diff --git a/benchmarks/longmemeval/results/benchmark_results.json b/benchmarks/longmemeval/results/benchmark_results.json index 0637a088..106dbbe4 100644 --- a/benchmarks/longmemeval/results/benchmark_results.json +++ b/benchmarks/longmemeval/results/benchmark_results.json @@ -1 +1,16128 @@ -[] \ No newline at end of file +{ + "overall_accuracy": 80.0, + "total_correct": 4, + "total_questions": 5, + "total_invalid": 0, + "total_valid": 5, + "num_items": 5, + "item_results": [ + { + "item_id": "e47becba", + "metrics": { + "accuracy": 100.0, + "correct": 1, + "total": 1, + "invalid": 0, + "valid_total": 1, + "category_stats": { + "single-session-user": { + "correct": 1, + "total": 1, + "invalid": 0 + } + }, + "detailed_results": [ + { + "question": "What degree did I graduate with?", + "correct_answer": "Business Administration", + "predicted_answer": "You earned a Bachelor\u2019s degree in Business Administration (from the University of Michigan).", + "reasoning": "", + "category": "single-session-user", + "retrieved_memories": [ + { + "id": "139f57c3-db64-4a44-bba1-784b8aee9332", + "text": "User graduated with a Bachelor's degree in Business Administration from the University of Michigan in 2012", + "context": "Session fab585c2_1", + "event_date": "2012-01-01T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6819747686386108, + "bm25_score": 0.0, + "rrf_score": 0.048651507139079855, + "semantic_rank": 2, + "bm25_rank": 1, + "graph_rank": 2, + "weight": 0.8689963167094209, + "cross_encoder_score": 1.892113447189331, + "cross_encoder_score_normalized": 0.8689963167094209, + "original_rank": 1, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "95aae747-fb86-4a52-b84c-139589c60498", + "text": "User graduated with a degree in Business Administration", + "context": "Session answer_280352e9", + "event_date": "2023-05-30T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.7092711925506592, + "bm25_score": 0.0, + "rrf_score": 0.048915917503966164, + "semantic_rank": 1, + "bm25_rank": 2, + "graph_rank": 1, + "weight": 0.8620277753504961, + "cross_encoder_score": 1.8322350978851318, + "cross_encoder_score_normalized": 0.8620277753504961, + "original_rank": 2, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "a177c9e2-4e74-4d5d-af77-b129d60fd348", + "text": "User is considering pursuing a Master's degree, possibly an online MBA program, but has not had time to commit to a full-time program", + "context": "Session fab585c2_1", + "event_date": "2023-05-28T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6230001449584961, + "bm25_score": 0.0, + "rrf_score": 0.04617537313432836, + "semantic_rank": 4, + "bm25_rank": 7, + "graph_rank": 4, + "weight": 0.016249462858615185, + "cross_encoder_score": -4.1033124923706055, + "cross_encoder_score_normalized": 0.016249462858615185, + "original_rank": 3, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "155471c2-cf7a-43df-8acd-7752eb98acce", + "text": "Maiko Haga earned a Bachelor of Arts in English from Tokyo University of Foreign Studies.", + "context": "Session sharegpt_dabXXRO_10", + "event_date": "2023-05-27T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.5946784373926196, + "bm25_score": 0.0, + "rrf_score": 0.014285714285714285, + "semantic_rank": 10, + "weight": 0.014294976231663205, + "cross_encoder_score": -4.2334489822387695, + "cross_encoder_score_normalized": 0.014294976231663205, + "original_rank": 4, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "b8c8ace4-e8d6-4987-be00-5386c2f1308f", + "text": "Juan Perez earned a Bachelor's Degree in Computer Science from University of Buenos Aires during 20XX-20XX", + "context": "Session sharegpt_KmMXLp3_0", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.554904666250667, + "bm25_score": 0.0, + "rrf_score": 0.024705882352941175, + "semantic_rank": 40, + "bm25_rank": 8, + "weight": 0.00691580738566319, + "cross_encoder_score": -4.967005729675293, + "cross_encoder_score_normalized": 0.00691580738566319, + "original_rank": 5, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "54db77b6-f1ff-402d-b435-1315579269f2", + "text": "Charles F. 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founder's motivations and what drove the founder to seek a magical being's power, to understand why the founder made the pact and what the founder hoped to achieve.", + "context": "Session febc8622", + "event_date": "2023-05-24T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.0, + "bm25_score": 0.0, + "rrf_score": 0.006711409395973154, + "graph_rank": 89, + "weight": 1.4138572241543819e-05, + "cross_encoder_score": -11.166589736938477, + "cross_encoder_score_normalized": 1.4138572241543819e-05, + "original_rank": 147, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "dde80fee-0b2d-4a1e-92dc-a46001459227", + "text": "User believes the higher retention is because audiobooks are more engaging and because they can multitask while listening", + "context": "Session answer_40a90d51", + "event_date": "2023-05-22T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.0, + "bm25_score": 0.0, + 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Teva-specific discounts", + "context": "Session 4d0d7984", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6088354639260124, + "bm25_score": 0.0, + "rrf_score": 0.011494252873563218, + "semantic_rank": 27, + "weight": 3.983138700866502e-05, + "cross_encoder_score": -10.130815505981445, + "cross_encoder_score_normalized": 3.983138700866502e-05, + "original_rank": 15, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "15cfcc9b-7e3c-4c3a-b8aa-836ad5febf52", + "text": "Phillips 66 Rewards offers points for every purchase, redeemable for fuel discounts, food, drinks, and other items at Phillips 66 stations.", + "context": "Session 0afa904a", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6534272456001212, + "bm25_score": 0.0, + "rrf_score": 0.014925373134328358, + "semantic_rank": 7, + "weight": 3.95986160823386e-05, + 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10% off coupon for Zara that they received when they bought the white silk blouse.", + "context": "Session c3991c02_2", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6387851859225238, + "bm25_score": 0.0, + "rrf_score": 0.014084507042253521, + "semantic_rank": 11, + "weight": 3.767203253085742e-05, + "cross_encoder_score": -10.186554908752441, + "cross_encoder_score_normalized": 3.767203253085742e-05, + "original_rank": 18, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "d29f369a-4a27-4c72-8744-85e85d9e78bd", + "text": "CITGO Rewards offers points for every purchase, redeemable for fuel discounts, food, drinks, and other items at CITGO stations.", + "context": "Session 0afa904a", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6168206194853088, + "bm25_score": 0.0, + "rrf_score": 0.0125, + "semantic_rank": 20, + "weight": 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received the old coffee table from their parents when they first moved out, and has now gotten rid of it.", + "context": "Session 793382f9_2", + "event_date": "2023-05-27T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.5852822806926474, + "bm25_score": 0.0, + "rrf_score": 0.0078125, + "semantic_rank": 68, + "weight": 3.6262316506929877e-05, + "cross_encoder_score": -10.224695205688477, + "cross_encoder_score_normalized": 3.6262316506929877e-05, + "original_rank": 21, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "c1f30522-6f28-4ae0-81e2-2f2454c63340", + "text": "Assistant recommended using OneNote to create sections and pages for digital coupons.", + "context": "Session aae4411b_2", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.5727798597347912, + "bm25_score": 0.0, + "rrf_score": 0.01318585990023856, + "semantic_rank": 99, + "bm25_rank": 85, + 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digital coupons and loyalty cards in digital wallets such as Apple Wallet or Google Pay.", + "context": "Session aae4411b_2", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.5994589150113321, + "bm25_score": 