{"type":"session","version":3,"id":"01a04ab5-40c0-7e27-afe0-a46055e5cc94","timestamp":"2026-08-28T23:29:47.712Z","cwd":"/app"}
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{"type":"message","id":"eba74cde","parentId":"b2511f8b","timestamp":"2026-08-28T23:29:52.693Z","message":{"role":"toolResult","toolCallId":"call_XJcWU4PLfbdhCx6Mbgu8TwMn|fc_075031e12bebdd06016a9219f0982887d0a03448349cde9ada","toolName":"read","content":[{"type":"text","text":"---\nid: quickstart.md\nsummary: Get started with Milvus.\ntitle: Quickstart\n---\n\n# Quickstart with Milvus Lite\n\n![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n![\"GitHub](\"https://img.shields.io/badge/View%20on%20GitHub-555555?style=flat&logo=github&logoColor=white\")
\n\nVectors, the output data format of Neural Network models, can effectively encode information and serve a pivotal role in AI applications such as knowledge base, semantic search, Retrieval Augmented Generation (RAG) and more.\n\nMilvus is an open-source vector database that suits AI applications of every size from running a demo chatbot in Jupyter notebook to building web-scale search that serves billions of users. In this guide, we will walk you through how to set up Milvus locally within minutes and use the Python client library to generate, store and search vectors.\n\n## Install Milvus\n\nIn this guide we use Milvus Lite, a python library included in `pymilvus` that can be embedded into the client application. Milvus also supports deployment on [Docker](https://milvus.io/docs/install_standalone-docker.md) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) for production use cases.\n\nBefore starting, make sure you have Python 3.8+ available in the local environment. Install `pymilvus` which contains both the python client library and Milvus Lite:\n\n```python\n$ pip install -U pymilvus\n```\n\n\n\n> If you are using Google Colab, to enable dependencies just installed, you may need to **restart the runtime**. (Click on the \"Runtime\" menu at the top of the screen, and select \"Restart session\" from the dropdown menu).\n\n
\n\n## Set Up Vector Database\n\nTo create a local Milvus vector database, simply instantiate a `MilvusClient` by specifying a file name to store all data, such as \"milvus_demo.db\".\n\n```python\nfrom pymilvus import MilvusClient\n\nclient = MilvusClient(\"milvus_demo.db\")\n```\n\n## Create a Collection\n\nIn Milvus, we need a collection to store vectors and their associated metadata. You can think of it as a table in traditional SQL databases. When creating a collection, you can define schema and index params to configure vector specs such as dimensionality, index types and distant metrics. There are also complex concepts to optimize the index for vector search performance. For now, let's just focus on the basics and use default for everything possible. At minimum, you only need to set the collection name and the dimension of the vector field of the collection.\n\n```python\nif client.has_collection(collection_name=\"demo_collection\"):\n client.drop_collection(collection_name=\"demo_collection\")\nclient.create_collection(\n collection_name=\"demo_collection\",\n dimension=768, # The vectors we will use in this demo has 768 dimensions\n)\n```\n\nIn the above setup,\n\n- The primary key and vector fields use their default names (\"id\" and \"vector\").\n- The metric type (vector distance definition) is set to its default value ([COSINE](https://milvus.io/docs/metric.md#Cosine-Similarity)).\n- The primary key field accepts integers and does not automatically increments (namely not using [auto-id feature](https://milvus.io/docs/schema.md))\n Alternatively, you can formally define the schema of the collection by following this [instruction](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Collections/create_schema.md).\n\n## Prepare Data\n\nIn this guide, we use vectors to perform semantic search on text. We need to generate vectors for text by downloading embedding models. This can be easily done by using the utility functions from `pymilvus[model]` library.\n\n## Represent text with vectors\n\nFirst, install the model library. This package includes essential ML tools such as PyTorch. The package download may take some time if your local environment has never installed PyTorch.\n\n```python\n$ pip install \"pymilvus[model]\"\n```\n\nGenerate vector embeddings with default model. Milvus expects data to be inserted organized as a list of dictionaries, where each dictionary represents a data record, termed as an entity.\n\n```python\nfrom pymilvus import model\n\n# If connection to https://huggingface.co/ failed, uncomment the following path\n# import os\n# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\n\n# This will download a small embedding model \"paraphrase-albert-small-v2\" (~50MB).\nembedding_fn = model.DefaultEmbeddingFunction()\n\n# Text strings to search from.\ndocs = [\n \"Artificial intelligence was founded as an academic discipline in 1956.\",\n \"Alan Turing was the first person to conduct substantial research in AI.\",\n \"Born in Maida Vale, London, Turing was raised in southern England.\",\n]\n\nvectors = embedding_fn.encode_documents(docs)\n# The output vector has 768 dimensions, matching the collection that we just created.\nprint(\"Dim:\", embedding_fn.dim, vectors[0].shape) # Dim: 768 (768,)\n\n# Each entity has id, vector representation, raw text, and a subject label that we use\n# to demo metadata filtering later.\ndata = [\n {\"id\": i, \"vector\": vectors[i], \"text\": docs[i], \"subject\": \"history\"}\n for i in range(len(vectors))\n]\n\nprint(\"Data has\", len(data), \"entities, each with fields: \", data[0].keys())\nprint(\"Vector dim:\", len(data[0][\"vector\"]))\n```\n\n```\nDim: 768 (768,)\nData has 3 entities, each with fields: dict_keys(['id', 'vector', 'text', 'subject'])\nVector dim: 768\n```\n\n## [Alternatively] Use fake representation with random vectors\n\nIf you couldn't download the model due to network issues, as a walkaround, you can use random vectors to represent the text and still finish the example. Just note that the search result won't reflect semantic similarity as the vectors are fake ones.\n\n```python\nimport random\n\n# Text strings to search from.\ndocs = [\n \"Artificial intelligence was founded as an academic discipline in 1956.\",\n \"Alan Turing was the first person to conduct substantial research in AI.\",\n \"Born in Maida Vale, London, Turing was raised in southern England.\",\n]\n# Use fake representation with random vectors (768 dimension).\nvectors = [[random.uniform(-1, 1) for _ in range(768)] for _ in docs]\ndata = [\n {\"id\": i, \"vector\": vectors[i], \"text\": docs[i], \"subject\": \"history\"}\n for i in range(len(vectors))\n]\n\nprint(\"Data has\", len(data), \"entities, each with fields: \", data[0].keys())\nprint(\"Vector dim:\", len(data[0][\"vector\"]))\n```\n\n```\nData has 3 entities, each with fields: dict_keys(['id', 'vector', 'text', 'subject'])\nVector dim: 768\n```\n\n## Insert Data\n\nLet's insert the data into the collection:\n\n```python\nres = client.insert(collection_name=\"demo_collection\", data=data)\n\nprint(res)\n```\n\n```\n{'insert_count': 3, 'ids': [0, 1, 2], 'cost': 0}\n```\n\n## Semantic Search\n\nNow we can do semantic searches by representing the search query text as vector, and conduct vector similarity search on Milvus.\n\n### Vector search\n\nMilvus accepts one or multiple vector search requests at the same time. The value of the query_vectors variable is a list of vectors, where each vector is an array of float numbers.\n\n```python\nquery_vectors = embedding_fn.encode_queries([\"Who is Alan Turing?