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{"type":"message","id":"a078786c","parentId":"df9e7aa8","timestamp":"2026-08-31T00:23:01.409Z","message":{"role":"toolResult","toolCallId":"call_3qVxLxEjTVV3ZLrRyBAEphhi|fc_0d51cdf92682941d016a94c965227887d09c5e1839c2b7a127","toolName":"read","content":[{"type":"text","text":"---\ntitle: \"Quickstart: Locally hosted with Docker\"\nsidebar_label: Local Quickstart (self-hosted)\nimage: og/docs/quickstart-tutorial.jpg\n# tags: ['getting started']\n---\n\nimport Tabs from \"@theme/Tabs\";\nimport TabItem from \"@theme/TabItem\";\nimport SkipLink from \"/src/components/SkipValidationLink\";\nimport CardsSection from \"/src/components/CardsSection\";\nimport Tooltip from \"/src/components/Tooltip\";\nimport styles from \"/src/components/CardsSection/styles.module.scss\";\n\nexport const quickstartOptions = [\n {\n title: (\n <>\n Vectorize objects during import\n
\n (recommended)\n \n ),\n description:\n \"Import objects and vectorize them with the Ollama embedding model.\",\n link: \"?import=vectorization#create-a-collection\",\n icon: \"fas fa-arrows-spin\",\n groupId: \"import\",\n activeTab: \"vectorization\",\n },\n {\n title: \"Import vectors\",\n description: \"Import pre-computed vector embeddings along with your data.\",\n link: \"?import=custom-embeddings#create-a-collection\",\n icon: \"fas fa-circle-nodes\",\n groupId: \"import\",\n activeTab: \"custom-embeddings\",\n },\n];\n\nWeaviate is an open-source vector database built to power AI applications. This quickstart guide will show you how to:\n\n1. **Set up a collection** - Create a collection and import data into it. \n2. **Search** - Perform a similarity (vector) search on your data.\n3. **RAG** - Perform Retrieval Augmented Generation (RAG) with a generative model.\n\nimport KapaAI from \"/src/components/KapaAI\";\n\nIf you encounter any issues along the way or have additional questions, use the Ask AI feature.\n\n## Prerequisites\n\nBefore we get started, install [Docker](https://docs.docker.com/get-started/get-docker/) on your machine.\nWe will be running Weaviate and Ollama language models locally. We recommend that you use a modern computer with at least 8GB of RAM, preferably 16GB or more.\n\nimport TelemetryNotice from '/_includes/telemetry-notice.mdx';\n\n\n\n---\n\n## Start Weaviate and Ollama with Docker Compose\n\nSave the following code to a file named `docker-compose.yml` in your project directory.\n\n```yaml\nservices:\n weaviate:\n command:\n - --host\n - 0.0.0.0\n - --port\n - '8080'\n - --scheme\n - http\n image: cr.weaviate.io/semitechnologies/weaviate:||site.weaviate_version||\n ports:\n - 8080:8080\n - 50051:50051\n volumes:\n - weaviate_data:/var/lib/weaviate\n restart: on-failure:0\n environment:\n AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'\n PERSISTENCE_DATA_PATH: '/var/lib/weaviate'\n ENABLE_MODULES: 'text2vec-ollama,generative-ollama'\n CLUSTER_HOSTNAME: 'node1'\n OLLAMA_API_ENDPOINT: 'http://ollama:11434'\n depends_on:\n - ollama\n\n ollama:\n image: ollama/ollama:0.12.9\n ports:\n - \"11434:11434\"\n volumes:\n - ollama_data:/root/.ollama\n\nvolumes:\n weaviate_data:\n ollama_data:\n```\n\nRun the following command to start a Weaviate instance and the Ollama server inside Docker containers:\n\n```bash\ndocker-compose up -d\n```\n\nOnce the Ollama service starts, you can pull the required embedding model ([`nomic-embed-text`](https://ollama.com/library/nomic-embed-text)) and generative model ([`llama3.2`](https://ollama.com/library/llama3.2)) in the `ollama` container:\n\n```bash\ndocker compose exec ollama ollama pull nomic-embed-text\ndocker compose exec ollama ollama pull llama3.2\n```\n\n---\n\n## Install a client library\n\nFollow the instructions below to install one of the official client libraries, available in [Python](../client-libraries/python/index.mdx), [JavaScript/TypeScript](../client-libraries/typescript/index.mdx), [Go](../client-libraries/go.md), and [Java](../client-libraries/java/index.mdx).\n\nimport CodeClientInstall from \"/_includes/code/quickstart/clients.install.new.mdx\";\n\n\n\n## Step 1: Create a collection & import data {#create-a-collection}\n\nThere are two paths you can choose from when importing data:\n\n\n
\n\n\n\n\nThe following example creates a collection called `Movie` with the [Ollama](../model-providers/ollama/embeddings.md) embedding model provider (`text2vec-ollama`) for vectorizing data during import and for querying. You are also free to use any other available [embedding model provider](../model-providers/index.md).\n\nimport CreateCollection from \"/_includes/code/quickstart/quickstart.short.local.create_collection.mdx\";\n\n\n\n\n\n\nThe following example creates a collection called `Movie`. The data should already contain the pre-computed vector embeddings. This option is useful for when you are migrating data from a different vector database.\n\nimport CreateCollectionCustomVectors from \"/_includes/code/quickstart/quickstart.short.local.import_vectors.create_collection.mdx\";\n\n\n\n\n\n\n## Step 2: Semantic (vector) search {#semantic-search}\n\n\n\n\nSemantic search finds results based on meaning. This is called `nearText` in Weaviate. The following example searches for 2 objects (_limit_) whose meaning is most similar to that of `sci-fi`.\n\nimport QueryNearText from \"/_includes/code/quickstart/quickstart.short.local.query.neartext.mdx\";\n\n\n\n\n\n\nSemantic search finds results based on meaning. This is called `nearVector` in Weaviate. The following example searches for 2 objects (_limit_) whose vector is most similar to the query vector.\n\nimport QueryNearVectorImportVectors from \"/_includes/code/quickstart/quickstart.short.local.import_vectors.query.nearvector.mdx\";\n\n\n\n\n\n\n\n\nExample response
\n\n```json\n{\n \"genre\": \"Science Fiction\",\n \"title\": \"The Matrix\",\n \"description\": \"A computer hacker learns about the true nature of reality and his role in the war against its controllers.\"\n}\n{\n \"genre\": \"Fantasy\",\n \"title\": \"The Lord of the Rings: The Fellowship of the Ring\",\n \"description\": \"A meek Hobbit and his companions set out on a perilous journey to destroy a powerful ring and save Middle-earth.\"\n}\n```\n\n \n\n:::tip Query Agent\n\nTry the [Query Agent](/query-agent/index.md) with a Weaviate Cloud instance. You simply provide a prompt/question in natural language, and the Query Agent takes care of all the needed steps to provide an answer.\n\n:::\n\n## Step 3: Retrieval augmented generation (RAG)\n\n\n\n\nRetrieval augmented generation (RAG), also called generative search, works by prompting a large language model (LLM) with a combination of a _user query_ and _data retrieved from a database_.\n\nThe following example combines the semantic search for the query `sci-fi` with a prompt to generate a tweet using the Ollama generative model (`generative-ollama`).\n\nimport QueryRAG from \"/_includes/code/quickstart/quickstart.short.local.query.rag.mdx\";\n\n\n\n\n\n\nRetrieval augmented generation (RAG), also called generative search, works by prompting a large language model (LLM) with a combination of a _user query_ and _data retrieved from a database_.\n\nThe following example combines the vector similarity search with a prompt to generate a tweet using the Ollama generative model (`generative-ollama`).\n\nimport QueryRAGCustomVectors from \"/_includes/code/quickstart/quickstart.short.local.import-vectors.query.rag.mdx\";\n\n\n\n\n\n\n\n\nExample response