0.0, + "rrf_score": 0.017260814428631817, + "semantic_rank": 34, + "bm25_rank": 91, + "weight": 3.454555327028535e-05, + "cross_encoder_score": -10.273197174072266, + "cross_encoder_score_normalized": 3.454555327028535e-05, + "original_rank": 24, + "mmr_score": null, + "mmr_relevance": null, + "mmr_max_similarity": null, + "mmr_diversified": false + }, + { + "id": "3ac6f1b2-efb5-4144-a559-d0d9162ca715", + "text": "Assistant recommended the RetailMeNot app for digital coupons, promo codes, and cash back deals.", + "context": "Session aae4411b_2", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6079246273130666, + "bm25_score": 0.0, + "rrf_score": 0.019931607230092817, + "semantic_rank": 29, + 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share insights as Senior Project Managers overseeing Europe, LatAM, and Canada; and the group will explore collaboration and communication tools to facilitate efficient global teamwork", + "context": "Session sharegpt_LG0uJvY_0", + "event_date": "2023-05-20T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.0, + "bm25_score": 2.0, + "rrf_score": 0.012658227848101266, + "bm25_rank": 19, + "weight": 1.8188698579732485e-05, + "cross_encoder_score": -10.914691925048828, + "cross_encoder_score_normalized": 1.8188698579732485e-05, + "original_rank": 37, + "mmr_score": -0.2897079610172999, + "mmr_relevance": 6.865310528236933e-06, + "mmr_max_similarity": 0.5794227873451281, + "mmr_diversified": true + }, + { + "id": "a42f7241-145e-4ac8-b851-7a76e868de44", + "text": "The government of Chhattisgarh can create policies such as family\u2011friendly work policies and equal\u2011pay laws to promote a supportive environment for women entrepreneurs.", + "context": "Session 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{ + "id": "d4e1b4ee-6cb9-4598-97cd-55387c20cf71", + "text": "Assistant suggested Lucy Dacus, an American indie rock singer-songwriter, as similar to Phoebe Bridgers.", + "context": "Session 3ea9f765", + "event_date": "2023-05-30T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.0, + "bm25_score": 0.0, + "rrf_score": 0.006756756756756757, + "graph_rank": 88, + "weight": 2.7087119988430155e-05, + "cross_encoder_score": -10.516425132751465, + "cross_encoder_score_normalized": 2.7087119988430155e-05, + "original_rank": 146, + "mmr_score": -0.3826377179662951, + "mmr_relevance": 1.576768052826664e-05, + "mmr_max_similarity": 0.7652912036131184, + "mmr_diversified": true + }, + { + "id": "6f575534-ab6a-4ad7-a276-3c7b432cafc8", + "text": "Assistant suggested the user search online for \"music venues near me\" or \"concerts in [your city]\" to find upcoming events and venues in their area.", + "context": "Session 7045db85_1", + "event_date": "2023-05-22T00:00:00+00:00", + 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recommendations, and it is available on iOS and Android.", + "context": "Session 127fd1c9", + "event_date": "2023-05-27T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.6015429496765137, + "bm25_score": 0.0, + "rrf_score": 0.01680672268907563, + "semantic_rank": 59, + "graph_rank": 59, + "weight": 1.6203242561834036e-05, + "cross_encoder_score": -11.030282974243164, + "cross_encoder_score_normalized": 1.6203242561834036e-05, + "original_rank": 150, + "mmr_score": -0.38655825840421565, + "mmr_relevance": 4.878973482787378e-06, + "mmr_max_similarity": 0.7731213957819141, + "mmr_diversified": true + }, + { + "id": "c2685764-3e52-4506-8482-bdc6c11f25b0", + "text": "User gave a beautiful vase to one couple as a wedding gift", + "context": "Session 7b1d1f43_1", + "event_date": "2023-05-21T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.0, + "bm25_score": 0.0, + "rrf_score": 0.014492753623188406, + "graph_rank": 9, + "weight": 1.3036000699888966e-05, + 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"id": "b78b3ac7-ea8b-4305-9184-62ba77fb7704", + "text": "User is interested in trying new snack bar options, specifically Kind Bar and LaraBar.", + "context": "Session 978c0e70", + "event_date": "2023-05-25T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.5773854851722717, + "bm25_score": 0.0, + "rrf_score": 0.006666666666666667, + "semantic_rank": 90, + "weight": 1.3073761348260839e-05, + "cross_encoder_score": -11.244890213012695, + "cross_encoder_score_normalized": 1.3073761348260839e-05, + "original_rank": 153, + "mmr_score": -0.3893766504972731, + "mmr_relevance": 1.7481035911749937e-06, + "mmr_max_similarity": 0.7787550490981374, + "mmr_diversified": true + }, + { + "id": "a50ca813-1fcf-4515-9a1b-80c62d7a5bcd", + "text": "User outlined steps to create a customer\u2011centric AR marketing design, including defining the target audience, identifying the focal goal, determining subordinate and super\u2011ordinate goals, designing the AR experience, and testing and refining it.", + "context": "Session sharegpt_UnjngE7_65", + "event_date": "2023-05-23T00:00:00+00:00", + "access_count": 0, + "semantic_similarity": 0.0, + "bm25_score": 1.0, + "rrf_score": 0.0070921985815602835, + "bm25_rank": 81, + "weight": 1.838411769312715e-05, + "cross_encoder_score": -10.90400505065918, + "cross_encoder_score_normalized": 1.838411769312715e-05, + "original_rank": 154, + "mmr_score": -0.38960931578566155, + "mmr_relevance": 7.0608163570367055e-06, + "mmr_max_similarity": 0.7792256923876801, + "mmr_diversified": true + } + ], + "is_invalid": false, + "error": null, + "is_correct": true, + "correctness_reasoning": "The generated answer correctly identifies the playlist name \"Summer Vibes\" matching the gold answer." + } + ] + }, + "num_sessions": -1 + } + ] +} \ No newline at end of file diff --git a/benchmarks/visualizer/server.py b/benchmarks/visualizer/server.py index 321f974a..5a4bde9d 100644 --- a/benchmarks/visualizer/server.py +++ b/benchmarks/visualizer/server.py @@ -1,10 +1,11 @@ """Benchmark Visualizer Web Service. A standalone web service for visualizing benchmark results. -Currently supports LoComo benchmark visualization. +Supports LoComo and LongMemEval benchmark visualization. """ import json +import logging from pathlib import Path from typing import Any @@ -63,10 +64,41 @@ async def get_locomo_results(mode: str = "search") -> dict[str, Any]: raise HTTPException(status_code=500, detail=str(e)) +@app.get("/api/longmemeval") +async def get_longmemeval_results() -> dict[str, Any]: + """Get LongMemEval benchmark results. + + Returns pre-computed benchmark results from the longmemeval directory. + """ + try: + results_path = BENCHMARKS_DIR / "longmemeval" / "results" / "benchmark_results.json" + print(f"chcking path {results_path}") + logging.info(f"chcking path {results_path}") + + if not results_path.exists(): + raise HTTPException( + status_code=404, + detail="Benchmark results not found. Please run the benchmark first." + ) + + 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 + logging.basicConfig(level=logging.INFO) uvicorn.run(app, host="127.0.0.1", port=8001) diff --git a/benchmarks/visualizer/static/index.html b/benchmarks/visualizer/static/index.html index 23e1b5a3..ee1af744 100644 --- a/benchmarks/visualizer/static/index.html +++ b/benchmarks/visualizer/static/index.html @@ -17,6 +17,7 @@ + diff --git a/benchmarks/visualizer/static/js/app.js b/benchmarks/visualizer/static/js/app.js index ecfee586..6e6766ff 100644 --- a/benchmarks/visualizer/static/js/app.js +++ b/benchmarks/visualizer/static/js/app.js @@ -22,6 +22,8 @@ function selectBenchmark() { loadLocomoResults('search'); } else if (currentBenchmark === 'locomo-think') { loadLocomoResults('think'); + } else if (currentBenchmark === 'longmemeval') { + loadLongMemEvalResults(); } } @@ -369,3 +371,259 @@ function filterAnswers() { } }); } + +// LongMemEval functions +async function loadLongMemEvalResults() { + try { + const response = await fetch('/api/longmemeval'); + + if (!response.ok) { + const errorData = await response.json(); + document.getElementById('benchmark-content').innerHTML = ` +
+

