{ "entries": [ { "kind": "message", "role": "user", "text": "Read /app/quickstart.md and implement the local Weaviate quickstart in /app. Use one rootless Podman Weaviate container and the documented custom-vectors path because multi-container bridge networking is unavailable. Run the first vector search and confirm it returns The Matrix. This machine provides rootless Podman instead of Docker; use Podman-compatible commands.", "time_seconds": 3.106 }, { "kind": "thinking", "role": "assistant", "text": "**Planning app modification and inspection**", "time_seconds": 3.118 }, { "kind": "tool_call", "role": "read", "text": "read {\"path\": \"/app/quickstart.md\"}", "time_seconds": 3.118 }, { "kind": "tool_result", "role": "read", "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\n\ud83d\udd76\ufe0f Unplug from the system & join Neo's journey \ud83d\udc8a\ud83d\udc30\n\n\"The Matrix\" will blow your mind \ud83e\udd2f as reality unravels \ud83c\udf00\n\nKung-fu, slow-mo & mind-bending sci-fi \ud83e\udd4b\ud83d\udd74\ufe0f\n\nAre you ready to see how deep the rabbit hole goes? \ud83d\udd34\ud83d\udd35 #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", "time_seconds": 8.103 }, { "kind": "thinking", "role": "assistant", "text": "**Planning local quickstart implementation**", "time_seconds": 8.104 }, { "kind": "tool_call", "role": "bash", "text": "$ pwd && printf '\\n-- files --\\n' && find . -maxdepth 2 -type f -not -path './.git/*' -print | sort && printf '\\n-- dirs --\\n' && find . -maxdepth 2 -type d -not -path './.git*' -print | sort", "time_seconds": 8.104 }, { "kind": "tool_result", "role": "bash", "text": "/app\n\n-- files --\n./quickstart.md\n./source-receipt.json\n\n-- dirs --\n.\n", "time_seconds": 13.508 }, { "kind": "thinking", "role": "assistant", "text": "**Planning Weaviate client scripting**", "time_seconds": 13.509 }, { "kind": "tool_call", "role": "read", "text": "read {\"path\": \"/app/source-receipt.json\"}", "time_seconds": 13.509 }, { "kind": "tool_result", "role": "read", "text": "{\n \"document\": {\n \"bytes\": 8799,\n \"path\": \"quickstart.md\",\n \"sha256\": \"a6c21317bc1f35607fc66c20610f431de06fc4155d2d8ba02d25e563d18c66ad\"\n },\n \"fetched_at\": \"2026-08-30T23:35:51.697620+00:00\",\n \"product\": \"Weaviate\",\n \"schema_version\": 1,\n \"source_commit\": \"016b79ae0b25275c70666fbebede91ea669767c2\",\n \"source_transform\": null,\n \"source_url\": \"https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/docs/weaviate/quickstart/local.md\",\n \"sources\": [\n {\n \"bytes\": 8799,\n \"sha256\": \"a6c21317bc1f35607fc66c20610f431de06fc4155d2d8ba02d25e563d18c66ad\",\n \"url\": \"https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/docs/weaviate/quickstart/local.md\"\n }\n ],\n \"subject\": \"weaviate\",\n \"version\": \"v1.39.2\"\n}\n", "time_seconds": 18.22 }, { "kind": "thinking", "role": "assistant", "text": "**Checking Podman installation and connectivity**", "time_seconds": 18.221 }, { "kind": "tool_call", "role": "bash", "text": "$ podman --version && podman info --format '{{.Host.Security.Rootless}} {{.Host.OCIRuntime.Name}}'", "time_seconds": 18.221 }, { "kind": "tool_call", "role": "bash", "text": "$ python --version && python -m pip --version && curl --version | head -1", "time_seconds": 18.221 }, { "kind": "tool_call", "role": "bash", "text": "$ git status --short 2>/dev/null || true", "time_seconds": 18.221 }, { "kind": "tool_result", "role": "bash", "text": "podman version 6.1.0\ntrue crun\n", "time_seconds": 24.004 }, { "kind": "tool_result", "role": "bash", "text": "Python 3.12.12\npip 26.2.1 from /opt/venv/lib/python3.12/site-packages/pip (python 3.12)\ncurl 8.21.0 (x86_64-pc-linux-gnu) libcurl/8.21.0 OpenSSL/3.6.4 zlib/1.3.1.zlib-ng brotli/1.2.0 zstd/1.5.7 libidn2/2.3.8 libpsl/0.21.5 libssh2/1.11.1 nghttp2/1.70.0 ngtcp2/1.25.0 nghttp3/1.18.0 mit-krb5/1.22.2\n", "time_seconds": 24.004 }, { "kind": "tool_result", "role": "bash", "text": "(no output)", "time_seconds": 24.005 }, { "kind": "thinking", "role": "assistant", "text": "**Designing rootless