{
"entries": [
{
"kind": "message",
"role": "user",
"text": "Read /app/quickstart.md and implement the Quickwit quickstart in /app. Run it and confirm that its first search returns results.",
"time_seconds": 3.15
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning quickstart review**",
"time_seconds": 3.159
},
{
"kind": "tool_call",
"role": "read",
"text": "read {\"path\": \"/app/quickstart.md\"}",
"time_seconds": 3.159
},
{
"kind": "tool_result",
"role": "read",
"text": "---\ntitle: Quickstart\nsidebar_position: 1\n---\n\nimport Tabs from '@theme/Tabs';\nimport TabItem from '@theme/TabItem';\n\nIn this quick start guide, we will install Quickwit, create an index, add documents and finally execute search queries. All the Quickwit commands used in this guide are documented [in the CLI reference documentation](../reference/cli.md).\n\n## Install Quickwit using Quickwit installer\n\nThe Quickwit installer automatically picks the correct binary archive for your environment and then downloads and unpacks it in your working directory.\nThis method works only for [some OS/architectures](installation.md#download), and you will also need to install some [external dependencies](installation.md#note-on-external-dependencies).\n\n```bash\ncurl -L https://install.quickwit.io | sh\n```\n\n```bash\ncd ./quickwit-v*/\n./quickwit --version\n```\n\nYou can now move this executable directory wherever sensible for your environment and possibly add it to your `PATH` environment.\n\n## Use Quickwit's Docker image\n\nYou can also pull and run the Quickwit binary in an isolated Docker container.\n\n```bash\n# Create first the data directory.\nmkdir qwdata\ndocker run --rm quickwit/quickwit:0.9.0 --version\n```\n\n## Start Quickwit server\n\n\n\n\n\n```bash\n./quickwit run\n```\n\n\n\n\n\n```bash\ndocker run --rm -v $(pwd)/qwdata:/quickwit/qwdata -p 127.0.0.1:7280:7280 quickwit/quickwit:0.9.0 run\n```\n\n\n\n\n\nTips: you can use the environment variable `RUST_LOG` to control quickwit verbosity.\n\nCheck it's working by browsing the [UI at http://localhost:7280](http://localhost:7280) or do a simple GET with cURL:\n\n```bash\ncurl http://localhost:7280/api/v1/version\n```\n\n## Create your first index\n\nBefore adding documents to Quickwit, you need to create an index configured with a YAML config file. This config file notably lets you define how to map your input documents to your index fields and whether these fields should be stored and indexed. See the [index config documentation](../configuration/index-config.md).\n\nLet's create an index configured to receive Stackoverflow posts (questions and answers).\n\n```bash\n# First, download the stackoverflow dataset config from Quickwit repository.\ncurl -o stackoverflow-index-config.yaml https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.9.0/config/tutorials/stackoverflow/index-config.yaml\n```\n\nThe index config defines three fields: `title`, `body` and `creationDate`. `title` and `body` are [indexed and tokenized](../configuration/index-config.md#text-type), and they are also used as default search fields, which means they will be used for search if you do not target a specific field in your query. `creationDate` serves as the timestamp for each record. There are no more explicit field definitions as we can use the default dynamic [mode](../configuration/index-config.md#mode): the undeclared fields will still be indexed, by default fast fields are enabled to enable aggregation queries. and the `raw` tokenizer is used for text.\n\nAnd here is the complete config:\n\n```yaml title=\"stackoverflow-index-config.yaml\"\n#\n# Index config file for stackoverflow dataset.\n#\nversion: 0.8\n\nindex_id: stackoverflow\n\ndoc_mapping:\n field_mappings:\n - name: title\n type: text\n tokenizer: default\n record: position\n stored: true\n - name: body\n type: text\n tokenizer: default\n record: position\n stored: true\n - name: creationDate\n type: datetime\n fast: true\n input_formats:\n - rfc3339\n fast_precision: seconds\n timestamp_field: creationDate\n\nsearch_settings:\n default_search_fields: [title, body]\n\nindexing_settings:\n commit_timeout_secs: 10\n```\n\nNow we can create the index with the command:\n\n\n\n\n\n```bash\n./quickwit index create --index-config ./stackoverflow-index-config.yaml\n```\n\n\n\n\n\n```bash\ncurl -XPOST http://127.0.0.1:7280/api/v1/indexes --header \"content-type: application/yaml\" --data-binary @./stackoverflow-index-config.yaml\n```\n\n\n\n\n\nCheck that a directory `./qwdata/indexes/stackoverflow` has been created, Quickwit will write index files here and a `metastore.json` which contains the [index metadata](../overview/architecture.md#index).\nYou're now ready to fill the index.\n\n\n## Let's add some documents\n\nQuickwit can index data from many [sources](../configuration/source-config.md). We will use a new line delimited json [ndjson](http://ndjson.org/) datasets as our data source.\nLet's download [a bunch of stackoverflow posts (10 000)](https://quickwit-datasets-public.s3.amazonaws.com/stackoverflow.posts.transformed-10000.json) in [ndjson](http://ndjson.org/) format and index it.\n\n```bash\n# Download the first 10_000 Stackoverflow posts articles.\ncurl -O https://quickwit-datasets-public.s3.amazonaws.com/stackoverflow.posts.transformed-10000.json\n```\n\n\n\n\n\n```bash\n# Index our 10k documents.\n./quickwit index ingest --index stackoverflow --input-path stackoverflow.posts.transformed-10000.json --force\n```\n\n\n\n\n\n```bash\n# Index our 10k documents.\ncurl -XPOST \"http://127.0.0.1:7280/api/v1/stackoverflow/ingest?commit=force\" --data-binary @stackoverflow.posts.transformed-10000.json\n```\n\n\n\n\n\nAs soon as the ingest command finishes you can start querying data by using the following `search` command:\n\n\n\n\n\n```bash\n./quickwit index search --index stackoverflow --query \"search AND engine\"\n```\n\n\n\n\n\n```bash\ncurl \"http://127.0.0.1:7280/api/v1/stackoverflow/search?query=search+AND+engine\"\n```\n\n\n\n\n\nIt should return 10 hits. Now you're ready to play with the search API.\n\n\n## Execute search queries\n\n\nLet's start with a query on the field `title`: `title:search AND engine`:\n```bash\ncurl \"http://127.0.0.1:7280/api/v1/stackoverflow/search?query=title:search+AND+engine\"\n```\n\nThe same request can be expressed as a JSON query:\n```bash\ncurl -XPOST \"http://localhost:7280/api/v1/stackoverflow/search\" -H 'Content-Type: application/json' -d '{\n \"query\": \"title:search AND engine\"\n}'\n```\n\nThis format is more verbose but it allows you to use more advanced features such as aggregations. The following query finds most popular tags used on the questions in this dataset:\n```bash\ncurl -XPOST \"http://localhost:7280/api/v1/stackoverflow/search\" -H 'Content-Type: application/json' -d '{\n \"query\": \"type:question\",\n \"max_hits\": 0,\n \"aggs\": {\n \"foo\": {\n \"terms\":{\n \"field\":\"tags\",\n \"size\": 10\n }\n }\n }\n}'\n```\n\nAs you are experimenting with different queries check out the server logs to see what's happening.\n\n:::note\n\nDon't forget to encode correctly the query params to avoid bad request (status 400).