\n\n```json\nš¶ļø Unplug from the system & join Neo's journey šš°\n\n\"The Matrix\" will blow your mind š¤Æ as reality unravels š\n\nKung-fu, slow-mo & mind-bending sci-fi š„š“ļø\n\nAre you ready to see how deep the rabbit hole goes? š“šµ #TheMatrix #WakeUp\n```\n\n \n\n## Next steps\n\nimport NextSteps from \"/_includes/quickstart.short.nextsteps.mdx\";\n\n\n\n## Questions and feedback\n\nimport DocsFeedback from \"/_includes/docs-feedback.mdx\";\n\n\n"}],"isError":false,"timestamp":1788135781409}}
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{"type":"message","id":"32809cba","parentId":"3e8cf1c4","timestamp":"2026-08-31T00:23:27.959Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Exploring documentation include paths**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94c97cc64c87d0b6b23bf085947d9a\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMl-EzXERd9j8hG70wLYeNWVIiNPEHe43rHMvrHw4uvC9harYLxpQcyZzzXCF62WNDectr9SBbOM88M3yz6LxIPPybOCTpEmYyYjYaCI7xRtnnhu7Z1mizr3CRrbpB2awiUFKUbxZgvM4rRQJ_C3UETkMye9EvMxEmlNvgUtpIKkB6t8NMrW8zs9s0OXrgT7JkiJez8sp1aVzBkzeCpRkAQKkFTJQWuIoNJAhkdX6f8bAWmZz8XNxLfX4ia3-Dcl9PwL-w1up_Lf74h-iC6wPObD4jAKqbLxoNYuYWffFy1UMp0v3XE4oZRCXrEkkYRx1otSMOE7JfOKUnrubEkxj5gOy91CyIHHAlOTKiaA8XDiGIo104WaMOjP_8c2RfMmnfUL7XyMtVro6pzoky-z5MvrrFdHa4XAS-DNL27M23Z7fr6IsobY7p2Cv9a5lTQFiii40VL2CTep9ghNX8RY9mXdVpYcl-IcoF6LduClcWJHaGYkhLjnVbWnfrXx7il-fVt9keyaqDxvSxcfIYLhJemFow5SXWrzgHg0Yh7IyCxIjaALiJ0kSfDIj8rehjbW17sc49o1U93oC7YBxV85AfnTTctIqjsz9lQeNj7_n3KogsRtQurXNtBDX_V4Y8n2SasoqnNGTEBd_L6QhshQNEdsTod9rH3fKfxHhoNqKzhobpxOKrscXnaU0F_tc_rP12pI8mVKbBNZXRYlALIB_SARaFI6oV4bgPYZJGUhMkCQLT44mzkAK4dtVf0ZraZukx2gII6mgDo5-SeRnMwM5zXVjIxyEjG95QT3Yu8yNI-d6Pv_-5F7NzFwIuWPEKCJrGS1b3LWLRLyCDLEf1CDRaqQbalWsfp6Jaf3alt6nb8RQ19W0YBHFdIdYpcFa7GfcrWI8CnI88rChBScfwFNMUKTHSO3V5L05QLJOvoVIIGGfz9nJ0XYPZBtqoECrG450UEfap69b0uciok26IbDBS7hM5e0KvTY8Q05v2ipLB1R-N-EG2YxKRbtCFSNtdGx8Kc3ozQby1U7wOprgjmXgwDpGd9eokXmKrNygMYb-ssR6MvNW7mBXpSzPb3VmG_NQ67N8yE7nt2QcxK2DGzOjpSKU4TDchYMuv2BNvnNxSYKlyDexgI-mddDghQw1C96na3-IfnV0OiMpneEe4VnY-iTRFs69sWMSxjw3-0rij-Lfdd71-SmlTILMBla0fdmiukdl-ks4Y2RAnUrlSMG7hn38jjWj_rc6uARYCZRcaMqU5CB6shxjDbG0XBdq-D5tkn1Z0lFHV8abywriTXVIevOhVweu8JkG7cUj4qVcfkEL_HQnqwS1pbo5PY1_y1Drv7VrauN0rfUEPUe1KgJi8ESicX2J7gQiFKaMF8VnFQ5p0VgaP64t9SxU-ve6w17M_hAKyWqAyNnlkpLwIMCC26rhYDrndrQMkC-w3f4cwP1axR6qdS1cKxB4rCQKi5oU7OJ\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Exploring documentation include paths**\"}]}"},{"type":"toolCall","id":"call_QnzNqEu2F317smFb4lWsjhpW|fc_0d51cdf92682941d016a94c97e0a4487d0a8593818614886f9","name":"bash","arguments":{"command":"for p in 'docs/_includes/code/quickstart' '_includes/code/quickstart' 'src/components'; do echo $p; curl -fsSL \"https://api.github.com/repos/weaviate/docs/contents/$p?ref=016b79ae0b25275c70666fbebede91ea669767c2\" | python -c 'import json,sys; d=json.load(sys.stdin); print(\"\\n\".join(x.get(\"name\",\"\") for x in d) if isinstance(d,list) else d)' || true; done","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1063,"output":174,"cacheRead":3584,"cacheWrite":0,"totalTokens":4821,"cost":{"input":0.005315,"output":0.00522,"cacheRead":0.001792,"cacheWrite":0,"total":0.012327}},"stopReason":"toolUse","timestamp":1788135803788,"responseId":"resp_0d51cdf92682941d016a94c97c1bc487d086e225e81a1dcae4"}}
{"type":"message","id":"f46e3c81","parentId":"32809cba","timestamp":"2026-08-31T00:23:29.098Z","message":{"role":"toolResult","toolCallId":"call_QnzNqEu2F317smFb4lWsjhpW|fc_0d51cdf92682941d016a94c97e0a4487d0a8593818614886f9","toolName":"bash","content":[{"type":"text","text":"docs/_includes/code/quickstart\ncurl: (22) The requested URL returned error: 404\nTraceback (most recent call last):\n File \"\", line 1, in \n File \"/opt/python-base/lib/python3.12/json/__init__.py\", line 293, in load\n return loads(fp.read(),\n ^^^^^^^^^^^^^^^^\n File \"/opt/python-base/lib/python3.12/json/__init__.py\", line 346, in loads\n return _default_decoder.decode(s)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python-base/lib/python3.12/json/decoder.py\", line 338, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python-base/lib/python3.12/json/decoder.py\", line 356, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n_includes/code/quickstart\nclients.install.mdx\nclients.install.new.mdx\ncollection.definition.mdx\nconnect.noheader.mdx\nconnect.partial.mdx\nconnect.withkey.mdx\nendtoend-v2.ts\nendtoend.mdx\nendtoend.py\nendtoend.py3.py\nendtoend.ts\ngenerativesearch.grouped.mdx\ngenerativesearch.single.mdx\ngo-add-objects.go\ngo-collection-define.go\ngo-connect.go\ngo-create-run-all.go\ngo-query-filter.go\ngo-query-generative-group.go\ngo-query-generative.go\ngo-query-neartext.go\nimport.custom.vectors.mdx\nimport.mdx\nlocal.quickstart.create_collection.mdx\nlocal.quickstart.import_objects.mdx\nlocal.quickstart.is_ready.mdx\nlocal.quickstart.query.neartext.mdx\nlocal.quickstart.query.rag.mdx\nneartext.mdx\nneartext.where.mdx\nquickstart.create_collection.mdx\nquickstart.import_objects.mdx\nquickstart.is_ready.mdx\nquickstart.query.neartext.mdx\nquickstart.query.rag.mdx\nquickstart.short.create_collection.mdx\nquickstart.short.import-vectors.query.rag.mdx\nquickstart.short.import_vectors.create_collection.mdx\nquickstart.short.import_vectors.query.nearvector.mdx\nquickstart.short.local.create_collection.mdx\nquickstart.short.local.import-vectors.query.rag.mdx\nquickstart.short.local.import_vectors.create_collection.mdx\nquickstart.short.local.import_vectors.query.nearvector.mdx\nquickstart.short.local.query.neartext.mdx\nquickstart.short.local.query.rag.mdx\nquickstart.short.query-agent.mdx\nquickstart.short.query.neartext.mdx\nquickstart.short.query.rag.mdx\nresponse.biology.generativesearch.grouped.mdx\nresponse.biology.generativesearch.single.mdx\nresponse.biology.questions.mdx\nresponse.biology.where.questions.mdx\nsrc/components\nAPITable\nAcademyAdmonition\nAcademyBadge\nCardsSection\nCloudOnlyBadge\nContextualMenu\nDeploymentCards\nDetailedOptions\nDocsImage\nDocumentation\nFeedback\nFeedbackModal\nIcons\nKapaAI\nNavDocsPointer\nPageRatingWidget\nPromptStarter\nQuickLinks\nRecipesCards\nSkipValidationLink\nThankYouModal\nTooltip\nUTM\nWeaviateConfigurator\nconfig.js\n"}],"isError":false,"timestamp":1788135809097}}
{"type":"message","id":"52139ae7","parentId":"f46e3c81","timestamp":"2026-08-31T00:23:35.668Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Fetching project paths**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94c981b43887d0bbdc6ce399b9a400\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMmH3p_btAWPTVTEwxVm0vAfBHO9rI_G2OP341-E-vhgtU6nu-nK3YQAXXn207BtN9GnU6YYBZJ_9SbryUF5K8bFlxDFBpjhODippfdGnRTzfzXufqvhOkKdBofEXoGSjPrtgZi5sdv_9gPeYLDeDEkGfGGz_dj9VtyU8alfmx-efqdOvc8ReV7NIG1ZytInsVUd0nGP2vHZGaIi7vghv5e5QT541aMU0AwaTCt7l8AJZgbSfoAo_UbOaNUTUxNvcRUJa-IZJcv7iRHWnNTiJs_qZCFR4CqVZy4ouW5hQY3jCXZeuI1mD9Rus9M_M2LXOjkmqabqyVnnUmdmXFsuZVkr7LLqim-SVNucHbj7nwFQ-mzT9KsBB1arCjwTeE022_zFIQ3ZiEDhGwDS2u8P2xR83z4JiGcKnvJHbmKRoBjBWcs9FZvotu_1cBRbZ_KiNh5w5sYBqqKTNprjTwIP9xbZAkRPEtQ75Q4tijLf6sdAFSY9-824zKkkIPucrM-v7DaTWlVXeXyejsMPHG-SW_ieJJCA7z77ob0iBI8o2uSMpUVVpzrQJFSu7c7mKSnptzq77ICqu2UOOec6WvSyDjgttblAYMiqaX3_4US7-kb0AQVT8Ff9eg3AtYusnuE7hI_5uyTlhWjn6g2YSpVDS8c7T_y7l_XjD2R5FPLJy2wQIR21hraJjfP7E6pUFH4x94gLvDmps2wFZcX5HEw9hkZQDGqs1cCs9Y--i_hsSbZvk6WhCeCpTQWT1nBBe9SwYIySPFeXmJQnEp60aJCPt2jUAYfrVw3QwLOSNR8MziTILuX5Y2i_I3P14A3yuRFjYexYmcETSfh_5S4-iqToW_hQoK5yNtFulq1H3XCpqI2LIobmAJPfQmUp700D41cE4z0QPsUtitABgHUAagnozLsoYCuod2sWThA51jZOC3PwkNL-yAcbjh43tUqAOkB8nBb41bC2YppvT7zOqOOgfaA9qIRuv6y_H8VDyjndBb505zSmdPzCeqFjqpbmwWMO5HOgthxPuwKW_ZyuRCM1p6293LkNbEl9Hgschz0MMA1oZa7AcuHpLsBNEDuF95FfNEWOqSDk3Qf5_NfmfCeO6dba6KAX9YzT9-UMFwOaSUFFgnuXymGJgBpPxzwSjbQDD2Mv7rmuz57HoX2OCiI30Reobrj9_81EMD3TtRu2P3vqMTgMUmnl9QkOkjTe0lz9w1-09_EQcpRahBta0NvTzVPvXflFg--Gju8M2pvkMBVlPoswiyYJMlskMoLG_RBQSkqVX6zG60FRzf6WR_vEUTDs0g==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Fetching