo Benchmark Results (Think Mode)
+
+**Overall Accuracy**: 100.00% (1/1)
+
+| Sample ID | Sessions | Questions | Correct | Accuracy | Multi-hop | Single-hop | Temporal | Open-domain |
+|-----------|----------|-----------|---------|----------|-----------|------------|----------|-------------|
+| conv-26 | -1 | 1 | 1 | 100.00% | N/A | N/A | N/A | N/A |

benchmarks/visualizer/README.md

@@ -0,0 +1,62 @@
+# Benchmark Visualizer
+
+A standalone web service for visualizing benchmark results. Currently supports the LoComo benchmark with plans to add more benchmarks in the future.
+
+## Features
+
+- Interactive web interface for viewing benchmark results
+- Detailed breakdown by category (Multi-hop, Single-hop, Temporal, Open-domain)
+- Filter options to view all, correct, or incorrect answers
+- Expandable Q&A details with reasoning and retrieved memories
+- Overall and per-item accuracy statistics
+
+## Running the Visualizer
+
+### Option 1: Using the serve script (recommended)
+
+```bash
+cd benchmarks/visualizer
+./serve.sh
+```
+
+### Option 2: Using uvicorn directly
+
+```bash
+cd benchmarks/visualizer
+uv run uvicorn server:app --reload --host 0.0.0.0 --port 8001
+```
+
+Then open your browser to: http://localhost:8001
+
+## Usage
+
+1. Select a benchmark from the dropdown:
+   - **LoComo (search)**: Traditional two-step approach (search → LLM answer generation)
+   - **LoComo (think)**: Integrated approach using think API (single call for retrieval + reasoning)
+2. The visualization will automatically load and display:
+   - Overall accuracy statistics
+   - Category-wise performance breakdown
+   - Detailed results for each conversation
+3. Use the filter controls to show all answers, only incorrect, or only correct answers
+4. Expand individual conversations to see Q&A details, reasoning, and retrieved memories
+
+## API Endpoints
+
+- `GET /` - Main visualizer page
+- `GET /api/locomo?mode={search|think}` - Returns LoComo benchmark results as JSON
+  - `mode=search` (default): Returns results from `benchmark_results.json`
+  - `mode=think`: Returns results from `benchmark_results_think.json`
+
+## Requirements
+
+- FastAPI
+- Uvicorn
+- Python 3.11+
+
+The visualizer reads benchmark results from:
+- `benchmarks/locomo/benchmark_results.json` for search mode
+- `benchmarks/locomo/benchmark_results_think.json` for think mode
+
+Make sure to run the benchmark first to generate results:
+- Search mode: `cd benchmarks/locomo && uv run python run_benchmark.py`
+- Think mode: `cd benchmarks/locomo && uv run python run_benchmark.py --use-think`

benchmarks/visualizer/serve.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+# Start the Benchmark Visualizer server with hot reload
+cd "$(dirname "$0")"
+uv run uvicorn server:app --reload --host 0.0.0.0 --port 8001

benchmarks/visualizer/server.py

@@ -0,0 +1,72 @@
+"""Benchmark Visualizer Web Service.
+
+A standalone web service for visualizing benchmark results.
+Currently supports LoComo benchmark visualization.
+"""
+
+import json
+from pathlib import Path
+from typing import Any
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import HTMLResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+
+app = FastAPI(title="Benchmark Visualizer")
+
+# Get the benchmarks directory
+BENCHMARKS_DIR = Path(__file__).parent.parent
+
+
+@app.get("/", response_class=HTMLResponse)
+async def index():
+    """Serve the main benchmark visualizer page."""
+    html_path = Path(__file__).parent / "static" / "index.html"
+    with open(html_path) as f:
+        return f.read()
+
+
+@app.get("/api/locomo")
+async def get_locomo_results(mode: str = "search") -> dict[str, Any]:
+    """Get LoComo benchmark results.
+
+    Returns pre-computed benchmark results from the locomo directory.
+
+    Args:
+        mode: Either "search" (default) or "think" to select which results to load
+    """
+    try:
+        # Determine filename based on mode
+        if mode == "think":
+            filename = "benchmark_results_think.json"
+        else:
+            filename = "benchmark_results.json"
+
+        results_path = BENCHMARKS_DIR / "locomo" / filename
+
+        if not results_path.exists():
+            raise HTTPException(
+                status_code=404,
+                detail=f"Benchmark results not found for mode '{mode}'. Please run the benchmark first with {'--use-think' if mode == 'think' else 'default settings'}."
+            )
+
+        with open(results_path) as f:
+            results = json.load(f)
+
+        return results
+    except json.JSONDecodeError as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to parse benchmark results: {str(e)}"
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# Mount static files
+app.mount("/static", StaticFiles(directory=Path(__file__).parent / "static"), name="static")
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8001)