Podman Weaviate setup**", "time_seconds": 24.005 }, { "kind": "tool_call", "role": "bash", "text": "$ for u in \\\n'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/docs/_includes/code/quickstart/quickstart.short.local.import_vectors.create_collection.mdx' \\\n'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/docs/_includes/code/quickstart/quickstart.short.local.import_vectors.query.nearvector.mdx'; do echo \"--- $u\"; curl -fsSL \"$u\" || true; echo; done", "time_seconds": 24.005 }, { "kind": "tool_result", "role": "bash", "text": "--- https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/docs/_includes/code/quickstart/quickstart.short.local.import_vectors.create_collection.mdx\ncurl: (22) The requested URL returned error: 404\n\n--- https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/docs/_includes/code/quickstart/quickstart.short.local.import_vectors.query.nearvector.mdx\ncurl: (22) The requested URL returned error: 404\n\n", "time_seconds": 30.481 }, { "kind": "thinking", "role": "assistant", "text": "**Exploring documentation include paths**", "time_seconds": 30.482 }, { "kind": "tool_call", "role": "bash", "text": "$ 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", "time_seconds": 30.482 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 35.791 }, { "kind": "thinking", "role": "assistant", "text": "**Fetching project paths**", "time_seconds": 35.792 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/quickstart/quickstart.short.local.import_vectors.create_collection.mdx'", "time_seconds": 35.792 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/quickstart/quickstart.short.local.import_vectors.query.nearvector.mdx'", "time_seconds": 35.792 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/quickstart/quickstart.short.local.import-vectors.query.rag.mdx'", "time_seconds": 35.792 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 42.565 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 42.566 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 42.566 }, { "kind": "thinking", "role": "assistant", "text": "**Planning code retrieval**", "time_seconds": 42.566 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.create_collection.py'", "time_seconds": 42.566 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.query.nearvector.py'", "time_seconds": 42.566 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.query.rag.py'", "time_seconds": 42.566 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsSL 'https://raw.githubusercontent.com/weaviate/docs/016b79ae0b25275c70666fbebede91ea669767c2/_includes/code/python/quickstart.short.local.import_vectors.create_collection.py' | grep -A100 -B3 'START'", "time_seconds": 42.566 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 49.938 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 49.938 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 49.938 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 49.939 }, { "kind": "thinking", "role": "assistant", "text": "**Checking client installation status**", "time_seconds": 49.939 }, { "kind": "tool_call", "role": "bash", "text": "$ python - <<'PY'\ntry:\n import weaviate; print('weaviate', getattr(weaviate,'__version__','unknown'))\nexcept Exception as e: print(type(e).__name__, e)\nPY\npodman ps -a --format '{{.Names}} {{.Image}} {{.Status}}'", "time_seconds": 49.939 }, { "kind": "tool_result", "role": "bash", "text": "ModuleNotFoundError No module named 'weaviate'\n", "time_seconds": 53.678 }, { "kind": "thinking", "role": "assistant", "text": "**Planning rootless Podman container setup**", "time_seconds": 53.679 }, { "kind": "tool_call", "role": "bash", "text": "$ podman pull cr.weaviate.io/semitechnologies/weaviate:1.39.2", "time_seconds": 53.679 }, { "kind": "tool_result", "role": "bash", "text": "time=\"2026-08-30T17:23:52-07:00\" level=warning msg=\"\\\"/run/user/1000\\\" directory set by $XDG_RUNTIME_DIR does not exist. Either create the directory or unset $XDG_RUNTIME_DIR.: faccessat /run/user/1000: no such file or directory: Trying to pull image in the event that it is a public image.