\n\n:::\n\n\n\n## Clean\n\nLet's do some cleanup by deleting the index:\n\n\n\n\n\n```bash\n./quickwit index delete --index stackoverflow\n```\n\n\n\n\n\n```bash\ncurl -XDELETE http://127.0.0.1:7280/api/v1/indexes/stackoverflow\n```\n\n\n\n\n\nCongrats! You can level up with the following tutorials to discover all Quickwit features.\n\n\n## TLDR\n\nRun the following command from within Quickwit's installation directory.\n\n```bash\ncurl -o stackoverflow-index-config.yaml https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.9.0/config/tutorials/stackoverflow/index-config.yaml\n./quickwit index create --index-config ./stackoverflow-index-config.yaml\ncurl -O https://quickwit-datasets-public.s3.amazonaws.com/stackoverflow.posts.transformed-10000.json\n./quickwit index ingest --index stackoverflow --input-path ./stackoverflow.posts.transformed-10000.json --force\n./quickwit index search --index stackoverflow --query \"search AND engine\"\n./quickwit index delete --index stackoverflow\n```\n\n\n## Next tutorials\n\n- [Search on logs with timestamp pruning](/docs/get-started/tutorials/tutorial-hdfs-logs)\n- [Setup a distributed search on AWS S3](/docs/get-started/tutorials/tutorial-hdfs-logs-distributed-search-aws-s3)\n",
"time_seconds": 5.028
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Inspecting app directory files**",
"time_seconds": 5.028
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ pwd && ls -la && find . -maxdepth 2 -type f -not -path './.git/*' -print | sort | head -100",
"time_seconds": 5.028
},
{
"kind": "tool_result",
"role": "bash",
"text": "/app\ntotal 16\ndrwxrwxr-x+ 1 root root 64 Aug 28 20:40 .\ndrwxr-xr-x 15 root root 340 Aug 28 20:40 ..\n-rw-rw-r--+ 1 root root 8286 Aug 28 20:35 quickstart.md\n-rw-rw-r--+ 1 root root 443 Aug 28 20:35 source-receipt.json\n./quickstart.md\n./source-receipt.json\n",
"time_seconds": 7.089
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Preparing quickstart installation**",
"time_seconds": 7.09
},
{
"kind": "tool_call",
"role": "read",
"text": "read {\"path\": \"/app/source-receipt.json\"}",
"time_seconds": 7.09
},
{
"kind": "tool_result",
"role": "read",
"text": "{\n \"document\": {\n \"bytes\": 8286,\n \"path\": \"quickstart.md\",\n \"sha256\": \"e29014fdf293a93221f8bddbe1eec7debb51463f2b6b06be0b57d7dc6733b68e\"\n },\n \"fetched_at\": \"2026-08-29T03:35:38.586038+00:00\",\n \"product\": \"Quickwit\",\n \"schema_version\": 1,\n \"source_commit\": \"v0.9.0\",\n \"source_url\": \"https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.9.0/docs/get-started/quickstart.md\",\n \"subject\": \"quickwit\",\n \"version\": \"v0.9.0\"\n}\n",
"time_seconds": 8.638
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning binary installation and setup**",
"time_seconds": 8.638
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ uname -a; command -v curl; command -v docker || true; command -v wget || true; df -h .; free -h",
"time_seconds": 8.638
},
{
"kind": "tool_result",
"role": "bash",
"text": "Linux rocinante 6.18.37-1-cachyos-lts #1 SMP PREEMPT_DYNAMIC Sun, 28 Jun 2026 21:09:31 +0000 x86_64 GNU/Linux\n/bin/curl\n/bin/docker\n/bin/wget\nFilesystem Size Used Avail Use% Mounted on\n/dev/mapper/luks-eaa980dc-be29-4b51-a76c-e33dbf3c4d17 930G 659G 266G 72% /app\n total used free shared buff/cache available\nMem: 15Gi 4.4Gi 6.3Gi 591Mi 5.7Gi 11Gi\nSwap: 15Gi 2.8Gi 12Gi\n",
"time_seconds": 11.384
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning Quickwit v0.9.0 installation and verification**",
"time_seconds": 11.384
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ docker info --format '{{.ServerVersion}}' 2>&1 || true; curl -fsSL https://install.quickwit.io | head -80",
"time_seconds": 11.384
},
{
"kind": "tool_result",
"role": "bash",
"text": "\nfailed to connect to the docker API at unix:///var/run/docker.sock; check if the path is correct and if the daemon is running: dial unix /var/run/docker.sock: connect: no such file or directory\ncurl: (6) Could not resolve host: install.quickwit.io\n",
"time_seconds": 14.423
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Investigating network DNS and cache issues**",
"time_seconds": 14.424
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ find / -maxdepth 4 -iname '*quickwit*' 2>/dev/null | head -100; env | sort | grep -Ei 'proxy|http|https|quick|cargo|rust' || true",
"time_seconds": 14.424
},
{
"kind": "tool_result",
"role": "bash",
"text": "CARGO_TERM_PROGRESS_WHEN=never\nDEBUGINFOD_URLS=https://debuginfod.archlinux.org https://debuginfod.cachyos.org \nPI_PROMPT=Read /app/quickstart.md and implement the Quickwit quickstart in /app. Run it and confirm that its first search returns results.\nQUICKSTART_SUBJECT=quickwit\n",
"time_seconds": 17.137
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Investigating network DNS issues**",
"time_seconds": 17.137
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ getent hosts raw.githubusercontent.com || true; cat /etc/resolv.conf; curl -I --max-time 10 https://github.com 2>&1 | head",
"time_seconds": 17.137
},
{
"kind": "tool_result",
"role": "bash",
"text": "2606:50c0:8001::154 raw.githubusercontent.com\n2606:50c0:8000::154 raw.githubusercontent.com\n2606:50c0:8003::154 raw.githubusercontent.com\n2606:50c0:8002::154 raw.githubusercontent.com\n# Generated by NetworkManager\nnameserver 127.0.0.53\noptions edns0 trust-ad\n % Total % Received % Xferd Average Speed Time Time Time Current\n Dload Upload Total Spent Left Speed\n\r 0 0 0 0 0 0 0 0 0HTTP/2 200 \r\ndate: Sat, 29 Aug 2026 03:40:51 GMT\r\ncontent-type: text/html; charset=utf-8\r\ncontent-language: en-US\r\nvary: X-PJAX, X-PJAX-Container, Turbo-Visit, Turbo-Frame, X-Requested-With, X-GitHub-Client-Version, Accept-Language, Sec-Fetch-Site,Accept-Encoding, Accept, X-Requested-With\r\netag: W/\"79e496389a3438a3ff632e716746ffd0\"\r\ncache-control: max-age=0, private, must-revalidate\r\nstrict-transport-security: max-age=31536000; includeSubdomains; preload\r\n",
"time_seconds": 20.147
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Searching Quickwit v0.9 release assets**",
"time_seconds": 20.148
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ curl -fsSL --max-time 30 https://api.github.com/repos/quickwit-oss/quickwit/releases/tags/v0.9.0 | python3 -c 'import json,sys; d=json.load(sys.stdin); print(d.get(\"tag_name\")); [print(a[\"name\"], a[\"browser_download_url\"]) for a in d.get(\"assets\",[])]'",
"time_seconds": 20.148
},
{
"kind": "tool_result",
"role": "bash",
"text": "v0.9.0\nquickwit-v0.9.0-aarch64-apple-darwin.tar.gz https://github.com/quickwit-oss/quickwit/releases/download/v0.9.0/quickwit-v0.9.0-aarch64-apple-darwin.tar.gz\nquickwit-v0.9.0-aarch64-unknown-linux-gnu.tar.gz https://github.com/quickwit-oss/quickwit/releases/download/v0.9.0/quickwit-v0.9.0-aarch64-unknown-linux-gnu.tar.gz\nquickwit-v0.9.0-x86_64-apple-darwin.tar.gz https://github.com/quickwit-oss/quickwit/releases/download/v0.9.0/quickwit-v0.9.0-x86_64-apple-darwin.tar.gz\nquickwit-v0.9.0-x86_64-unknown-linux-gnu.tar.gz https://github.com/quickwit-oss/quickwit/releases/download/v0.9.0/quickwit-v0.9.0-x86_64-unknown-linux-gnu.tar.gz\n",