benchmarks/visualizer/static/css/styles.css

@@ -0,0 +1,140 @@
+body {
+    font-family: Tahoma, sans-serif;
+    margin: 0;
+    padding: 0;
+    background: #f5f5f5;
+}
+
+.header {
+    background: #333;
+    color: white;
+    padding: 20px;
+    border-bottom: 3px solid #42a5f5;
+}
+
+.header h1 {
+    margin: 0 0 5px 0;
+}
+
+.header p {
+    margin: 0;
+    color: #ccc;
+}
+
+.benchmark-selector {
+    background: #f0f0f0;
+    padding: 15px 20px;
+    border-bottom: 2px solid #333;
+    display: flex;
+    gap: 15px;
+    align-items: center;
+}
+
+.benchmark-selector label {
+    font-weight: bold;
+    font-size: 14px;
+}
+
+.benchmark-selector select {
+    padding: 8px 12px;
+    border: 2px solid #42a5f5;
+    border-radius: 4px;
+    background: white;
+    color: #333;
+    font-size: 14px;
+    font-weight: bold;
+    cursor: pointer;
+    min-width: 200px;
+}
+
+#benchmark-content {
+    padding: 20px;
+}
+
+.welcome-message {
+    text-align: center;
+    padding: 60px 20px;
+    color: #666;
+}
+
+.welcome-message h2 {
+    color: #333;
+}
+
+.load-button {
+    padding: 8px 20px;
+    background: #66bb6a;
+    color: white;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    font-weight: bold;
+    font-size: 14px;
+    margin-bottom: 20px;
+}
+
+.load-button:hover {
+    background: #43a047;
+}
+
+.error-message {
+    color: #d32f2f;
+    padding: 20px;
+    background: #ffebee;
+    border: 2px solid #ef5350;
+    border-radius: 8px;
+    margin: 20px;
+    max-width: 800px;
+}
+
+.error-message h3 {
+    margin-top: 0;
+    color: #c62828;
+}
+
+.error-message pre {
+    background: #f5f5f5;
+    padding: 10px;
+    border-radius: 4px;
+    overflow-x: auto;
+    color: #333;
+    font-family: monospace;
+    font-size: 13px;
+}
+
+.stats-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+    gap: 10px;
+    margin-top: 10px;
+}
+
+.stat-item {
+    padding: 12px;
+    background: white;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    text-align: center;
+}
+
+.stat-label {
+    font-weight: bold;
+    color: #666;
+    font-size: 12px;
+    margin-bottom: 5px;
+}
+
+.stat-value {
+    font-size: 24px;
+    color: #333;
+    font-weight: bold;
+}
+
+.qa-results {
+    margin-top: 20px;
+}
+
+.qa-item {
+    margin-bottom: 15px;
+    border-radius: 8px;
+}

benchmarks/visualizer/static/index.html

@@ -0,0 +1,32 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Benchmark Visualizer</title>
+    <meta charset="utf-8">
+    <link rel="stylesheet" href="/static/css/styles.css">
+</head>
+<body>
+    <div class="header">
+        <h1>Benchmark Visualizer</h1>
+        <p>Analyze and visualize benchmark results</p>
+    </div>
+
+    <div class="benchmark-selector">
+        <label>Select Benchmark:</label>
+        <select id="benchmark-select" onchange="selectBenchmark()">
+            <option value="">-- Select a benchmark --</option>
+            <option value="locomo-search">LoComo (search)</option>
+            <option value="locomo-think">LoComo (think)</option>
+        </select>
+    </div>
+
+    <div id="benchmark-content">
+        <div class="welcome-message">
+            <h2>Welcome to Benchmark Visualizer</h2>
+            <p>Select a benchmark from the dropdown above to view results.</p>
+        </div>
+    </div>
+
+    <script src="/static/js/app.js"></script>
+</body>
+</html>