\"])\n# If you don't have the embedding function you can use a fake vector to finish the demo:\n# query_vectors = [ [ random.uniform(-1, 1) for _ in range(768) ] ]\n\nres = client.search(\n collection_name=\"demo_collection\", # target collection\n data=query_vectors, # query vectors\n limit=2, # number of returned entities\n output_fields=[\"text\", \"subject\"], # specifies fields to be returned\n)\n\nprint(res)\n```\n\n```\ndata: [\"[{'id': 2, 'distance': 0.5859944820404053, 'entity': {'text': 'Born in Maida Vale, London, Turing was raised in southern England.', 'subject': 'history'}}, {'id': 1, 'distance': 0.5118255615234375, 'entity': {'text': 'Alan Turing was the first person to conduct substantial research in AI.', 'subject': 'history'}}]\"] , extra_info: {'cost': 0}\n```\n\nThe output is a list of results, each mapping to a vector search query. Each query contains a list of results, where each result contains the entity primary key, the distance to the query vector, and the entity details with specified `output_fields`.\n\n## Vector Search with Metadata Filtering\n\nYou can also conduct vector search while considering the values of the metadata (called \"scalar\" fields in Milvus, as scalar refers to non-vector data). This is done with a filter expression specifying certain criteria. Let's see how to search and filter with the `subject` field in the following example.\n\n```python\n# Insert more docs in another subject.\ndocs = [\n \"Machine learning has been used for drug design.\",\n \"Computational synthesis with AI algorithms predicts molecular properties.\",\n \"DDR1 is involved in cancers and fibrosis.\",\n]\nvectors = embedding_fn.encode_documents(docs)\ndata = [\n {\"id\": 3 + i, \"vector\": vectors[i], \"text\": docs[i], \"subject\": \"biology\"}\n for i in range(len(vectors))\n]\n\nclient.insert(collection_name=\"demo_collection\", data=data)\n\n# This will exclude any text in \"history\" subject despite close to the query vector.\nres = client.search(\n collection_name=\"demo_collection\",\n data=embedding_fn.encode_queries([\"tell me AI related information\"]),\n filter=\"subject == 'biology'\",\n limit=2,\n output_fields=[\"text\", \"subject\"],\n)\n\nprint(res)\n```\n\n```\ndata: [\"[{'id': 4, 'distance': 0.27030569314956665, 'entity': {'text': 'Computational synthesis with AI algorithms predicts molecular properties.', 'subject': 'biology'}}, {'id': 3, 'distance': 0.16425910592079163, 'entity': {'text': 'Machine learning has been used for drug design.', 'subject': 'biology'}}]\"] , extra_info: {'cost': 0}\n```\n\nBy default, the scalar fields are not indexed. If you need to perform metadata filtered search in large dataset, you can consider using fixed schema and also turn on the [index](https://milvus.io/docs/scalar_index.md) to improve the search performance.\n\nIn addition to vector search, you can also perform other types of searches:\n\n### Query\n\nA query() is an operation that retrieves all entities matching a criteria, such as a [filter expression](https://milvus.io/docs/boolean.md) or matching some ids.\n\nFor example, retrieving all entities whose scalar field has a particular value:\n\n```python\nres = client.query(\n collection_name=\"demo_collection\",\n filter=\"subject == 'history'\",\n output_fields=[\"text\", \"subject\"],\n)\n```\n\nDirectly retrieve entities by primary key:\n\n```python\nres = client.query(\n collection_name=\"demo_collection\",\n ids=[0, 2],\n output_fields=[\"vector\", \"text\", \"subject\"],\n)\n```\n\n## Delete Entities\n\nIf you'd like to purge data, you can delete entities specifying the primary key or delete all entities matching a particular filter expression.\n\n```python\n# Delete entities by primary key\nres = client.delete(collection_name=\"demo_collection\", ids=[0, 2])\n\nprint(res)\n\n# Delete entities by a filter expression\nres = client.delete(\n collection_name=\"demo_collection\",\n filter=\"subject == 'biology'\",\n)\n\nprint(res)\n```\n\n```\n[0, 2]\n[3, 4, 5]\n```\n\n## Load Existing Data\n\nSince all data of Milvus Lite is stored in a local file, you can load all data into memory even after the program terminates, by creating a `MilvusClient` with the existing file. For example, this will recover the collections from \"milvus_demo.db\" file and continue to write data into it.\n\n```python\nfrom pymilvus import MilvusClient\n\nclient = MilvusClient(\"milvus_demo.db\")\n```\n\n## Drop the collection\n\nIf you would like to delete all the data in a collection, you can drop the collection with\n\n```python\n# Drop collection\nclient.drop_collection(collection_name=\"demo_collection\")\n```\n\n## Learn More\n\nMilvus Lite is great for getting started with a local python program. If you have large scale data or would like to use Milvus in production, you can learn about deploying Milvus on [Docker](https://milvus.io/docs/install_standalone-docker.md) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md). All deployment modes of Milvus share the same API, so your client side code doesn't need to change much if moving to another deployment mode. Simply specify the [URI and Token](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Client/MilvusClient.md) of a Milvus server deployed anywhere:\n\n```python\nclient = MilvusClient(uri=\"http://localhost:19530\", token=\"root:Milvus\")\n```\n\nTo migrate data from Milvus Lite to Milvus deployed on Docker or Kubernetes, refer to [Migrating data from Milvus Lite](https://github.com/milvus-io/milvus-lite?tab=readme-ov-file#migrating-data-from-milvus-lite).\n\nMilvus provides REST and gRPC API, with client libraries in languages such as [Python](https://milvus.io/docs/install-pymilvus.md), [Java](https://milvus.io/docs/install-java.md), [Go](https://milvus.io/docs/install-go.md), C# and [Node.js](https://milvus.io/docs/install-node.md).\n\nFor schema design, Milvus supports flexible schema design, where you can define the fields and their data types, including vector fields. You can also define the index type and parameters for each field. For more information, see [Data Model Design for Search](https://milvus.io/docs/schema-hands-on.md).\n\n## Milvus for AI Agents\n\nIf you are using AI coding assistants like Claude Code or Cursor, you can install [Milvus Skill](https://github.com/zilliztech/milvus-skill) to help your AI tools write correct Milvus code.\n\nFor more agent tools including MCP servers and curated prompts, see [Milvus for AI Agents](milvus_for_agents.md).\n"}],"isError":false,"timestamp":1787959792693}}
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protobuf>=5.27.2 (from pymilvus)\n Downloading protobuf-7.36.0-cp310-abi3-manylinux2014_x86_64.whl.metadata (595 bytes)\nCollecting python-dotenv<2.0.0,>=1.0.1 (from pymilvus)\n Downloading python_dotenv-1.2.3-py3-none-any.whl.metadata (29 kB)\nCollecting requests>=2.20.0 (from pymilvus)\n Downloading requests-2.34.2-py3-none-any.whl.metadata (4.8 kB)\nCollecting pymilvus-model>=0.3.0 (from pymilvus[model])\n Downloading pymilvus_model-0.3.2-py3-none-any.whl.metadata (2.0 kB)\nCollecting typing-extensions~=4.12 (from grpcio!=1.68.0,!=1.68.1,!=1.69.0,!=1.70.0,!=1.70.1,!=1.71.0,!=1.72.1,!=1.73.0,>=1.66.2->pymilvus)\n Downloading typing_extensions-4.16.0-py3-none-any.whl.metadata (3.3 kB)\nCollecting numpy>=1.26.0 (from pandas>=1.2.4->pymilvus)\n Downloading numpy-2.5.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.6 kB)\nCollecting python-dateutil>=2.8.2 (from pandas>=1.2.4->pymilvus)\n Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\nCollecting transformers>=4.36.0 (from pymilvus-model>=0.3.0->pymilvus[model])\n Downloading transformers-5.16.1-py3-none-any.whl.metadata (32 kB)\nCollecting onnxruntime (from pymilvus-model>=0.3.0->pymilvus[model])\n Downloading onnxruntime-1.29.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (5.6 kB)\nCollecting scipy>=1.10.0 (from pymilvus-model>=0.3.0->pymilvus[model])\n Downloading scipy-1.18.