project paths**\"}]}"},{"type":"toolCall","id":"call_Jzn5yyZwIOis6CnAj7LaEZBq|fc_0d51cdf92682941d016a94c9878bf087d0ae8bae1de9829c78","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/quickstart/quickstart.short.local.import_vectors.create_collection.mdx'","timeout":20}},{"type":"toolCall","id":"call_5eLGHo376oYeDIXSDI3KQom3|fc_0d51cdf92682941d016a94c9878c0487d0b901f8cee5923d3e","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/quickstart/quickstart.short.local.import_vectors.query.nearvector.mdx'","timeout":20}},{"type":"toolCall","id":"call_pgAUsXLJKmuFlaFOcibqDbAr|fc_0d51cdf92682941d016a94c9878c0c87d0a66dfe4ec4734633","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/quickstart/quickstart.short.local.import-vectors.query.rag.mdx'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1977,"output":243,"cacheRead":3584,"cacheWrite":0,"totalTokens":5804,"cost":{"input":0.009885000000000001,"output":0.0072900000000000005,"cacheRead":0.001792,"cacheWrite":0,"total":0.018967}},"stopReason":"toolUse","timestamp":1788135809098,"responseId":"resp_0d51cdf92682941d016a94c98143f487d093d7ba584d48d7a1"}}
{"type":"message","id":"640c51e6","parentId":"52139ae7","timestamp":"2026-08-31T00:23:35.871Z","message":{"role":"toolResult","toolCallId":"call_Jzn5yyZwIOis6CnAj7LaEZBq|fc_0d51cdf92682941d016a94c9878bf087d0ae8bae1de9829c78","toolName":"bash","content":[{"type":"text","text":"import Tabs from \"@theme/Tabs\";\nimport TabItem from \"@theme/TabItem\";\nimport FilteredTextBlock from \"@site/src/components/Documentation/FilteredTextBlock\";\nimport PyCode from \"!!raw-loader!/_includes/code/python/quickstart.short.local.import_vectors.create_collection.py\";\nimport TSCode from \"!!raw-loader!/_includes/code/typescript/quickstart.short.local.import_vectors.create_collection.ts\";\nimport GoCode from \"!!raw-loader!/_includes/code/howto/go/docs/quickstart/short_local_vectors_1/quickstart.short.local.import_vectors.create_collection.go\";\nimport JavaV6Code from \"!!raw-loader!/_includes/code/java-v6/src/test/java/quickstart/QuickstartLocalCreateVectors.java\";\nimport CSharpCode from \"!!raw-loader!/_includes/code/csharp/quickstart/QuickstartLocalCreateVectors.cs\";\n\n\n\n \n\n\n\n\n\n\n\nThe collection also contains a configuration for the generative (RAG) integration:\n\n- Ollama [generative AI integrations](/weaviate/model-providers/ollama/generative) for retrieval augmented generation (RAG).\n\n\n\n\n \n\n\n \n\n\n"}],"isError":false,"timestamp":1788135815871}}
{"type":"message","id":"e3d9b699","parentId":"640c51e6","timestamp":"2026-08-31T00:23:35.872Z","message":{"role":"toolResult","toolCallId":"call_5eLGHo376oYeDIXSDI3KQom3|fc_0d51cdf92682941d016a94c9878c0487d0b901f8cee5923d3e","toolName":"bash","content":[{"type":"text","text":"import Tabs from \"@theme/Tabs\";\nimport TabItem from \"@theme/TabItem\";\nimport FilteredTextBlock from \"@site/src/components/Documentation/FilteredTextBlock\";\nimport PyCode from \"!!raw-loader!/_includes/code/python/quickstart.short.local.import_vectors.query.nearvector.py\";\nimport TSCode from \"!!raw-loader!/_includes/code/typescript/quickstart.short.local.import_vectors.query.nearvector.ts\";\nimport GoCode from \"!!raw-loader!/_includes/code/howto/go/docs/quickstart/short_local_vectors_2/quickstart.short.local.import_vectors.query.nearvector.go\";\nimport JavaV6Code from \"!!raw-loader!/_includes/code/java-v6/src/test/java/quickstart/QuickstartLocalQueryNearVector.java\";\nimport CSharpCode from \"!!raw-loader!/_includes/code/csharp/quickstart/QuickstartLocalQueryNearVector.cs\";\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n"}],"isError":false,"timestamp":1788135815872}}
{"type":"message","id":"bcbbcb7c","parentId":"e3d9b699","timestamp":"2026-08-31T00:23:35.872Z","message":{"role":"toolResult","toolCallId":"call_pgAUsXLJKmuFlaFOcibqDbAr|fc_0d51cdf92682941d016a94c9878c0c87d0a66dfe4ec4734633","toolName":"bash","content":[{"type":"text","text":"import Tabs from \"@theme/Tabs\";\nimport TabItem from \"@theme/TabItem\";\nimport FilteredTextBlock from \"@site/src/components/Documentation/FilteredTextBlock\";\nimport PyCode from \"!!raw-loader!/_includes/code/python/quickstart.short.local.import_vectors.query.rag.py\";\nimport TSCode from \"!!raw-loader!/_includes/code/typescript/quickstart.short.local.import_vectors.query.rag.ts\";\nimport GoCode from \"!!raw-loader!/_includes/code/howto/go/docs/quickstart/short_local_vectors_3/quickstart.short.local.import_vectors.query.rag.go\";\nimport JavaV6Code from \"!!raw-loader!/_includes/code/java-v6/src/test/java/quickstart/QuickstartLocalQueryNearVectorRAG.java\";\nimport CSharpCode from \"!!raw-loader!/_includes/code/csharp/quickstart/QuickstartLocalQueryNearVectorRAG.cs\";\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n"}],"isError":false,"timestamp":1788135815872}}
{"type":"message","id":"9be4a5cb","parentId":"bcbbcb7c","timestamp":"2026-08-31T00:23:42.951Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Planning code retrieval**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94c9890c3087d099ed90bed58421f6\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMmOzFv21Z62UMCEKB5ACmrF2KzoskV-pXVkybDylJqkE2q2i7fJ6JtshK7R8zc_jqhYYxo-NaTBmNElNsO4e1eh27F41UIHqOON-U4tJ_4W1wyFNneCpj6s9cS4gZb5Wh_R17Mlvgn8AbnBfK2QxXQXCNFGRfly0SHBIox1owkFJJhVkJdUVxaY5l7egTtVl3rBXMNaVU2Cn_z5xNLSOGarogeUTG_dQrVvthcvYhLhgpNBVZDMo_f-qDPbNSBZrCqxSyCE-OPKpqdkMcpzWMfWuagztdbrwD1xcxgl9jM5C7EC76Enh8ktKpQvOLaAqklJhmWaLVz-Nb_0rJN-5FRIeOmgM5_JgWJPr4In8-f91MCN_GB8XYZEBK2bMsYFEs8NZQUU3lL5iXfh5InPPgjdG4GjghM1LFQEBlM7BGHb-4ib0IYSx0g4Gh9SRX6cEWemQNnBfKglzw64sGhGZyZV0ht3-0xCaILKYl_NXPQHkuvjWUwutjIJ2Yaoq_nOU14ztUWCP6yzMI_oIyeRwyYS2FWvUcJNMKjFNPRflxRIayWmjjpMy72f0ZnSAWW0X_vBi-OR54lTE0N5YX5wXF3DZwQZlCv7ifLyPYdcA5ElBk2MW75OQPig6p0AW97xdj7-wnAGrzdBaMF9NO9KUu5fPkVlDggi0EN5m1JlN8dces-nsqJewJt-dUmERugzUvi4pwu-PAlUoXqgjaKG4w6YtsdwOAM75OmCtuX0UDyZ9Opt4ch8I38vscnM_VdddrmhL0hf33HlfILUcd70tulgnJv1W4cxPmGyPln0PndGKfDNB9OOaTa6X0NvcBC7y2-Upy-IOdh_4CeWTZnKjolawtNBt8Hw0ggBBP5aKQ0MUBpTIWGK8flMTNqzmBAEcme5ewspnegTLe4Ms3HXhSwaXoEOT7v8iOObA92WgHh3_XuEGiOuSZ7nllKdw3MhLUtv3kNSjDVoHrPdXzkFphurerKCJCHdIUhajD4PBSFaDOm2Emp2ucor5SPyr5JSVD0jbqJxsRYyQknWpZAvUO5LRYN0PnfjkQxrc2kaGAT3TcXe7JMODvdZ6LEwZL3pXEAHuJbAgANF7wZ9w81-qQKZzb1BwVb3pBC2xWRe9WyVfN6taHyeOocegJ_PfTH4J3Tq0rGMJgtGq5VJUvu6g4lEcejWcvpyvC0jlRAJXSMLJmGLH3Yb8dgkFuw-7SpKV_2GV753JwW72GgZ2QFTagoJjidORXdLNyt_iW5b4W9o5FpAZPq3AhvQiLWn-A4knXGQrqAGdBGX8OAT1hBtXpSxpBZcCDIZwQlObBkmZzuBCUc=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Planning code retrieval**\"}]}"},{"type":"toolCall","id":"call_DvquOqFcz6qNsjPpglJvCyof|fc_0d51cdf92682941d016a94c98ed13c87d08762c63f229e64c1","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.create_collection.py'","timeout":20}},{"type":"toolCall","id":"call_3Zj6Q8bZlPjflD1SK1n9ZN1e|fc_0d51cdf92682941d016a94c98ed14c87d099007dc5c94f46b8","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.query.nearvector.py'","timeout":20}},{"type":"toolCall","id":"call_PsCr1hAHkjzuwJnmZTzjXOtd|fc_0d51cdf92682941d016a94c98ed15087d0a2abe89699aa3e19","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.query.rag.py'","timeout":20}},{"type":"toolCall","id":"call_1eZGSg8aMt6GF41EtkFd9iKb|fc_0d51cdf92682941d016a94c98ed15887d08c5bca3eae6b22a4","name":"bash","arguments":{"command":"curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.create_collection.py' | grep -A100 -B3 'START'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":2669,"output":313,"cacheRead":4608,"cacheWrite":0,"totalTokens":7590,"cost":{"input":0.013345000000000001,"output":0.00939,"cacheRead":0.002304,"cacheWrite":0,"total":0.025039000000000002}},"stopReason":"toolUse","timestamp":1788135815872,"responseId":"resp_0d51cdf92682941d016a94c9882de887d0bae650bc39a42315"}}