⚠️ Benchmark Results Not Found

+

${errorData.detail || 'The requested benchmark results are not available.'}

+

To generate results:

+
cd benchmarks/longmemeval
+uv run python longmemeval_benchmark.py
+

+ Once the benchmark completes, refresh this page and select "LongMemEval" again. +

+
+ `; + return; + } + + benchmarkData = await response.json(); + console.log('Loaded longmemeval data:', benchmarkData); + renderLongMemEvalResults(); + } catch (e) { + console.error('Error loading longmemeval results:', e); + document.getElementById('benchmark-content').innerHTML = ` +
+

❌ Error Loading Results

+

${e.message}

+

Check the browser console for more details.

+
+ `; + } +} + +function renderLongMemEvalResults() { + if (!benchmarkData) return; + + const content = document.getElementById('benchmark-content'); + + try { + const results = benchmarkData.item_results || []; + const numItems = benchmarkData.num_items || results.length; + + console.log('Rendering longmemeval results:', { resultsCount: results.length, numItems }); + + // Calculate per-category statistics + const categoryStats = {}; + + // Aggregate across all items + let totalInvalid = 0; + results.forEach(item => { + if (item.metrics && item.metrics.category_stats) { + Object.entries(item.metrics.category_stats).forEach(([category, stats]) => { + if (!categoryStats[category]) { + categoryStats[category] = { name: category, correct: 0, total: 0, invalid: 0 }; + } + categoryStats[category].correct += stats.correct || 0; + categoryStats[category].total += stats.total || 0; + categoryStats[category].invalid += stats.invalid || 0; + }); + } + if (item.metrics && item.metrics.detailed_results) { + item.metrics.detailed_results.forEach(result => { + if (result.is_invalid) { + totalInvalid++; + } + }); + } + }); + + // Overall stats + const totalInvalidDisplay = totalInvalid > 0 + ? `
+
Invalid Questions
+
${totalInvalid}
+
` + : ''; + + const overallHtml = ` +
+