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)\nCollecting six>=1.5 (from python-dateutil>=2.8.2->pandas>=1.2.4->pymilvus)\n Downloading six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)\nCollecting charset_normalizer<4,>=2 (from requests>=2.20.0->pymilvus)\n Downloading charset_normalizer-3.5.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (45 kB)\nCollecting idna<4,>=2.5 (from requests>=2.20.0->pymilvus)\n Downloading idna-3.19-py3-none-any.whl.metadata (9.2 kB)\nCollecting urllib3<3,>=1.26 (from requests>=2.20.0->pymilvus)\n Downloading urllib3-2.7.0-py3-none-any.whl.metadata (6.9 kB)\nCollecting certifi>=2023.5.7 (from requests>=2.20.0->pymilvus)\n Downloading certifi-2026.7.22-py3-none-any.whl.metadata (2.5 kB)\nCollecting huggingface-hub<2.0,>=1.5.0 (from transformers>=4.36.0->pymilvus-model>=0.3.0->pymilvus[model])\n Downloading huggingface_hub-1.29.0-py3-none-any.whl.metadata (16 kB)\nCollecting packaging>=20.0 (from transformers>=4.36.0->pymilvus-model>=0.3.0->pymilvus[model])\n Downloading packaging-26.3-py3-none-any.whl.metadata (3.5 kB)\nCollecting pyyaml>=5.1 (from transformers>=4.36.0->pymilvus-model>=0.3.0->pymilvus[model])\n Downloading pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.4 kB)\nCollecting regex>=2025.10.22 (from transformers>=4.36.0->pymilvus-model>=0.3.0->pymilvus[model])\n Downloading regex-2026.7.19-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (40 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pymilvus-model\n\nSuccessfully installed annotated-doc-0.0.5 anyio-4.14.2 cachetools-7.1.7 certifi-2026.7.22 charset_normalizer-3.5.1 click-8.5.0 filelock-3.32.4 flatbuffers-25.12.19 fsspec-2026.7.0 grpcio-1.83.1 h11-0.16.0 hf-xet-1.6.0 httpcore-1.0.9 httpx-0.28.1 huggingface-hub-1.29.0 idna-3.19 markdown-it-py-4.2.0 mdurl-0.1.2 numpy-2.5.2 onnxruntime-1.29.0 orjson-3.12.0 packaging-26.3 pandas-3.0.5 protobuf-7.36.0 pygments-2.21.0 pymilvus-3.0.1 pymilvus-model-0.3.2 python-dateutil-2.9.0.post0 python-dotenv-1.2.3 pyyaml-6.0.3 regex-2026.7.19 requests-2.34.2 rich-15.0.0 safetensors-0.8.0 scipy-1.18.1 shellingham-1.5.4 six-1.17.0 tokenizers-0.23.1 tqdm-4.70.0 transformers-5.16.1 typer-0.27.2 typing-extensions-4.16.0 urllib3-2.7.0\n"}],"isError":false,"timestamp":1787959866348}}
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If its download is not\navailable, the documented random-vector fallback keeps this example runnable.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport random\nfrom pathlib import Path\nfrom typing import Any\n\nfrom pymilvus import MilvusClient\n\nCOLLECTION_NAME = \"demo_collection\"\nDIMENSION = 768\nDATABASE_PATH = Path(__file__).with_name(\"milvus_demo.db\")\n\nDOCS = [\n \"Artificial intelligence was founded as an academic discipline in 1956.\",\n \"Alan Turing was the first person to conduct substantial research in AI.\",\n \"Born in Maida Vale, London, Turing was raised in southern England.\",\n]\n\n\ndef random_vectors(texts: list[str]) -> list[list[float]]:\n \"\"\"Create the fallback representation described in the quickstart.\"\"\"\n rng = random.Random(42)\n return [\n [rng.uniform(-1, 1) for _ in range(DIMENSION)]\n for _ in texts\n ]\n\n\ndef make_embeddings(texts: list[str], embedding_fn: Any | None, *, query: bool = False):\n if embedding_fn is not None:\n if query:\n return embedding_fn.encode_queries(texts)\n return embedding_fn.encode_documents(texts)\n return random_vectors(texts)\n\n\ndef main() -> None:\n # Milvus Lite stores the database in this local file.\n client = MilvusClient(str(DATABASE_PATH))\n if client.has_collection(collection_name=COLLECTION_NAME):\n client.drop_collection(collection_name=COLLECTION_NAME)\n client.create_collection(\n collection_name=COLLECTION_NAME,\n dimension=DIMENSION,\n )\n\n # The quickstart's default model is small (~50 MB), but network access is\n # not required to run the example because the docs provide a fallback.\n embedding_fn = None\n try:\n from pymilvus import model\n\n embedding_fn = model.DefaultEmbeddingFunction()\n print(f\"Using embedding model (dimension {embedding_fn.dim})\")\n except Exception as exc:\n print(f\"Embedding model unavailable ({exc}); using random vectors\")\n\n vectors = make_embeddings(DOCS, embedding_fn)\n if len(vectors[0]) != DIMENSION:\n raise ValueError(f\"Expected {DIMENSION}-dimensional vectors\")\n\n data = [\n {\"id\": i, \"vector\": vectors[i], \"text\": text, \"subject\": \"history\"}\n for i, text in enumerate(DOCS)\n ]\n insert_result = client.insert(collection_name=COLLECTION_NAME, data=data)\n print(f\"Inserted {insert_result['insert_count']} entities\")\n\n # First vector search from the quickstart.\n query_vectors = make_embeddings([\"Who is Alan Turing?\"], embedding_fn, query=True)\n results = client.search(\n collection_name=COLLECTION_NAME,\n data=query_vectors,\n limit=2,\n output_fields=[\"text\", \"subject\"],\n )\n if not results or not results[0]:\n raise RuntimeError(\"The first vector search returned no results\")\n print(\"FIRST_SEARCH_RESULTS:\", results)\n\n # Search with a scalar metadata filter.\n biology_docs = [\n \"Machine learning has been used for drug design.\",\n \"Computational synthesis with AI algorithms predicts molecular properties.\",\n \"DDR1 is involved in cancers and fibrosis.\",\n ]\n biology_vectors = make_embeddings(biology_docs, embedding_fn)\n client.insert(\n collection_name=COLLECTION_NAME,\n data=[\n {\n \"id\": 3 + i,\n \"vector\": biology_vectors[i],\n \"text\": text,\n \"subject\": \"biology\",\n }\n for i, text in enumerate(biology_docs)\n ],\n )\n filtered = client.search(\n collection_name=COLLECTION_NAME,\n data=make_embeddings([\"tell me AI related information\"], embedding_fn, query=True),\n filter=\"subject == 'biology'\",\n limit=2,\n output_fields=[\"text\", \"subject\"],\n )\n print(\"FILTERED_RESULTS:\", filtered)\n\n print(\"HISTORY_COUNT:\", len(client.query(\n collection_name=COLLECTION_NAME,\n filter=\"subject == 'history'\",\n output_fields=[\"text\", \"subject\"],\n )))\n print(\"IDS:\", client.query(\n collection_name=COLLECTION_NAME,\n ids=[0, 2],\n output_fields=[\"vector\", \"text\", \"subject\"],\n ))\n\n client.delete(collection_name=COLLECTION_NAME, ids=[0, 2])\n client.delete(collection_name=COLLECTION_NAME, filter=\"subject == 'biology'\")\n\n # Verify the file-backed database can be opened again, then clean up.\n client.close()\n reopened = MilvusClient(str(DATABASE_PATH))\n if not reopened.has_collection(collection_name=COLLECTION_NAME):\n raise RuntimeError(\"Collection was not persisted to the Lite database\")\n reopened.drop_collection(collection_name=COLLECTION_NAME)\n reopened.close()\n DATABASE_PATH.unlink(missing_ok=True)\n print(\"Quickstart completed successfully\")\n\n\nif __name__ == \"__main__\":\n main()\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":5696,"output":1626,"cacheRead":4608,"cacheWrite":0,"totalTokens":11930,"cost":{"input":0.028480000000000002,"output":0.048780000000000004,"cacheRead":0.002304,"cacheWrite":0,"total":0.07956400000000001}},"stopReason":"toolUse","timestamp":1787959866349,"responseId":"resp_075031e12bebdd06016a921a3a87bc87d088c084ce67d8ae68"}}
{"type":"message","id":"c38a11d8","parentId":"69fcbf3e","timestamp":"2026-08-28T23:31:36.680Z","message":{"role":"toolResult","toolCallId":"call_rchMjYxh4lzwtQsMdNg6gpIQ|fc_075031e12bebdd06016a921a410bc087d0afaa4a4b8b898da0","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4894 bytes to /app/quickstart.py"}],"isError":false,"timestamp":1787959896680}}
