{
"entries": [
{
"kind": "message",
"role": "user",
"text": "Read /app/quickstart.md and implement the Chroma OSS Python getting-started example in /app. Run it and confirm that its first query returns results.",
"time_seconds": 3.172
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning quickstart inspection**",
"time_seconds": 3.181
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{
"kind": "tool_call",
"role": "read",
"text": "read {\"path\": \"/app/quickstart.md\"}",
"time_seconds": 3.181
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{
"kind": "tool_result",
"role": "read",
"text": "> ## Documentation Index\n> Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt\n> Use this file to discover all available pages before exploring further.\n\n# Getting Started\n\n> Chroma is the open-source data infrastructure for AI. It comes with everything you need to get started built-in, and runs on your machine.\n\nexport const YouTube = ({src, title, allow, allowFullScreen = true, referrerPolicy}) => {\n const [isVisible, setIsVisible] = useState(false);\n const wrapperRef = useRef(null);\n useEffect(() => {\n const wrapper = wrapperRef.current;\n if (!wrapper) return;\n const observer = new IntersectionObserver(([entry]) => {\n if (entry.isIntersecting) {\n setIsVisible(true);\n observer.disconnect();\n }\n }, {\n threshold: 0\n });\n observer.observe(wrapper);\n return () => observer.disconnect();\n }, []);\n return
\n\n \n For production, Chroma offers [Chroma Cloud](https://trychroma.com/signup?utm_source=docs-getting-started) - a fast, scalable, and serverless database-as-a-service. Get started in 30 seconds - \\$5 in free credits included.\n \n\n ## Install with AI\n\n Give the following prompt to Claude Code, Cursor, Codex, or your favorite AI agent. It will quickly set you up with Chroma.\n\n \n ```prompt Chroma Cloud expandable theme={null}\n In this directory create a new Python project with Chroma set up.\n Use a virtual environment.\n\n Write a small example that adds some data to a collection and queries it.\n Do not delete the data from the collection when it's complete.\n Run the script when you are done setting up the environment and writing the\n script. The output should show what data was ingested, what was the query,\n and the results.\n Your own summary should include this output so the user can see it.\n\n First, install `chromadb`.\n\n The project should be set up with Chroma Cloud. When you install `chromadb`,\n you get access to the Chroma CLI. You can run `chroma login` to authenticate.\n This will open a browser for authentication and save a connection profile\n locally.\n\n You can also use `chroma profile show` to see if the user already has an\n active profile saved locally. If so, you can skip the login step.\n\n Then create a DB using the CLI with `chroma db create chroma-getting-started`.\n This will create a DB with this name.\n\n Then use the CLI command `chroma db connect chroma-getting-started --env-file`.\n This will create a .env file in the current directory with the connection\n variables for this DB and account, so the CloudClient can be instantiated\n with chromadb.CloudClient(api_key=os.getenv(\"CHROMA_API_KEY\"), ...).\n ```\n\n ```text OSS expandable theme={null}\n In this directory create a new Python project with Chroma set up.\n Use a virtual environment.\n\n Write a small example that adds some data to a collection and queries it.\n Do not delete the data from the collection when it's complete.\n Run the script when you are done setting up the environment and writing the\n script. The output should show what data was ingested, what was the query,\n and the results.\n Your own summary should include this output so the user can see it.