LongMemEval Benchmark - Overall Performance

+ ${totalInvalid > 0 ? `
+ ⚠️ Note: ${totalInvalid} question(s) marked as invalid due to errors (excluded from accuracy calculation) +
` : ''} +
+
+
Overall Accuracy
+
${benchmarkData.overall_accuracy.toFixed(2)}%
+ ${totalInvalid > 0 ? `
(${benchmarkData.total_correct} / ${benchmarkData.total_valid || (benchmarkData.total_questions - totalInvalid)})
` : ''} +
+
+
Correct Answers
+
${benchmarkData.total_correct} / ${benchmarkData.total_questions}
+
+ ${totalInvalidDisplay} +
+
Items
+
${numItems}
+
+
+ +

Accuracy by Category

+
+ ${Object.values(categoryStats).map(cat => { + const invalidCount = cat.invalid || 0; + const validTotal = cat.total - invalidCount; + const accuracy = validTotal > 0 ? ((cat.correct / validTotal) * 100).toFixed(1) : 0; + const color = accuracy >= 70 ? '#43a047' : accuracy >= 50 ? '#ff9800' : '#e53935'; + const invalidNote = invalidCount > 0 ? ` (${invalidCount} invalid)` : ''; + return ` +
+
${cat.name}
+
${accuracy}%
+
${cat.correct} / ${cat.total}${invalidNote}
+
+ `; + }).join('')} +
+
+ `; + + // Filter controls + const filterHtml = ` +
+ + + + + ${totalInvalid > 0 ? '' : ''} +
+ `; + + // Build item sections + let itemsHtml = ''; + results.forEach((item, idx) => { + const itemId = item.item_id || `item-${idx}`; + const accuracy = item.metrics.accuracy.toFixed(2); + const correctCount = item.metrics.correct; + const totalCount = item.metrics.total; + + itemsHtml += ` +
+
+