{"type":"message","id":"5f76ad0e","parentId":"c38a11d8","timestamp":"2026-08-28T23:31:38.752Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Verifying MilvusClient close method**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a59d37c87d0a712daf86a533b62\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhpaIAh6MKu66zgMW67uDhUx8WkYrPuQjwSVb1elHl-ktDI1X3Jju-bkcMPHssas0tBf8SJwWNbbTBZnraVHXg_vdEc9R70pMdnrNM4lOL3rlyM7p7rDdf6se0V9F6sJtPwintVxzsfaHpA5qzXKeipFkWVnUJrBjtcMH_--YJFlFUo7pFdUEh4StwOnO_eSPMpTHs10GA4fB3gI3aWXsYcsL3W5IXDhHRdfYGJRRyAHk8JKsMH1JszixaI_zH_GFFA3oqmcSEprOHA63XqFNEVFP3aEERlwEjJBs-0Us6M7hjZugvDMmM8rJyrOoz5Ts-TGCE6SULTD4rncFyCTreBTy7w9X9QpgUT8z2ZxAwC0aB6sAb2-vOK6I5Aik39FkwpAw8yE1yvjbNgwg4AFm4FO9ID_gzxopwddc4jtfBAcD8OjgTYrEFISLQJIZrl9SJ8XVjZS2y-TBAmWnVccYezD4MdN3dYGIkZkK9ZB5MlbrFR3fW_I2sdbeu7vEMKHoacZcCYVPRpk2VLZQHsLRPmuQ9nrmRgrl8VaFeynqXpOFotEK7llDz--fXSvv_asQzr24LlKoQdatVmGql8ENy_h18UQuuuZCBO1MnMBSeWp7_-E4wpDvIkLbdAf3xPE5M6NRtl_M_50uD9pMNPZbNNkJ9ZESQZEFmAu1Mt5zTj-jveJ5IUz69jZ-mvyNC8XyOHWWuvUI2ovrCmhUn5gl6usV3e0dHkSGhboUWdzNHtiz9efsBiAfnk3tCdLiirscX6ljcg85l1sw312KZ1r7SNM-Irjj3XOy9CtfFJZqznaXfidSoQzsFdWWECIAhsUca7Ar5bXkXVliUvf7JcMPp4SmkFHaOI8xDp1EwBkZNoxPnSc2evQ8NE7sGAtGRYe9XhAvet6PN-2w341plRaMbVH0CS5PmTFztW0oERyz85GC3QcLnK6lTXqiV3xcN-zYawEhWo5u7IrVl-ubXZZXYo_8RyPZxSVEDzlvCEGISJNy__rduWHeC0j82gI1-hg5G6HdjTVHiytBQ1m1xErhJgrhQjKrIsfClbY7GiQLtCQvbbwoApj9ggKeglcMp1lLS2uTJByeCKhFNWGyW_rdY83gn-XR9HUpvfxyRuwXZZEdCX0HXLjwccrBl9UO9Z8d9e2YAYwrxbWgnxp8lhGsfTbvN9NC1X9MXi7beqsglQuBMyxAuJ3OBft55kqsLAlHXOgKUGe4_hHg_aZQyvHGFWV0ZtsSNI4WsnQun-IUNjxmWuejQ6KbcQ1cNBklK9sTyWMoxwVAptwiRPF1l1URBH8J4g-LOxXyE0X6Ys0fXDWcn2T_wquuCtRL7wRP7DIN-mgNkwo6OWKBtTW_yujVETUfFEQ9QJ9abX8Qbp9oP0XmIUSp7ObuFGwQJpRXX0lGCFazL1ce0nRu5xOUb5qS5jv9g==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Verifying MilvusClient close method**\"}]}"},{"type":"toolCall","id":"call_14Y5TY2T8TEvJ7tKPGx0cF38|fc_075031e12bebdd06016a921a5aa3dc87d0af9ad20a131707f1","name":"bash","arguments":{"command":"python quickstart.py","timeout":240}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":2225,"output":50,"cacheRead":9728,"cacheWrite":0,"totalTokens":12003,"cost":{"input":0.011125000000000001,"output":0.0015,"cacheRead":0.004863999999999999,"cacheWrite":0,"total":0.017489}},"stopReason":"toolUse","timestamp":1787959896680,"responseId":"resp_075031e12bebdd06016a921a59394c87d086548ad908da9baf"}}
{"type":"message","id":"e4c6de1f","parentId":"5f76ad0e","timestamp":"2026-08-28T23:31:39.518Z","message":{"role":"toolResult","toolCallId":"call_14Y5TY2T8TEvJ7tKPGx0cF38|fc_075031e12bebdd06016a921a5aa3dc87d0af9ad20a131707f1","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n File \"/opt/venv/lib/python3.12/site-packages/pymilvus/client/connection_manager.py\", line 102, in from_uri\n from milvus_lite.server_manager import ( # noqa: PLC0415\nModuleNotFoundError: No module named 'milvus_lite'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/app/quickstart.py\", line 145, in \n main()\n File \"/app/quickstart.py\", line 49, in main\n client = MilvusClient(str(DATABASE_PATH))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/pymilvus/milvus_client/milvus_client.py\", line 91, in __init__\n self._config = ConnectionConfig.from_uri(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/pymilvus/client/connection_manager.py\", line 106, in from_uri\n raise ConnectionConfigException(\npymilvus.exceptions.ConnectionConfigException: \n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1787959899518}}
{"type":"message","id":"7098eb87","parentId":"e4c6de1f","timestamp":"2026-08-28T23:31:41.241Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Planning pymilvus[milvus_lite] installation**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a5c186087d09cd26a02a766fb90\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhpd_DyukJ9xP3Bfp4fKvOItvLXqlNbRnIl9A3Tz4Ev6nY6dsC1vXyWQSTFqpTfsyz0KpxF5NKNRtVnOjuH1kBpOJ7h2bshFLunKb0gF0xu11sHpO1S805MBCRIkTHZK1wBvwNnQ0rFOKY6W670GLUuzoSC-reDlhGP_vb3-odWRV7HjCFQuTF4A3lkhRGg6P5HOZi5N5zihrpqiau35LtH0hK5ve_Mfijv_haeyP_4jV-XFBhuoPcXn7QlkH0nOdPczzVz7_zZKN33OJg2DEMP2pBK40uTU15pB1GgYixFkOEBdChvEjpN5UjmAyLICEm0X8wzjAiSsM2QYR_-dosM5tFM4i30yuBdI5qjwcnTnu2q0v6pjsGCAzaI9Mqob8XWa2rWZus6xfN9OELQ38rXQUCl-TJZ4_BdBTP3rT6kswhXVxEmfM8DSzim7XgoeDx964CgPNqRhPzhynAhISylcVWNik7FA7BQ8ODjGwryP_axR6l7sbPhIat03ymFmKU7Uw-1RegSorzqN-XccjKzH5GpUnjalK2PIQXGQwiti_guLzltzRTbFZxhc-Ev-CgTY6Fcpo6-w0rbb23l5LonpynpsvBT7afeSwySLxjrjc0M46NFK7O-gS4MQk6D83lHSkXkQkIgH2XChJvF2yTBYbaS8_RjyvXKHTb8BshJCOs5iOz_rrmp_gGyi-eTav106QH2yvl0vNZO2AS6rZib4pSaZLvrO3zX74L7nhKJsJV9e0l7Bm1LTC1iVzFThkPDGmQIBi-T86Enfvhi0g4Iik1xFSVBPHopcpUEVwXZF5eYtJDY6WtCQONWCZELLxOTpMzklkczBM86k2PaECLKZhTFhE4-XYLVNu48FQLmjHiskFgfxN0y0_y8blUNXg2Mx2BU41WZmYFTUQiisVHAy13b0cIr7lYgLAtAO6YumQSjhQeo0f1HR9xxYCYGb_bZ2SgL5L0CconlLjKPn_YfwMkUw6vPA0R0juyYH4zTpDQFxv_qkIg-mjkZGzXv-JbM1IPUBXXj_Fn5iLH_dmBFzEujVM_DSq8KgjPCxYym18iWaJRllb0W-OH9eEjEJuQWicNwc6i3-6Ojo8rHuP2ZDuhBrZiOLXiirZCAfh_4z94VcOigkv0XMSi45GybV1KNSwNjHfNnqWLajtT8rtuwd5QoxG-Sr5VDvM_YU3NoxStAwsO459C9kCt_sY_aj_ZVX44Upgt8o8Bf_MoMMR8-qv9aRKrx583Y51kd41rEyFA6pmiJkWP7ifpvN76aOnyBPBIgNii1XfyTVwoVaBgZ6ocSoI10I-yVhnUiH4FV6iBmJ08EWIyCRxQHxWzo2Wm6KRiA0kLtZNjTMo-B5t_vN81Kr1nj6s1F1ZboCjyY-Vu4bgR5bMX_VpthoLyd5w9Oj\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Planning pymilvus[milvus_lite] installation**\"}]}"},{"type":"toolCall","id":"call_m2HXGA5nqRnU6vosrMxJhcxn|fc_075031e12bebdd06016a921a5d150087d0a9d1caedcb2b9b9c","name":"bash","arguments":{"command":"python -m pip install -U 'pymilvus[milvus_lite]'","timeout":180}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":534,"output":63,"cacheRead":11776,"cacheWrite":0,"totalTokens":12373,"cost":{"input":0.00267,"output":0.00189,"cacheRead":0.005888,"cacheWrite":0,"total":0.010447999999999999}},"stopReason":"toolUse","timestamp":1787959899518,"responseId":"resp_075031e12bebdd06016a921a5bb70887d08c3e5b9caf53970c"}}
{"type":"message","id":"fbbc542e","parentId":"7098eb87","timestamp":"2026-08-28T23:31:52.849Z","message":{"role":"toolResult","toolCallId":"call_m2HXGA5nqRnU6vosrMxJhcxn|fc_075031e12bebdd06016a921a5d150087d0a9d1caedcb2b9b9c","toolName":"bash","content":[{"type":"text","text":"Requirement already satisfied: pymilvus[milvus_lite] in /opt/venv/lib/python3.12/site-packages (3.0.1)\nRequirement already satisfied: cachetools>=5.0.0 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (7.1.7)\nRequirement already satisfied: grpcio!=1.68.0,!=1.68.1,!=1.69.0,!=1.70.0,!=1.70.1,!=1.71.0,!=1.72.1,!=1.73.0,>=1.66.2 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (1.83.1)\nRequirement already satisfied: orjson>=3.10.15 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (3.12.0)\nRequirement already satisfied: pandas>=1.2.4 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (3.0.5)\nRequirement already satisfied: protobuf>=5.27.2 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (7.36.0)\nRequirement already satisfied: python-dotenv<2.0.0,>=1.0.1 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (1.2.3)\nRequirement already satisfied: requests>=2.20.0 in /opt/venv/lib/python3.12/site-packages (from pymilvus[milvus_lite]) (2.34.2)\nCollecting milvus-lite>=2.4.0 (from pymilvus[milvus_lite])\n Downloading milvus_lite-3.2.1-py3-none-any.whl.metadata (15 kB)\nRequirement already satisfied: typing-extensions~=4.12 in /opt/venv/lib/python3.12/site-packages (from grpcio!=1.68.0,!=1.68.1,!=1.69.0,!=1.70.0,!=1.70.1,!=1.71.0,!=1.72.1,!=1.73.0,>=1.66.2->pymilvus[milvus_lite]) (4.16.0)\nCollecting faiss-cpu>=1.7.4 (from milvus-lite>=2.4.0->pymilvus[milvus_lite])\n Downloading faiss_cpu-1.15.0-cp310-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (7.8 kB)\nRequirement already satisfied: numpy>=1.24 in /opt/venv/lib/python3.12/site-packages (from milvus-lite>=2.4.0->pymilvus[milvus_lite]) (2.5.2)\nCollecting pyarrow>=15.0 (from milvus-lite>=2.4.0->pymilvus[milvus_lite])\n Downloading pyarrow-25.0.