benchmarks/visualizer/static/js/app.js

@@ -0,0 +1,342 @@
+// Benchmark Visualizer App
+
+let currentBenchmark = null;
+let benchmarkData = null;
+
+function selectBenchmark() {
+    const select = document.getElementById('benchmark-select');
+    currentBenchmark = select.value;
+
+    if (!currentBenchmark) {
+        document.getElementById('benchmark-content').innerHTML = `
+            <div class="welcome-message">
+                <h2>Welcome to Benchmark Visualizer</h2>
+                <p>Select a benchmark from the dropdown above to view results.</p>
+            </div>
+        `;
+        return;
+    }
+
+    // Load the selected benchmark
+    if (currentBenchmark === 'locomo-search') {
+        loadLocomoResults('search');
+    } else if (currentBenchmark === 'locomo-think') {
+        loadLocomoResults('think');
+    }
+}
+
+async function loadLocomoResults(mode = 'search') {
+    try {
+        const response = await fetch(`/api/locomo?mode=${mode}`);
+
+        if (!response.ok) {
+            const errorData = await response.json();
+            const modeLabel = mode === 'think' ? 'think' : 'search';
+            const runCommand = mode === 'think'
+                ? 'uv run python run_benchmark.py --use-think'
+                : 'uv run python run_benchmark.py';
+
+            document.getElementById('benchmark-content').innerHTML = `
+                <div class="error-message">
+                    <h3>⚠️ Benchmark Results Not Found</h3>
+                    <p>${errorData.detail || 'The requested benchmark results are not available.'}</p>
+                    <p><strong>To generate ${modeLabel} mode results:</strong></p>
+                    <pre style="background: #f5f5f5; padding: 10px; border-radius: 4px; overflow-x: auto;">cd benchmarks/locomo
+${runCommand}</pre>
+                    <p style="margin-top: 15px; font-size: 14px; color: #666;">
+                        Once the benchmark completes, refresh this page and select "${mode === 'think' ? 'LoComo (think)' : 'LoComo (search)'}" again.
+                    </p>
+                </div>
+            `;
+            return;
+        }
+
+        benchmarkData = await response.json();
+        console.log(`Loaded locomo data (${mode} mode):`, benchmarkData);
+        renderLocomoResults(mode);
+    } catch (e) {
+        console.error('Error loading benchmark results:', e);
+        document.getElementById('benchmark-content').innerHTML = `
+            <div class="error-message">
+                <h3>❌ Error Loading Results</h3>
+                <p>${e.message}</p>
+                <p style="font-size: 12px; color: #666; margin-top: 10px;">Check the browser console for more details.</p>
+            </div>
+        `;
+    }
+}
+
+function renderLocomoResults(mode = 'search') {
+    if (!benchmarkData) return;
+
+    const content = document.getElementById('benchmark-content');
+
+    try {
+        // Handle both old and new structure
+        const results = benchmarkData.item_results || benchmarkData.conversation_results || [];
+        const numItems = benchmarkData.num_items || results.length;
+
+        console.log('Rendering results:', { resultsCount: results.length, numItems });
+
+        // Calculate per-category statistics
+        const categoryStats = {
+            1: { name: 'Multi-hop', correct: 0, total: 0 },
+            2: { name: 'Single-hop', correct: 0, total: 0 },
+            3: { name: 'Temporal', correct: 0, total: 0 },
+            4: { name: 'Open-domain', correct: 0, total: 0 }
+        };
+
+        // Aggregate across all items
+        results.forEach(item => {
+            if (item.metrics && item.metrics.detailed_results) {
+                item.metrics.detailed_results.forEach(result => {
+                    const category = result.category;
+                    if (categoryStats[category]) {
+                        categoryStats[category].total++;
+                        if (result.is_correct) {
+                            categoryStats[category].correct++;
+                        }
+                    }
+                });
+            }
+        });
+
+        // Determine title based on mode
+        const modeLabel = mode === 'think' ? ' (Think Mode)' : ' (Search Mode)';
+
+        // Overall stats
+        const overallHtml = `
+            <div style="background: #f9f9f9; padding: 20px; border: 2px solid #333; border-radius: 8px; margin-bottom: 20px;">
+                <h3 style="margin-top: 0;">LoComo Benchmark${modeLabel} - Overall Performance</h3>
+                <div class="stats-grid">
+                    <div class="stat-item">
+                        <div class="stat-label">Overall Accuracy</div>
+                        <div class="stat-value">${benchmarkData.overall_accuracy.toFixed(2)}%</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-label">Correct Answers</div>
+                        <div class="stat-value">${benchmarkData.total_correct} / ${benchmarkData.total_questions}</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-label">Items</div>
+                        <div class="stat-value">${numItems}</div>
+                    </div>