+ 📊 ${itemId} + + ${accuracy}% (${correctCount}/${totalCount}) + +

+
+ +
+ `; + }); + + content.innerHTML = overallHtml + filterHtml + itemsHtml; + } catch (e) { + console.error('Error rendering LongMemEval results:', e); + content.innerHTML = ` +
+ Error rendering results: ${e.message}
+
${e.stack}
+
+ `; + } +} + +function renderLongMemEvalItemDetails(item) { + if (!item || !item.metrics) { + return '
No metrics available
'; + } + + const results = item.metrics.detailed_results; + if (!results || !Array.isArray(results) || results.length === 0) { + return '
No detailed results available
'; + } + + let html = '
'; + + results.forEach((result, idx) => { + const isInvalid = result.is_invalid || false; + const isCorrect = result.is_correct; + const bgColor = isInvalid ? '#fff3cd' : (isCorrect ? '#e8f5e9' : '#ffebee'); + const icon = isInvalid ? '⚠️' : (isCorrect ? '✅' : '❌'); + const category = result.category || 'Unknown'; + + html += ` +
+
+
+
+ ${icon} Question ${idx + 1} ${isInvalid ? 'INVALID' : ''} ${category} +
+
+ Q: ${result.question} +
+
+
+ +
+
+
✓ Correct Answer:
+
+ ${result.correct_answer} +
+
+
+
+ ${isCorrect ? '✓' : '✗'} Predicted Answer: +
+
+ ${result.predicted_answer} +
+
+
+ +
+ + 📝 Show Reasoning & Retrieved Memories + +
+ ${isInvalid ? `
+ ⚠️ Error: +
${result.error || 'Question marked as invalid'}
+
` : ''} +
+ System Reasoning: +
+ ${result.reasoning || 'N/A'} +
+
+
+ Judge Reasoning: +
+ ${result.correctness_reasoning || 'N/A'} +
+
+
+ Retrieved Memories (${result.retrieved_memories ? result.retrieved_memories.length : 0}): + ${renderRetrievedMemories(result.retrieved_memories)} +
+
+
+
+ `; + }); + + html += '
'; + return html; +} diff --git a/memora/fact_extraction.py b/memora/fact_extraction.py index abd6e4a3..d87bcc3c 100644 --- a/memora/fact_extraction.py +++ b/memora/fact_extraction.py @@ -12,6 +12,7 @@ from datetime import datetime from typing import List, Dict, Optional, Literal from openai import AsyncOpenAI from pydantic import BaseModel, Field +from .llm_wrapper import OutputTooLongError class Entity(BaseModel): @@ -439,6 +440,112 @@ Remember: raise last_error +async def _extract_facts_with_auto_split( + chunk: str, + chunk_index: int, + total_chunks: int, + event_date: datetime, + context: str, + llm_config: 'LLMConfig' +) -> List[Dict[str, str]]: + """ + Extract facts from a chunk with automatic splitting if output exceeds token limits. + + If the LLM output is too long (OutputTooLongError), this function automatically + splits the chunk in half and processes each half recursively. + + Args: + chunk: Text chunk to process + chunk_index: Index of this chunk in the original list + total_chunks: Total number of original chunks + event_date: Reference date for temporal information + context: Context about the conversation/document + llm_config: LLM configuration to use + + Returns: + List of fact dictionaries extracted from the chunk (possibly from sub-chunks) + """ + import logging + logger = logging.getLogger(__name__) + + try: + # Try to extract facts from the full chunk + return await _extract_facts_from_chunk( + chunk=chunk, + chunk_index=chunk_index, + total_chunks=total_chunks, + event_date=event_date, + context=context, + llm_config=llm_config + ) + except OutputTooLongError as e: + # Output exceeded token limits - split the chunk in half and retry + logger.warning( + f"Output too long for chunk {chunk_index + 1}/{total_chunks} " + f"({len(chunk)} chars). Splitting in half and retrying..." + ) + + # Split at the midpoint, preferring sentence boundaries + mid_point = len(chunk) // 2 + + # Try to find a sentence boundary near the midpoint + # Look for ". ", "! ", "? " within 20% of midpoint + search_range = int(len(chunk) * 0.2) + search_start = max(0, mid_point - search_range) + search_end = min(len(chunk), mid_point + search_range) + + sentence_endings = ['. ', '! ', '? ', '\n\n'] + best_split = mid_point + + for ending in sentence_endings: + pos = chunk.rfind(ending, search_start, search_end) + if pos != -1: + best_split = pos + len(ending) + break + + # Split the chunk + first_half = chunk[:best_split].strip() + second_half = chunk[best_split:].strip() + + logger.info( + f"Split chunk {chunk_index + 1} into two sub-chunks: " + f"{len(first_half)} chars and {len(second_half)} chars" + ) + + # Process both halves recursively (in parallel) + sub_tasks = [ + _extract_facts_with_auto_split( + chunk=first_half, + chunk_index=chunk_index, + total_chunks=total_chunks, + event_date=event_date, + context=context, + llm_config=llm_config + ), + _extract_facts_with_auto_split( + chunk=second_half, + chunk_index=chunk_index, + total_chunks=total_chunks, + event_date=event_date, + context=context, + llm_config=llm_config + ) + ] + + sub_results = await asyncio.gather(*sub_tasks) + + # Combine results from both halves + all_facts = [] + for sub_result in sub_results: + all_facts.extend(sub_result) + + logger.info( + f"Successfully extracted {len(all_facts)} facts from split chunk {chunk_index + 1}" + ) + + return all_facts + + async def extract_facts_from_text( text: str, event_date: datetime, @@ -452,6 +559,9 @@ async def extract_facts_from_text( For large texts (>chunk_size chars), automatically chunks at sentence boundaries to avoid hitting output token limits. Processes ALL chunks in PARALLEL for speed. + If a chunk produces output that exceeds token limits (OutputTooLongError), it is + automatically split in half and retried recursively until successful. + Args: text: Input text (conversation, article, etc.) event_date: Reference date for resolving relative times @@ -473,7 +583,7 @@ async def extract_facts_from_text( chunks = chunk_text(text, max_chars=chunk_size) tasks = [ - _extract_facts_from_chunk( + _extract_facts_with_auto_split( chunk=chunk, chunk_index=i, total_chunks=len(chunks), diff --git a/memora/llm_wrapper.py b/memora/llm_wrapper.py index a6028910..35f61ef6 100644 --- a/memora/llm_wrapper.py +++ b/memora/llm_wrapper.py @@ -5,7 +5,7 @@ import os import time import asyncio from typing import Optional, Any, Dict, List -from openai import AsyncOpenAI, RateLimitError, APIError, APIStatusError +from openai import AsyncOpenAI, RateLimitError, APIError, APIStatusError, LengthFinishReasonError import logging logger = logging.getLogger(__name__) @@ -14,6 +14,17 @@ logger = logging.getLogger(__name__) logging.getLogger("httpx").setLevel(logging.WARNING) +class OutputTooLongError(Exception): + """ + Bridge exception raised when LLM output exceeds token limits. + + This wraps provider-specific errors (e.g., OpenAI's LengthFinishReasonError) + to allow callers to handle output length issues without depending on + provider-specific implementations. + """ + pass + + class LLMConfig: """Configuration for an LLM provider.""" @@ -134,6 +145,13 @@ class LLMConfig: return result + except LengthFinishReasonError as e: + # Output exceeded token limits - raise bridge exception for caller to handle + logger.warning(f"LLM output exceeded token limits: {str(e)}") + raise OutputTooLongError( + f"LLM output exceeded token limits. Input may need to be split into smaller chunks." + ) from e + except APIStatusError as e: last_exception = e if attempt < max_retries: diff --git a/memora/temporal_semantic_memory.py b/memora/temporal_semantic_memory.py index f2cb5e11..aafbcd46 100644 --- a/memora/temporal_semantic_memory.py +++ b/memora/temporal_semantic_memory.py @@ -551,8 +551,7 @@ class