\n\n Use Chroma's in-memory client: `chromadb.Client()`\n ```\n \n\n ## Install Manually\n\n \n \n \n ```bash pip theme={null}\n pip install chromadb\n ```\n\n ```bash poetry theme={null}\n poetry add chromadb\n ```\n\n ```bash uv theme={null}\n uv pip install chromadb\n ```\n \n \n\n \n ```python Python theme={null}\n import chromadb\n chroma_client = chromadb.Client()\n ```\n \n\n \n Collections are where you'll store your embeddings, documents, and any additional metadata. Collections index your embeddings and documents, and enable efficient retrieval and filtering. You can create a collection with a name:\n\n ```python Python theme={null}\n collection = chroma_client.create_collection(name=\"my_collection\")\n ```\n \n\n \n Chroma will store your text and handle embedding and indexing automatically. You can also customize the embedding model. You must provide unique string IDs for your documents.\n\n ```python Python theme={null}\n collection.add(\n ids=[\"id1\", \"id2\"],\n documents=[\n \"This is a document about pineapple\",\n \"This is a document about oranges\"\n ]\n )\n ```\n \n\n \n You can query the collection with a list of query texts, and Chroma will return the n most similar results. It's that easy!\n\n ```python Python theme={null}\n results = collection.query(\n query_texts=[\"This is a query document about hawaii\"], # Chroma will embed this for you\n n_results=2 # how many results to return\n )\n print(results)\n ```\n\n If n\\_results is not provided, Chroma will return 10 results by default. Here we only added 2 documents, so we set n\\_results=2.\n \n\n \n From the above - you can see that our query about hawaii is semantically most similar to the document about pineapple.\n\n ```python Python theme={null}\n {\n 'documents': [[\n 'This is a document about pineapple',\n 'This is a document about oranges'\n ]],\n 'ids': [['id1', 'id2']],\n 'distances': [[1.0404009819030762, 1.243080496788025]],\n 'uris': None,\n 'data': None,\n 'metadatas': [[None, None]],\n 'embeddings': None,\n }\n ```\n \n\n \n What if we tried querying with \"This is a document about florida\"? Here is a full example.\n\n ```python Python expandable theme={null}\n import chromadb\n chroma_client = chromadb.Client()\n\n # switch \\`create_collection\\` to \\`get_or_create_collection\\` to avoid creating a new collection every time\n collection = chroma_client.get_or_create_collection(name=\"my_collection\")\n\n # switch \\`add\\` to \\`upsert\\` to avoid adding the same documents every time\n collection.upsert(\n documents=[\n \"This is a document about pineapple\",\n \"This is a document about oranges\"\n ],\n ids=[\"id1\", \"id2\"]\n )\n\n results = collection.query(\n query_texts=[\"This is a query document about florida\"], # Chroma will embed this for you\n n_results=2 # how many results to return\n )\n\n print(results)\n ```\n \n \n\n ## Next steps\n\n In this guide we used Chroma's [in-memory client](/docs/run-chroma/clients#in-memory-client) for simplicity. It starts a Chroma server in-memory, so any data you ingest will be lost when your program terminates. You can use the [persistent client](/docs/run-chroma/clients#persistent-client) or run Chroma in [client-server mode](/docs/run-chroma/client-server) if you need data persistence.\n\n * Learn how to [Deploy Chroma](/guides/deploy/client-server-mode) to a server\n * Join Chroma's [Discord Community](https://discord.com/invite/MMeYNTmh3x) to ask questions and get help\n * Follow Chroma on [X (@trychroma)](https://twitter.com/trychroma) for updates\n \n\n \n
\n \n