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (3.0 kB)\nRequirement already satisfied: packaging in /opt/venv/lib/python3.12/site-packages (from faiss-cpu>=1.7.4->milvus-lite>=2.4.0->pymilvus[milvus_lite]) (26.3)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/venv/lib/python3.12/site-packages (from pandas>=1.2.4->pymilvus[milvus_lite]) (2.9.0.post0)\nRequirement already satisfied: six>=1.5 in /opt/venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.2.4->pymilvus[milvus_lite]) (1.17.0)\nRequirement already satisfied: charset_normalizer<4,>=2 in /opt/venv/lib/python3.12/site-packages (from requests>=2.20.0->pymilvus[milvus_lite]) (3.5.1)\nRequirement already satisfied: idna<4,>=2.5 in /opt/venv/lib/python3.12/site-packages (from requests>=2.20.0->pymilvus[milvus_lite]) (3.19)\nRequirement already satisfied: urllib3<3,>=1.26 in /opt/venv/lib/python3.12/site-packages (from requests>=2.20.0->pymilvus[milvus_lite]) (2.7.0)\nRequirement already satisfied: certifi>=2023.5.7 in /opt/venv/lib/python3.12/site-packages (from requests>=2.20.0->pymilvus[milvus_lite]) (2026.7.22)\nDownloading milvus_lite-3.2.1-py3-none-any.whl (269 kB)\nDownloading faiss_cpu-1.15.0-cp310-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (18.8 MB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18.8/18.8 MB 11.7 MB/s 0:00:01\nDownloading pyarrow-25.0.1-cp312-cp312-manylinux_2_28_x86_64.whl (50.1 MB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.1/50.1 MB 12.7 MB/s 0:00:03\nInstalling collected packages: pyarrow, faiss-cpu, milvus-lite\n\nSuccessfully installed faiss-cpu-1.15.0 milvus-lite-3.2.1 pyarrow-25.0.1\n"}],"isError":false,"timestamp":1787959912849}}
{"type":"message","id":"58c6b1c1","parentId":"fbbc542e","timestamp":"2026-08-28T23:31:55.182Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Adding model and milvus_lite requirements**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a69fa9487d083284daded524d08\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhpqWSJG_LMKJvX6h1eROzY8VWNWqDBV2RqgBGQkYcLstb6NuEViu20tIMfFLJ8Nk2G3fjCcEYqrYsRx-9y4F5I37qqGIaDWmsOUJhLVTqSVSfyw2LHGjOL9mXlzqTZLZczVDmiLFyB8lqE3YGCFvGRPZaEl1s_YZSVaxkaz4NvaZHimh-bmOVf1mv0QFFuZkRdDO2JruCGJYC3KWCgQVOK0zPw75Bbj_J4M5nJMqq8_skqvKv-dO_dLe59OmtiCczNxrX_pzQR4gGq3OJaWSlTLRYPmniCv-t8IKoXrG0qfRx-V25hLE1vNDK17snB3W3h_EUVN3XfjvhBz-GzktKISEBzIkvDk4jeiemp0oSlCkYnIO34k_zcp362vR_qsjf9FTQpSiCAR6pK-jTFCvPHUl8N1jBMk3L1PwWo8xIeQbE9_1LZL5dJ159nIzwO6S9-CPqxDYOQFlH4VLZi33Aovt72b1JAJp1ZvqCruVfniy6Ke0nrfpI35ZeLxjvWPVePbH5d4SY156vaTeytp-hzByeuNJmG9X1ayYgLkMV6odRol9aIqNIZsB2TvURuTmx84cu4NbNE_0inYND6SZ7MaVwpYWvT8oZlHfmNqCaTMnOnb2MBUatHDOKQYEfq0H-Y4wE2eqZLE2kvXod4pg0BpR43PS_0fNwpQQVnJX_ZQwyXkui6JXGz1fJfRtApY-rMZcPH7kOgiw6PcRAQiS4iKnCV2a4O-R7bG3sAVJyUJSDe8pE-CitXZGI3s3fjjjNa7zmnj4Y39ttDC7XOoW4-aSbmNYTx9UmGpNkF3L1J52FOwoVaGIXylp1mbPTP9aC3-7FcFkXiXEusjfSS8YH9gs18VUpoF9amRqX-Dl1LAo2NX1EzzVN4VtyzDM9U8bHVlrqu_v_NzfPyrEKupw55NwuTW32ctE-cHHZr9alQBJ9G1dg1oF0VAf8Pc5CNpylsml8digsAd4T_q4v32cR9uBOaX4B6eHginzp8QLmyfzzPA2B5SwCrnGjpGdNGzknJUuGDce0gYYPG9_PqmRcnq_jYFqiOxH_OUe_mmTUeaY70wUBFz6o0ayYTk090txUz63uKDf0OvzZVWwdF2VUGeBWoygwZW1ngcLE5V9bY_u7fwIlBC7UDv5KIkIkJK2cYwk553CkcnzpL6a99_WKxDr-azm0mcOC_KJSwWlG3jAzo2bP9BbhhiLX7MHGBLL7W3_m3jnOy5s6SxQI8EFmL77NpfP8roEeT9CAOzyte4Sf3zo1aRgbNWTXKgNadmyWmqqCkx0RqYs5BYwarvXs1kZaQNqVMEKwp3fwVXbXwq7dIrRN8dolnKmQPX6NoQPlaXj6aKunaEr6PZac4FhUW4rR9w68moZua9kxByw1L4eSM=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Adding model and milvus_lite requirements**\"}]}"},{"type":"toolCall","id":"call_A0s7mgNXK80dimuECLOVD9Fo|fc_075031e12bebdd06016a921a6af64487d08036abbf5d9fe64f","name":"write","arguments":{"path":"/app/requirements.txt","content":"pymilvus[model,milvus_lite]>=3.0.1\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1971,"output":66,"cacheRead":11776,"cacheWrite":0,"totalTokens":13813,"cost":{"input":0.009855000000000001,"output":0.00198,"cacheRead":0.005888,"cacheWrite":0,"total":0.017723000000000003}},"stopReason":"toolUse","timestamp":1787959912850,"responseId":"resp_075031e12bebdd06016a921a6977d087d0b617ab46aba2c3a5"}}
{"type":"message","id":"48d8c3e8","parentId":"58c6b1c1","timestamp":"2026-08-28T23:31:55.184Z","message":{"role":"toolResult","toolCallId":"call_A0s7mgNXK80dimuECLOVD9Fo|fc_075031e12bebdd06016a921a6af64487d08036abbf5d9fe64f","toolName":"write","content":[{"type":"text","text":"Successfully wrote 35 bytes to /app/requirements.txt"}],"isError":false,"timestamp":1787959915184}}
{"type":"message","id":"4339b04d","parentId":"48d8c3e8","timestamp":"2026-08-28T23:31:56.609Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a6bf3fc87d09238743dd34c12dc\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhpstx46yN4MwhOFv_Xg6YRwVIhz98WTvjIUlBq9rQmwF95Lvo79WGylVxYNQy4rYhYBAStKxp-CS066C2S65WKe5b95C8iGjJsZ--hQ-vC2Q6fag5SIXU2sIt9zHkZOM2cybukIM1nwuTernM0A-HumWpTY33mc3k0C1htMnGTt4m1EgzDilG4Iky2hRBXzOfWAFk2MYeP3r8bvqvOe6Zse44wrC8koCJBxkq1vCGbxUdhTFlJTw382MjqGOPJWs7-AODsURNFmOCjpg23Fi6Tm779czg601d8fHayB_hxVfxXKwvhxGzUMwihWm-p13McSIZG-OwtJuGmzTAdbhvwl2sd8rxp9YABmgzhVXsadI9FXi0uvCMX-amyPX2G-bPLVUIR9r6LIA4zj8XNMk982QZamkPtviqfK7UYXN5rwPei2X6mNpe9Nl7D6tONSHHNxP9oo268adzPNrEUykKOfoIU0JjcTnQG5jNcNPlJplvftiR8rg_5wrcwj7cFut5r-PhglFf_PArpbr63oT7NNPf-IAMNjdzGTDNjyHAaJBjdvkQekzVNoxEVP0I-_GzTP3IiJmc_kBGryJiVRSEIyIGXm32VKVJ44k4wwxdAW181yq2f-jYPWzCu1Hcb6G6A1INtyTYFaiWUO-xJ89P8IOpzgZWcaJ5r3TMqw8wMSyEpUjgEl2NK8PZWJeXajNGVI_dlQQwGMNiaHdjUYGvFIHwvYXFhlIX3_8WQSMdO-BW_h-18rkgfeiVsJyKarT-ZUyg556GMboLJ6hn3wTFWs9elodABdxAOQyOopB9WpkEPGodVaqIzSQHrVMXLEm5lWmU54WyZfnNzfwTBTD1qJWrZEuCrvBUNKKA_lGLmZokzf-nzHdunAwKe6Rut-Nj1VVI7qepX5dU1izUmSgo5XJS7YVrYJPTdFMFwDqi_O97W2mqgf7G-GEkB1PoiDehf5lplPvHwzoaks_jh44HZ8lyQLfQkD305PcN6d3NvsgILIUdLUlqwVI4YdNEA90WVfM3OGX_3lw-B9fTT0dpTjxQ143NQzl0P1a97I8qRaTs2BG1whRcf_EJJWz8GcUi7esz1g30zXagFGOtehuPG5mvNk_C-dacxIPIzzuK25saZqpdNcpJMyo0HN6a8tO7Mq2IvjSGNviUyFSA5c42ysvsRa-gY0XT7_1-c3vIrevaOKZdyX_Gqkyadme9OUjU3k9N53s4uGUqe2hkc3xPV355SgrkvBzq2T2DiSdt1UEaqGecllaho6QN1sbYDOzwXwU9RjCUxPv8zED0RDiQohsw==\",\"summary\":[]}"},{"type":"toolCall","id":"call_cGviHizNpM5TnEcc9tZTguLG|fc_075031e12bebdd06016a921a6c785c87d095630c51ccc2d539","name":"bash","arguments":{"command":"python quickstart.py","timeout":300}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1034,"output":34,"cacheRead":12800,"cacheWrite":0,"totalTokens":13868,"cost":{"input":0.00517,"output":0.00102,"cacheRead":0.0063999999999999994,"cacheWrite":0,"total":0.01259}},"stopReason":"toolUse","timestamp":1787959915184,"responseId":"resp_075031e12bebdd06016a921a6b76e087d0837d672e222c5aa2"}}
{"type":"message","id":"07dbc11c","parentId":"4339b04d","timestamp":"2026-08-28T23:32:07.358Z","message":{"role":"toolResult","toolCallId":"call_cGviHizNpM5TnEcc9tZTguLG|fc_075031e12bebdd06016a921a6c785c87d095630c51ccc2d539","toolName":"bash","content":[{"type":"text","text":"[transformers] PyTorch was not found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\nWarning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\nUsing embedding model (dimension 768)\nTraceback (most recent call last):\n File \"/app/quickstart.py\", line 145, in \n main()\n File \"/app/quickstart.py\", line 68, in main\n vectors = make_embeddings(DOCS, embedding_fn)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/quickstart.py\", line 43, in make_embeddings\n return embedding_fn.encode_documents(texts)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/pymilvus/model/dense/onnx.py\", line 33, in encode_documents\n return self._encode(documents)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/pymilvus/model/dense/onnx.py\", line 36, in _encode\n return [self._to_embedding(text) for text in texts]\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/pymilvus/model/dense/onnx.py\", line 39, in _to_embedding\n encoded_text = self.tokenizer.encode_plus(data, padding=\"max_length\", truncation=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/transformers/tokenization_utils_base.py\", line 1308, in __getattr__\n raise AttributeError(f\"{self.__class__.__name__} has no attribute {key}\")\nAttributeError: AlbertTokenizer has no attribute encode_plus. Did you mean: '_encode_plus'?\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1787959927358}}