+                </div>
+
+                <h4 style="margin: 20px 0 10px 0; padding-top: 15px; border-top: 1px solid #ddd;">Accuracy by Category</h4>
+                <div class="stats-grid" style="grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));">
+                    ${Object.values(categoryStats).map(cat => {
+                        const accuracy = cat.total > 0 ? ((cat.correct / cat.total) * 100).toFixed(1) : 0;
+                        const color = accuracy >= 70 ? '#43a047' : accuracy >= 50 ? '#ff9800' : '#e53935';
+                        return `
+                            <div class="stat-item">
+                                <div class="stat-label">${cat.name}</div>
+                                <div class="stat-value" style="color: ${color};">${accuracy}%</div>
+                                <div style="font-size: 11px; color: #666; margin-top: 4px;">${cat.correct} / ${cat.total}</div>
+                            </div>
+                        `;
+                    }).join('')}
+                </div>
+            </div>
+        `;
+
+        // Filter controls
+        const filterHtml = `
+            <div style="margin-bottom: 20px; display: flex; gap: 10px; align-items: center;">
+                <label style="font-weight: bold;">Show:</label>
+                <label><input type="radio" name="answer-filter" value="all" checked onchange="filterAnswers()"> All Answers</label>
+                <label><input type="radio" name="answer-filter" value="incorrect" onchange="filterAnswers()"> ❌ Incorrect Only</label>
+                <label><input type="radio" name="answer-filter" value="correct" onchange="filterAnswers()"> ✅ Correct Only</label>
+            </div>
+        `;
+
+        // Build item sections
+        let itemsHtml = '';
+        results.forEach((item, idx) => {
+            const itemId = item.item_id || item.sample_id || `item-${idx}`;
+            const accuracy = item.metrics.accuracy.toFixed(2);
+            const correctCount = item.metrics.correct;
+            const totalCount = item.metrics.total;
+
+            itemsHtml += `
+                <div style="margin-bottom: 30px; border: 2px solid #333; border-radius: 8px; overflow: hidden;">
+                    <div style="background: #f0f0f0; padding: 15px; border-bottom: 2px solid #333; cursor: pointer;" onclick="toggleConversation(${idx})">
+                        <h3 style="margin: 0; display: flex; justify-content: space-between; align-items: center;">
+                            <span>📊 ${itemId}</span>
+                            <span style="font-size: 18px; color: ${accuracy >= 70 ? '#43a047' : accuracy >= 50 ? '#ff9800' : '#e53935'};">
+                                ${accuracy}% (${correctCount}/${totalCount})
+                            </span>
+                        </h3>
+                    </div>
+                    <div id="conv-${idx}" style="display: none; padding: 20px;">
+                        ${renderConversationDetails(item)}
+                    </div>
+                </div>
+            `;
+        });
+
+        content.innerHTML = overallHtml + filterHtml + itemsHtml;
+    } catch (e) {
+        console.error('Error rendering Locomo results:', e);
+        content.innerHTML = `
+            <div class="error-message">
+                <strong>Error rendering results:</strong> ${e.message}<br>
+                <pre style="margin-top: 10px; font-size: 11px; overflow: auto;">${e.stack}</pre>
+            </div>
+        `;
+    }
+}
+
+function renderConversationDetails(conv) {
+    if (!conv || !conv.metrics) {
+        return '<div style="padding: 20px; color: #666;">No metrics available</div>';
+    }
+
+    const results = conv.metrics.detailed_results;
+    if (!results || !Array.isArray(results) || results.length === 0) {
+        return '<div style="padding: 20px; color: #666;">No detailed results available</div>';
+    }
+
+    let html = '<div class="qa-results">';
+
+    results.forEach((result, idx) => {
+        const isCorrect = result.is_correct;
+        const bgColor = isCorrect ? '#e8f5e9' : '#ffebee';
+        const icon = isCorrect ? '✅' : '❌';
+        const category = getCategoryName(result.category);
+
+        html += `
+            <div class="qa-item" data-correct="${isCorrect}" style="background: ${bgColor}; padding: 15px; margin-bottom: 15px; border: 1px solid #ddd; border-radius: 8px;">
+                <div style="display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 10px;">
+                    <div style="flex: 1;">
+                        <div style="font-weight: bold; font-size: 16px; margin-bottom: 8px;">
+                            ${icon} Question ${idx + 1} <span style="font-size: 12px; background: #666; color: white; padding: 2px 8px; border-radius: 4px; margin-left: 8px;">${category}</span>
+                        </div>
+                        <div style="margin-bottom: 8px;">
+                            <b>Q:</b> ${result.question}
+                        </div>
+                    </div>
+                </div>
+
+                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px; margin-bottom: 10px;">