\"\nTrying to pull cr.weaviate.io/semitechnologies/weaviate:1.39.2...\n\n\nCommand timed out after 120 seconds", "time_seconds": 178.904 }, { "kind": "thinking", "role": "assistant", "text": "**Checking process and image sizes**", "time_seconds": 178.905 }, { "kind": "tool_call", "role": "bash", "text": "$ podman images --format '{{.Repository}}:{{.Tag}} {{.Size}}' | head", "time_seconds": 178.905 }, { "kind": "tool_call", "role": "bash", "text": "$ podman ps -a --format '{{.Names}} {{.Image}} {{.Status}}'", "time_seconds": 178.905 }, { "kind": "tool_call", "role": "bash", "text": "$ env | grep -E 'XDG_RUNTIME|HTTP_PROXY|HTTPS_PROXY|NO_PROXY' || true", "time_seconds": 178.905 }, { "kind": "tool_result", "role": "bash", "text": "docker.io/semitechnologies/weaviate:1.39.2 208 MB\ndocker.io/library/neo4j:2026.07.1 669 MB\ndocker.elastic.co/elasticsearch/elasticsearch:9.5.2 1.63 GB\ndocker.io/memgraph/memgraph:latest 675 MB\ndocker.io/library/alpine:3.22 8.59 MB\n", "time_seconds": 182.44 }, { "kind": "tool_result", "role": "bash", "text": "(no output)", "time_seconds": 182.44 }, { "kind": "tool_result", "role": "bash", "text": "XDG_RUNTIME_DIR=/run/user/1000\n", "time_seconds": 182.44 }, { "kind": "thinking", "role": "assistant", "text": "**Planning rootless Docker run with volume**", "time_seconds": 182.44 }, { "kind": "tool_call", "role": "bash", "text": "$ podman volume exists weaviate_data || podman volume create weaviate_data\npodman run -d --name weaviate \\\n -p 8080:8080 -p 50051:50051 \\\n -v weaviate_data:/var/lib/weaviate \\\n -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \\\n -e PERSISTENCE_DATA_PATH=/var/lib/weaviate \\\n -e CLUSTER_HOSTNAME=node1 \\\n docker.io/semitechnologies/weaviate:1.39.2 \\\n --host 0.0.0.0 --port 8080 --scheme http\npodman ps --format '{{.Names}} {{.Status}} {{.Ports}}'", "time_seconds": 182.44 }, { "kind": "tool_result", "role": "bash", "text": "weaviate_data\ntime=\"2026-08-30T17:26:00-07:00\" level=warning msg=\"\\\"/run/user/1000\\\" directory set by $XDG_RUNTIME_DIR does not exist. Either create the directory or unset $XDG_RUNTIME_DIR.: faccessat /run/user/1000: no such file or directory: Trying to pull image in the event that it is a public image.\"\n931405ff6518afddefc2f2ec08c07901506eb91bafdb598c2717006c875f64ba\nweaviate Up 1 second 0.0.0.0:8080->8080/tcp, 0.0.0.0:50051->50051/tcp\n", "time_seconds": 187.738 }, { "kind": "thinking", "role": "assistant", "text": "**Waiting for readiness with curl**", "time_seconds": 187.739 }, { "kind": "tool_call", "role": "bash", "text": "$ for i in $(seq 1 30); do if curl -fsS http://127.0.0.1:8080/v1/.well-known/ready >/dev/null; then echo ready; exit 0; fi; sleep 1; done; podman logs --tail 100 weaviate; exit 1", "time_seconds": 187.739 }, { "kind": "tool_result", "role": "bash", "text": "curl: (56) Recv failure: Connection reset by peer\ncurl: (56) Recv failure: Connection reset by peer\ncurl: (22) The requested URL returned error: 503\ncurl: (22) The requested URL returned error: 503\ncurl: (22) The requested URL returned error: 503\ncurl: (22) The requested URL returned error: 503\nready\n", "time_seconds": 196.41 }, { "kind": "thinking", "role": "assistant", "text": "**Planning quickstart implementation**\n\n**Ensuring client package compatibility**", "time_seconds": 196.411 }, { "kind": "tool_call", "role": "bash", "text": "$ python -m pip install 'weaviate-client>=4.16.0,<5'", "time_seconds": 196.411 }, { "kind": "tool_result", "role": "bash", "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 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 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 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 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 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 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 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 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", "time_seconds": 213.229 }, { "kind": "thinking", "role": "assistant", "text": "**Testing local connection defaults**", "time_seconds": 213.23 }, { "kind": "tool_call", "role": "bash", "text": "$ 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", "time_seconds": 213.23 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 228.033 }, { "kind": "thinking", "role": "assistant", "text": "**Planning rootless Podman Weaviate container script**", "time_seconds": 228.033 }, { "kind": "tool_call", "role": "bash", "text": "$ 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}}'", "time_seconds": 228.033 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 233.348 }, { "kind": "thinking", "role": "assistant", "text": "**Designing idempotent single-container startup script**\n\n**Verifying Podman image registry and alias usage**", "time_seconds": 233.348 }, { "kind": "tool_call", "role": "bash", "text": "$ podman image exists cr.weaviate.io/semitechnologies/weaviate:1.39.2; echo cr_exists=$?; podman image exists docker.io/semitechnologies/weaviate:1.39.2; echo docker_exists=$?", "time_seconds": 233.348 }, { "kind": "tool_result", "role": "bash", "text": "cr_exists=1\ndocker_exists=0\n", "time_seconds": 241.619 }, { "kind": "thinking", "role": "assistant", "text": "**Choosing docker.io for Weaviate image**\n\n**Designing robust rootless Podman script**\n\n**Verifying collection existence and batch handling**", "time_seconds": 241.619 }, { "kind": "tool_call", "role": "write", "text": "write {\"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\", \"path\": \"/app/start_weaviate.sh\"}", "time_seconds": 241.619 }, { "kind": "tool_call", "role": "write", "text": "write {\"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\", \"path\": \"/app/quickstart.py\"}", "time_seconds": 241.619 }, { "kind": "tool_call", "role": "write", "text": "write {\"content\": \"weaviate-client>=4.16.0,<5\\n\", \"path\": \"/app/requirements.txt\"}", "time_seconds": 241.619 }, { "kind": "tool_call", "role": "write", "text": "write {\"content\": \"#!/usr/bin/env bash\\nset -euo pipefail\\n\\ncd \\\"$(dirname \\\"$0\\\")\\\"\\n\\\"$PWD/start_weaviate.sh\\\"\\nexec python \\\"$PWD/quickstart.py\\\"\\n\", \"path\": \"/app/run_quickstart.sh\"}", "time_seconds": 241.619 }, { "kind": "tool_result", "role": "write", "text": "Successfully wrote 1442 bytes to /app/start_weaviate.sh", "time_seconds": 277.642 }, { "kind": "tool_result", "role": "write", "text": "Successfully wrote 2511 bytes to /app/quickstart.py", "time_seconds": 277.643 }, { "kind": "tool_result", "role": "write", "text": "Successfully wrote 27 bytes to /app/requirements.txt", "time_seconds": 277.643 }, { "kind": "tool_result", "role": "write", "text": "Successfully wrote 118 bytes to /app/run_quickstart.sh", "time_seconds": 277.643 }, { "kind": "thinking", "role": "assistant", "text": "**Adding chmod and testing script execution**", "time_seconds": 277.643 }, { "kind": "tool_call", "role": "write", "text": "write {\"content\": \"# Local Weaviate quickstart\\n\\nThis is the local quickstart's **custom-vectors** path. It uses one rootless\\nPodman Weaviate container; no Ollama or container bridge network is required.\\n\\n```bash\\npython -m pip install -r requirements.txt\\n./run_quickstart.sh\\n```\\n\\nThe command creates the `Movie` collection, imports the three documented\\nobjects with their pre-computed vectors, runs the first `nearVector` search,\\nand verifies that its first result is **The Matrix**.\\n\\nUseful commands:\\n\\n```bash\\npodman ps # inspect the Weaviate container\\npodman logs weaviate\\npodman stop weaviate # stop it without deleting persisted data\\n```\\n\\nWeaviate is published on `http://127.0.0.1:8080` (HTTP) and `50051` (gRPC).\\n\", \"path\": \"/app/README.md\"}", "time_seconds": 277.643 }, { "kind": "tool_result", "role": "write", "text": "Successfully wrote 736 bytes to /app/README.md", "time_seconds": 282.981 }, { "kind": "tool_call", "role": "bash", "text": "$ chmod +x /app/start_weaviate.sh /app/run_quickstart.sh && /app/run_quickstart.sh", "time_seconds": 282.982 }, { "kind": "tool_result", "role": "bash", "text": "Weaviate is ready at http://127.0.0.1:8080\n/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{\n \"description\": \"A computer hacker learns about the true nature of reality and his role in the war against its controllers.