{"type":"message","id":"c2569c89","parentId":"07dbc11c","timestamp":"2026-08-28T23:32:09.809Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Pinning transformers version below 5**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a77e03c87d091c9e8fc5e6815ad\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhp52KcEAzdjyhGDiqQtTDVafm4i09M8MyUx1_PubxrTC1cvbWK3xrZd4lqJmScp_3DXJpHDKI5Gcfb1zcsqDlB2zRYwA1zcFQCUZJOrKW6r-DYGKjK-FJ3HQoSGcVNzzKxisqIBfQU2PdmMX_3_gsAvRwbxfnTeOY7sfHftak3qzxEgg8ZFwRjdhLPnMfhoCuMkePNI8XwnR8sEQVx_k4bDwSexfYHGkKwylWDeSwl1SUCm2yMKFNE8zSSvpCnYze72EplkcKhfiu5eNlRSGkTo6B0-S3sgnmQXbZa8458bj06FAoug3DcC0t3zd-U5oV8VnLcpoGETARjSLmZaoaPq1U4OFz2ncD6jENnXsmG68RfM6ng1TZJyf4lo0NCN3dafvQH__d2RoJeKB1dxjunFOgofP8dbKsTZzudvm5tP02BpSprhwsFuhs8vZS--yGnLxns6_gvKf_xtHHSdTepqapAGqbENin3qmn9nQRx8c26Swjy_vsBOg5Sfv6ANjhNmqmSf8M2QjBfWBG5Ejc7yJ1Q25cnZG5gBNvIyy2zGjyvksoWcgz7CR8o2xzJkiEhrOkjnsgQh08J1O9oN9RknUiz4P_s81Q9bprMYE47MQMMACN4iy3YK0NHQUSFa8JMm7iV1Q0griwznFcJwcA2gwXH8oFI6n04vpx-DSEQKTnbZtjbDLVqh2NxZZ3zEVob7O8Zuiry6HRq-Sx2yX4wy43zxNAPJeIZys-sz6e9_X5AsIYbJi6mVyo-AxPBGeTTlKGHAnOqoyW6Se8Egs-h6Rznhr6axtjjOxJboayZi0EDdLvpM3j2h3qt4MHK5tgdDFKZg1DLr4gKAM5pAn7XxFRbmtwZ5nptEP6BvllSyusrN9HhZCsAchDNXcZwXVio0v8Kgqiz_ZlpV64oI4Ig7JSF1-x-i98FDhd9K0jrIUVr-khkFtr4NAKCH6aa-Q9D_8vFKpwZoxmKULqD_KA3rh3ZPYFe8U_p5yShHwEFfrC8SuuKVJ-XUGgptyHLSF_ctkH6ZkMsSE1xp23xDp52DqnS3h0gFiAwHuE9Bv44J1Gdr_nnt5ZBUl3yLy4WYPqegCnSoOYJw_8Mkz-8yVfZl3vzzUaTD7PJ3XWfBQ2kaJCvCWzjD6ubNf_I7h_QXaGjSNHfX8xDmMHOypZ4Ke3UYiHw7DDbG23k5MAW03aEfU4ZZiC8mFTvLDYB89uDXn-w2RQIT_ETwNdXNLghVYJWNLDYLpIOMwHZ_GQ9teMhPaQuR9CNRqysAwDgBgI-T3dzHlfQ50KwPurH_71y-jNB3msZ8yLaOXEs8OjBHTPU9OyiCrHRTF2TQ0kPUfZykT-ZfVzrBLOqn_taKsFNKwWFIHkgBf8QLOA4jgTj3x_Gs4BQGeTO6uC-0Sez4Q4A1WgHM-x8wUj9In_I9d4-NnctfzLCVGa8RigWpN8MRQ1NOo5U5Qc8dblDDXo4x-vUuGDeOcTXjuVfxqS9BZsAPErHrP_YqHVNwUu1uAI3S4Po52KSsScivoZ-aR26-qkKUSV8Orl5g5v6ufVR5pDSLm62s6KGPnBq7XkUZDEXkJHWg8GI0x0xNqPsIL-ul0XNpMYSwyP70VNOMGLwuEMDj03FXfgRMjWncZRDcPnBquZaD9HqFlyGFy9TyL9gNieKjDoA1U9FNs7Oq1fBmfwEe9dp0Dw==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Pinning transformers version below 5**\"}]}"},{"type":"toolCall","id":"call_4HrMZV60LexQ1lN3Sfb0B3ff|fc_075031e12bebdd06016a921a79a55887d08750be1baff0349f","name":"bash","arguments":{"command":"python -m pip install 'transformers<5'","timeout":180}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1491,"output":106,"cacheRead":12800,"cacheWrite":0,"totalTokens":14397,"cost":{"input":0.007455000000000001,"output":0.00318,"cacheRead":0.0063999999999999994,"cacheWrite":0,"total":0.017035}},"stopReason":"toolUse","timestamp":1787959927358,"responseId":"resp_075031e12bebdd06016a921a777f8887d0b7922e5a855b5388"}}
{"type":"message","id":"dbcb8426","parentId":"c2569c89","timestamp":"2026-08-28T23:32:24.762Z","message":{"role":"toolResult","toolCallId":"call_4HrMZV60LexQ1lN3Sfb0B3ff|fc_075031e12bebdd06016a921a79a55887d08750be1baff0349f","toolName":"bash","content":[{"type":"text","text":"Collecting transformers<5\n Downloading transformers-4.57.6-py3-none-any.whl.metadata (43 kB)\nRequirement already satisfied: filelock in /opt/venv/lib/python3.12/site-packages (from transformers<5) (3.32.4)\nCollecting huggingface-hub<1.0,>=0.34.0 (from transformers<5)\n Downloading huggingface_hub-0.36.2-py3-none-any.whl.metadata (15 kB)\nRequirement already satisfied: numpy>=1.17 in /opt/venv/lib/python3.12/site-packages (from transformers<5) (2.5.2)\nRequirement already satisfied: packaging>=20.0 in /opt/venv/lib/python3.12/site-packages (from transformers<5) (26.3)\nRequirement already satisfied: pyyaml>=5.1 in /opt/venv/lib/python3.12/site-packages (from transformers<5) (6.0.3)\nRequirement already satisfied: regex!=2019.12.17 in /opt/venv/lib/python3.12/site-packages (from transformers<5) (2026.7.19)\nRequirement already satisfied: requests in /opt/venv/lib/python3.12/site-packages (from transformers<5) (2.34.2)\nCollecting tokenizers<=0.23.0,>=0.22.0 (from transformers<5)\n Downloading tokenizers-0.22.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.3 kB)\nRequirement already satisfied: safetensors>=0.4.3 in /opt/venv/lib/python3.12/site-packages (from transformers<5) (0.8.0)\nRequirement already satisfied: tqdm>=4.27 in /opt/venv/lib/python3.12/site-packages (from transformers<5) (4.70.0)\nRequirement already satisfied: fsspec>=2023.5.0 in /opt/venv/lib/python3.12/site-packages (from huggingface-hub<1.0,>=0.34.0->transformers<5) (2026.7.0)\nRequirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /opt/venv/lib/python3.12/site-packages (from huggingface-hub<1.0,>=0.34.0->transformers<5) (1.6.0)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/venv/lib/python3.12/site-packages (from huggingface-hub<1.0,>=0.34.0->transformers<5) (4.16.0)\nRequirement already satisfied: charset_normalizer<4,>=2 in /opt/venv/lib/python3.12/site-packages (from requests->transformers<5) (3.5.1)\nRequirement already satisfied: idna<4,>=2.5 in /opt/venv/lib/python3.12/site-packages (from requests->transformers<5) (3.19)\nRequirement already satisfied: urllib3<3,>=1.26 in /opt/venv/lib/python3.12/site-packages (from requests->transformers<5) (2.7.0)\nRequirement already satisfied: certifi>=2023.5.7 in /opt/venv/lib/python3.12/site-packages (from requests->transformers<5) (2026.7.22)\nDownloading transformers-4.57.6-py3-none-any.whl (12.0 MB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.0/12.0 MB 12.5 MB/s 0:00:00\nDownloading huggingface_hub-0.36.2-py3-none-any.whl (566 kB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 566.4/566.4 kB 6.4 MB/s 0:00:00\nDownloading tokenizers-0.22.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB)\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.3/3.3 MB 9.9 MB/s 0:00:00\nInstalling collected packages: huggingface-hub, tokenizers, transformers\n Attempting uninstall: huggingface-hub\n Found existing installation: huggingface_hub 1.29.0\n Uninstalling huggingface_hub-1.29.0:\n Successfully uninstalled huggingface_hub-1.29.0\n Attempting uninstall: tokenizers\n Found existing installation: tokenizers 0.23.1\n Uninstalling tokenizers-0.23.1:\n Successfully uninstalled tokenizers-0.23.1\n Attempting uninstall: transformers\n Found existing installation: transformers 5.16.1\n Uninstalling transformers-5.16.1:\n Successfully uninstalled transformers-5.16.1\n\nSuccessfully installed huggingface-hub-0.36.2 tokenizers-0.22.2 transformers-4.57.6\n"}],"isError":false,"timestamp":1787959944762}}
{"type":"message","id":"761a663a","parentId":"dbcb8426","timestamp":"2026-08-28T23:32:27.842Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Adding transformers<5 constraint**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a8943b887d0889d2e6c50622592\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhqKWkJYDMzKKTj7cE_gdXCCR6I0BwSsZp12nLGI7-XBXWDxkJRCZ-Nqa61Sn85b2DHrASFjJkRU9QY5bt0-incNYRjIxFBhxrGwcuT976G5T9bS_0WSzempqKR9-lehIU3hJmNv0ozXLdTUykQFwqcb47a__NLI8nEAEXlxbo5k3A3EO44zpy7aA_yeJvs6USkWCWM5LumZZprwmNQ5_9liTGRnAl0Fs9BZbHXkwuzvf0zjdnAU3AgXUyRXOXYJ8yiPE7PEl1-da7U2o8gQnFXF8spg_CnQvXS-iWfdULMR185tAEjOndzKmhur6YPsUcdmoXCFIWxUtLqKVvL--nAUgl9gSRW404tfRQ1kQEzJLMP2TfzEZJI1KNi2Vp_qx6hdfphwAAxnf0CRXpneer9lcZ2Nn1H9wtsd5Vzy6B9QNdORUDY9oqYVpk3M2cg21SINcRRhKCQhRjmmj9YXipA5-MNGEXbGiCnkwrEvAy3VleM_uxMCGeagSvQ1XVnBktnY0uYTZYwMqAqQngWxWS3Z29CNMJBfF4XRr6V-gs1RgI4Fl3HOXm9QYfIvEqF5QqovwxC64ukbKA_Cfxk2Kxzhs-KaHu1mFHGg7EttrOqiBsgCovMCkg_XEmUDHhp_m7KZhNIIjWGRxyExqxwWJNmBa4vaJQI-WLe3uI9YNDbbs_mhTRyUXck6EuXKh_WOYEnIsFj-YaHVZEbdv1jYjKtkrPA7QMym-3jMs8H9rBG_-tuWZQK1KPmYT7tCA08GsT1gSs3XfnfxKluK1pALnmRU_WieCH19fl-tgNZo42gnjkrCl4jV0zWw5dx86JG7ptTeVkLMCtRjG5-js7h21VwAsTKTI477g7LGiphO8TShUUSyOJ9eXtJsbNSeEOIjQFipj__rj1SjJtsAjCLHyhR2RUinAbxOl8KKhcApDPNe0vYPiky4WLyfcgSgE3qjGr8YOGLu5cZV7lwiS3KVStZtCoa9y6YkGJnlPJxTE-fx4TZkm9cB8RVGB_jJBMfWAJfORTDduj_oe3jeuy48VsVmwYVw3lY50r8h4U7ygbZED0bklMB_ZeALQ8GxnYOICvK2j-riEl9TxqXM0aVXTqiVl32GmIiR3-c2IMhZQDGDXTK6r0oKicXwzT9oDzB7Du734egtpgBf9XRT7pqIBVdegajOXXORP9lvt7LPvWPe2Hxo6qWraAvlVHDjBWQlQnn1HXFm1rbsFMPk0vV3NiZy-sKqrfB9iJo8g-ocQ6EDHJwU2GsTxrEJKm5iJQ_wqBA09kFAXIt35IMxkMQXe6lMCjPC9OqESnNhzuKBEoK25h7Ym5mSQWBCGMLwSDBvouzFEk8BuZOs1hmyYdLanvTLJBuDvz264Jkbj174ywwXqnqwPDE2SWVwuL_2k_OAYberueWylth74PHAONw6OcbdJhCOEZ1faodyR3Du5nSzbFK52FIBYyEwcTQoKDPSxWyODdGszZ_txRmgk9pnGxVuUvfqlxKC-eawlvqmDbjMagFtNmUU6lMukL2zh0dzTHRYbmJJlykQHR22hhHs-LpzdpcoXNF_9prPm3ehbl6lg-M=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Adding transformers<5 constraint**\"}]}"},{"type":"toolCall","id":"call_x893FVNgfGdD4OOSjPcVyE09|fc_075031e12bebdd06016a921a8aab0487d0b4f04acc9ca52611","name":"edit","arguments":{"path":"/app/requirements.txt","edits":[{"oldText":"pymilvus[model,milvus_lite]>=3.0.1\n","newText":"pymilvus[model,milvus_lite]>=3.0.1\n# pymilvus-model currently uses tokenizer APIs removed by Transformers 5.\ntransformers<5\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1748,"output":142,"cacheRead":13824,"cacheWrite":0,"totalTokens":15714,"cost":{"input":0.008740000000000001,"output":0.00426,"cacheRead":0.006912,"cacheWrite":0,"total":0.019912}},"stopReason":"toolUse","timestamp":1787959944763,"responseId":"resp_075031e12bebdd06016a921a88e83c87d09bed633edacb836a"}}