\n\n \n For production, Chroma offers [Chroma Cloud](https://trychroma.com/signup?utm_source=docs-getting-started) - a fast, scalable, and serverless database-as-a-service. Get started in 30 seconds - \\$5 in free credits included.\n \n\n ## Install with AI\n\n Give the following prompt to Claude Code, Cursor, Codex, or your favorite AI agent. It will quickly set you up with Chroma.\n\n \n ```prompt Chroma Cloud expandable theme={null}\n In this directory create a new Typescript project with Chroma set up.\n\n Write a small example that adds some data to a collection and queries it.\n Do not delete the data from the collection when it's complete.\n Run the script when you are done setting up the environment and writing the\n script. The output should show what data was ingested, what was the query,\n and the results.\n Your own summary should include this output so the user can see it.\n\n First, install `chromadb`.\n\n The project should be set up with Chroma Cloud. When you install `chromadb`,\n you get access to the Chroma CLI. You can run `chroma login` to authenticate.\n This will open a browser for authentication and save a connection profile\n locally.\n\n You can also use `chroma profile show` to see if the user already has an\n active profile saved locally. If so, you can skip the login step.\n\n Then create a DB using the CLI with `chroma db create chroma-getting-started`.\n This will create a DB with this name.\n\n Then use the CLI command `chroma db connect chroma-getting-started --env-file`.\n This will create a .env file in the current directory with the connection\n variables for this DB and account, so the CloudClient can be instantiated\n with: new CloudClient().\n ```\n\n ```prompt OSS expandable theme={null}\n In this directory create a new Typescript project with Chroma set up.\n\n Write a small example that adds some data to a collection and queries it.\n Do not delete the data from the collection when it's complete.\n Run the script when you are done setting up the environment and writing the\n script. The output should show what data was ingested, what was the query,\n and the results.\n Your own summary should include this output so the user can see it.\n\n You will have to run a local Chroma server to make this work. When you install\n `chromadb` you get access to the Chroma CLI, which can start a local server\n for you with `chroma run`.\n\n Make sure to instruct the user on how to start a local Chroma server in your\n summary.\n ```\n \n\n ## Install Manually\n\n \n \n \n ```bash npm theme={null}\n npm install chromadb @chroma-core/default-embed\n ```\n\n ```bash pnpm theme={null}\n pnpm add chromadb @chroma-core/default-embed\n ```\n\n ```bash bun theme={null}\n bun add chromadb @chroma-core/default-embed\n ```\n\n ```bash yarn theme={null}\n yarn add chromadb @chroma-core/default-embed\n ```\n \n \n\n \n Run the Chroma backend:\n\n \n ```bash npm theme={null}\n npx chroma run --path ./getting-started\n ```\n\n ```bash pnpm theme={null}\n pnpm exec chroma run --path ./getting-started\n ```\n\n ```bash bun theme={null}\n bunx chroma run --path ./getting-started\n ```\n\n ```bash yarn theme={null}\n yarn chroma run --path ./getting-started\n ```\n\n ```bash docker theme={null}\n docker pull chromadb/chroma\n docker run -p 8000:8000 chromadb/chroma\n ```\n \n\n Then create a client which connects to it:\n\n \n ```typescript TypeScript ESM theme={null}\n import { ChromaClient } from \"chromadb\";\n const client = new ChromaClient();\n ```\n\n ```typescript TypeScript CJS theme={null}\n const { ChromaClient } = require(\"chromadb\");\n const client = new ChromaClient();\n ```\n \n \n\n \n Collections are where you'll store your embeddings, documents, and any additional metadata. Collections index your embeddings and documents, and enable efficient retrieval and filtering. You can create a collection with a name:\n\n ```typescript TypeScript theme={null}\n const collection = await client.createCollection({\n name: \"my_collection\",\n });\n ```\n \n\n \n Chroma will store your text and handle embedding and indexing automatically. You can also customize the embedding model. You must provide unique string IDs for your documents.