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data/retrieval_individual-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

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 {"tstamp": 1723473599.7214, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723473599.4837, "finish": 1723473599.7214, "ip": "", "conv_id": "6376c813b3a94bb99f23e44460b892d7", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Stackoverflow Stackexchange\n\nQ: Can't install gatsby-plugin-sharp.(libpng-dev may not installed) OS: Win 10\nwindows-build-tools is installed.\nWhen I was trying to install gatsby-plugin-sharp with npm install --save gatsby-plugin-sharp. I received two errors.\nI've tried this npm install --save-dev pngquant --msvs_version=2013  as someone mentioned. But it caused the same error with no 'libpng-dev'.\n\\> mozjpeg@6.0.1 postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\mozjpeg\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ mozjpeg pre-build test failed\ni compiling from source\n× Error: Command failed: C:\\Windows\\system32\\cmd.exe /s /c \"autoreconf -fiv\"\n'autoreconf' �����ڲ����ⲿ���Ҳ���ǿ����еij���\n���������ļ���\n\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat runMicrotasks (<anonymous>)\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n\\> pngquant-bin@5.0.2 postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\pngquant-bin\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ pngquant pre-build test failed\ni compiling from source\n× Error: pngquant failed to build, make sure that libpng-dev is installed\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1723473626.8478, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723473624.3478, "finish": 1723473626.8478, "ip": "", "conv_id": "ec1508740221411c80ef762aaeb99fcb", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "output": [["A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "Title: Is there anything non-classical?\n\nAbstract: It is argued that quantum logic and quantum probability theory are fascinating mathematical theories but without any relevance to our real world."]], "corpus": "arxiv"}
 {"tstamp": 1723473626.8478, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723473624.3478, "finish": 1723473626.8478, "ip": "", "conv_id": "61639fb40f3b49d8856bec9352cf5a13", "model_name": "text-embedding-3-large", "prompt": "A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "output": [["A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "Title: Autoregressive Image Generation using Residual Quantization\n\nAbstract: For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a 256$\\times$256 image as 8$\\times$8 resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework outperforms the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images."]], "corpus": "arxiv"}
+{"tstamp": 1723478511.1514, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1723478508.9234, "finish": 1723478511.1514, "ip": "", "conv_id": "767e8e6e540a4a3484f52959bdca7746", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "theory for functional group test for aldehyde and ketones", "output": [["theory for functional group test for aldehyde and ketones", "Aldehyde\n\nEnolization Aldehydes (except those without an alpha carbon, or without protons on the alpha carbon, such as formaldehyde and benzaldehyde) can exist in either the keto or the enol tautomer. Keto–enol tautomerism is catalyzed by either acid or base. In neutral solution, the enol is the minority tautomer, reversing several times per second. But it becomes the dominant tautomer in strong acid or base solutions, and enolized aldehydes undergo nucleophilic attack at the α position.\nReduction\nThe formyl group can be readily reduced to a primary alcohol (). Typically this conversion is accomplished by catalytic hydrogenation either directly or by transfer hydrogenation. Stoichiometric reductions are also popular, as can be effected with sodium borohydride.\nOxidation\nThe formyl group readily oxidizes to the corresponding carboxyl group (). The preferred oxidant in industry is oxygen or air. In the laboratory, popular oxidizing agents include potassium permanganate, nitric acid, chromium(VI) oxide, and chromic acid. The combination of manganese dioxide, cyanide, acetic acid and methanol will convert the aldehyde to a methyl ester.\nAnother oxidation reaction is the basis of the silver-mirror test. In this test, an aldehyde is treated with Tollens' reagent, which is prepared by adding a drop of sodium hydroxide solution into silver nitrate solution to give a precipitate of silver(I) oxide, and then adding just enough dilute ammonia solution to redissolve the precipitate in aqueous ammonia to produce  complex. This reagent converts aldehydes to carboxylic acids without attacking carbon–carbon double bonds. The name silver-mirror test arises because this reaction produces a precipitate of silver, whose presence can be used to test for the presence of an aldehyde."]], "corpus": "wikipedia"}
+{"tstamp": 1723478511.1514, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723478508.9234, "finish": 1723478511.1514, "ip": "", "conv_id": "668f03f91f274000a5ddfe8cee96f8a8", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "theory for functional group test for aldehyde and ketones", "output": [["theory for functional group test for aldehyde and ketones", "Aldehyde\n\nIn organic chemistry, an aldehyde () is an organic compound containing a functional group with the structure . The functional group itself (without the \"R\" side chain) can be referred to as an aldehyde but can also be classified as a formyl group. Aldehydes are a common motif in many chemicals important in technology and biology."]], "corpus": "wikipedia"}