+                    <div>
+                        <div style="font-weight: bold; color: #43a047; margin-bottom: 4px;">✓ Correct Answer:</div>
+                        <div style="background: white; padding: 8px; border-radius: 4px; border: 1px solid #ccc;">
+                            ${result.correct_answer}
+                        </div>
+                    </div>
+                    <div>
+                        <div style="font-weight: bold; color: ${isCorrect ? '#43a047' : '#e53935'}; margin-bottom: 4px;">
+                            ${isCorrect ? '✓' : '✗'} Predicted Answer:
+                        </div>
+                        <div style="background: white; padding: 8px; border-radius: 4px; border: 1px solid #ccc;">
+                            ${result.predicted_answer}
+                        </div>
+                    </div>
+                </div>
+
+                <details style="margin-top: 10px;">
+                    <summary style="cursor: pointer; font-weight: bold; padding: 5px; background: rgba(255,255,255,0.5); border-radius: 4px;">
+                        📝 Show Reasoning & Retrieved Memories
+                    </summary>
+                    <div style="margin-top: 10px; padding: 10px; background: white; border-radius: 4px;">
+                        <div style="margin-bottom: 10px;">
+                            <b>System Reasoning:</b>
+                            <div style="padding: 8px; background: #f5f5f5; border-radius: 4px; margin-top: 4px;">
+                                ${result.reasoning}
+                            </div>
+                        </div>
+                        <div style="margin-bottom: 10px;">
+                            <b>Judge Reasoning:</b>
+                            <div style="padding: 8px; background: #f5f5f5; border-radius: 4px; margin-top: 4px;">
+                                ${result.correctness_reasoning || 'N/A'}
+                            </div>
+                        </div>
+                        <div>
+                            <b>Retrieved Memories (${result.retrieved_memories ? result.retrieved_memories.length : 0}):</b>
+                            ${renderRetrievedMemories(result.retrieved_memories)}
+                        </div>
+                    </div>
+                </details>
+            </div>
+        `;
+    });
+
+    html += '</div>';
+    return html;
+}
+
+function renderRetrievedMemories(memories) {
+    if (!memories || !Array.isArray(memories) || memories.length === 0) {
+        return '<div style="padding: 8px; color: #999;">No memories retrieved</div>';
+    }
+
+    let html = '<div style="margin-top: 8px;">';
+    memories.forEach((mem, idx) => {
+        if (!mem) return;
+        const eventDate = mem.event_date ? new Date(mem.event_date).toLocaleString() : 'N/A';
+
+        // Determine border color based on fact type
+        let borderColor = '#42a5f5'; // default blue
+        let factTypeLabel = '';
+        if (mem.fact_type) {
+            factTypeLabel = `<span style="background: #666; color: white; padding: 2px 6px; border-radius: 3px; font-size: 10px; margin-left: 8px;">${mem.fact_type.toUpperCase()}</span>`;
+            if (mem.fact_type === 'world') {
+                borderColor = '#4caf50'; // green
+            } else if (mem.fact_type === 'agent') {
+                borderColor = '#ff9800'; // orange
+            } else if (mem.fact_type === 'opinion') {
+                borderColor = '#9c27b0'; // purple
+            }
+        }
+
+        html += `
+            <div style="padding: 8px; background: #f5f5f5; border-left: 3px solid ${borderColor}; margin-bottom: 8px;">
+                <div style="font-size: 11px; color: #666; margin-bottom: 4px;">
+                    Rank #${idx + 1} | Score: ${mem.score ? mem.score.toFixed(4) : 'N/A'} | Event Date: ${eventDate}${factTypeLabel}
+                </div>
+                <div style="font-size: 13px;">${mem.text}</div>
+            </div>
+        `;
+    });
+    html += '</div>';
+    return html;
+}
+
+function getCategoryName(category) {
+    const categories = {
+        1: 'Multi-hop',
+        2: 'Single-hop',
+        3: 'Temporal',
+        4: 'Open-domain'
+    };
+    return categories[category] || 'Unknown';
+}
+
+function toggleConversation(idx) {
+    const elem = document.getElementById(`conv-${idx}`);
+    if (elem.style.display === 'none') {
+        elem.style.display = 'block';
+    } else {
+        elem.style.display = 'none';
+    }
+}
+
+function filterAnswers() {
+    const filter = document.querySelector('input[name="answer-filter"]:checked').value;
+    const items = document.querySelectorAll('.qa-item');
+
+    items.forEach(item => {
+        const isCorrect = item.dataset.correct === 'true';
+
+        if (filter === 'all') {
+            item.style.display = 'block';
+        } else if (filter === 'correct' && isCorrect) {
+            item.style.display = 'block';
+        } else if (filter === 'incorrect' && !isCorrect) {
+            item.style.display = 'block';
+        } else {
+            item.style.display = 'none';
+        }
+    });
+}