{"type":"message","id":"3f570aed","parentId":"761a663a","timestamp":"2026-08-28T23:32:27.851Z","message":{"role":"toolResult","toolCallId":"call_x893FVNgfGdD4OOSjPcVyE09|fc_075031e12bebdd06016a921a8aab0487d0b4f04acc9ca52611","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in /app/requirements.txt."}],"details":{"diff":" 1 pymilvus[model,milvus_lite]>=3.0.1\n+2 # pymilvus-model currently uses tokenizer APIs removed by Transformers 5.\n+3 transformers<5","patch":"--- /app/requirements.txt\n+++ /app/requirements.txt\n@@ -1,1 +1,3 @@\n pymilvus[model,milvus_lite]>=3.0.1\n+# pymilvus-model currently uses tokenizer APIs removed by Transformers 5.\n+transformers<5\n","firstChangedLine":2},"isError":false,"timestamp":1787959947851}}
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{"type":"message","id":"f690f3b7","parentId":"42366105","timestamp":"2026-08-28T23:32:37.700Z","message":{"role":"toolResult","toolCallId":"call_yLm74gMifwgNKW7f7kSXsxpm|fc_075031e12bebdd06016a921a8d101087d0ad667045f94fb0ed","toolName":"bash","content":[{"type":"text","text":"None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\nUsing embedding model (dimension 768)\nInserted 3 entities\nFIRST_SEARCH_RESULTS: data: [[{'id': 2, 'distance': 0.5859946012496948, 'entity': {'id': 2, 'text': 'Born in Maida Vale, London, Turing was raised in southern England.', 'subject': 'history'}}, {'id': 1, 'distance': 0.5118256211280823, 'entity': {'id': 1, 'text': 'Alan Turing was the first person to conduct substantial research in AI.', 'subject': 'history'}}]]\nFILTERED_RESULTS: data: [[{'id': 4, 'distance': 0.2703056335449219, 'entity': {'id': 4, 'text': 'Computational synthesis with AI algorithms predicts molecular properties.', 'subject': 'biology'}}, {'id': 3, 'distance': 0.16425907611846924, 'entity': {'id': 3, 'text': 'Machine learning has been used for drug design.', 'subject': 'biology'}}]]\nHISTORY_COUNT: 3\nIDS: data: [\"{'id': 0, 'vector': [0.010727839544415474, -0.035895150154829025, 0.018749738112092018, 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0.052917614579200745, 0.03040403500199318, 0.06543507426977158, 0.04979304224252701, 0.04340166971087456, -0.04959817975759506, 0.024344919249415398, 0.019741827622056007, 0.029212063178420067, -0.04641685262322426, 0.003497771453112364, 0.0758170410990715, -0.058527931571006775, -0.007137339096516371, -0.04129267483949661], 'text': 'Born in Maida Vale, London, Turing was raised in southern England.', 'subject': 'history'}\"], extra_info: {}\nTraceback (most recent call last):\n File \"/app/quickstart.py\", line 145, in \n main()\n File \"/app/quickstart.py\", line 140, in main\n DATABASE_PATH.unlink(missing_ok=True)\n File \"/opt/python-base/lib/python3.12/pathlib.py\", line 1342, in unlink\n os.unlink(self)\nIsADirectoryError: [Errno 21] Is a directory: '/app/milvus_demo.db'\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1787959957700}}
{"type":"message","id":"1d700406","parentId":"f690f3b7","timestamp":"2026-08-28T23:32:53.283Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Adding database cleanup with shutil.rmtree**\n\n**Implementing robust embedding fallback handling**\n\n**Defining embedding generation with error fallback**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921a96640c87d0aaec72ee80be51a1\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhqfFEtmnMR3fbhimbNOVc3yBA3t5fj3rmGfsK59B_6Gu5aXkX59atl9H8mSTicjRcYOuSwQUEoA8sbDJdJPvTFAz9QebgG5FAlPgObiSHfIpUrdyqD-1nGpyQXXk_TN2_7C_wRZ_-gYuAcgXdSPlAPJSiDXSY3VE15qkZYV7yEmt_QFmmhfDjoYrJTKIn2QOmdvAbUUJIPDaZJPzonyAUA0NFQx5g5TZSv7p4tlOGjLHhdeI1RjPNn22QwACBVUcnnsSK4fGd3TivF2FScFC9cWpuhpyyxRgA8p6xUhiHIxH4bfGX83UQu-kCCMdjUCxTk--R6Vl0fLhur4Q779UcXqROXUOrJvRFwjVcLYKaUFdItufCW2Oj6jH7qJAFe72hqNkHrvwyqKSVTn0cN3yyI-_4SasXkiuxznHcJ02KXqpXo1ZTfh3nJCi15hr4m-6vjBPFDsHPnvjp20PxsM1oWIwAJ7idqaxSsV0wpQXbSM9qJEqQGoXG1fpd34LIZtj2BBVqLfto_NBswhHfLf4rNE6lyqSlWbeUj6n7Zd8h73c5N-RylSab-eu2KlxtKMMimgRIqEqpcTLroORbA8wc0J28AkS-eKE2EpbYMAbiUjlExwl3ludODl4INvoqipIo2g_qJJhGts7BmZTtyICJ_6pAvgRr8Dp9f7Dv2eMemIS5eiTWCc_Tk_PX95n3bSFRe-IRJ_FJbjgC0SZJHoNwYbFUiFOQJwvaRa00Dxgfa9Zt4reB02Q-CDa5f8iKuCLlrYpfxLI3Ie2zyF1NLekmlj4Vei6ZsVLOyxZ3wEqUQqbQd76yyVsAzR094npIgLXSYVELdPzpQnNN2Yxyx9f_HaqA2G5gV62nFRbljSTGroagNhBVwcPh9pQTY9jaQ6qpis96jSDBREUbgNxJD4z9GYYpfLf7ekDfxvfBu-uiRTrTwaaizZe5NbMj--4TLWzp8qR3UT0E8YHF_G9Qkuuu3QEf7CrS1hallEJpFDpMkovliZCRGCQH7iQhpXIOm1-NCZ2RR9EnNkmDYlRsaetWk8lx0sVLR0VJlfSfI-hHqyVPD_wmGCws2KnLe2Tp2rY6i8dviHRHsBoH0JGNxvroV0xnx7dH0ctaVJSB1yRDBRc6YIM0eNQV_xGsL_VdD3Df77_lPTr4cAyNfa57oQi5lNudDAgrUwFOGnTdnSzvo8z7xx3EExPt61ep8R7Uov1LUyVjtV_WWXl2d0jDJE1XZKdP4ypwXcVR-Kvw0Udmf6iHnF_oTK3jV6DwTTKAPLvJ3YDvDYACIMGL7JEPx2eZUlkLRtJGCAsmPewRIUUj2pJWkPik1CEE5O1Gnn4dcLdfv979I20Am2Ele97hntVQDTBSGabd2DWP0s4Tj6zCwu2kotL8Txzpdw0yKKJFv0ky5p-Cx2lLxufWoe7oB6HhAnC-v8EYa_5ao0WHZa9H_Q5tmIu896GZuxlXl-uUDWHJZDxEf1zPc7pHdul4BUFrPjv-MWjT-XJSyYMB5S4OQgGmOIeozn-ooa4uirqlXudj3N6MH-sv0rtabzC9La93jkGGrfpgWbYcZml9r7kVBsLQ2NnOr4d24SDjHd-3z8uMPzPZSnizHkjA4FyUyP_bRxBA_beUSIXhNPjuLMfnufeRHArTxX_fe9gm-lk9VsN-RSehcqrvs0DJaXhov_iAF-LjROAK4v0aHfMBPaepYwcljDLZ1rfLVCnLsAxpUKybvvvD6dUD7sG9H7gW8IhIB5Hf-eG1TEXRiF7ycRR7MVx2fKzcMavm1CC_VhPiDbCSSoWU7jnikKwlKQbQuwd5uCAoFcPCgGdGNkbBi5XTc2Af58tVBn8rhYxxIdzypyuIze1uc-CRpPPpXbRi-dRma4ddScCqvVxEoZ4WWb_BqkCMZ-shk3r6FSjyrTajCsgtcC4BoJfc9l0bvSNe4YCEloOIzPG3XcsbI-bkVmB3htYTvQ56ZYMzmP3_jK78YXGFrb7WNxbLXUtQQ-Ml99AZ8ZTZqzZABm3GjGw8Q4DPAdrA6iP-p5qPe5bZTaRaLqb4G2twkasvJYds7dvJdxI4L6vj5aovOtMs5RHyk3JEd2TfHaTUSrxSyIPkqCG7kR9kKCui_imbjT-0HxHdosTK90KAwGIPtRuS00zgtvYk0g9_TADNMme6E-q2_-RA8mzKGVNJYziciMIwKPWFwpFowgiUGiuPpJKmrhgrxm3P3t8q0GyKSp8Bykp4JPK5Ed3vWMd7zLa6x__NxpnWKLPAUq2_OpAMAdlZQCr3PZLdfb0ri02_P6erLBTVlE1XmhardJDASouNAKry37Pxj5pH3Hg90rm7yl2NbicDrAjMmvn19omhnbTRLPwfD-t9XMNKBrt6R1ctIsYGCYqNZR5p1UcONVF71egrLDteY7QPau5PyjLD6Zkk-0dbm7C9K63mv6Y0kCQPhBmIIH4jd9Vr59e_tx6g5UX7d8nz10PYXvAiNLfUHD4kFhb46PLTCz2g3J33Zq9QjqgUrrhhF2lrCkgDah63t-gmD0ZFnn3HX6dvwOBSY3KuyjkGg_odZkK786HuLKiNzqZTtvx2aqngnijGVg7RYTm9URFL33Vy2FD8u_6C1Jpe1jbMvG4uHxPbvvaCsn2TdcqkpmGTbTK2x4Ix_Q6MOuopNv7-fii6fIT0N3wCebGPRdVx4UXxD_XG07pJPxb1yklM0XVGDvUT4zE5pEvolW2hXFt3fFDN-4dk_VPu6D_O8GE5HyF9dOE-dmPpKSlR81HaB24ZFRbq2lsmXhWbo8a8AfIi5LoMqRKfGhka64KWVM8SNQbMi3hhxVIEZmpcPnEv0T32lJDBpMOxuinJCotMOU58lyFyXrXFfu3f6p6FcqPkh1zmpJt7xKtcVLh5Ityz6obWmyOyrduAmYqITiFtvBC9bmvz_RZyzvd9jnFQFqL_nFT8cB6izwuPcMnxr4CthWeBZOg4S7rk4PeFRZK96I5t9GL6QRGS6wKvW5bNCfVBDopd4PLMRL7Bop6gyzAUkFACSAMeDrb_1shee6OjaCeje2ursNcjlaRYPmMeLa0sRwrh5OnP4-RRhNNduI88d8J8VD-fKDuzsPj6D1FkMNi8eiJGJcw3Y9CBsSdAtl6CUeGDM1J25fOIx0kLbcdwF5HqAuz53XsOsSDObnQbgcCSEGil0fndyMPACDpKOuFwY_rLP1gYPDwCjQg3NIgO2wHewSwK1j6YQQcHRKhcnq-Zj6z4XP6dJzVLoLg7aC0AMfQiuTpDlPhCN3YJ5n_ISpLAm5HIZtf02sZ3CtemCqC6_SkOB3YZYY2nyZ39sfXC5Z-PF6eqftMucz9wsNjRMhwkEeDsQhGxWaZD1YvOGBY5HHdhew7F5BkAst1Bg3kwk0hG2VuQlS6Cjuo-PBCgpZq97U0-9PlB0JqVAmP0v_MXZLtmpNdBNakUaKvPh1jX3CzghJ0M12sUr4DIQrVF1R8Er4BeR155_lTuOx4XTwWe8OYONV8xQhp9ANDLKNTpS4KVdDFxhZ5yNguNU2_tAlWQF6_o9ZVPhFS6wz_4PTgSQQoLsyztGiRU32dXvsNBNEGsDh0lmssR6WEZJtb3oKYVF47vOtcgA6NQStTtb8WS78Gv7Yf2FRJF9ldZXPijwOxutejmvbjGNYjji_BakTmHO_UJfAChJms2DANxXZStmMKymoxCRyy5082jyF6Toc7v5ds72F_GZKBzqw1B7A5Bj0U510Xgy_Kfr19qC2VrQC8prf1VE2bp35L0I4aAPxFY-QrdybIwJjUm9UZoZuRicqB5ArQPFYj6wpAOSBLKlKXsSG7sWTuqlMuXfPOb-EEG5uA0ppxvzBQs4oz_myXWBrFKaABA6Uaxp95Opt0tGwpsYX2VamnkKWgDLYM1xOGGYp6S_0wiMKFluZs-vLIkT8sf5oGB6qzv0Gow6jhEVrxozfQoK4dpZ2lRhOYuEwz2a-PJTQXVYZTGOuqkUHlVZU2y05l0_3lfqPKWs-LcHNT2pI1DRWnO28O3zoDFYFnzq-Mx_GnPvJxqSrxSiqvv6BvriPzpw7HNlmSRkoi4ORPXhNHeJAVxU2ya1tyRyiBqCOqtgHhwEXIgx_vdLKEy6Yu0LeBhQa67DBCskBihN5ZRs3w-l5ubR6n6eUnUlIcW_kUfYyhY2iY7VqNJZUEBa9NRj5DvPgljqiUkHSFReDRKFH3CZL55Dm5qikHvw2XREiPPRFUEh1aX_HDn9JkRJUX8LVKQxnc_BizjvDKi0L_VvnOaRvYKKpigQisZr8EZeyOwB_tbAbTarTi8AjnS3QP9kobZoDk60HGOdD0suZ26mmZozYv75_D63HKt1Ax0_EQqYwKWqtUV0XHclKdZCP772V-hziQ6oA9HVpIwxc0fzXIP8=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Adding