TemporalSemanticMemory( # Calculate total character count total_chars = sum(len(item.get("content", "")) for item in contents) - # Threshold: 50,000 characters per sub-batch (roughly 12k tokens with average 4 chars/token) - CHARS_PER_BATCH = 50_000 + CHARS_PER_BATCH = 500_000 if total_chars > CHARS_PER_BATCH: # Split into smaller batches based on character count diff --git a/memora/web/__init__.py b/memora/web/__init__.py index 924cf08d..c9433fc6 100644 --- a/memora/web/__init__.py +++ b/memora/web/__init__.py @@ -3,6 +3,7 @@ Web interface for memory system. Provides FastAPI app and visualization interface. """ -from .server import app, create_app +from memora.api import create_app +from .server import app __all__ = ["app", "create_app"] diff --git a/memora/web/server.py b/memora/web/server.py index 5f8a8058..2db9b7de 100644 --- a/memora/web/server.py +++ b/memora/web/server.py @@ -4,758 +4,12 @@ FastAPI server for memory graph visualization and API. Provides REST API endpoints for memory operations and serves the interactive visualization interface. """ -import asyncio -from fastapi import FastAPI, HTTPException -from fastapi.staticfiles import StaticFiles -from fastapi.responses import FileResponse -from pydantic import BaseModel -from dotenv import load_dotenv -import os -import sys -from pathlib import Path -from typing import Optional, List, Dict, Any -from datetime import datetime - -# Import from parent memora package -from memora import TemporalSemanticMemory -from memora.embeddings import Embeddings - import logging +import os +import argparse - - -# Environment variables are loaded by the shell script that calls this module -# No need to load .env files here as they're sourced by start-server.sh - - -def create_app(memory: TemporalSemanticMemory) -> FastAPI: - """ - Create and configure the FastAPI application. - - Args: - memory: TemporalSemanticMemory instance (already initialized with required parameters) - - Returns: - Configured FastAPI application - """ - app = FastAPI( - title="Agent Memory API", - version="1.0.0", - description=""" -A temporal-semantic memory system for AI agents that stores, retrieves, and reasons over memories. - -## Features - -* **Batch Memory Storage**: Store multiple memories efficiently with automatic fact extraction -* **Semantic Search**: Find relevant memories using natural language queries -* **Fact Type Filtering**: Search across world facts, agent actions, and opinions separately -* **Think Endpoint**: Generate contextual answers based on agent identity and memories -* **Graph Visualization**: Interactive memory graph visualization -* **Document Tracking**: Track and manage memory documents with upsert support - -## Architecture - -The system uses: -- **Temporal Links**: Connect memories that are close in time -- **Semantic Links**: Connect semantically similar memories -- **Entity Links**: Connect memories that mention the same entities -- **Spreading Activation**: Intelligent traversal for memory retrieval - """, - contact={ - "name": "Memory System", - }, - license_info={ - "name": "Apache 2.0", - "url": "https://www.apache.org/licenses/LICENSE-2.0.html", - } - ) - - # Mount static files - app.mount("/static", StaticFiles(directory=str(Path(__file__).parent / "static")), name="static") - - @app.on_event("startup") - async def startup_event(): - """Initialize memory system on startup.""" - await memory.initialize() - logging.info("Memory system initialized") - - @app.on_event("shutdown") - async def shutdown_event(): - """Cleanup memory system on shutdown.""" - await memory.close() - logging.info("Memory system closed") - - # Store memory instance on app for route handlers to access - app.state.memory = memory - - # Register all routes - _register_routes(app) - - return app - - -class SearchRequest(BaseModel): - """Request model for search endpoint.""" - query: str - agent_id: str = "default" - thinking_budget: int = 100 - max_tokens: int = 4096 - reranker: str = "heuristic" - trace: bool = False - fact_type: Optional[str] = None - - class Config: - json_schema_extra = { - "example": { - "query": "What did Alice say about machine learning?", - "agent_id": "user123", - "thinking_budget": 100, - "max_tokens": 4096, - "reranker": "heuristic", - "trace": True, - "fact_type": "world" - } - } - - -class SearchResponse(BaseModel): - """Response model for search endpoints.""" - results: List[Dict[str, Any]] - trace: Optional[Dict[str, Any]] = None - - class Config: - json_schema_extra = { - "example": { - "results": [ - { - "text": "Alice works at Google on the AI team", - "score": 0.95, - "id": "123e4567-e89b-12d3-a456-426614174000" - } - ], - "trace": { - "query": "What did Alice say about machine learning?", - "num_results": 1, - "time_seconds": 0.123 - } - } - } - - -class MemoryItem(BaseModel): - """Single memory item for batch put.""" - content: str - event_date: Optional[datetime] = None - context: Optional[str] = None - - class Config: - json_schema_extra = { - "example": { - "content": "Alice mentioned she's working on a new ML model", - "event_date": "2024-01-15T10:30:00Z", - "context": "team meeting" - } - } - - -class BatchPutRequest(BaseModel): - """Request model for batch put endpoint.""" - agent_id: str - items: List[MemoryItem] - document_id: Optional[str] = None - document_metadata: Optional[Dict[str, Any]] = None - upsert: bool = False - - class Config: - json_schema_extra = { - "example": { - "agent_id": "user123", - "items": [ - { - "content": "Alice works at Google", - "context": "work" - }, - { - "content": "Bob went hiking yesterday", - "event_date": "2024-01-15T10:00:00Z" - } - ], - "document_id": "conversation_123", - "upsert": False - } - } - - -class BatchPutResponse(BaseModel): - """Response model for batch put endpoint.""" - success: bool - message: str - agent_id: str - document_id: Optional[str] = None - items_count: int - - class Config: - json_schema_extra = { - "example": { - "success": True, - "message": "Successfully stored 2 memory items", - "agent_id": "user123", - "document_id": "conversation_123", - "items_count": 2 - } - } - - -class BatchPutAsyncResponse(BaseModel): - """Response model for async batch put endpoint.""" - success: bool - message: str - agent_id: str - document_id: Optional[str] = None - items_count: int - queued: bool - - class Config: - json_schema_extra = { - "example": { - "success": True, - "message": "Batch put task queued for background processing", - "agent_id": "user123", - "document_id": "conversation_123", - "items_count": 2, - "queued": True - } - } - - -class ThinkRequest(BaseModel): - """Request model for think endpoint.""" - query: str - agent_id: str = "default" - thinking_budget: int = 50 - - class Config: - json_schema_extra = { - "example": { - "query": "What do you think about artificial intelligence?", - "agent_id": "user123", - "thinking_budget": 50 - } - } - - -class OpinionItem(BaseModel): - """Model for an opinion with confidence score.""" - text: str - confidence: float - - -class ThinkResponse(BaseModel): - """Response model for think endpoint.""" - text: str - based_on: Dict[str, List[Dict[str, Any]]] # {"world": [...], "agent": [...], "opinion": [...]