examples/parallel_think.py

@@ -0,0 +1,46 @@
+"""
+Example: Running many think operations in parallel with optimized connection pooling.
+
+For 100 parallel think operations:
+- Each think does 3 searches (world, agent, opinion)
+- Each search acquires 1-3 connections briefly
+- Total: ~300 concurrent connection requests
+
+Solution: Increase pool_max_size to handle the concurrency.
+"""
+import asyncio
+from memora import TemporalSemanticMemory
+
+
+async def main():
+    # For 100 parallel think operations, use a larger pool
+    # Rule of thumb: pool_max_size >= (num_parallel_thinks * 3)
+    memory = TemporalSemanticMemory(
+        pool_min_size=10,        # Keep some connections warm
+        pool_max_size=200        # Allow up to 200 concurrent connections
+    )
+    await memory.initialize()
+
+    # Example: Run 100 think operations in parallel
+    queries = [f"Query {i}" for i in range(100)]
+
+    tasks = [
+        memory.think_async(
+            agent_id="test_agent",
+            query=query,
+            thinking_budget=50,
+            top_k=10
+        )
+        for query in queries
+    ]
+
+    # Run all thinks in parallel
+    results = await asyncio.gather(*tasks)
+
+    print(f"Completed {len(results)} think operations")
+
+    await memory.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