"time_seconds": 23.655
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Downloading version 0.9.0 x86**",
"time_seconds": 23.656
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ curl -fL --retry 3 -o quickwit-v0.9.0-x86_64-unknown-linux-gnu.tar.gz https://github.com/quickwit-oss/quickwit/releases/download/v0.9.0/quickwit-v0.9.0-x86_64-unknown-linux-gnu.tar.gz && tar -xzf quickwit-v0.9.0-x86_64-unknown-linux-gnu.tar.gz && ls -la",
"time_seconds": 23.656
},
{
"kind": "tool_result",
"role": "bash",
"text": " % Total % Received % Xferd Average Speed Time Time Time Current\n Dload Upload Total Spent Left Speed\n\r 0 0 0 0 0 0 0 0 0\r 0 0 0 0 0 0 0 0 0\r 0 0 0 0 0 0 0 0 0\n\r 0 0 0 0 0 0 0 0 0\r 17 77.91M 17 13.48M 0 0 10.00M 0 00:07 00:01 00:06 13.48M\r 38 77.91M 38 29.63M 0 0 12.61M 0 00:06 00:02 00:04 14.80M\r 59 77.91M 59 46.49M 0 0 13.88M 0 00:05 00:03 00:02 15.49M\r 81 77.91M 81 63.40M 0 0 14.58M 0 00:05 00:04 00:01 15.84M\r100 77.91M 100 77.91M 0 0 14.95M 0 00:05 00:05 15.84M\r100 77.91M 100 77.91M 0 0 14.95M 0 00:05 00:05 15.84M\r100 77.91M 100 77.91M 0 0 14.95M 0 00:05 00:05 15.84M\ntar: ./quickwit-v0.9.0/qwdata: Cannot change ownership to uid 1001, gid 1001: Invalid argument\ntar: ./quickwit-v0.9.0/quickwit: Cannot change ownership to uid 1001, gid 1001: Invalid argument\ntar: ./quickwit-v0.9.0/config/quickwit.yaml: Cannot change ownership to uid 1001, gid 1001: Invalid argument\ntar: ./quickwit-v0.9.0/config: Cannot change ownership to uid 1001, gid 1001: Invalid argument\ntar: ./quickwit-v0.9.0/LICENSE: Cannot change ownership to uid 1001, gid 1001: Invalid argument\ntar: ./quickwit-v0.9.0: Cannot change ownership to uid 1001, gid 1001: Invalid argument\ntar: Exiting with failure status due to previous errors\n\n\nCommand exited with code 2",
"time_seconds": 33.594
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Testing extraction with adjusted ownership**",
"time_seconds": 33.597
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ rm -rf quickwit-v0.9.0 && tar --no-same-owner -xzf quickwit-v0.9.0-x86_64-unknown-linux-gnu.tar.gz && ls -la quickwit-v0.9.0 && ./quickwit-v0.9.0/quickwit --version",
"time_seconds": 33.597
},
{
"kind": "tool_result",
"role": "bash",
"text": "total 205628\ndrwxr-xr-x+ 1 root root 54 Jul 25 11:23 .\ndrwxrwxr-x+ 1 root root 188 Aug 28 20:41 ..\ndrwxr-xr-x+ 1 root root 26 Jul 25 11:23 config\n-rw-r--r--+ 1 root root 11351 Jul 25 11:23 LICENSE\n-rwxr-xr-x+ 1 root root 210550008 Jul 25 11:23 quickwit\ndrwxr-xr-x+ 1 root root 0 Jul 25 11:23 qwdata\nQuickwit 0.9.0 (x86_64-unknown-linux-gnu 2026-07-25T17:48:09Z cc420c3)\n",
"time_seconds": 37.857
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Preparing Quickwit server setup**",
"time_seconds": 37.857
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ curl -fL --retry 3 -o stackoverflow-index-config.yaml https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.9.0/config/tutorials/stackoverflow/index-config.yaml && sha256sum stackoverflow-index-config.yaml && sed -n '1,120p' stackoverflow-index-config.yaml",
"time_seconds": 37.857
},
{
"kind": "tool_result",
"role": "bash",
"text": " % Total % Received % Xferd Average Speed Time Time Time Current\n Dload Upload Total Spent Left Speed\n\r 0 0 0 0 0 0 0 0 0\r100 591 100 591 0 0 3169 0 0\r100 591 100 591 0 0 3167 0 0\r100 591 100 591 0 0 3165 0 0\n8041fb31f62650265fddc53f5f07754b7028c9f650d7db59d188ca8bd88d3a51 stackoverflow-index-config.yaml\n#\n# Index config file for stackoverflow dataset.\n#\nversion: 0.8\n\nindex_id: stackoverflow\n\ndoc_mapping:\n field_mappings:\n - name: title\n type: text\n tokenizer: default\n record: position\n stored: true\n - name: body\n type: text\n tokenizer: default\n record: position\n stored: true\n - name: creationDate\n type: datetime\n fast: true\n input_formats:\n - rfc3339\n fast_precision: seconds\n timestamp_field: creationDate\n\nsearch_settings:\n default_search_fields: [title, body]\n\nindexing_settings:\n commit_timeout_secs: 10\n",
"time_seconds": 41.203
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Starting server with process fork**",
"time_seconds": 41.204
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ cd /app/quickwit-v0.9.0 && ./quickwit run > /app/quickwit.log 2>&1 & echo $!",
"time_seconds": 41.204
},
{
"kind": "tool_result",
"role": "bash",
"text": "120\n",
"time_seconds": 43.957
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Verifying curl process logs**",
"time_seconds": 43.957
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ sleep 2; ps -ef | grep '[q]uickwit' || true; printf '\\n--- log ---\\n'; tail -80 /app/quickwit.log; printf '\\n--- version ---\\n'; curl -fsS --max-time 10 http://127.0.0.1:7280/api/v1/version",
"time_seconds": 43.957
},
{
"kind": "tool_result",
"role": "bash",
"text": "root 1 0 0 20:40 ? 00:00:00 bwrap --tmpfs / --ro-bind /usr /usr --ro-bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/hide/antfly /usr/lib/node_modules/@antfly/cli/bin/antfly.js --ro-bind /etc /etc --ro-bind /sys /sys --dir /bin --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/bin /bin --symlink usr/lib /lib --symlink usr/lib /lib64 --proc /proc --dev /dev --tmpfs /run --dir /app --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/app /app --dir /usr-local --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/usr-local /usr/local --dir /root --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/root /root --dir /logs --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/logs /logs --dir /tmp --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/tmp /tmp --dir /opt --dir /opt/evaluator --ro-bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/benchmarks/quickstart-cinema-harness /opt/evaluator --dir /opt/python-base --ro-bind /home/rowan/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu /opt/python-base --dir /opt/venv --bind /home/rowan/Documents/antfly/antfly-circus-quickstart-cinema/results/campaigns/2026-08-28-natural-quickstarts/raw/quickwit/trial-01/attempt-01/.runtime/venv /opt/venv --unshare-user --unshare-pid --die-with-parent --uid 0 --gid 0 --chdir /app --setenv HOME /root --setenv TERM xterm-256color --setenv PATH /root/.local/bin:/opt/venv/bin:/usr/local/bin:/bin --setenv PI_PROMPT Read /app/quickstart.md and implement the Quickwit quickstart in /app. Run it and confirm that its first search returns results. --setenv PI_MODEL openai-codex/gpt-5.6-luna --setenv QUICKSTART_SUBJECT quickwit /bin/bash -c ulimit -v 8388608; taskset -c 0-3 pi --model \"$PI_MODEL\" --thinking high --no-extensions --no-skills --no-prompt-templates --no-themes --no-context-files --session-dir /root/.pi/agent/sessions --name \"Natural quickstart session\" \"$PI_PROMPT\"; p=$?; python3 /opt/evaluator/verify.py --subject \"$QUICKSTART_SUBJECT\" --workspace /app --out /logs/verifier/result.json >/logs/verifier/verify.log 2>&1; v=$?; if [ $p -ne 0 ]; then exit $p; else exit $v; fi\nroot 120 1 0 20:41 ? 00:00:00 /bin/bash -c cd /app/quickwit-v0.9.0 && ./quickwit run > /app/quickwit.log 2>&1 & echo $!\nroot 121 120 0 20:41 ? 00:00:00 ./quickwit run\nroot 132 3 0 20:41 ? 