\n\n ```typescript TypeScript theme={null}\n await collection.add({\n ids: [\"id1\", \"id2\"],\n documents: [\n \"This is a document about pineapple\",\n \"This is a document about oranges\",\n ],\n });\n ```\n \n\n \n You can query the collection with a list of query texts, and Chroma will return the n most similar results. It's that easy!\n\n ```typescript TypeScript theme={null}\n const results = await collection.query({\n queryTexts: [\"This is a query document about hawaii\"], // Chroma will embed this for you\n nResults: 2, // how many results to return\n });\n\n console.log(results);\n ```\n\n If n\\_results is not provided, Chroma will return 10 results by default. Here we only added 2 documents, so we set n\\_results=2.\n \n\n \n From the above - you can see that our query about hawaii is semantically most similar to the document about pineapple.\n\n ```typescript TypeScript theme={null}\n {\n documents: [\n [\n 'This is a document about pineapple',\n 'This is a document about oranges'\n ]\n ],\n ids: [\n ['id1', 'id2']\n ],\n distances: [[1.0404009819030762, 1.243080496788025]],\n uris: null,\n data: null,\n metadatas: [[null, null]],\n embeddings: null\n }\n ```\n \n\n \n What if we tried querying with \"This is a document about florida\"? Here is a full example.\n\n ```typescript TypeScript expandable theme={null}\n import { ChromaClient } from \"chromadb\";\n const client = new ChromaClient();\n\n // switch `createCollection` to `getOrCreateCollection` to avoid creating a new collection every time\n const collection = await client.getOrCreateCollection({\n name: \"my_collection\",\n });\n\n // switch `addRecords` to `upsertRecords` to avoid adding the same documents every time\n await collection.upsert({\n documents: [\n \"This is a document about pineapple\",\n \"This is a document about oranges\",\n ],\n ids: [\"id1\", \"id2\"],\n });\n\n const results = await collection.query({\n queryTexts: [\"This is a query document about florida\"], // Chroma will embed this for you\n nResults: 2, // how many results to return\n });\n\n console.log(results);\n ```\n \n \n\n ## Next steps\n\n * We offer [first class support](/docs/embeddings/embedding-functions) for various embedding providers via our embedding function interface. Each embedding function ships in its own npm package.\n * Learn how to [Deploy Chroma](/guides/deploy/client-server-mode) to a server\n * Join Chroma's [Discord Community](https://discord.com/invite/MMeYNTmh3x) to ask questions and get help\n * Follow Chroma on [X (@trychroma)](https://twitter.com/trychroma) for updates\n \n\n \n Our Rust docs are hosted on [docs.rs](https://docs.rs/chroma/latest/chroma/)!\n\n ## Install Manually\n\n ```bash theme={null}\n cargo add chroma\n ```\n\n ## Create a Chroma Client\n\n Run the Chroma backend:\n\n ```bash theme={null}\n chroma run --path ./getting-started\n ```\n\n Then create a client which connects to it:\n\n ```rust theme={null}\n use chroma::ChromaHttpClient;\n\n let client = ChromaHttpClient::new(Default::default());\n ```\n\n ## Create a collection\n\n ```rust theme={null}\n let collection = client\n .create_collection(\"my_collection\", None, None)\n .await?;\n ```\n\n ## Add some text documents to the collection\n\n The Rust client expects embeddings to be provided directly. Generate embeddings with your provider SDK, then pass them along with documents.