scripts/profile_queries.py

@@ -0,0 +1,166 @@
+"""
+Profile slow database queries to identify optimization opportunities.
+
+Usage:
+    uv run python scripts/profile_queries.py
+"""
+import asyncio
+import os
+import asyncpg
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+async def profile_entry_points_query():
+    """Profile the vector similarity entry points query."""
+    db_url = os.getenv("DATABASE_URL")
+    conn = await asyncpg.connect(db_url)
+
+    # Generate a dummy embedding vector (384 dimensions for bge-small-en-v1.5)
+    dummy_embedding = str([0.1] * 384)
+
+    print("=" * 80)
+    print("PROFILING: Entry Points Query (Vector Similarity)")
+    print("=" * 80)
+
+    # Run EXPLAIN ANALYZE
+    explain = await conn.fetch("""
+        EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
+        SELECT id, text, context, event_date, access_count, embedding,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM memory_units
+        WHERE agent_id = $2
+          AND embedding IS NOT NULL
+          AND (1 - (embedding <=> $1::vector)) >= 0.5
+        ORDER BY embedding <=> $1::vector
+        LIMIT 3
+    """, dummy_embedding, "test_agent")
+
+    for row in explain:
+        print(row[0])
+
+    await conn.close()
+
+
+async def profile_neighbors_query(sample_node_ids):
+    """Profile the neighbors JOIN query."""
+    db_url = os.getenv("DATABASE_URL")
+    conn = await asyncpg.connect(db_url)
+
+    print("\n" + "=" * 80)
+    print("PROFILING: Neighbors Query (Graph Traversal)")
+    print(f"Sample size: {len(sample_node_ids)} nodes")
+    print("=" * 80)
+
+    # Run EXPLAIN ANALYZE
+    explain = await conn.fetch("""
+        EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
+        SELECT ml.from_unit_id, ml.to_unit_id, ml.weight, ml.link_type, ml.entity_id,
+               mu.text, mu.context, mu.event_date, mu.access_count,
+               mu.id as neighbor_id
+        FROM memory_links ml
+        JOIN memory_units mu ON ml.to_unit_id = mu.id
+        WHERE ml.from_unit_id = ANY($1::uuid[])
+          AND ml.weight >= 0.1
+        ORDER BY ml.from_unit_id, ml.weight DESC
+    """, sample_node_ids)
+
+    for row in explain:
+        print(row[0])
+
+    await conn.close()
+
+
+async def profile_embeddings_query(sample_node_ids):
+    """Profile the batch embeddings fetch query."""
+    db_url = os.getenv("DATABASE_URL")
+    conn = await asyncpg.connect(db_url)
+
+    print("\n" + "=" * 80)
+    print("PROFILING: Embeddings Query (Batch Fetch)")
+    print(f"Sample size: {len(sample_node_ids)} nodes")
+    print("=" * 80)
+
+    # Run EXPLAIN ANALYZE
+    explain = await conn.fetch("""
+        EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
+        SELECT id, embedding
+        FROM memory_units
+        WHERE id = ANY($1::uuid[])
+    """, sample_node_ids)
+
+    for row in explain:
+        print(row[0])
+
+    await conn.close()
+
+
+async def get_sample_node_ids(batch_size=50):
+    """Get sample node IDs for profiling."""
+    db_url = os.getenv("DATABASE_URL")
+    conn = await asyncpg.connect(db_url)
+
+    rows = await conn.fetch(f"""
+        SELECT id FROM memory_units
+        LIMIT {batch_size}
+    """)
+
+    await conn.close()
+    return [row['id'] for row in rows]
+
+
+async def check_indexes():
+    """Check what indexes exist."""
+    db_url = os.getenv("DATABASE_URL")
+    conn = await asyncpg.connect(db_url)
+
+    print("\n" + "=" * 80)
+    print("CURRENT INDEXES")
+    print("=" * 80)
+
+    indexes = await conn.fetch("""
+        SELECT
+            tablename,
+            indexname,
+            indexdef
+        FROM pg_indexes
+        WHERE schemaname = 'public'
+          AND tablename IN ('memory_units', 'memory_links')
+        ORDER BY tablename, indexname
+    """)
+
+    for idx in indexes:
+        print(f"\nTable: {idx['tablename']}")
+        print(f"Index: {idx['indexname']}")
+        print(f"Definition: {idx['indexdef']}")
+
+    await conn.close()
+
+
+async def main():
+    print("Starting Query Profiling...")
+
+    # Check indexes first
+    await check_indexes()
+
+    # Get sample node IDs
+    sample_ids = await get_sample_node_ids(50)
+
+    if sample_ids:
+        print(f"\nGot {len(sample_ids)} sample node IDs for profiling")
+
+        # Profile each query type
+        await profile_entry_points_query()
+        await profile_neighbors_query(sample_ids)
+        await profile_embeddings_query(sample_ids)
+    else:
+        print("\nNo data in database - run ingestion first")
+
+    print("\n" + "=" * 80)
+    print("PROFILING COMPLETE")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())