00:00:00 /bin/bash -c sleep 2; ps -ef | grep '[q]uickwit' || true; printf '\\n--- log ---\\n'; tail -80 /app/quickwit.log; printf '\\n--- version ---\\n'; curl -fsS --max-time 10 http://127.0.0.1:7280/api/v1/version\n\n--- log ---\n2026-08-29T03:41:16.547Z INFO quickwit_cli::service: quickwit version: 0.9.0 (x86_64-unknown-linux-gnu 2026-07-25T17:48:09Z cc420c3)\n2026-08-29T03:41:16.549Z INFO quickwit_config::node_config::serialize: using listen address `127.0.0.1` as advertise address advertise_address=127.0.0.1\n2026-08-29T03:41:16.549Z INFO quickwit_config::node_config: ingestion shard throughput limit: 5.2 MB\n2026-08-29T03:41:16.550Z WARN quickwit_config::node_config::serialize: cluster ID is not set, falling back to default value `quickwit-default-cluster`\n2026-08-29T03:41:16.550Z WARN quickwit_config::node_config::serialize: peer seeds are empty\n2026-08-29T03:41:16.550Z INFO quickwit_cli: loaded node config config_uri=file:///app/quickwit-v0.9.0/config/quickwit.yaml config=NodeConfig { cluster_id: \"quickwit-default-cluster\", node_id: NodeId(\"rocinante\"), availability_zone: None, enabled_services: {Indexer, Searcher, Janitor, ControlPlane, Metastore}, gossip_listen_addr: 127.0.0.1:7280, grpc_listen_addr: 127.0.0.1:7281, gossip_advertise_addr: 127.0.0.1:7280, grpc_advertise_addr: 127.0.0.1:7281, gossip_interval: 1s, peer_seeds: [], data_dir_path: \"/app/quickwit-v0.9.0/qwdata\", metastore_uri: Uri { uri: \"file:///app/quickwit-v0.9.0/qwdata/indexes#polling_interval=30s\" }, metastore_read_replica_uri: None, default_index_root_uri: Uri { uri: \"file:///app/quickwit-v0.9.0/qwdata/indexes\" }, rest_config: RestConfig { listen_addr: 127.0.0.1:7280, cors_allow_origins: [], extra_headers: {}, tls_config: None, max_connection_age: None, max_connection_age_grace: None }, health_config: None, grpc_config: GrpcConfig { max_message_size: 20.0 MiB (20971520 bytes), tls_config: None, keep_alive: None, max_connection_age: None, max_connection_age_grace: None }, storage_configs: StorageConfigs([]), metastore_configs: MetastoreConfigs([]), indexer_config: IndexerConfig { split_store_max_num_bytes: 100.0 GiB (107374182400 bytes), split_store_max_num_splits: 1000, max_concurrent_split_uploads: 12, max_merge_write_throughput: None, merge_concurrency: 2, enable_otlp_endpoint: true, enable_cooperative_indexing: false, cpu_capacity: CpuCapacity(4000), parquet_merge_use_streaming_engine: false }, searcher_config: SearcherConfig { aggregation_memory_limit: 476.8 MiB (500000000 bytes), aggregation_bucket_limit: 65000, fast_field_cache: CacheConfig { capacity: Some(953.7 MiB (1000000000 bytes)), policy: None, virtual_caches: [] }, split_footer_cache: CacheConfig { capacity: Some(476.8 MiB (500000000 bytes)), policy: None, virtual_caches: [] }, partial_request_cache: CacheConfig { capacity: Some(61.0 MiB (64000000 bytes)), policy: None, virtual_caches: [] }, predicate_cache: CacheConfig { capacity: Some(244.1 MiB (256000000 bytes)), policy: None, virtual_caches: [] }, max_num_concurrent_split_searches: 100, max_splits_per_search: None, _max_num_concurrent_split_streams: None, split_cache: None, request_timeout_secs: 30, leaf_request_timeout_secs: 30, storage_timeout_policy: None, use_metastore_read_replica: false, warmup_memory_budget: 93.1 GiB (100000000000 bytes), warmup_single_split_initial_allocation: 286.1 MiB (300000000 bytes), lambda: None }, ingest_api_config: IngestApiConfig { max_queue_memory_usage: 2.0 GiB (2147483648 bytes), max_queue_disk_usage: 4.0 GiB (4294967296 bytes), replication_factor: 1, content_length_limit: 10.0 MiB (10485760 bytes), shard_throughput_limit: 5.0 MiB (5242880 bytes), shard_burst_limit: 50.0 MiB (52428800 bytes), shard_scale_up_factor: 1.5, grpc_compression_algorithm: None }, jaeger_config: JaegerConfig { enable_endpoint: true, lookback_period_hours: 72, max_trace_duration_secs: 3600, max_fetch_spans: 10000 }, compactor_config: CompactorConfig { max_concurrent_merge_executions: 3, pipeline_slots_per_merge_execution: 2, max_concurrent_split_uploads: 12, max_merge_write_throughput: None }, enable_standalone_compactors: false }\n2026-08-29T03:41:16.550Z INFO quickwit_common::env: using environment variable `QW_DISABLE_TOKIO_LIFO_SLOT` default value default_value=true\n2026-08-29T03:41:16.551Z INFO quickwit_cluster::cluster: joining cluster cluster_id=quickwit-default-cluster node_id=rocinante generation_id=1787974876551845362 enabled_services={Indexer, Searcher, Janitor, ControlPlane, Metastore} gossip_listen_addr=127.0.0.1:7280 gossip_advertise_addr=127.0.0.1:7280 grpc_advertise_addr=127.0.0.1:7281 peer_seed_addrs=\n2026-08-29T03:41:16.552Z INFO quickwit_serve::metastore: starting local metastore service metastore_kind=\"primary\"\n2026-08-29T03:41:16.553Z INFO quickwit_cluster::change: node `rocinante` has joined the cluster node_id=rocinante generation_id=1787974876551845362\n2026-08-29T03:41:16.581Z INFO quickwit_control_plane::control_plane: starting control plane\n2026-08-29T03:41:16.585Z INFO load_from_metastore: quickwit_common::env: using environment variable `QW_ENABLE_INGEST_V2` default value default_value=true\n2026-08-29T03:41:16.585Z INFO load_from_metastore: quickwit_control_plane::model: adding ingest v2 source to 0 indexes\n2026-08-29T03:41:16.585Z INFO load_from_metastore: quickwit_control_plane::model: added ingest v2 source to 0 indexes in 0ms\n2026-08-29T03:41:16.585Z INFO load_from_metastore: quickwit_control_plane::model: enabling 0 ingest v2 sources\n2026-08-29T03:41:16.585Z INFO load_from_metastore: quickwit_control_plane::model: enabled 0 ingest v2 sources in 0ms\n2026-08-29T03:41:16.585Z INFO load_from_metastore: quickwit_control_plane::model: synced control plane model with metastore in 3ms (0 indexes, 0 sources, 0 shards)\n2026-08-29T03:41:16.585Z INFO quickwit_serve: control plane is ready\n2026-08-29T03:41:16.585Z INFO quickwit_common::env: using environment variable `QW_DISABLE_INGEST_V1` default value default_value=false\n2026-08-29T03:41:16.585Z WARN quickwit_control_plane::indexing_scheduler: no ready indexer available, falling back to retiring indexers for shard draining\n2026-08-29T03:41:16.586Z INFO quickwit_ingest::ingest_api_service: Ingest API partition id ingest_partition_id=ingest_partition_01M15SPZD93GM8YBJTG4GSPH2G\n2026-08-29T03:41:16.586Z INFO quickwit_indexing: starting indexer service\n2026-08-29T03:41:16.587Z INFO quickwit_ingest::ingest_v2::state: opening WAL located at `/app/quickwit-v0.9.0/qwdata/wal`\n2026-08-29T03:41:16.587Z INFO quickwit_ingest::ingest_v2::ingester: subscribing ingester to shard positions updates\n2026-08-29T03:41:16.587Z INFO quickwit_ingest::ingest_v2::state: opened WAL successfully in 0ms\n2026-08-29T03:41:16.587Z INFO ingester.reset_shards: quickwit_ingest::ingest_v2::ingester: resetting shards\n2026-08-29T03:41:16.587Z INFO quickwit_indexing::models::shard_positions: initialized shard positions in 0ms (0 keys)\n2026-08-29T03:41:16.587Z INFO quickwit_control_plane::ingest::ingest_controller: received advise reset shards request from `rocinante`\n2026-08-29T03:41:16.588Z INFO ingester.reset_shards: quickwit_ingest::ingest_v2::state: resetting shards\n2026-08-29T03:41:16.588Z INFO ingester.reset_shards: quickwit_ingest::ingest_v2::ingester: deleted 0 and truncated 0 shard(s) in 0ms\n2026-08-29T03:41:16.588Z INFO quickwit_config::index_config::serialize: index config does not specify `index_uri`, falling back to default value index_id=otel-logs-v0_9 index_uri=file:///app/quickwit-v0.9.0/qwdata/indexes/otel-logs-v0_9\n2026-08-29T03:41:16.590Z INFO quickwit_config::index_config::serialize: index config does not specify `index_uri`, falling back to default value index_id=otel-traces-v0_9 index_uri=file:///app/quickwit-v0.9.0/qwdata/indexes/otel-traces-v0_9\n2026-08-29T03:41:16.620Z INFO quickwit_control_plane::control_plane: created index index_uid=otel-logs-v0_9:01M15SPZDKHFQW6KB43013BTXB\n2026-08-29T03:41:18.586Z WARN quickwit_control_plane::indexing_scheduler: no ready indexer available, falling back to retiring indexers for shard draining\n2026-08-29T03:41:18.586Z WARN quickwit_control_plane::indexing_scheduler: no indexing capacity available, cannot schedule an indexing plan\n2026-08-29T03:41:18.627Z INFO quickwit_control_plane::control_plane: created index index_uid=otel-traces-v0_9:01M15SQ1BT6PQAP0NQHX7BTDY6\n2026-08-29T03:41:20.587Z WARN quickwit_control_plane::indexing_scheduler: no ready indexer available, falling back to retiring indexers for shard draining\n2026-08-29T03:41:20.587Z WARN quickwit_control_plane::indexing_scheduler: no indexing capacity available, cannot schedule an indexing plan\n2026-08-29T03:41:20.587Z INFO quickwit_serve: configured search metastore client metastore_kind=\"primary\"\n2026-08-29T03:41:20.587Z INFO quickwit_common::env: using environment variable `QW_DISABLE_DELETE_TASK_SERVICE` default value default_value=false\n2026-08-29T03:41:20.587Z INFO quickwit_janitor: starting janitor service\n2026-08-29T03:41:20.588Z INFO quickwit_serve: configured metastore readiness dependency metastore_kind=\"primary\"\n2026-08-29T03:41:20.589Z INFO quickwit_common::env: using environment variable `QW_ENABLE_CORS_DEBUG` default value default_value=false\n2026-08-29T03:41:20.589Z INFO quickwit_serve::rest: starting REST server listening on 127.0.0.1:7280 listen_addr=127.0.0.1:7280\n2026-08-29T03:41:20.589Z INFO quickwit_janitor::actors::garbage_collector: loaded 2 indexes from the metastore\n2026-08-29T03:41:20.589Z INFO quickwit_common::env: using environment variable `QW_SPLIT_DELETION_GRACE_PERIOD_SECS` default value default_value=1920\n2026-08-29T03:41:20.589Z INFO quickwit_janitor::actors::retention_policy_executor: loaded 2 indexes from the metastore\n2026-08-29T03:41:20.590Z INFO quickwit_janitor::actors::delete_task_pipeline: spawning delete tasks pipeline index_uid=otel-logs-v0_9:01M15SPZDKHFQW6KB43013BTXB root_dir=/app/quickwit-v0.9.0/qwdata/delete_task_service\n2026-08-29T03:41:20.590Z INFO quickwit_serve::grpc: starting gRPC server listening on 127.0.0.1:7281 enabled_grpc_services={\"control-plane\", \"developer\", \"health\", \"indexing\", \"ingest-api\", \"ingest-router\", \"ingester\", \"jaeger\", \"jaeger-v2\", \"metastore\", \"otlp-logs\", \"otlp-traces\", \"reflection\", \"search\"} grpc_listen_addr=127.0.0.1:7281\n2026-08-29T03:41:20.590Z INFO quickwit_serve: gRPC server is ready\n2026-08-29T03:41:20.590Z INFO quickwit_serve: REST server is ready\n2026-08-29T03:41:20.590Z INFO quickwit_janitor::actors::delete_task_pipeline: spawning delete tasks pipeline index_uid=otel-traces-v0_9:01M15SQ1BT6PQAP0NQHX7BTDY6 root_dir=/app/quickwit-v0.9.0/qwdata/delete_task_service\n2026-08-29T03:41:21.553Z INFO quickwit_cluster::change: node `rocinante` has transitioned to ready state node_id=rocinante generation_id=1787974876551845362\n2026-08-29T03:41:21.553Z INFO quickwit_serve: adding node `rocinante` to searcher pool node_id=rocinante generation_id=1787974876551845362\n2026-08-29T03:41:21.554Z INFO quickwit_serve: adding node `rocinante` with ingester status `ready` to indexer pool node_id=rocinante generation_id=1787974876551845362\n2026-08-29T03:41:21.554Z INFO quickwit_serve: adding node `rocinante` with ingester status `ready` to ingester pool node_id=rocinante generation_id=1787974876551845362\n2026-08-29T03:41:21.587Z INFO quickwit_indexing::actors::indexing_service: fetched 0 splits candidates for merge\n2026-08-29T03:41:21.587Z INFO spawn_merge_pipeline{index_uid=otel-logs-v0_9:01M15SPZDKHFQW6KB43013BTXB generation=0}: quickwit_indexing::actors::merge_pipeline: spawning merge pipeline index_uid=otel-logs-v0_9:01M15SPZDKHFQW6KB43013BTXB source_id=_ingest-api-source root_dir=/app/quickwit-v0.9.0/qwdata/indexing/otel-logs-v0_9%01M15SPZDKHFQW6KB43013BTXB%_ingest-api-source%01M15SQ49JY9F03FJMGRZG0DNV%L5tqlU merge_policy=StableLogMergePolicy { config: StableLogMergePolicyConfig { min_level_num_docs: 100000, merge_factor: 10, max_merge_factor: 12, maturation_period: 172800s }, split_num_docs_target: 10000000 }\n2026-08-29T03:41:21.587Z INFO spawn_pipeline{index=otel-logs-v0_9 gen=0}: quickwit_indexing::actors::indexing_pipeline: spawning indexing pipeline index_id=\"otel-logs-v0_9\" source_id=\"_ingest-api-source\" pipeline_uid=01M15SQ49JY9F03FJMGRZG0DNV root_dir=/app/quickwit-v0.9.0/qwdata/indexing/otel-logs-v0_9%01M15SPZDKHFQW6KB43013BTXB%_ingest-api-source%01M15SQ49JY9F03FJMGRZG0DNV%L5tqlU\n2026-08-29T03:41:21.587Z INFO spawn_pipeline{index=otel-logs-v0_9 gen=0}: quickwit_common::env: using environment variable `QW_DISABLE_PER_INDEX_METRICS` default value default_value=false\n2026-08-29T03:41:21.588Z INFO spawn_merge_pipeline{index_uid=otel-traces-v0_9:01M15SQ1BT6PQAP0NQHX7BTDY6 generation=0}: quickwit_indexing::actors::merge_pipeline: spawning merge pipeline index_uid=otel-traces-v0_9:01M15SQ1BT6PQAP0NQHX7BTDY6 source_id=_ingest-api-source root_dir=/app/quickwit-v0.9.0/qwdata/indexing/otel-traces-v0_9%01M15SQ1BT6PQAP0NQHX7BTDY6%_ingest-api-source%01M15SQ49JRNBSJBWWPDHC0F5N%F7oAvE merge_policy=StableLogMergePolicy { config: StableLogMergePolicyConfig { min_level_num_docs: 100000, merge_factor: 10, max_merge_factor: 12, maturation_period: 172800s }, split_num_docs_target: 10000000 }\n2026-08-29T03:41:21.588Z INFO spawn_pipeline{index=otel-traces-v0_9 gen=0}: quickwit_indexing::actors::indexing_pipeline: spawning indexing pipeline index_id=\"otel-traces-v0_9\" source_id=\"_ingest-api-source\" pipeline_uid=01M15SQ49JRNBSJBWWPDHC0F5N root_dir=/app/quickwit-v0.9.0/qwdata/indexing/otel-traces-v0_9%01M15SQ1BT6PQAP0NQHX7BTDY6%_ingest-api-source%01M15SQ49JRNBSJBWWPDHC0F5N%F7oAvE\n2026-08-29T03:41:21.610Z INFO spawn_pipeline{index=otel-logs-v0_9 gen=0}: quickwit_indexing::source::ingest_api_source: created queue successfully index_id=\"otel-logs-v0_9\" partition_id=ingest_partition_01M15SPZD93GM8YBJTG4GSPH2G\n2026-08-29T03:41:21.615Z INFO spawn_pipeline{index=otel-traces-v0_9 gen=0}: quickwit_indexing::source::ingest_api_source: created queue successfully index_id=\"otel-traces-v0_9\" partition_id=ingest_partition_01M15SPZD93GM8YBJTG4GSPH2G\n\n--- version ---\n{\"build\":{\"build_date\":\"2026-07-25T18:12:04Z\",\"build_profile\":\"release\",\"build_target\":\"x86_64-unknown-linux-gnu\",\"cargo_pkg_version\":\"0.9.0\",\"commit_date\":\"2026-07-25T17:48:09Z\",\"commit_hash\":\"cc420c34d686a75f412b7cd450564c5c99ad6f21\",\"commit_short_hash\":\"cc420c3\",\"commit_tags\":[\"v0.9.0\"],\"version\":\"v0.9.0\"},\"runtime\":{\"num_cpus\":4,\"num_threads_blocking\":3,\"num_threads_non_blocking\":1}}",