{"type":"message","id":"92043494","parentId":"9be4a5cb","timestamp":"2026-08-31T00:23:43.244Z","message":{"role":"toolResult","toolCallId":"call_DvquOqFcz6qNsjPpglJvCyof|fc_0d51cdf92682941d016a94c98ed13c87d08762c63f229e64c1","toolName":"bash","content":[{"type":"text","text":"# START CreateCollection\nimport weaviate\nfrom weaviate.classes.config import Configure\n\n# Step 1.1: Connect to your local Weaviate instance\nwith weaviate.connect_to_local() as client:\n # END CreateCollection\n\n # NOT SHOWN TO THE USER - DELETE EXISTING COLLECTION\n client.collections.delete(\"Movie\")\n\n # START CreateCollection\n\n # Step 1.2: Create a collection\n # highlight-start\n movies = client.collections.create(\n name=\"Movie\",\n vector_config=Configure.Vectors.self_provided(), # No automatic vectorization since we're providing vectors\n )\n # highlight-end\n # START CreateCollection\n\n # END CreateCollection\n # fmt: off\n # START CreateCollection\n # Step 1.3: Import three objects\n data_objects = [\n {\"properties\": {\"title\": \"The Matrix\", \"description\": \"A computer hacker learns about the true nature of reality and his role in the war against its controllers.\", \"genre\": \"Science Fiction\"},\n \"vector\": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]},\n {\"properties\": {\"title\": \"Spirited Away\", \"description\": \"A young girl becomes trapped in a mysterious world of spirits and must find a way to save her parents and return home.\", \"genre\": \"Animation\"},\n \"vector\": [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},\n {\"properties\": {\"title\": \"The Lord of the Rings: The Fellowship of the Ring\", \"description\": \"A meek Hobbit and his companions set out on a perilous journey to destroy a powerful ring and save Middle-earth.\", \"genre\": \"Fantasy\"},\n \"vector\": [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]}\n ]\n # END CreateCollection\n # fmt: on\n # START CreateCollection\n\n # Insert the objects with vectors\n movies = client.collections.get(\"Movie\")\n with movies.batch.fixed_size(batch_size=200) as batch:\n for obj in data_objects:\n batch.add_object(properties=obj[\"properties\"], vector=obj[\"vector\"])\n\n print(\n f\"Imported {len(data_objects)} objects with vectors into the Movie collection\"\n )\n # END CreateCollection\n"}],"isError":false,"timestamp":1788135823244}}
{"type":"message","id":"fa21a954","parentId":"92043494","timestamp":"2026-08-31T00:23:43.244Z","message":{"role":"toolResult","toolCallId":"call_3Zj6Q8bZlPjflD1SK1n9ZN1e|fc_0d51cdf92682941d016a94c98ed14c87d099007dc5c94f46b8","toolName":"bash","content":[{"type":"text","text":"# START NearText\nimport weaviate\nimport json\n\n# Step 2.1: Connect to your local Weaviate instance\nwith weaviate.connect_to_local() as client:\n\n # Step 2.2: Use this collection\n movies = client.collections.use(\"Movie\")\n\n # Step 2.3: Perform a vector search with NearVector\n # highlight-start\n # END NearText\n # fmt: off\n # START NearText\n response = movies.query.near_vector(\n near_vector=[0.11, 0.21, 0.31, 0.41, 0.51, 0.61, 0.71, 0.81], \n limit=2\n )\n # END NearText\n # fmt: on\n # START NearText\n # highlight-end\n\n for obj in response.objects:\n print(json.dumps(obj.properties, indent=2)) # Inspect the results\n # END NearText\n"}],"isError":false,"timestamp":1788135823244}}
{"type":"message","id":"e2742f53","parentId":"fa21a954","timestamp":"2026-08-31T00:23:43.244Z","message":{"role":"toolResult","toolCallId":"call_PsCr1hAHkjzuwJnmZTzjXOtd|fc_0d51cdf92682941d016a94c98ed15087d0a2abe89699aa3e19","toolName":"bash","content":[{"type":"text","text":"import time\n# START RAG\nimport weaviate\nfrom weaviate.classes.generate import GenerativeConfig\n\n# Step 2.1: Connect to your local Weaviate instance\nwith weaviate.connect_to_local() as client:\n # END RAG\n time.sleep(1)\n # START RAG\n\n # Step 2.2: Use this collection\n movies = client.collections.use(\"Movie\")\n\n # Step 2.3: Perform RAG with on NearVector results\n # highlight-start\n response = movies.generate.near_vector(\n near_vector=[0.11, 0.21, 0.31, 0.41, 0.51, 0.61, 0.71, 0.81],\n limit=1,\n grouped_task=\"Write a tweet with emojis about this movie.\",\n generative_provider=GenerativeConfig.ollama( # Configure the Ollama generative integration\n api_endpoint=\"http://ollama:11434\", # If using Docker you might need: http://host.docker.internal:11434\n model=\"llama3.2\", # The model to use\n ),\n )\n # highlight-end\n\n print(response.generative.text) # Inspect the results\n # END RAG\n"}],"isError":false,"timestamp":1788135823244}}
{"type":"message","id":"6c3cc040","parentId":"e2742f53","timestamp":"2026-08-31T00:23:43.245Z","message":{"role":"toolResult","toolCallId":"call_1eZGSg8aMt6GF41EtkFd9iKb|fc_0d51cdf92682941d016a94c98ed15887d08c5bca3eae6b22a4","toolName":"bash","content":[{"type":"text","text":"# START CreateCollection\nimport weaviate\nfrom weaviate.classes.config import Configure\n\n# Step 1.1: Connect to your local Weaviate instance\nwith weaviate.connect_to_local() as client:\n # END CreateCollection\n\n # NOT SHOWN TO THE USER - DELETE EXISTING COLLECTION\n client.collections.delete(\"Movie\")\n\n # START CreateCollection\n\n # Step 1.2: Create a collection\n # highlight-start\n movies = client.collections.create(\n name=\"Movie\",\n vector_config=Configure.Vectors.self_provided(), # No automatic vectorization since we're providing vectors\n )\n # highlight-end\n # START CreateCollection\n\n # END CreateCollection\n # fmt: off\n # START CreateCollection\n # Step 1.3: Import three objects\n data_objects = [\n {\"properties\": {\"title\": \"The Matrix\", \"description\": \"A computer hacker learns about the true nature of reality and his role in the war against its controllers.\", \"genre\": \"Science Fiction\"},\n \"vector\": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]},\n {\"properties\": {\"title\": \"Spirited Away\", \"description\": \"A young girl becomes trapped in a mysterious world of spirits and must find a way to save her parents and return home.\", \"genre\": \"Animation\"},\n \"vector\": [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},\n {\"properties\": {\"title\": \"The Lord of the Rings: The Fellowship of the Ring\", \"description\": \"A meek Hobbit and his companions set out on a perilous journey to destroy a powerful ring and save Middle-earth.\", \"genre\": \"Fantasy\"},\n \"vector\": [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]}\n ]\n # END CreateCollection\n # fmt: on\n # START CreateCollection\n\n # Insert the objects with vectors\n movies = client.collections.get(\"Movie\")\n with movies.batch.fixed_size(batch_size=200) as batch:\n for obj in data_objects:\n batch.add_object(properties=obj[\"properties\"], vector=obj[\"vector\"])\n\n print(\n f\"Imported {len(data_objects)} objects with vectors into the Movie collection\"\n )\n # END CreateCollection\n"}],"isError":false,"timestamp":1788135823245}}