\n\n ```rust theme={null}\n let embeddings = vec![vec![0.1, 0.2, 0.3], vec![0.4, 0.5, 0.6]];\n\n collection\n .add(\n vec![\"id1\".to_string(), \"id2\".to_string()],\n embeddings,\n Some(vec![\n Some(\"This is a document about pineapple\".to_string()),\n Some(\"This is a document about oranges\".to_string()),\n ]),\n None,\n None,\n )\n .await?;\n ```\n\n ## Query the collection\n\n ```rust theme={null}\n let results = collection\n .query(vec![vec![0.1, 0.2, 0.3]], Some(2), None, None, None)\n .await?;\n ```\n\n ## Next steps\n\n * Read the Rust API docs on [docs.rs](https://docs.rs/chroma/latest/chroma/)\n * Learn how to [Deploy Chroma](/guides/deploy/client-server-mode) to a server\n * Join Chroma's [Discord Community](https://discord.com/invite/MMeYNTmh3x) to ask questions and get help\n \n\n",
"time_seconds": 5.386
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Inspecting /app for OSS Python starter**",
"time_seconds": 5.387
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ pwd && ls -la && find . -maxdepth 2 -type f -not -path './.git/*' -print",
"time_seconds": 5.387
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ python --version && pip --version && (command -v chroma || true) && (python -c 'import chromadb; print(chromadb.__version__)' 2>/dev/null || true)",
"time_seconds": 5.387
},
{
"kind": "tool_result",
"role": "bash",
"text": "/app\ntotal 24\ndrwxrwxr-x+ 1 root root 64 Aug 28 20:38 .\ndrwxr-xr-x 15 root root 340 Aug 28 20:38 ..\n-rw-rw-r--+ 1 root root 19373 Aug 28 20:35 quickstart.md\n-rw-rw-r--+ 1 root root 405 Aug 28 20:35 source-receipt.json\n./quickstart.md\n./source-receipt.json\n",
"time_seconds": 9.364
},
{
"kind": "tool_result",
"role": "bash",
"text": "Python 3.12.12\npip 25.3 from /opt/python-base/lib/python3.12/site-packages/pip (python 3.12)\n",
"time_seconds": 9.367
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Setting up virtual environment and installing ChromaDB**",
"time_seconds": 9.369
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ python -m venv .venv && .venv/bin/python -m pip install chromadb",
"time_seconds": 9.369
},
{
"kind": "tool_result",
"role": "bash",
"text": "Collecting chromadb\n Downloading chromadb-1.5.9-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.0 kB)\nCollecting build>=1.0.3 (from chromadb)\n Downloading build-1.6.0-py3-none-any.whl.metadata (5.6 kB)\nCollecting pydantic>=2.0 (from chromadb)\n Downloading pydantic-2.13.5-py3-none-any.whl.metadata (110 kB)\nCollecting pydantic-settings>=2.0 (from chromadb)\n Downloading pydantic_settings-2.15.0-py3-none-any.whl.metadata (3.9 kB)\nCollecting pybase64>=1.4.1 (from chromadb)\n Downloading pybase64-1.5.0-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl.metadata (11 kB)\nCollecting uvicorn>=0.18.3 (from uvicorn[standard]>=0.18.3->chromadb)\n Downloading uvicorn-0.52.4-py3-none-any.whl.metadata (6.6 kB)\nCollecting numpy>=1.22.5 (from chromadb)\n Downloading numpy-2.5.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.6 kB)\nCollecting typing-extensions>=4.5.0 (from chromadb)\n 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websocket_client-1.9.0-py3-none-any.whl (82 kB)\nDownloading websockets-17.1-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (224 kB)\nDownloading flatbuffers-25.12.19-py2.py3-none-any.whl (26 kB)\nDownloading pyproject_hooks-1.2.0-py3-none-any.whl (10 kB)\nDownloading requests-2.34.2-py3-none-any.whl (73 kB)\nDownloading requests_oauthlib-2.0.0-py2.py3-none-any.whl (24 kB)\nDownloading aiohappyeyeballs-2.7.1-py3-none-any.whl (15 kB)\nDownloading aiosignal-1.4.0-py3-none-any.whl (7.5 kB)\nDownloading charset_normalizer-3.5.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (248 kB)\nDownloading filelock-3.32.4-py3-none-any.whl (99 kB)\nDownloading frozenlist-1.8.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (242 kB)\nDownloading fsspec-2026.7.0-py3-none-any.whl (206 kB)\nDownloading hf_xet-1.6.0-cp38-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.5 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 4.5/4.5 MB 16.4 MB/s eta 0:00:00\nDownloading mdurl-0.1.2-py3-none-any.whl (10.0 kB)\nDownloading multidict-6.7.