"time_seconds": 49.151
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Creating index using CLI**",
"time_seconds": 49.151
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ cd /app/quickwit-v0.9.0 && ./quickwit index create --index-config /app/stackoverflow-index-config.yaml",
"time_seconds": 49.151
},
{
"kind": "tool_result",
"role": "bash",
"text": "\u276f Creating index...\n\u2714 Index successfully created.\n",
"time_seconds": 51.519
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning dataset download**",
"time_seconds": 51.519
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ cd /app && curl -fL --retry 3 -o stackoverflow.posts.transformed-10000.json https://quickwit-datasets-public.s3.amazonaws.com/stackoverflow.posts.transformed-10000.json && wc -l -c stackoverflow.posts.transformed-10000.json && head -c 500 stackoverflow.posts.transformed-10000.json",
"time_seconds": 51.519
},
{
"kind": "tool_result",
"role": "bash",
"text": " % Total % Received % Xferd Average Speed Time Time Time Current\n Dload Upload Total Spent Left Speed\n\r 0 0 0 0 0 0 0 0 0\r100 6.37M 100 6.37M 0 0 5.81M 0 00:01 00:01 0\r100 6.37M 100 6.37M 0 0 5.81M 0 00:01 00:01 0\r100 6.37M 100 6.37M 0 0 5.81M 0 00:01 00:01 0\n 10000 6684940 stackoverflow.posts.transformed-10000.json\n{\"user\":\"8\",\"tags\":[\"c#\",\"winforms\",\"type-conversion\",\"decimal\",\"opacity\"],\"questionId\":4,\"creationDate\":\"2008-07-31T21:42:52Z\",\"title\":\"While applying opacity to a form should we use a decimal or double value?\",\"acceptedAnswerId\":7,\"type\":\"question\",\"body\":\"I want to use a track-bar to change a form's opacity. This is my code: decimal trans = trackBar1.Value / 5000; this.Opacity = trans; When I build the application, it gives the following error: Cannot implicitly convert type 'decimal' to 'dou",
"time_seconds": 55.148
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ cd /app/quickwit-v0.9.0 && ./quickwit index ingest --index stackoverflow --input-path /app/stackoverflow.posts.transformed-10000.json --force",
"time_seconds": 55.149
},
{
"kind": "tool_result",
"role": "bash",
"text": "\u276f Ingesting documents from /app/stackoverflow.posts.transformed-10000.json.\n\u2714 Ingested 10000 document(s) successfully.\n",
"time_seconds": 60.234
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Executing and capturing search results**",
"time_seconds": 60.234
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ cd /app/quickwit-v0.9.0 && ./quickwit index search --index stackoverflow --query \"search AND engine\"",
"time_seconds": 60.234
},
{
"kind": "tool_result",
"role": "bash",
"text": "{\n \"num_hits\": 10,\n \"hits\": [\n {\n \"answerId\": 16049,\n \"body\": \"Not sure if this is what you are after but you can search source code on Google. Follow the link for a search on 'function:reduce() lang:python' on Google Code search At first glance the following projects use reduce() MoinMoin Zope Numeric ScientificPython etc. etc. but then these are hardly surprising since they are huge projects. The functionality of reduce can be done using function recursion which I guess Guido thought was more explicit. Update: Since Google's Code Search was discontinued on 15-Jan-2012, besides reverting to regular Google searches, there's something called Code Snippets Collection that looks promising. A number of other resources are mentioned in answers this (closed) question Replacement for Google Code Search?. Update 2 (29-May-2017): A good source for Python examples (in open-source code) is the Nullege search engine. \",\n \"creationDate\": \"2008-08-19T12:16:01Z\",\n \"questionId\": 15995,\n \"type\": \"answer\",\n \"user\": \"199\"\n },\n {\n \"answerId\": 14908,\n \"body\": \"This is a HUGE questions, I wouldn't know how to begin... So let me just try giving you the right \\\"terms\\\" so you can refine your quest: First, understand that Speech Recognition is a diverse and complicated subject, and it has many different applications. People tend to map this domain to the first thing that comes to their head (usually, that would be computers understanding what you are saying like in IVR systems). So first lets distinguise the concept into the main categories: Human-to-Machine: Applications that deal with understanding what a human is saying, but the human knows he is talking to a machine and the grammar is very limited. Examples are Computer automation Specialized: Pilots automating some controls for example (noise a huge problem) IVR (Interactive Voice Response) systems like Google-411 or when you call the bank and the computer on the other side says \\\"say 'service' to get customer service\\\" human-to-human (Spontaneous speech): This is a bigger, more complex problem. Here we can also break it down into different applciations: Call Center: conversation between Agent-Customer, phone quality, compressed Intelligence: radio/phone/live conversations between 2 or more individuals Now, Speech-To-Text is not what you should be saying that you care about. What you care about is solving a problem. Different technologies are used to solve different problems. See an overview here of some of them. to summarize, other approaches are Phonetic transcription, LVCSR and direct based. Also, are you interested in being the PHd behind the technology? you would need a Masters equivalent involving Signal processing and probably a PHd to be cutting edge. In which case, you will work for a company that develops the actual speech engine. Companies like Nuance and IBM are the big ones, but also Phillips and other startups exist. On the other hand, if you want to be the one implementing applications, you will not be working on the engine, but working on building application that USE the engine. A good analogy I think is form the gaming industry: Are you developing the graphic engine (like the Cry engine), or working on one of several hundred games, all use the same graphic engine? Don't get me wrong, there is plenty to work on the quality of the search also outside the IBM/Nuance of the world. The engine is usually very open, and there are a lot of algorithmic tweaking to be done that can dramatically affect performance. Each business application has different constraints and cost/benefit function, so you can make experiments for many years building better voice recognition based applications. one more thing: in general, you would also want to have good statistics background the lower in the stack you want to be. At this point in time, I'm mainly interested in being able to create applications that allow automation Good, we are converging here... Then you have no interest in \\\"Speech-to-Text\\\". That buzzwords takes you to the world of full transcription, a place you do not need to go to. You should be focusing on some of the more Human-to-Machine technologies like Voice XML and the ones used in IVR systems (Nuance is the biggest player there) \",\n \"creationDate\": \"2008-08-18T16:56:43Z\",\n \"questionId\": 14837,\n \"type\": \"answer\",\n \"user\": \"350\"\n },\n {\n \"acceptedAnswerId\": 14908,\n \"body\": \"I'm really interested in speech-to-text algorithms, but I'm not sure where to start studying up on them. A bunch of searching around led me to this, but it's from 1996 and I'm fairly certain that there have been improvements since then. Does anyone who has any experience with this sort of stuff have any recommendations for reading / source code to examine? Or just general advice on what I should be trying to learn about if I want to get into the world of writing speech recognition programs (sometimes it's hard to know what to search for if you don't have much knowledge about the domain). Edit: I'd like to do something cross-platform, but for the moment I'd be targeting linux. Edit 2: Thanks csmba for the well-thought out reply. At this point in time, I'm mainly interested in being able to create applications that allow automation, or execution of different commands through voice. So, a limited amount of recognizable commands being able to be strung together. An example would be a music player that took commands like \\\"Play the album Hello Everything by Squarepusher\\\", or an application launcher that allowed the user to create voice-shortcuts to launch specific apps. I realize that it's a pretty giant problem, and that I have nowhere near the level of knowledge required right now to tackle implementing an entire recognition engine, although the techniques involved with doing so fascinate me, and it is something I'd like to work myself up to doing. In all likelihood, I'll probably end up picking up a book or two on the subject and studying up / playing with \\\"simple\\\" implementations in my free time. \",\n \"creationDate\": \"2008-08-18T16:05:43Z\",\n \"questionId\": 14837,\n \"tags\": [\n \"language-agnostic\",\n \"speech-recognition\"\n ],\n \"title\": \"How to get started with speech-to-text?\",\n \"type\": \"question\",\n \"user\": \"242853\"\n },\n {\n \"answerId\": 13871,\n \"body\": \"Depends a lot on the kind of queries you've got to parse and somewhat on the structure of the data in your database. I'm going to assume that you're not trying to do full text search in a DB (i.e. a search engine across your entire DB) because, as most Information Retrieval people will tell you, the performance for that is terrible. Inverted indexes are most certainly the best way of doing that. Tell us a bit more about the actual problem: what are the users going to input, what are they expecting as output, and what's the data model like. Design a search solution without those pieces of information, and you'll get a far from optimal result. \",\n \"creationDate\": \"2008-08-17T19:28:04Z\",\n \"questionId\": 13863,\n \"type\": \"answer\",\n \"user\": \"1035\"\n },\n {\n \"body\": \"I have been trying to find an easy way to parse a search query and convert it to an SQL query for my DB. I have found two solutions: Lucene: Powerful Java-based search engine, contains a query parser but it isn't very configurable and I could find a way to easily hack/adapt it to create SQL queries. ANTLR: A veteran text lexer-parser. Used for building anything from compilers to sky scrapers. ANTLR is highly configurable but everyone touching the code from now on will have to learn a new language... Any other ideas? \",\n \"creationDate\": \"2008-08-17T19:21:26Z\",\n \"questionId\": 13863,\n \"tags\": [\n \"java\",\n \"search\"\n ],\n \"title\": \"Parsing search queries in Java\",\n \"type\": \"question\",\n \"user\": \"1651\"\n },\n {\n \"acceptedAnswerId\": 12560,\n \"body\": \"I know google has things like linkto:yourdomain.com and Alexa is around, but there's nothing that I can query and get the number of sites that link to me. Anyone know of such a thing, other than using Google or another search engine and scraping the HTML results? \",\n \"creationDate\": \"2008-08-15T17:58:54Z\",\n \"questionId\": 12555,\n \"tags\": [\n \"api\",\n \"analytics\",\n \"metrics\",\n \"counter\",\n \"hit\"\n ],\n \"title\": \"Is there an API that allows you to find out how many sites link to yours?\",\n \"type\": \"question\",\n \"user\": \"572\"\n },\n {\n \"answerId\": 5474,\n \"body\": \"If you have a public facing website, the real business case is SEO. Accessibility is important and maintaining semantic (X)HTML is much easier than maintaining table layouts, but that #1 spot on Google will bring home the bacon. For example: Monthly web report: 127 million page views for July Monthly web report: 127 million page views for July ... Latimes.com keeps getting better at SEO (search engine optimization), which means our stories are ranking higher in Google and other search engines. We are also performing better on sites like Digg.com. All that adds up to more exposure and more readership than ever before. If you look at their site, they've got a pretty decent CSS layout going. Generally, you find relatively few table layouts performing well in the SERPs these days. \",\n \"creationDate\": \"2008-08-07T23:22:57Z\",\n \"questionId\": 5323,\n \"type\": \"answer\",\n \"user\": \"60\"\n },\n {\n \"answerId\": 3929,\n \"body\": \"This might be a bit of a long shot, but if you're using (or planning on using) SQL Server 2008 I'd recommend having a look at the new FileStream data type. FileStream solves most of the problems around storing the files in the DB: The Blobs are actually stored as files in a folder. The Blobs can be accessed using either a database connection or over the filesystem. Backups are integrated. Migration \\\"just works\\\". However SQL's \\\"Transparent Data Encryption\\\" does not encrypt FileStream objects, so if that is a consideration, you may be better off just storing them as varbinary. From the MSDN Article: Transact-SQL statements can insert, update, query, search, and back up FILESTREAM data. Win32 file system interfaces provide streaming access to the data. FILESTREAM uses the NT system cache for caching file data. This helps reduce any effect that FILESTREAM data might have on Database Engine performance. The SQL Server buffer pool is not used; therefore, this memory is available for query processing. \",\n \"creationDate\": \"2008-08-06T20:15:31Z\",\n \"questionId\": 3748,\n \"type\": \"answer\",\n \"user\": \"233\"\n },\n {\n \"answerId\": 3548,\n \"body\": \"If you're trying to prevent search engine bots from accessing certain pages, make sure you're using a properly formatted robots.txt file. Using HTTP_REFERER is unreliable because it is easily faked. Another option is to check the user agent string for known bots (this may require code modification). \",\n \"creationDate\": \"2008-08-06T15:32:11Z\",\n \"questionId\": 3486,\n \"type\": \"answer\",\n \"user\": \"357\"\n },\n {\n \"answerId\": 1130,\n \"body\": \"Why is it needed? When data is stored on disk based storage devices, it is stored as blocks of data. These blocks are accessed in their entirety, making them the atomic disk access operation. Disk blocks are structured in much the same way as linked lists; both contain a section for data, a pointer to the location of the next node (or block), and