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{"type":"message","id":"2fcd7fe4","parentId":"a74d2709","timestamp":"2026-08-31T00:23:46.984Z","message":{"role":"toolResult","toolCallId":"call_ebShQcCZsiXNZyp6gTO9gb24|fc_0d51cdf92682941d016a94c99148ac87d0b7336d335371000b","toolName":"bash","content":[{"type":"text","text":"ModuleNotFoundError No module named 'weaviate'\n"}],"isError":false,"timestamp":1788135826984}}
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{"type":"message","id":"505b8070","parentId":"f46c6dbc","timestamp":"2026-08-31T00:26:26.535Z","message":{"role":"toolResult","toolCallId":"call_fWqv1CuSsXHJtLNYDIhm43HF|fc_0d51cdf92682941d016a94ca287f1c87d0a02186139ac7dce7","toolName":"bash","content":[{"type":"text","text":"Collecting weaviate-client<5,>=4.16.0\n Downloading weaviate_client-4.23.0-py3-none-any.whl.metadata (3.5 kB)\nCollecting httpx<0.29.0,>=0.26.0 (from weaviate-client<5,>=4.16.0)\n Downloading httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)\nCollecting validators<1.0.0,>=0.34.0 (from weaviate-client<5,>=4.16.0)\n Downloading validators-0.35.0-py3-none-any.whl.metadata (3.9 kB)\nCollecting authlib<2.0.0,>=1.6.7 (from weaviate-client<5,>=4.16.0)\n Downloading authlib-1.8.0-py2.py3-none-any.whl.metadata (10 kB)\nCollecting pydantic<3.0.0,>=2.12.0 (from weaviate-client<5,>=4.16.0)\n Downloading pydantic-2.13.5-py3-none-any.whl.metadata (110 kB)\nCollecting grpcio<1.80.0,>=1.59.5 (from weaviate-client<5,>=4.16.0)\n Downloading grpcio-1.78.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (3.8 kB)\nCollecting protobuf<7.0.0,>=4.21.6 (from weaviate-client<5,>=4.16.0)\n Downloading protobuf-6.33.6-cp39-abi3-manylinux2014_x86_64.whl.metadata (593 bytes)\nCollecting packaging>=21.0 (from weaviate-client<5,>=4.16.0)\n Downloading packaging-26.3-py3-none-any.whl.metadata (3.5 kB)\nCollecting cryptography>=45.0.1 (from authlib<2.0.0,>=1.6.7->weaviate-client<5,>=4.16.0)\n Downloading cryptography-50.0.1-cp311-abi3-manylinux_2_34_x86_64.whl.metadata (4.3 kB)\nCollecting joserfc>=1.6.1 (from authlib<2.0.0,>=1.6.7->weaviate-client<5,>=4.16.0)\n Downloading joserfc-1.7.5-py3-none-any.whl.metadata (3.1 kB)\nCollecting typing-extensions~=4.12 (from grpcio<1.80.0,>=1.59.5->weaviate-client<5,>=4.16.0)\n Downloading typing_extensions-4.16.0-py3-none-any.whl.metadata (3.3 kB)\nCollecting anyio (from httpx<0.29.0,>=0.26.0->weaviate-client<5,>=4.16.0)\n Downloading anyio-4.14.2-py3-none-any.whl.metadata (4.6 kB)\nCollecting certifi (from httpx<0.29.0,>=0.26.0->weaviate-client<5,>=4.16.0)\n Downloading certifi-2026.7.22-py3-none-any.whl.metadata (2.5 kB)\nCollecting httpcore==1.* (from httpx<0.29.0,>=0.26.0->weaviate-client<5,>=4.16.0)\n Downloading httpcore-1.0.9-py3-none-any.whl.metadata (21 kB)\nCollecting idna (from httpx<0.29.0,>=0.26.0->weaviate-client<5,>=4.16.0)\n Downloading idna-3.19-py3-none-any.whl.metadata (9.2 kB)\nCollecting h11>=0.16 (from httpcore==1.*->httpx<0.29.0,>=0.26.0->weaviate-client<5,>=4.16.0)\n Downloading h11-0.16.0-py3-none-any.whl.metadata (8.3 kB)\nCollecting annotated-types>=0.6.0 (from pydantic<3.0.0,>=2.12.0->weaviate-client<5,>=4.16.0)\n Downloading annotated_types-0.8.0-py3-none-any.whl.metadata (15 kB)\nCollecting pydantic-core==2.46.5 (from pydantic<3.0.0,>=2.12.0->weaviate-client<5,>=4.16.0)\n Downloading pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\nCollecting typing-inspection>=0.4.2 (from pydantic<3.0.0,>=2.12.0->weaviate-client<5,>=4.16.0)\n Downloading typing_inspection-0.4.4-py3-none-any.whl.metadata (2.6 kB)\nCollecting cffi>=2.0.0 (from cryptography>=45.0.1->authlib<2.0.0,>=1.6.7->weaviate-client<5,>=4.16.0)\n Downloading cffi-2.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.5 kB)\nCollecting pycparser (from cffi>=2.0.0->cryptography>=45.0.1->authlib<2.0.0,>=1.6.7->weaviate-client<5,>=4.16.0)\n Downloading pycparser-3.0-py3-none-any.whl.metadata (8.2 kB)\nDownloading weaviate_client-4.23.0-py3-none-any.whl (656 kB)\n āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā 656.2/656.2 kB 5.3 MB/s 0:00:00\nDownloading authlib-1.8.0-py2.py3-none-any.whl (260 kB)\nDownloading grpcio-1.78.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (6.7 MB)\n āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā 6.7/6.7 MB 12.0 MB/s 0:00:00\nDownloading httpx-0.28.1-py3-none-any.whl (73 kB)\nDownloading httpcore-1.0.9-py3-none-any.whl (78 kB)\nDownloading protobuf-6.33.6-cp39-abi3-manylinux2014_x86_64.whl (323 kB)\nDownloading pydantic-2.13.5-py3-none-any.whl (472 kB)\nDownloading pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā 2.1/2.1 MB 10.9 MB/s 0:00:00\nDownloading typing_extensions-4.16.0-py3-none-any.whl (45 kB)\nDownloading validators-0.35.0-py3-none-any.whl (44 kB)\nDownloading annotated_types-0.8.0-py3-none-any.whl (13 kB)\nDownloading cryptography-50.0.1-cp311-abi3-manylinux_2_34_x86_64.whl (4.7 MB)\n āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā 4.7/4.7 MB 16.0 MB/s 0:00:00\nDownloading cffi-2.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (221 kB)\nDownloading h11-0.16.0-py3-none-any.whl (37 kB)\nDownloading joserfc-1.7.5-py3-none-any.whl (71 kB)\nDownloading packaging-26.3-py3-none-any.whl (129 kB)\nDownloading typing_inspection-0.4.4-py3-none-any.whl (14 kB)\nDownloading anyio-4.14.2-py3-none-any.whl (125 kB)\nDownloading idna-3.19-py3-none-any.whl (68 kB)\nDownloading certifi-2026.7.22-py3-none-any.whl (136 kB)\nDownloading pycparser-3.0-py3-none-any.whl (48 kB)\nInstalling collected packages: validators, typing-extensions, pycparser, protobuf, packaging, idna, h11, certifi, annotated-types, typing-inspection, pydantic-core, httpcore, grpcio, cffi, anyio, pydantic, httpx, cryptography, joserfc, authlib, weaviate-client\n\nSuccessfully installed annotated-types-0.8.0 anyio-4.14.2 authlib-1.8.0 certifi-2026.7.22 cffi-2.1.1 cryptography-50.0.1 grpcio-1.78.0 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 idna-3.19 joserfc-1.7.5 packaging-26.3 protobuf-6.33.6 pycparser-3.0 pydantic-2.13.5 pydantic-core-2.46.5 typing-extensions-4.16.0 typing-inspection-0.4.4 validators-0.35.0 weaviate-client-4.23.0\n"}],"isError":false,"timestamp":1788135986535}}
{"type":"message","id":"9e02ac17","parentId":"505b8070","timestamp":"2026-08-31T00:26:38.979Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Testing local connection defaults**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94ca3488f887d0b7a9b3ea288f0753\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMo1qol2K8ppp9NHPNHDC8DqAno8gZINrCGY8H6EY3Jji9opTCuUctcv_Eb45uauR1sFO500NUlAMtRMOjUdX0-jxFAZmC_irolSnPi-r2aWwqDFEu9qRSnMx6IhbHhMDgT-RVMXMRAN4j7fBxQnLMHHuIsreRz-XD69ypusIe9tbHDh7cTdd2M_931lBn3VoHPzn8Ealnvx1e4CGjbM4zLZl6RbWM3xljQm6f1SGHn7dQQKKs2RKcZg55_aqiiuaeVVzoPzAFoggt89LnWwa1GqNpSMKv2RC4ik0qFKdrik1IXtd7iczqE2Rk61t5lp0J-PbsjNC0wEeEaQDk9b8AnVIZ1uTBNvvsIAdv8q6OVIMr4zuyaKQForbN6LAmdEJY9mY1eW1dlk5kootXHOC3aYiLmfoao9Zo0vgFmZviYacXdXNqzmqioFl8B2bb8sm0MHSV5rLH0OkSZTzLh76bRPs8kGyYMOaEZu0saJPxX4Q1JnPzUiauAY8ktw6TgX-jQTiIlxxy1JSopn6ubczKyYD8j-wAZv5pFKWc5iT9M7NAGSIVF39wij2367vTyBGqqAQ-VNVEY7sOOo3hddxzUahSEXNiFVkC36BgEv2gVfSq5V0l5MeZY5iKrRemfHzzQGNktFcql1WP6TlgagzpwQntWdP4sI9hws_UlbA7bXf8eutE0r9kUwVZaOBfiDY-7hUtiOYFAwVTYbXbD7LjHM9lwAnbfeXjPkaM6Ily_E6zKqZRVS57Y2jBqxKfCGiv2uf8cZaLZADfBkOPE0NchRyErnFmKsa-yhb2pmHzFIKIYqisP4FOLdudvGLOl4v2qXcss42x8ORIR46WIbRGdhNTP__3ArRlDS84T3vVUQFkMoVxgjF5bYSxH_-PhM3GRUgLlM0DUKYmzRl4lsM0XgXGxkCRpQENH_uadlIlxc6rZawyRHXiW8bPH2D5A37R3M0AkWBD7N0LghoBx7fBQNP3it5Os4BXAL22D8HvpkDSOJ0RSTQkJjgnMQqPB6cOv37ofQiJFiRIanZjD9o1OjW8q9Av1QnJnvJm-cNhjRKIdKAvgXF7SkZPFv1uoZa3da5Scdd7xEwd3yvTU_MFKG6tE9m-Rl5CfOLTPJsp3DlvcOhbuiXYqbmyP45AlIfkg1ai437ELRJWRegnsTNVYLOUN-c4AcGelOhBn3anJY_BUT7iGVl_sqC2QyG-PFDJw5NC2NJiduDy2tralyh7i2FVfw74nsozQkk1JWwSC7FiStTG2eHDrrwio9tro6Vfinla7xWNDZPlEA4q99G9gHI_vajmcmrC2QmPxqdREOCP5aT6yGPW3KKWMICS1qZxEggkceArfm_c1GRFWo8Auw0TjhNuvONHCY9sq8DoK12dP8tzGN3SwTkAYzqRYVldgDWS2KBYZtuPf29IfKzhPJsg==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Testing