\",\n \"title\": \"The Matrix\",\n \"genre\": \"Science Fiction\"\n}\n{\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 \"title\": \"Spirited Away\",\n \"genre\": \"Animation\"\n}\nCONFIRMED: first vector search returns The Matrix\n", "time_seconds": 289.125 }, { "kind": "thinking", "role": "assistant", "text": "**Inspecting git changes**", "time_seconds": 289.126 }, { "kind": "tool_call", "role": "bash", "text": "$ git status --short 2>/dev/null || true && ls -l /app", "time_seconds": 289.126 }, { "kind": "tool_call", "role": "bash", "text": "$ podman ps -a --format 'table {{.Names}}\\t{{.Image}}\\t{{.Status}}\\t{{.Ports}}'", "time_seconds": 289.126 }, { "kind": "tool_call", "role": "bash", "text": "$ curl -fsS http://127.0.0.1:8080/v1/schema | python -m json.tool | head -80", "time_seconds": 289.126 }, { "kind": "tool_result", "role": "bash", "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", "time_seconds": 292.834 }, { "kind": "tool_result", "role": "bash", "text": "NAMES IMAGE STATUS PORTS\nweaviate docker.io/semitechnologies/weaviate:1.39.2 Up About a minute 0.0.0.0:8080->8080/tcp, 0.0.0.0:50051->50051/tcp\n", "time_seconds": 292.834 }, { "kind": "tool_result", "role": "bash", "text": "{\n \"classes\": [\n {\n \"class\": \"Movie\",\n \"invertedIndexConfig\": {\n \"bm25\": {\n \"b\": 0.75,\n \"k1\": 1.2\n },\n \"cleanupIntervalSeconds\": 60,\n \"stopwords\": {\n \"additions\": null,\n \"preset\": \"en\",\n \"removals\": null\n },\n \"usingBlockMaxWAND\": true\n },\n \"multiTenancyConfig\": {\n \"autoTenantActivation\": false,\n \"autoTenantCreation\": false,\n \"enabled\": false\n },\n \"properties\": [\n {\n \"dataType\": [\n \"text\"\n ],\n \"description\": \"This property was generated by Weaviate's auto-schema feature on Mon Aug 31 00:27:42 2026\",\n \"indexFilterable\": true,\n \"indexRangeFilters\": false,\n \"indexSearchable\": true,\n \"name\": \"description\",\n \"tokenization\": \"word\"\n },\n {\n \"dataType\": [\n \"text\"\n ],\n \"description\": \"This property was generated by Weaviate's auto-schema feature on Mon Aug 31 00:27:42 2026\",\n \"indexFilterable\": true,\n \"indexRangeFilters\": false,\n \"indexSearchable\": true,\n \"name\": \"genre\",\n \"tokenization\": \"word\"\n },\n {\n \"dataType\": [\n \"text\"\n ],\n \"description\": \"This property was generated by Weaviate's auto-schema feature on Mon Aug 31 00:27:42 2026\",\n \"indexFilterable\": true,\n \"indexRangeFilters\": false,\n \"indexSearchable\": true,\n \"name\": \"title\",\n \"tokenization\": \"word\"\n }\n ],\n \"shardingConfig\": {\n \"actualCount\": 1,\n \"actualVirtualCount\": 128,\n \"desiredCount\": 1,\n \"desiredVirtualCount\": 128,\n \"function\": \"murmur3\",\n \"key\": \"_id\",\n \"strategy\": \"hash\",\n \"virtualPerPhysical\": 128\n },\n \"vectorConfig\": {\n \"default\": {\n \"vectorIndexConfig\": {\n \"bq\": {\n \"enabled\": false\n },\n \"cleanupIntervalSeconds\": 300,\n \"distance\": \"cosine\",\n \"dynamicEfFactor\": 8,\n \"dynamicEfMax\": 500,\n \"dynamicEfMin\": 100,\n \"ef\": -1,\n \"efConstruction\": 128,\n", "time_seconds": 292.834 }, { "kind": "thinking", "role": "assistant", "text": "**Assessing container startup strategy**\n\n**Verifying Podman image sources and dependencies**\n\n**Clarifying quickstart implementation location**", "time_seconds": 292.835 }, { "kind": "thinking", "role": "assistant", "text": "**Configuring rootless Podman networking**\n\n**Verifying rootless Podman container states**", "time_seconds": 292.835 }, { "kind": "text", "role": "assistant", "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**.", "time_seconds": 292.835 } ], "schema_version": 1, "source": "pi-session-jsonl", "source_sha256": "50fde34c37075f24969a9d134e7979afc6f03ba9646f357b4caa86b51c518bd5", "started_at_epoch_ms": 1788135773306 }