both need not be stored contiguously. Due to the fact that a number of records can only be sorted on one field, we can state that searching on a field that isn\u2019t sorted requires a Linear Search which requires N/2 block accesses (on average), where N is the number of blocks that the table spans. If that field is a non-key field (i.e. doesn\u2019t contain unique entries) then the entire table space must be searched at N block accesses. Whereas with a sorted field, a Binary Search may be used, this has log2 N block accesses. Also since the data is sorted given a non-key field, the rest of the table doesn\u2019t need to be searched for duplicate values, once a higher value is found. Thus the performance increase is substantial. What is indexing? Indexing is a way of sorting a number of records on multiple fields. Creating an index on a field in a table creates another data structure which holds the field value, and pointer to the record it relates to. This index structure is then sorted, allowing Binary Searches to be performed on it. The downside to indexing is that these indexes require additional space on the disk, since the indexes are stored together in a table using the MyISAM engine, this file can quickly reach the size limits of the underlying file system if many fields within the same table are indexed. How does it work? Firstly, let\u2019s outline a sample database table schema; Field name Data type Size on disk id (Primary key) Unsigned INT 4 bytes firstName Char(50) 50 bytes lastName Char(50) 50 bytes emailAddress Char(100) 100 bytes Note: char was used in place of varchar to allow for an accurate size on disk value. This sample database contains five million rows, and is unindexed. The performance of several queries will now be analyzed. These are a query using the id (a sorted key field) and one using the firstName (a non-key unsorted field). Example 1 - sorted vs unsorted fields Given our sample database of r = 5,000,000 records of a fixed size giving a record length of R = 204 bytes and they are stored in a table using the MyISAM engine which is using the default block size B = 1,024 bytes. The blocking factor of the table would be bfr = (B/R) = 1024/204 = 5 records per disk block. The total number of blocks required to hold the table is N = (r/bfr) = 5000000/5 = 1,000,000 blocks. A linear search on the id field would require an average of N/2 = 500,000 block accesses to find a value, given that the id field is a key field. But since the id field is also sorted, a binary search can be conducted requiring an average of log2 1000000 = 19.93 = 20 block accesses. Instantly we can see this is a drastic improvement. Now the firstName field is neither sorted nor a key field, so a binary search is impossible, nor are the values unique, and thus the table will require searching to the end for an exact N = 1,000,000 block accesses. It is this situation that indexing aims to correct. Given that an index record contains only the indexed field and a pointer to the original record, it stands to reason that it will be smaller than the multi-field record that it points to. So the index itself requires fewer disk blocks than the original table, which therefore requires fewer block accesses to iterate through. The schema for an index on the firstName field is outlined below; Field name Data type Size on disk firstName Char(50) 50 bytes (record pointer) Special 4 bytes Note: Pointers in MySQL are 2, 3, 4 or 5 bytes in length depending on the size of the table. Example 2 - indexing Given our sample database of r = 5,000,000 records with an index record length of R = 54 bytes and using the default block size B = 1,024 bytes. The blocking factor of the index would be bfr = (B/R) = 1024/54 = 18 records per disk block. The total number of blocks required to hold the index is N = (r/bfr) = 5000000/18 = 277,778 blocks. Now a search using the firstName field can utilise the index to increase performance. This allows for a binary search of the index with an average of log2 277778 = 18.08 = 19 block accesses. To find the address of the actual record, which requires a further block access to read, bringing the total to 19 + 1 = 20 block accesses, a far cry from the 1,000,000 block accesses required to find a firstName match in the non-indexed table. When should it be used? Given that creating an index requires additional disk space (277,778 blocks extra from the above example, a ~28% increase), and that too many indexes can cause issues arising from the file systems size limits, careful thought must be used to select the correct fields to index. Since indexes are only used to speed up the searching for a matching field within the records, it stands to reason that indexing fields used only for output would be simply a waste of disk space and processing time when doing an insert or delete operation, and thus should be avoided. Also given the nature of a binary search, the cardinality or uniqueness of the data is important. Indexing on a field with a cardinality of 2 would split the data in half, whereas a cardinality of 1,000 would return approximately 1,000 records. With such a low cardinality the effectiveness is reduced to a linear sort, and the query optimizer will avoid using the index if the cardinality is less than 30% of the record number, effectively making the index a waste of space. \",\n \"creationDate\": \"2008-08-04T10:41:04Z\",\n \"questionId\": 1108,\n \"type\": \"answer\",\n \"user\": \"264\"\n }\n ],\n \"snippets\": null,\n \"elapsed_time_micros\": 16752,\n \"errors\": [],\n \"aggregations\": null\n}\n",
"time_seconds": 61.978
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning quickstart implementation in /app**\n\n**Inspecting index directory for issues**",
"time_seconds": 61.979
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ find /app/quickwit-v0.9.0/qwdata/indexes/stackoverflow -maxdepth 2 -type f -printf '%P %s bytes\\n' | head -30; printf '\\nSearch summary:\\n'; ./quickwit-v0.9.0/quickwit index search --index stackoverflow --query 'search AND engine' 2>/dev/null | python3 -c 'import json,sys; d=json.load(sys.stdin); print({\"num_hits\": d[\"num_hits\"], \"returned_hits\": len(d[\"hits\"]), \"first_title\": d[\"hits\"][0].get(\"title\")})'",
"time_seconds": 61.979
},
{
"kind": "tool_result",
"role": "bash",
"text": "01M15SQDDNDPSPAD3AM6FDCSWZ.split 4712661 bytes\nmetastore.json 5274 bytes\n\nSearch summary:\n{'num_hits': 10, 'returned_hits': 10, 'first_title': None}\n",
"time_seconds": 68.489
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Verifying Quickwit first search functionality**\n\n**Confirming Quickwit setup aligns with docs**",
"time_seconds": 68.49
},
{
"kind": "text",
"role": "assistant",
"text": "Quickwit v0.9.0 quickstart completed in `/app`.\n\n- Server running at `127.0.0.1:7280`\n- `stackoverflow` index created\n- 10,000 documents ingested\n- First search (`search AND engine`) returned **10 hits**.",
"time_seconds": 68.49
}
],
"schema_version": 1,
"source": "pi-session-jsonl",
"source_sha256": "2e28a1baf9394e1c702b9252b0b7df67d5aa1efd8799b6962af25dac7b12c0d7",
"started_at_epoch_ms": 1787974832666
}