local connection defaults**\"}]}"},{"type":"toolCall","id":"call_3mjpqWWr6ilY9WaugrYuxxjz|fc_0d51cdf92682941d016a94ca35858487d0a3e51e50246b5f41","name":"bash","arguments":{"command":"python - <<'PY'\nimport weaviate\nfrom weaviate.classes.config import Configure\n\nwith weaviate.connect_to_local() as client:\n if client.collections.exists('Movie'):\n client.collections.delete('Movie')\n movies = client.collections.create(\n name='Movie',\n vector_config=Configure.Vectors.self_provided(),\n )\n data_objects = [\n {'properties': {'title': 'The Matrix', 'description': 'A computer hacker learns about the true nature of reality and his role in the war against its controllers.', 'genre': 'Science Fiction'}, 'vector': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]},\n {'properties': {'title': 'Spirited Away', 'description': 'A young girl becomes trapped in a mysterious world of spirits and must find a way to save her parents and return home.', 'genre': 'Animation'}, 'vector': [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},\n {'properties': {'title': 'The Lord of the Rings: The Fellowship of the Ring', 'description': 'A meek Hobbit and his companions set out on a perilous journey to destroy a powerful ring and save Middle-earth.', 'genre': 'Fantasy'}, 'vector': [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]},\n ]\n movies = client.collections.get('Movie')\n with movies.batch.fixed_size(batch_size=200) as batch:\n for obj in data_objects:\n batch.add_object(properties=obj['properties'], vector=obj['vector'])\n response = movies.query.near_vector(near_vector=[0.11, 0.21, 0.31, 0.41, 0.51, 0.61, 0.71, 0.81], limit=2)\n for obj in response.objects:\n print(obj.properties)\n assert response.objects[0].properties['title'] == 'The Matrix'\n print('CONFIRMED: first vector search returns The Matrix')\nPY","timeout":60}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":3508,"output":578,"cacheRead":9728,"cacheWrite":0,"totalTokens":13814,"cost":{"input":0.01754,"output":0.01734,"cacheRead":0.004863999999999999,"cacheWrite":0,"total":0.039744}},"stopReason":"toolUse","timestamp":1788135986536,"responseId":"resp_0d51cdf92682941d016a94ca33cda087d09ebbdd6e42b4e455"}}
{"type":"message","id":"95cd4789","parentId":"9e02ac17","timestamp":"2026-08-31T00:26:41.339Z","message":{"role":"toolResult","toolCallId":"call_3mjpqWWr6ilY9WaugrYuxxjz|fc_0d51cdf92682941d016a94ca35858487d0a3e51e50246b5f41","toolName":"bash","content":[{"type":"text","text":"/opt/venv/lib/python3.12/site-packages/authlib/integrations/httpx_client/assertion_client.py:5: AuthlibDeprecationWarning: The httpx module is deprecated; please use httpx2 instead.\n from ._compat import httpx2\n{'genre': 'Science Fiction', 'description': 'A computer hacker learns about the true nature of reality and his role in the war against its controllers.', 'title': 'The Matrix'}\n{'genre': 'Animation', 'description': 'A young girl becomes trapped in a mysterious world of spirits and must find a way to save her parents and return home.', 'title': 'Spirited Away'}\nCONFIRMED: first vector search returns The Matrix\n"}],"isError":false,"timestamp":1788136001339}}
{"type":"message","id":"d54f687d","parentId":"95cd4789","timestamp":"2026-08-31T00:26:46.431Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Planning rootless Podman Weaviate container script**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94ca4232e887d097f297edc26fcbf0\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMpFf9DTSz8MIlDLePaBmctQGDK4Pl0vADkCHPcE1hrrY_Cfq3Rs5lHngQIudg0skdrJOw714zOGVcx8oDixC10K0Z3aov6jvaYrT-vm3YMlXFOLN2gl1Jotd9Mz09DshONmOC8lyg86G8tetfZ-dL2ynIuIpPtl6DrBI3tLBTvdV8KE57iu42wQ_W8GVsvXWpkFO07O9EK9gMWfL5AI4Wso0zf0dMYInJ1UkoT5V1X_DQ5Zjts1bPeJgBcuQnk5251TewVHomIxVwyIjYRG8BvjOlRx-K4kDlDoqMoQFIjOGDxR2HRMymcOGovZDbqE-bA1fNQ2ViOve5_HJfQPBLenIBaXcKM6JRUmBE07q0XxzGcGNGfYqZMjlRZ1vVF5Xcd-BclykXyug_GgenK-NbBZaBuUI7D9Q53zKbfbEnlpq31fUxdGn_kvVoslt2wqlxr5coz3THA6iSK56qJvqunebw4_trG1FKM-GvC1_1MhXm3W5RmL2QvXFfWzILcZow9A5hlZbd9wnDQKssWKI2E-MBXVkaH29_H3A8lwPe12gKpDiTw8xtuyMImrUhOPiZciUJ8kBIJtzvQrIMJxgv7UGI2r2oVvLsJfFtdahBpO6Sx9TFGEfTUnC3zZcL7g-ukojOrQAepB6HeumShJb02a_h5E6I_bl3bWD9r7nEt6sAhNCS9HmGo5IYVnouHabDgFYsoKoiHw22G8PUxrsJDhHLNDGLHtQQyd_49xwdTvAwD0st1y_Sl8ThOSyTZQ1AYQakb6pjPLvFenTRG-tlZp0_MvgHJ3ZhvmFtcNyNatcFVjLQSEKwaqnGrd_cIkM35f7xjf1LW59Qw6w2BM6x47ycygSe0QdqvXjnDAkDpjDytzHc7rmSNWYf6TmP3xei5naOoTGwxOd-GRBtDckjJSndq1vfWl6-Gy-Jl_2c-pvv4C0FnRBr0zT-Hc0aUA8u9yfF3LsWNxQfq-rZGTtqIvFvJ2QGLs1_gvYMoKs2_9siyp8v0mOdFchlOuiL82ucabzS8Z5BrJGRD4LN_lh-mze589fnvWjbP3dkr2SYFhftLIELxrTxnRC1V7-V1DwMfZeOsRnK1zILCdYUoNB-z6GZ4ktrHunWJq9yzoDQCzPR5O4rdfI5KbPD3xsCLoSaNtrFqNW_sSAxnhov8FqqDlVJJl5ElTnXh0i48qoSVIAsjNukpwcRDwW74r-mD2nn2eSrLkVu694i5Jr8ykhDsbxruHr0kM0O2l_aVs55PMww5PN5B8pvFdqMbUH0uaRYjSsRRFSXnhEW4OLaC6wGvq6dk3JfOGN_W9_z1yOMJIj5UJuySv_5zqN6hogWn41JBas8E4zlnY6yUVuamTi0WI15Dbw5LY1ZoDsjCLuu7IcWSYrf9-bE7r1xSCGJtyFnn_9G8po5IsWorEkw_Ng606i0hW0NyrwQ5mBGR759MuojXcDbFwP1WCd5JE2hAsrQU7qF38sY5NzGE3DEHn0eB6glVrS2Uh6cKCzQTt56fZ7iQF89EUrG61ZhP_DJrmgkb9-6xReSzUiTrBIZJ1XTTlOQfkMseKdJkdFcZElSlnkjub1WK3fqMZM03iDtkE4E7WPzWu_hiDO3Uqf7Vau10DGCbHoQhHCXSpf3zIZVmIZNmzjaEiGN2rUJ61u8fWM6AtAcPBTBzM4raOnDsXzkPBZ1p1rqLaRXR-WhDnxSSRbOzg2teZ30StPu1STyXQaCo2Y1uqPBIkpMRL-TPWGZ2GuN7ZvaiiC_MLA0nxh3ZHmPf09G5trnpe1hFw_23W6riWY5-TFvklLOvVXKjQI9pALS7WgwA3vFCJnP7w8Qx1nJLqvkf7cFAXjDMdImhwxmsyvTSkkjjsqJAGS6MK_MlIbiRPnTF35iwfCg05kT8r40yR7SClQ2NuoMcokW3Zd7cLl7aPJLdpseh6HBvSU1eBx6lPmKpe1SjwV8M8HF6-_l3_1w-nagCTRCaubCz1Jqeuqw_yhhNQkaqF_AaHhjkl87TGrOCxAFeVL0UysChCtuGGwA-VKrr5XghT02KNw0bSxaHv8VCBi6yPqoSMbgY9QKXz7JmNhS81zK74_bVnADINeizxBeQCWw329kptJaG7\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Planning rootless Podman Weaviate container script**\"}]}"},{"type":"toolCall","id":"call_iCFfUjsQS6Rk5qYUlud7F9H9|fc_0d51cdf92682941d016a94ca452e9887d0bd69de1824e16851","name":"bash","arguments":{"command":"podman image inspect docker.io/semitechnologies/weaviate:1.39.2 --format '{{.Id}} {{range .RepoTags}}{{.}} {{end}}' && podman inspect weaviate --format '{{.Config.Image}} {{.HostConfig.NetworkMode}} {{range .Config.Env}}{{println .}}{{end}}'","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1176,"output":231,"cacheRead":12800,"cacheWrite":0,"totalTokens":14207,"cost":{"input":0.005880000000000001,"output":0.00693,"cacheRead":0.0063999999999999994,"cacheWrite":0,"total":0.01921}},"stopReason":"toolUse","timestamp":1788136001339,"responseId":"resp_0d51cdf92682941d016a94ca41a14087d0968012f48708fdb9"}}
{"type":"message","id":"955737a3","parentId":"d54f687d","timestamp":"2026-08-31T00:26:46.654Z","message":{"role":"toolResult","toolCallId":"call_iCFfUjsQS6Rk5qYUlud7F9H9|fc_0d51cdf92682941d016a94ca452e9887d0bd69de1824e16851","toolName":"bash","content":[{"type":"text","text":"63b16b7783ae63f990f5b1388b2e77fe501407d4583732a50bf65ff4973f3c41 docker.io/semitechnologies/weaviate:1.39.2 \ndocker.io/semitechnologies/weaviate:1.39.2 pasta PERSISTENCE_DATA_PATH=/var/lib/weaviate\nCLUSTER_HOSTNAME=node1\nPATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\ncontainer=podman\nAUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true\nHOME=/root\nHOSTNAME=931405ff6518\n\n"}],"isError":false,"timestamp":1788136006654}}