} - new_opinions: List[OpinionItem] = [] # List of newly formed opinions with confidence - - class Config: - json_schema_extra = { - "example": { - "text": "Based on my understanding, AI is a transformative technology...", - "based_on": { - "world": [{"text": "AI is used in healthcare", "score": 0.9}], - "agent": [{"text": "I discussed AI applications last week", "score": 0.85}], - "opinion": [{"text": "I believe AI should be used ethically", "score": 0.8}] - }, - "new_opinions": [ - {"text": "AI has great potential when used responsibly", "confidence": 0.95} - ] - } - } - - -class AgentsResponse(BaseModel): - """Response model for agents list endpoint.""" - agents: List[str] - - class Config: - json_schema_extra = { - "example": { - "agents": ["user123", "agent_alice", "agent_bob"] - } - } - - -class GraphDataResponse(BaseModel): - """Response model for graph data endpoint.""" - nodes: List[Dict[str, Any]] - edges: List[Dict[str, Any]] - table_rows: List[Dict[str, Any]] - total_units: int - - class Config: - json_schema_extra = { - "example": { - "nodes": [ - {"id": "1", "label": "Alice works at Google", "type": "world"}, - {"id": "2", "label": "Bob went hiking", "type": "world"} - ], - "edges": [ - {"from": "1", "to": "2", "type": "semantic", "weight": 0.8} - ], - "table_rows": [ - {"id": "abc12345...", "text": "Alice works at Google", "context": "Work info", "date": "2024-01-15 10:30", "entities": "Alice (PERSON), Google (ORGANIZATION)"} - ], - "total_units": 2 - } - } - - -def _register_routes(app: FastAPI): - """Register all API routes on the given app instance.""" - - @app.get("/", include_in_schema=False) - async def index(): - """Serve the visualization page.""" - return FileResponse(str(Path(__file__).parent / "templates" / "index.html")) - - - @app.get( - "/api/graph", - response_model=GraphDataResponse, - tags=["Visualization"], - summary="Get memory graph data", - description="Retrieve graph data for visualization, optionally filtered by agent_id and fact_type (world/agent/opinion)" - ) - async def api_graph( - agent_id: Optional[str] = None, - fact_type: Optional[str] = None - ): - """Get graph data from database, optionally filtered by agent_id and fact_type.""" - try: - data = await app.state.memory.get_graph_data(agent_id, fact_type) - return data - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/graph: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.post( - "/api/search", - response_model=SearchResponse, - tags=["Search"], - summary="Search memory", - description="Search memory using semantic similarity and spreading activation. Optionally filter by fact_type (world, agent, opinion)" - ) - async def api_search(request: SearchRequest): - """Run a search and return results with trace.""" - try: - # Run search with tracing - results, trace = await app.state.memory.search_async( - agent_id=request.agent_id, - query=request.query, - thinking_budget=request.thinking_budget, - max_tokens=request.max_tokens, - enable_trace=request.trace, - reranker=request.reranker, - fact_type=request.fact_type - ) - - # Convert trace to dict - trace_dict = trace.to_dict() if trace else None - - return SearchResponse( - results=results, - trace=trace_dict - ) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/search: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.post( - "/api/world_search", - response_model=SearchResponse, - tags=["Search"], - summary="Search world facts", - description="Search only world facts - general knowledge about people, places, events, and things that happen" - ) - async def api_world_search(request: SearchRequest): - """Search only world facts (general knowledge about the world).""" - try: - # Run search with fact_type filter for 'world' - results, trace = await app.state.memory.search_async( - agent_id=request.agent_id, - query=request.query, - thinking_budget=request.thinking_budget, - max_tokens=request.max_tokens, - enable_trace=request.trace, - reranker=request.reranker, - fact_type='world' - ) - - # Convert trace to dict - trace_dict = trace.to_dict() if trace else None - - return SearchResponse( - results=results, - trace=trace_dict - ) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/world_search: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.post( - "/api/agent_search", - response_model=SearchResponse, - tags=["Search"], - summary="Search agent action facts", - description="Search only agent facts - memories about what the AI agent did, actions taken, and tasks performed" - ) - async def api_agent_search(request: SearchRequest): - """Search only agent facts (facts about what the agent did).""" - try: - # Run search with fact_type filter for 'agent' - results, trace = await app.state.memory.search_async( - agent_id=request.agent_id, - query=request.query, - thinking_budget=request.thinking_budget, - max_tokens=request.max_tokens, - enable_trace=request.trace, - reranker=request.reranker, - fact_type='agent' - ) - - # Convert trace to dict - trace_dict = trace.to_dict() if trace else None - - return SearchResponse( - results=results, - trace=trace_dict - ) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/agent_search: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.post( - "/api/opinion_search", - response_model=SearchResponse, - tags=["Search"], - summary="Search agent opinions", - description="Search only opinion facts - the agent's formed beliefs, perspectives, and viewpoints" - ) - async def api_opinion_search(request: SearchRequest): - """Search only opinion facts (agent's formed opinions and perspectives).""" - try: - # Run search with fact_type filter for 'opinion' - results, trace = await app.state.memory.search_async( - agent_id=request.agent_id, - query=request.query, - thinking_budget=request.thinking_budget, - max_tokens=request.max_tokens, - enable_trace=request.trace, - reranker=request.reranker, - fact_type='opinion' - ) - - # Convert trace to dict - trace_dict = trace.to_dict() if trace else None - - return SearchResponse( - results=results, - trace=trace_dict - ) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/opinion_search: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.post( - "/api/think", - response_model=ThinkResponse, - tags=["Reasoning"], - summary="Think and generate answer", - description=""" - Think and formulate an answer using agent identity, world facts, and opinions. - - This endpoint: - 1. Retrieves agent facts (agent's identity) - 2. Retrieves world facts relevant to the query - 3. Retrieves existing opinions (agent's perspectives) - 4. Uses LLM to formulate a contextual answer - 5. Extracts and stores any new opinions formed - 6. Returns plain text answer, the facts used, and new opinions - """ - ) - async def api_think(request: ThinkRequest): - try: - # Use the memory system's think_async method - result = await app.state.memory.think_async( - agent_id=request.agent_id, - query=request.query, - thinking_budget=request.thinking_budget - ) - - return ThinkResponse( - text=result["text"], - based_on=result["based_on"], - new_opinions=result.get("new_opinions", []) - ) - - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/think: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.get( - "/api/agents", - response_model=AgentsResponse, - tags=["Management"], - summary="List all agents", - description="Get a list of all agent IDs that have stored memories in the system" - ) - async def api_agents(): - """Get list of available agents from database.""" - try: - agent_list = await app.state.memory.list_agents() - return AgentsResponse(agents=agent_list) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/agents: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - @app.get( - "/api/stats/{agent_id}", - tags=["Memory