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (256 kB)\nDownloading oauthlib-3.3.1-py3-none-any.whl (160 kB)\nDownloading propcache-0.5.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (61 kB)\nDownloading yarl-1.24.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (109 kB)\nInstalling collected packages: pypika, flatbuffers, durationpy, websockets, websocket-client, uvloop, urllib3, typing-extensions, tqdm, tenacity, six, shellingham, rpds-py, pyyaml, python-dotenv, pyproject_hooks, pygments, pybase64, protobuf, propcache, packaging, overrides, orjson, oauthlib, numpy, multidict, mmh3, mdurl, importlib-resources, idna, httptools, hf-xet, h11, fsspec, frozenlist, filelock, click, charset_normalizer, certifi, bcrypt, attrs, annotated-types, annotated-doc, aiohappyeyeballs, yarl, uvicorn, typing-inspection, requests, referencing, python-dateutil, pydantic-core, opentelemetry-proto, opentelemetry-api, onnxruntime, markdown-it-py, httpcore, grpcio, googleapis-common-protos, build, anyio, aiosignal, watchfiles, rich, requests-oauthlib, pydantic, opentelemetry-semantic-conventions, opentelemetry-exporter-otlp-proto-common, jsonschema-specifications, httpx, aiohttp, typer, pydantic-settings, opentelemetry-sdk, kubernetes, jsonschema, huggingface-hub, tokenizers, opentelemetry-exporter-otlp-proto-grpc, chromadb\nSuccessfully installed aiohappyeyeballs-2.7.1 aiohttp-3.14.3 aiosignal-1.4.0 annotated-doc-0.0.5 annotated-types-0.8.0 anyio-4.14.2 attrs-26.1.0 bcrypt-5.0.0 build-1.6.0 certifi-2026.7.22 charset_normalizer-3.5.1 chromadb-1.5.9 click-8.5.0 durationpy-0.11 filelock-3.32.4 flatbuffers-25.12.19 frozenlist-1.8.0 fsspec-2026.7.0 googleapis-common-protos-1.75.2 grpcio-1.83.1 h11-0.16.0 hf-xet-1.6.0 httpcore-1.0.9 httptools-0.8.0 httpx-0.28.1 huggingface-hub-1.29.0 idna-3.19 importlib-resources-7.1.0 jsonschema-4.26.0 jsonschema-specifications-2025.9.1 kubernetes-36.0.3 markdown-it-py-4.2.0 mdurl-0.1.2 mmh3-5.3.0 multidict-6.7.1 numpy-2.5.2 oauthlib-3.3.1 onnxruntime-1.29.0 opentelemetry-api-1.44.0 opentelemetry-exporter-otlp-proto-common-1.44.0 opentelemetry-exporter-otlp-proto-grpc-1.44.0 opentelemetry-proto-1.44.0 opentelemetry-sdk-1.44.0 opentelemetry-semantic-conventions-0.65b0 orjson-3.12.0 overrides-7.7.0 packaging-26.3 propcache-0.5.2 protobuf-7.36.0 pybase64-1.5.0 pydantic-2.13.5 pydantic-core-2.46.5 pydantic-settings-2.15.0 pygments-2.21.0 pypika-0.51.1 pyproject_hooks-1.2.0 python-dateutil-2.9.0.post0 python-dotenv-1.2.3 pyyaml-6.0.3 referencing-0.37.0 requests-2.34.2 requests-oauthlib-2.0.0 rich-15.0.0 rpds-py-2026.6.3 shellingham-1.5.4 six-1.17.0 tenacity-9.1.4 tokenizers-0.23.1 tqdm-4.70.0 typer-0.27.2 typing-extensions-4.16.0 typing-inspection-0.4.4 urllib3-2.7.0 uvicorn-0.52.4 uvloop-0.22.1 watchfiles-1.2.0 websocket-client-1.9.0 websockets-17.1 yarl-1.24.5\n",
"time_seconds": 51.98
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Designing robust ChromaDB example script**\n\n**Planning README and requirements writing**",
"time_seconds": 51.981
},
{
"kind": "tool_call",
"role": "read",
"text": "read {\"path\": \"/app/source-receipt.json\"}",
"time_seconds": 51.981
},
{
"kind": "tool_result",
"role": "read",
"text": "{\n \"document\": {\n \"bytes\": 19373,\n \"path\": \"quickstart.md\",\n \"sha256\": \"e4725a60cc3fa934e4869f53cde3e80d9ab3cf45c9ad8213ad3e602925732656\"\n },\n \"fetched_at\": \"2026-08-29T03:35:38.388063+00:00\",\n \"product\": \"Chroma\",\n \"schema_version\": 1,\n \"source_commit\": \"1.5.9\",\n \"source_url\": \"https://docs.trychroma.com/docs/overview/getting-started.md\",\n \"subject\": \"chroma\",\n \"version\": \"v1.5.9\"\n}\n",