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docker.io for Weaviate image**\"},{\"type\":\"summary_text\",\"text\":\"**Designing robust rootless Podman script**\"},{\"type\":\"summary_text\",\"text\":\"**Verifying collection existence and batch handling**\"}]}"},{"type":"toolCall","id":"call_58ZHuL4QJT3aIYruZxLoG2KY|fc_0d51cdf92682941d016a94ca72c81087d08548e199572be35f","name":"write","arguments":{"path":"/app/start_weaviate.sh","content":"#!/usr/bin/env bash\n# Start the single, rootless Podman container used by the local custom-vectors quickstart.\nset -euo pipefail\n\nCONTAINER_NAME=\"${WEAVIATE_CONTAINER_NAME:-weaviate}\"\nIMAGE=\"${WEAVIATE_IMAGE:-docker.io/semitechnologies/weaviate:1.39.2}\"\nVOLUME_NAME=\"${WEAVIATE_VOLUME_NAME:-weaviate_data}\"\n\nif ! command -v podman >/dev/null 2>&1; then\n echo \"podman is required\" >&2\n exit 1\nfi\n\npodman volume exists \"$VOLUME_NAME\" || podman volume create \"$VOLUME_NAME\" >/dev/null\n\nif podman container exists \"$CONTAINER_NAME\"; then\n running=\"$(podman inspect --format '{{.State.Running}}' \"$CONTAINER_NAME\")\"\n if [[ \"$running\" != \"true\" ]]; then\n podman start \"$CONTAINER_NAME\" >/dev/null\n fi\nelse\n # No Ollama container is used: imported vectors make this path self-contained.\n podman run --detach --name \"$CONTAINER_NAME\" \\\n --publish 8080:8080 \\\n --publish 50051:50051 \\\n --volume \"$VOLUME_NAME:/var/lib/weaviate\" \\\n --env AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \\\n --env PERSISTENCE_DATA_PATH=/var/lib/weaviate \\\n --env CLUSTER_HOSTNAME=node1 \\\n \"$IMAGE\" \\\n --host 0.0.0.0 --port 8080 --scheme http >/dev/null\nfi\n\nfor _ in {1..60}; do\n if curl --fail --silent http://127.0.0.1:8080/v1/.well-known/ready >/dev/null; then\n echo \"Weaviate is ready at http://127.0.0.1:8080\"\n exit 0\n fi\n sleep 1\ndone\n\necho \"Weaviate did not become ready\" >&2\npodman logs \"$CONTAINER_NAME\" >&2 || true\nexit 1\n"}},{"type":"toolCall","id":"call_fjmdADVxe7MatRSyXz1UyRth|fc_0d51cdf92682941d016a94ca72c82087d0ad8b134211a81c38","name":"write","arguments":{"path":"/app/quickstart.py","content":"\"\"\"Weaviate local quickstart using the documented custom-vectors path.\"\"\"\n\nimport json\n\nimport weaviate\nfrom weaviate.classes.config import Configure\n\n\nMOVIES = [\n {\n \"properties\": {\n \"title\": \"The Matrix\",\n \"description\": \"A computer hacker learns about the true nature of reality and his role in the war against its controllers.\",\n \"genre\": \"Science Fiction\",\n },\n \"vector\": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],\n },\n {\n \"properties\": {\n \"title\": \"Spirited Away\",\n \"description\": \"A young girl becomes trapped in a mysterious world of spirits and must find a way to save her parents and return home.\",\n \"genre\": \"Animation\",\n },\n \"vector\": [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n },\n {\n \"properties\": {\n \"title\": \"The Lord of the Rings: The Fellowship of the Ring\",\n \"description\": \"A meek Hobbit and his companions set out on a perilous journey to destroy a powerful ring and save Middle-earth.\",\n \"genre\": \"Fantasy\",\n },\n \"vector\": [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n },\n]\n\n\ndef main() -> None:\n # connect_to_local uses the published HTTP (8080) and gRPC (50051) ports.\n with weaviate.connect_to_local() as client:\n if client.collections.exists(\"Movie\"):\n client.collections.delete(\"Movie\")\n\n # Self-provided vectors are the custom-vectors configuration from the docs.\n client.collections.create(\n name=\"Movie\",\n vector_config=Configure.Vectors.self_provided(),\n )\n\n movies = client.collections.get(\"Movie\")\n with movies.batch.fixed_size(batch_size=200) as batch:\n for movie in MOVIES:\n batch.add_object(\n properties=movie[\"properties\"], vector=movie[\"vector\"]\n )\n\n # The first semantic search in the custom-vectors path: nearVector.\n response = movies.query.near_vector(\n near_vector=[0.11, 0.21, 0.31, 0.41, 0.51, 0.61, 0.71, 0.81],\n limit=2,\n )\n for obj in response.objects:\n print(json.dumps(obj.properties, indent=2))\n\n if not response.objects or response.objects[0].properties.get(\"title\") != \"The Matrix\":\n raise RuntimeError(\"The first vector-search result was not The Matrix\")\n print(\"CONFIRMED: first vector search returns The Matrix\")\n\n\nif __name__ == \"__main__\":\n main()\n"}},{"type":"toolCall","id":"call_SgiOgZrMFupNKebNauETH7DU|fc_0d51cdf92682941d016a94ca72c82887d0a51f45c9b3763d22","name":"write","arguments":{"path":"/app/requirements.txt","content":"weaviate-client>=4.16.0,<5\n"}},{"type":"toolCall","id":"call_ZHm4P7dBNvmGnAmrksFYE8Qr|fc_0d51cdf92682941d016a94ca72c83087d087b9a7fd6801cfda","name":"write","arguments":{"path":"/app/run_quickstart.sh","content":"#!/usr/bin/env bash\nset -euo pipefail\n\ncd \"$(dirname \"$0\")\"\n\"$PWD/start_weaviate.sh\"\nexec python \"$PWD/quickstart.py\"\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":964,"output":1915,"cacheRead":13824,"cacheWrite":0,"totalTokens":16703,"cost":{"input":0.0048200000000000005,"output":0.05745,"cacheRead":0.006912,"cacheWrite":0,"total":0.069182}},"stopReason":"toolUse","timestamp":1788136014925,"responseId":"resp_0d51cdf92682941d016a94ca4f413487d0a30e220e3532710e"}}
{"type":"message","id":"4e63921f","parentId":"b346293e","timestamp":"2026-08-31T00:27:30.948Z","message":{"role":"toolResult","toolCallId":"call_58ZHuL4QJT3aIYruZxLoG2KY|fc_0d51cdf92682941d016a94ca72c81087d08548e199572be35f","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1442 bytes to /app/start_weaviate.sh"}],"isError":false,"timestamp":1788136050948}}
{"type":"message","id":"9526487d","parentId":"4e63921f","timestamp":"2026-08-31T00:27:30.949Z","message":{"role":"toolResult","toolCallId":"call_fjmdADVxe7MatRSyXz1UyRth|fc_0d51cdf92682941d016a94ca72c82087d0ad8b134211a81c38","toolName":"write","content":[{"type":"text","text":"Successfully wrote 2511 bytes to /app/quickstart.py"}],"isError":false,"timestamp":1788136050949}}
{"type":"message","id":"94727ca9","parentId":"9526487d","timestamp":"2026-08-31T00:27:30.949Z","message":{"role":"toolResult","toolCallId":"call_SgiOgZrMFupNKebNauETH7DU|fc_0d51cdf92682941d016a94ca72c82887d0a51f45c9b3763d22","toolName":"write","content":[{"type":"text","text":"Successfully wrote 27 bytes to /app/requirements.txt"}],"isError":false,"timestamp":1788136050949}}