Statistics"], - summary="Get memory statistics for an agent", - description="Get statistics about nodes and links for a specific agent" - ) - async def api_stats(agent_id: str): - """Get statistics about memory nodes and links for an agent.""" - try: - pool = await app.state.memory._get_pool() - async with pool.acquire() as conn: - # Get node counts by fact_type - node_stats = await conn.fetch( - """ - SELECT fact_type, COUNT(*) as count - FROM memory_units - WHERE agent_id = $1 - GROUP BY fact_type - """, - agent_id - ) - - # Get link counts by link_type - link_stats = await conn.fetch( - """ - SELECT ml.link_type, COUNT(*) as count - FROM memory_links ml - JOIN memory_units mu ON ml.from_unit_id = mu.id - WHERE mu.agent_id = $1 - GROUP BY ml.link_type - """, - agent_id - ) - - # Format results - nodes_by_type = {row['fact_type']: row['count'] for row in node_stats} - links_by_type = {row['link_type']: row['count'] for row in link_stats} - - total_nodes = sum(nodes_by_type.values()) - total_links = sum(links_by_type.values()) - - return { - "agent_id": agent_id, - "total_nodes": total_nodes, - "total_links": total_links, - "nodes_by_type": nodes_by_type, - "links_by_type": links_by_type - } - - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/stats/{agent_id}: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - @app.post( - "/api/memories/batch", - response_model=BatchPutResponse, - tags=["Memory Storage"], - summary="Store multiple memories", - description=""" - Store multiple memory items in batch with automatic fact extraction. - - Features: - - Efficient batch processing - - Automatic fact extraction from natural language - - Entity recognition and linking - - Document tracking with optional upsert - - Temporal and semantic linking - - The system automatically: - 1. Extracts semantic facts from the content - 2. Generates embeddings - 3. Deduplicates similar facts - 4. Creates temporal, semantic, and entity links - 5. Tracks document metadata - """ - ) - async def api_batch_put(request: BatchPutRequest): - try: - # Validate agent_id - prevent writing to reserved agents - RESERVED_AGENT_IDS = {"locomo"} - if request.agent_id in RESERVED_AGENT_IDS: - raise HTTPException( - status_code=403, - detail=f"Cannot write to reserved agent_id '{request.agent_id}'. Reserved agents: {', '.join(RESERVED_AGENT_IDS)}" - ) - - # Prepare contents for put_batch_async - contents = [] - for item in request.items: - content_dict = {"content": item.content} - if item.event_date: - content_dict["event_date"] = item.event_date - if item.context: - content_dict["context"] = item.context - contents.append(content_dict) - - # Call put_batch_async - result = await app.state.memory.put_batch_async( - agent_id=request.agent_id, - contents=contents, - document_id=request.document_id, - document_metadata=request.document_metadata, - upsert=request.upsert - ) - logging.info(f"Batch put result: {result}") - - return BatchPutResponse( - success=True, - message=f"Successfully stored {len(contents)} memory items", - agent_id=request.agent_id, - document_id=request.document_id, - items_count=len(contents) - ) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/memories/batch: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.post( - "/api/memories/batch_async", - response_model=BatchPutAsyncResponse, - tags=["Memory Storage"], - summary="Store multiple memories asynchronously", - description=""" - Store multiple memory items in batch asynchronously using the task backend. - - This endpoint returns immediately after queuing the task, without waiting for completion. - The actual processing happens in the background. - - Features: - - Immediate response (non-blocking) - - Background processing via task queue - - Efficient batch processing - - Automatic fact extraction from natural language - - Entity recognition and linking - - Document tracking with optional upsert - - Temporal and semantic linking - - The system automatically: - 1. Queues the batch put task - 2. Returns immediately with success=True, queued=True - 3. Processes in background: extracts facts, generates embeddings, creates links - """ - ) - async def api_batch_put_async(request: BatchPutRequest): - try: - # Validate agent_id - prevent writing to reserved agents - RESERVED_AGENT_IDS = {"locomo"} - if request.agent_id in RESERVED_AGENT_IDS: - raise HTTPException( - status_code=403, - detail=f"Cannot write to reserved agent_id '{request.agent_id}'. Reserved agents: {', '.join(RESERVED_AGENT_IDS)}" - ) - - # Prepare contents for put_batch_async - contents = [] - for item in request.items: - content_dict = {"content": item.content} - if item.event_date: - content_dict["event_date"] = item.event_date - if item.context: - content_dict["context"] = item.context - contents.append(content_dict) - - # Submit task to background queue - await app.state.memory._task_backend.submit_task({ - 'type': 'batch_put', - 'agent_id': request.agent_id, - 'contents': contents, - 'document_id': request.document_id, - 'document_metadata': request.document_metadata, - 'upsert': request.upsert - }) - - logging.info(f"Batch put task queued for agent_id={request.agent_id}, {len(contents)} items") - - return BatchPutAsyncResponse( - success=True, - message=f"Batch put task queued for background processing ({len(contents)} items)", - agent_id=request.agent_id, - document_id=request.document_id, - items_count=len(contents), - queued=True - ) - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/memories/batch_async: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - - @app.delete( - "/api/memory/{unit_id}", - tags=["Memory Storage"], - summary="Delete a memory unit", - description="Delete a single memory unit and all its associated links (temporal, semantic, and entity links)" - ) - async def api_delete_memory_unit(unit_id: str): - """Delete a memory unit and all its links.""" - try: - result = await app.state.memory.delete_memory_unit(unit_id) - - if not result["success"]: - raise HTTPException(status_code=404, detail=result["message"]) - - return result - except HTTPException: - raise - except Exception as e: - import traceback - error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}" - print(f"Error in /api/memory/{unit_id}: {error_detail}") - raise HTTPException(status_code=500, detail=str(e)) - - +from memora import TemporalSemanticMemory +from memora.api import create_app # Create app at module level (required for uvicorn import string) @@ -771,7 +25,7 @@ app = create_app(_memory) if __name__ == "__main__": import uvicorn - import argparse + logging.basicConfig(level=logging.INFO) # Parse CLI arguments diff --git a/scripts/benchmarks/start-visualizer.sh b/scripts/benchmarks/start-visualizer.sh index 08b9c2b2..f9d6883f 100755 --- a/scripts/benchmarks/start-visualizer.sh +++ b/scripts/benchmarks/start-visualizer.sh @@ -9,5 +9,4 @@ echo "Server will be available at: http://localhost:8001" echo "" cd benchmarks/visualizer -open http://localhost:8001 uv run uvicorn server:app --reload --host 0.0.0.0 --port 8001 diff --git a/scripts/start-server.sh b/scripts/start-server.sh index 735ea1c8..2c54c391 100755 --- a/scripts/start-server.sh +++ b/scripts/start-server.sh @@ -82,5 +82,4 @@ if [[ ${#SERVER_ARGS[@]} -eq 0 ]]; then SERVER_ARGS=(--reload --host 0.0.0.0 --port 8080) fi -open "http://localhost:${PORT}" uv run python -m memora.web.server "${SERVER_ARGS[@]}"