"time_seconds": 56.057
},
{
"kind": "thinking",
"role": "assistant",
"text": "**Planning chromadb integration implementation**",
"time_seconds": 56.057
},
{
"kind": "tool_call",
"role": "write",
"text": "write {\"content\": \"import chromadb\\n\\n\\nclient = chromadb.Client()\\ncollection = client.get_or_create_collection(name=\\\"my_collection\\\")\\n\\ndocuments = [\\n \\\"This is a document about pineapple\\\",\\n \\\"This is a document about oranges\\\",\\n]\\nids = [\\\"id1\\\", \\\"id2\\\"]\\n\\n# Upsert keeps this example safe to run more than once in the same process.\\ncollection.upsert(ids=ids, documents=documents)\\nprint(f\\\"Ingested: {documents}\\\")\\n\\nquery = \\\"This is a query document about hawaii\\\"\\nresults = collection.query(query_texts=[query], n_results=2)\\nprint(f\\\"Query: {query}\\\")\\nprint(f\\\"Results: {results}\\\")\\n\\nif not results[\\\"ids\\\"] or not results[\\\"ids\\\"][0]:\\n raise RuntimeError(\\\"The first query returned no results\\\")\\n\", \"path\": \"/app/main.py\"}",
"time_seconds": 56.057
},
{
"kind": "tool_call",
"role": "write",
"text": "write {\"content\": \"[project]\\nname = \\\"chroma-getting-started\\\"\\nversion = \\\"0.1.0\\\"\\ndescription = \\\"Chroma OSS Python getting-started example\\\"\\nrequires-python = \\\">=3.9\\\"\\ndependencies = [\\n \\\"chromadb==1.5.9\\\",\\n]\\n\", \"path\": \"/app/pyproject.toml\"}",
"time_seconds": 56.057
},
{
"kind": "tool_call",
"role": "write",
"text": "write {\"content\": \"# Chroma OSS Python getting started\\n\\nThis example uses Chroma's in-memory client, adds two documents, and queries\\nfor the documents most similar to a Hawaii-related query.\\n\\n## Run\\n\\n```bash\\npython -m venv .venv\\n.venv/bin/python -m pip install -e .\\n.venv/bin/python main.py\\n```\\n\\nThe data is intentionally not deleted. Because this uses `chromadb.Client()`\\n(the in-memory client), it is discarded when the process exits.\\n\", \"path\": \"/app/README.md\"}",
"time_seconds": 56.057
},
{
"kind": "tool_result",
"role": "write",
"text": "Successfully wrote 664 bytes to /app/main.py",
"time_seconds": 66.139
},
{
"kind": "tool_result",
"role": "write",
"text": "Successfully wrote 186 bytes to /app/pyproject.toml",
"time_seconds": 66.139
},
{
"kind": "tool_result",
"role": "write",
"text": "Successfully wrote 418 bytes to /app/README.md",
"time_seconds": 66.14
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ .venv/bin/python main.py",
"time_seconds": 66.14
},
{
"kind": "tool_result",
"role": "bash",
"text": "\r/root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx.tar.gz: 0%| | 0.00/79.3M [00:00/dev/null || true && find . -maxdepth 2 -type f -not -path './.venv/*' -print",
"time_seconds": 79.073
},
{
"kind": "tool_call",
"role": "bash",
"text": "$ .venv/bin/python -m pip show chromadb | grep -E '^(Name|Version):'",
"time_seconds": 79.073
},
{
"kind": "tool_result",
"role": "bash",
"text": "./quickstart.md\n./source-receipt.json\n./main.py\n./pyproject.toml\n./README.md\n",
"time_seconds": 83.874
},
{
"kind": "tool_result",
"role": "bash",
"text": "Name: chromadb\nVersion: 1.5.9\n",
"time_seconds": 83.875
},
{
"kind": "text",
"role": "assistant",
"text": "Implemented the Chroma OSS Python example:\n\n- `/app/main.py`\n- `/app/pyproject.toml`\n- `/app/README.md`\n- Virtual environment: `/app/.venv`\n\nRan successfully with Chroma `1.5.9`. The first query returned:\n\n```text\nids: [['id1', 'id2']]\ndocuments: [['This is a document about pineapple', 'This is a document about oranges']]\n```\n\nThe pineapple document ranked first.",
"time_seconds": 83.876
}
],
"schema_version": 1,
"source": "pi-session-jsonl",
"source_sha256": "02d78c289f8d36d14eef2c7f1e0dcf38a039ab72d4c000cdc54bca01b681cd34",
"started_at_epoch_ms": 1787974696181
}