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{"type":"message","id":"6701177e","parentId":"513fa194","timestamp":"2026-08-31T00:27:45.984Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Inspecting git changes**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94ca7f027887d08f15e974d0513ed3\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMqBovu5dnKVXj4NG5yYNn8cdejDaevklktePugMcX_VvWFibh_sYHUTzciKNHxvkGZXe1SlxZ8h6CJPH0dPo2ftM6DfDAMqjl-TA6h4JUKIoNZvPgJxELFapotGIu4Bmz4tF1ZZ5NaC1hBn1wfr6RpPM2wNb1G0VEr-PzgK-09k6hN4HKLS2hlrXZELJkqU2wLsZbFiU7P2Jh7vveY4ERl8M8k7z_ips_1ObOcEWTYKkuB80m0Yk41ZzoPbTnsKStLbB-08nLqTH2-VnXkTQvU1Ba1cDoE8NhcAbUBWYnSX0iCQI-wydCM89aTYIfyN_cwwphS2-JjLBPrErpyOQM1iGMAksXF2RB9P8f6TntKe7UZKXils1HOjIuv6jtpXe2j5ZHl9ZxxBL9Ue0jGjefFMB58dpOWiTRKPwrk8EmLgJdZJ_3_48YBA-z5-vwhDfUJS8vxzmm1SAHckw2vlEScALilIbNKfMmyBh22CT9tMskEUEDsN0aHYmZTMcUZvsJsBQen7buLj0r2LwPl07eXSLh1_TZhSXcJtpJ0pIHg8w4DCKp_tnbL6Tvh5IBxJquACTsc8-fJvRA5zui8iyEzZrrodHPWErzr7wVLs-EsFL5Eg1yGYo7vjXdYYhTZAbwH_q4c1X4orJD6-xA1FcBlMwCgRQFPz0wZ9gseIabCTFa6qB4EwDzZq6GuYMFdyfwQ7IkiLfci0vE3o1WERsuVbvjuskaQ1o61RESDMY2IZ8IhhKInHUTYiDxw5lpF4jIqC56b3HOlvxcyXAZAISrMNDjBVEfp3uTLUrFUpWY5tlkVefHC06L1bfj-Z9zC-IXqJa4vZeb8llvYqyMH4lijtP31izpswjw6Bm3xr9gjdRWP1TRihrqV4kpnXOSBydrUSHb_THs7YqAbzkmKJ554-XqEULBdhY4prkFMCLOtKsUi35rSL0JL4OYxUkP4VO0fE5jpO5rXb3EkqOEpkqNeJMAahczjazty3-O2thpuJMS0h_CoWVvbRZo_NM1M3te64Teyz1oszsDn6xBKTZw4HyuRJnaXZPeSBsO6q58ppFc-8hdL7rNcfniWXyCA7oXcWZ6aQ5qHZOsvkzoHVZbOTuH1yOnSJnxvtrZJeuxjVGvILiuhG80n6GaClrSUeCt1HEZ7a0yGo0YaP7Xu_xucqI87EOVD7qYx3lUh7t94KFNKMtbrkJbF7eA_5lE2B4ej14Gft2Eo0_sU8UxM5KxzBMx3k6ig9Q2JzPLSGG7x4OsPB1PKf93rSQbKOAsK_E472VhGWLAE7aUHzFARVJIVP-VTvFprFWyFxA_mYIZ0-p-I=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting git changes**\"}]}"},{"type":"toolCall","id":"call_pmfmpxL5sQIaxh7UvZ4ZYwda|fc_0d51cdf92682941d016a94ca81e71c87d09c474bae1f118844","name":"bash","arguments":{"command":"git status --short 2>/dev/null || true && ls -l /app","timeout":10}},{"type":"toolCall","id":"call_ksDkSaGSfxtM7TkOt6vaem5d|fc_0d51cdf92682941d016a94ca81e73487d099b060da6728ef7e","name":"bash","arguments":{"command":"podman ps -a --format 'table {{.Names}}\\t{{.Image}}\\t{{.Status}}\\t{{.Ports}}'","timeout":20}},{"type":"toolCall","id":"call_l442jEtUQMDOCnfXy4eGGOAU|fc_0d51cdf92682941d016a94ca81e73c87d09e920c3b93b550f9","name":"bash","arguments":{"command":"curl -fsS http://127.0.0.1:8080/v1/schema | python -m json.tool | head -80","timeout":20}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":394,"output":161,"cacheRead":16896,"cacheWrite":0,"totalTokens":17451,"cost":{"input":0.00197,"output":0.00483,"cacheRead":0.008447999999999999,"cacheWrite":0,"total":0.015248}},"stopReason":"toolUse","timestamp":1788136062432,"responseId":"resp_0d51cdf92682941d016a94ca7e97e487d0b5f78e96bf137520"}}
{"type":"message","id":"8ff71707","parentId":"6701177e","timestamp":"2026-08-31T00:27:46.140Z","message":{"role":"toolResult","toolCallId":"call_pmfmpxL5sQIaxh7UvZ4ZYwda|fc_0d51cdf92682941d016a94ca81e71c87d09c474bae1f118844","toolName":"bash","content":[{"type":"text","text":"total 36\n-rw-rw-r--+ 1 root root 8799 Aug 30 16:35 quickstart.md\n-rw-rw-r--+ 1 root root 2511 Aug 30 17:27 quickstart.py\n-rw-rw-r--+ 1 root root 736 Aug 30 17:27 README.md\n-rw-rw-r--+ 1 root root 27 Aug 30 17:27 requirements.txt\n-rwxrwxr-x+ 1 root root 118 Aug 30 17:27 run_quickstart.sh\n-rw-rw-r--+ 1 root root 810 Aug 30 16:35 source-receipt.json\n-rwxrwxr-x+ 1 root root 1442 Aug 30 17:27 start_weaviate.sh\n"}],"isError":false,"timestamp":1788136066140}}
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{"type":"message","id":"8a2b850c","parentId":"1b77c61d","timestamp":"2026-08-31T00:28:02.215Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Assessing container startup strategy**\n\n**Verifying Podman image sources and dependencies**\n\n**Clarifying quickstart implementation 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container startup strategy**\"},{\"type\":\"summary_text\",\"text\":\"**Verifying Podman image sources and dependencies**\"},{\"type\":\"summary_text\",\"text\":\"**Clarifying quickstart implementation location**\"}]}"},{"type":"thinking","thinking":"**Configuring rootless Podman networking**\n\n**Verifying rootless Podman container states**","thinkingSignature":"{\"id\":\"rs_0d51cdf92682941d016a94ca8c6a9487d08780c565b977e68d\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqlMqQFgtebAnQGJLyURLGu3Fxm4UNuCVe21XxlJvBfYkO6IeU18wh4FCZd5XAzEn3AkFpmxGYpoqPa8P7mlSb1T16Z6gWdDvps1pcWAgnM5Kqc-7UHe4t38jC90byQNBY4rh6qbZxpVdjPQN4RLyTqxjPYnyJLMvEAxOo2ja8CEfV4uFuvcZR2vtYlJ-N3lUGUa3UKvyLsszMrE6ECHv87j7mYsZg-G63Ww9tq-KRkTGC1eWn-csGJ53xpBTmmyMAal9Ai9EZRJ4aV58rToUBdOVF-AOdWL8WtwsoONPDyKkSW1LuVL8fQrUnHQJO-b_5Eji_JTcqZNaiGf1DEmtJZp4ekJ0jC7CjOMEG84vEraCUxXFQ5NrECy0CBvcA-bbD1bmSLy-OhPX5THJQNVRn1qS_lI5x-xlLo74CxD92HpPOvzIVlHMx2WO7sCzCxMfhOIefpo9r-DyVlC_hplbLvNtufD4XaicLOnEc7SQW8msDm7uqXKz0y3HIWgXMW6NLV4217d7ru78ctXSaMFBqt_D53OSq_-GKniGecDJro9DPbCMtA795WgFOEq1LB30t86NrWcXSMgo3UfL4wtCuz_NclCOoZ92RO3iiH-8pnfH9C72MaAlWvFTWBFqMT1W7jPOQQAaV0B_19BRdrszbM5ZQiTX2QOQnEtsLfIa4wQZSM4MkOL3JsUY9H5lwNxlCYzFKHQUxgy5scJaGEBsni5ufbKSiTBqvEkh_YFQzDEHm-cvgKMuSVCUO1ttj28oR0IX1lsUWnRgYccd7p9BKfPLbtCUc_od2aa8bpWLkr8YaESSwYv8m2FLBE-myQPz7oRp4tEXMUw430z-Bn47fyasYmwebFWdiTJk_n6PnEDEHT9iobJh-NdSxbaAKplgcE1PJC9-bPn3Y6qBbLVm2qEjdjbs9fmcqYplAg-0RgLv7NTNcpr_SeyolhnigMbUoJQchsZT-YCX5piZOi2c_TV4kRm1yGb5kSWJnO98Rq6diuUg-aaawA-7OTolYEqWLTkDaiC4DEWTiygWAeyVFjHfqA5iICNvVmZ5ZcQSH8Mc9DF9HzORNZ-v76DnNmLb4VuQLgW0YFfd9VZ16eXRVyd4jBtihf9p3-6GOXDIvpymXvVO_5PGvIN8nayTaO-No3OcLlXIGZlvWi7ysIeeQaQT6Agaxm6NfaMcmJTiQbPMDiVxgO9mgJsnSF0Wc7S-rBuL_IQouUZ669NV_fqJlTRDz_OwYqNduVDoIFOg2fv_c_32PaIY4DOQO8WtAvwL6cmyQBRyzpvuN_f2h5zl-E4s18EyyO_mHZJONPyHql-gP9ed6v3bk8tZDBAG4qMypzVUL10P_W19DxN2htE7YO4zT0YGjj1UFBYLu6afj_U7amyrQsbMqThg1KhRthba70YArRnx9BzdWTVYUd6d35-yXG8BV9i-TJe1NPMUXyQRK_r8VNdS3LuV2VXYThPc7A-GYmglk8WxAKozxc38Hkmv5pHUGYzRAXrvi0_3PNxM1bZ1JanxSAGneG84PFX-0Ca0VBoQ3qEzZegtJcJfuW8PiIHdILmCHoM8_D0spmNHTxRsfbczH3xeSG7_EfUGdvWZWLGyjoYfOa1V1c00368ZlB-WcYwmD9eQE1OE1nNeuoqoGwMOR8Vuvsih7t2qLxo8klhT5LVYzVO5Y7sKFoBlQTFMVMIhUUlfcrHN-79uKPep9d2xAaQwCA_4V-kqJ52Yy7nfpY4OMfRjdz2yWfswhfN8HsoXSaNtsrhFBLSw4ovqxmgey7rF7Mt966Ro-c7bIsTDpo2lOd-X2AvdBuvo8E_RGx6xSPRKZzhYz5FXcM1meWPTAoV2rad-IXsoCOgrNs_RzEcLiuk8ASErk3ExHAR1iBrQ3o8tWJ2HCsiZnzVTonpCCPVYUeVAsaEFqolIbz3ZSCVumyvy5ey98Dx9UfUVrHZY3TRlSpfxcXvi2z5kENYgtvWRGkX965NIOaDi7PDQiKtO-deYjTCBLDFq5FBZ2B9gH0SEC7j4JjD2VkrEQv9eGJI0CNH0115obSmFDnGaOP3u2wGPF1CskGv4UI3Q1oldMIcZGXlQ52zfhyjpHMqm6DywaaLdUSrai03nqiUQSQ710ZdJLypcM0QP1vXEWFn_JDkC12WqKdp6Z1ldgUfbpn1kSHDN7p93PJuqCzhJ2yy4Ptc977tPSXrWxfpdSqr2IGhVrL_Y_6IH8mLrCVF2aTjY_TCEajFXfyWQkP99vJbp1SjekNnjagUqjMaKHzGkoDBvqC0gLcYQ3-8DuUhQB0kGGfSHDAbESLLmO9mBltYk_khB_Odfl5hB6jpr1sdlACmhRjpDWaXjf7-lIeHRsBIaFXSqlk3oXIvtaaM6XPrEh-8yyBEg9oUVyMDDvg95bt9AyUopojfQdEIgcHbnhWkKHaXnZKM6zoWQHH3TEIqaIJDIo8zftACkSD6KgzmbwR6Nvl5PkFjEH8Q8k17n-fxhwIC-wfxFpr7eulRioVQNhTJuBnj-BiO5LodI0c8qi06K1ImIZzkV6zyH7gxIPCbLKZmlLdmbgLygLon7xYj7aQtASCstbyQUe5Dv-JRvuREgJZvWTTK5LTiuEXl_XkTBrt4DztPiwNUNQM1x2syjCHxF5R8N7Crd0fsNGMjzZsTCwIl89A4pbxyUtrna5SXU-GgtYedB2RGMucpa62LSQt_CChW2eX2FRCki1u_ENvT87bqnfbhYbI9lZhFFqeDT87xYwOiTson0LWExG81HAcHD7EBe34pdm61D4mRWeDARgyz-VHxbh6-GNYL_OudHBIMJoGciPw8t6_HFlOr33Nl3JRqIvSlJnN_wJPeseCZp5fK4Qj8myJUM=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Configuring rootless Podman networking**\"},{\"type\":\"summary_text\",\"text\":\"**Verifying rootless Podman container states**\"}]}"},{"type":"text","text":"Implemented and ran the custom-vectors quickstart.\n\n- Added `/app/start_weaviate.sh`\n- Added `/app/quickstart.py`\n- Added `/app/run_quickstart.sh`, `requirements.txt`, and `README.md`\n- Started one rootless Podman Weaviate container\n- Created/imported the `Movie` collection\n- Ran `nearVector` search\n\nConfirmed result: **The Matrix**.","textSignature":"{\"v\":1,\"id\":\"msg_0d51cdf92682941d016a94ca90a41c87d0b80380e9d45ba65b\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1405,"output":848,"cacheRead":16896,"cacheWrite":0,"totalTokens":19149,"cost":{"input":0.007025000000000001,"output":0.02544,"cacheRead":0.008447999999999999,"cacheWrite":0,"total":0.040913}},"stopReason":"stop","timestamp":1788136066141,"responseId":"resp_0d51cdf92682941d016a94ca8260c087d09013c5fc0aeea58e"}}