database cleanup with shutil.rmtree**\"},{\"type\":\"summary_text\",\"text\":\"**Implementing robust embedding fallback handling**\"},{\"type\":\"summary_text\",\"text\":\"**Defining embedding generation with error fallback**\"}]}"},{"type":"toolCall","id":"call_rR5xwgLQxSgmsUAGuhtmYKQL|fc_075031e12bebdd06016a921a9f2e3087d08e047d6d44a87ae1","name":"edit","arguments":{"path":"/app/quickstart.py","edits":[{"oldText":"import random\nfrom pathlib import Path\nfrom typing import Any\n","newText":"import random\nimport shutil\nfrom pathlib import Path\nfrom typing import Any\n"},{"oldText":" vectors = make_embeddings(DOCS, embedding_fn)\n if len(vectors[0]) != DIMENSION:\n","newText":" try:\n vectors = make_embeddings(DOCS, embedding_fn)\n except Exception as exc:\n print(f\"Embedding failed ({exc}); using random vectors\")\n embedding_fn = None\n vectors = make_embeddings(DOCS, embedding_fn)\n if len(vectors[0]) != DIMENSION:\n"},{"oldText":" print(\"IDS:\", client.query(\n collection_name=COLLECTION_NAME,\n ids=[0, 2],\n output_fields=[\"vector\", \"text\", \"subject\"],\n ))\n","newText":" ids = client.query(\n collection_name=COLLECTION_NAME,\n ids=[0, 2],\n output_fields=[\"vector\", \"text\", \"subject\"],\n )\n print(\"IDS:\", [row[\"id\"] for row in ids])\n"},{"oldText":" DATABASE_PATH.unlink(missing_ok=True)\n print(\"Quickstart completed successfully\")\n","newText":" if DATABASE_PATH.is_dir():\n shutil.rmtree(DATABASE_PATH)\n else:\n DATABASE_PATH.unlink(missing_ok=True)\n print(\"Quickstart completed successfully\")\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":16881,"output":826,"cacheRead":14848,"cacheWrite":0,"totalTokens":32555,"cost":{"input":0.08440500000000001,"output":0.02478,"cacheRead":0.007424,"cacheWrite":0,"total":0.116609}},"stopReason":"toolUse","timestamp":1787959957700,"responseId":"resp_075031e12bebdd06016a921a95ec8887d08e13c765cd0111c0"}}
{"type":"message","id":"327980fe","parentId":"1d700406","timestamp":"2026-08-28T23:32:53.295Z","message":{"role":"toolResult","toolCallId":"call_rR5xwgLQxSgmsUAGuhtmYKQL|fc_075031e12bebdd06016a921a9f2e3087d08e047d6d44a87ae1","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 4 block(s) in /app/quickstart.py."}],"details":{"diff":" ...\n 10 \n 11 from __future__ import annotations\n 12 \n 13 import random\n+ 14 import shutil\n 14 from pathlib import Path\n 15 from typing import Any\n 16 \n 17 from pymilvus import MilvusClient\n ...\n 64 print(f\"Using embedding model (dimension {embedding_fn.dim})\")\n 65 except Exception as exc:\n 66 print(f\"Embedding model unavailable ({exc}); using random vectors\")\n 67 \n- 68 vectors = make_embeddings(DOCS, embedding_fn)\n+ 69 try:\n+ 70 vectors = make_embeddings(DOCS, embedding_fn)\n+ 71 except Exception as exc:\n+ 72 print(f\"Embedding failed ({exc}); using random vectors\")\n+ 73 embedding_fn = None\n+ 74 vectors = make_embeddings(DOCS, embedding_fn)\n 69 if len(vectors[0]) != DIMENSION:\n 70 raise ValueError(f\"Expected {DIMENSION}-dimensional vectors\")\n 71 \n 72 data = [\n ...\n 120 collection_name=COLLECTION_NAME,\n 121 filter=\"subject == 'history'\",\n 122 output_fields=[\"text\", \"subject\"],\n 123 )))\n-124 print(\"IDS:\", client.query(\n+130 ids = client.query(\n 125 collection_name=COLLECTION_NAME,\n 126 ids=[0, 2],\n 127 output_fields=[\"vector\", \"text\", \"subject\"],\n-128 ))\n+134 )\n+135 print(\"IDS:\", [row[\"id\"] for row in ids])\n 129 \n 130 client.delete(collection_name=COLLECTION_NAME, ids=[0, 2])\n 131 client.delete(collection_name=COLLECTION_NAME, filter=\"subject == 'biology'\")\n 132 \n ...\n 136 if not reopened.has_collection(collection_name=COLLECTION_NAME):\n 137 raise RuntimeError(\"Collection was not persisted to the Lite database\")\n 138 reopened.drop_collection(collection_name=COLLECTION_NAME)\n 139 reopened.close()\n-140 DATABASE_PATH.unlink(missing_ok=True)\n+147 if DATABASE_PATH.is_dir():\n+148 shutil.rmtree(DATABASE_PATH)\n+149 else:\n+150 DATABASE_PATH.unlink(missing_ok=True)\n 141 print(\"Quickstart completed successfully\")\n 142 \n 143 \n 144 if __name__ == \"__main__\":\n ...","patch":"--- /app/quickstart.py\n+++ /app/quickstart.py\n@@ -10,8 +10,9 @@\n \n from __future__ import annotations\n \n import random\n+import shutil\n from pathlib import Path\n from typing import Any\n \n from pymilvus import MilvusClient\n@@ -64,9 +65,14 @@\n print(f\"Using embedding model (dimension {embedding_fn.dim})\")\n except Exception as exc:\n print(f\"Embedding model unavailable ({exc}); using random vectors\")\n \n- vectors = make_embeddings(DOCS, embedding_fn)\n+ try:\n+ vectors = make_embeddings(DOCS, embedding_fn)\n+ except Exception as exc:\n+ print(f\"Embedding failed ({exc}); using random vectors\")\n+ embedding_fn = None\n+ vectors = make_embeddings(DOCS, embedding_fn)\n if len(vectors[0]) != DIMENSION:\n raise ValueError(f\"Expected {DIMENSION}-dimensional vectors\")\n \n data = [\n@@ -120,13 +126,14 @@\n collection_name=COLLECTION_NAME,\n filter=\"subject == 'history'\",\n output_fields=[\"text\", \"subject\"],\n )))\n- print(\"IDS:\", client.query(\n+ ids = client.query(\n collection_name=COLLECTION_NAME,\n ids=[0, 2],\n output_fields=[\"vector\", \"text\", \"subject\"],\n- ))\n+ )\n+ print(\"IDS:\", [row[\"id\"] for row in ids])\n \n client.delete(collection_name=COLLECTION_NAME, ids=[0, 2])\n client.delete(collection_name=COLLECTION_NAME, filter=\"subject == 'biology'\")\n \n@@ -136,9 +143,12 @@\n if not reopened.has_collection(collection_name=COLLECTION_NAME):\n raise RuntimeError(\"Collection was not persisted to the Lite database\")\n reopened.drop_collection(collection_name=COLLECTION_NAME)\n reopened.close()\n- DATABASE_PATH.unlink(missing_ok=True)\n+ if DATABASE_PATH.is_dir():\n+ shutil.rmtree(DATABASE_PATH)\n+ else:\n+ DATABASE_PATH.unlink(missing_ok=True)\n print(\"Quickstart completed successfully\")\n \n \n if __name__ == \"__main__\":\n","firstChangedLine":14},"isError":false,"timestamp":1787959973295}}
{"type":"message","id":"2238e82e","parentId":"327980fe","timestamp":"2026-08-28T23:32:54.674Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921aa6075887d08167dc67553ba036\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhqmG7iLKY5eYbmMZ2b-dI6FTSIM9DBHWu6wV-XBj9iqnQKQnkvTDKMra-0jv2ULonGtA0XnJBFadllWFlJJzX-Rt8lBp0HA1-mnf5uki8oZFyB6pevFLVzv159nKSFHmyAf9WgdOPxGjdyp9KukPQ74zAGQkThtCZ_cDk_buq53K5WoTvkd4UiigOD7kmqXYHyyF6kcW5tUoY28ILMl1M5QIkwXgVFZN-H4fxejZQyVFjD21Qqutu9YsALcLLJribCBqrC6js8LcbPcpcVjCWxL-bIuReXXsv8jPEETmV6hPkxbtftpkcCrkKRo5s2bEH5ScXdC-_8tskkM1Toy-YzV8Rhu6NuaDrVBeDHsy9g76VkFmLou5Js3UY9GW_A4dk2CAwObuJ0qiyF-2dVPP4IMDTvf2encQCkGu54Hw0Y0MVwR3rAt_F4hC64VxxNAUB9ZYodgqMCcfgUDGMRMXuixCiN5Y1q_Q0OFDM9vP4T30dyZoMhMWe61U4PcLBY_dugX1SDtk9R2qlsfOBsYQOk__MDnHLv52rbIgqks0TUQFO21_VvjSHFk6TacWbXjB92sOZyPHc8_il5KPeNcY80VWLcVykgUjoxUdRridiIIQ-pBNk5GPi6XCPAUBmgejEx_wM9Oxvk2xrPKAQhutOyDQbybFLrWVdQDC-iX-xjxqqR7rUFGgGJr48q3BV4IJtjPQ2YfxYv019fzB3wdqqWo8bEK1K83PYnUMSRjn49nVBxUlNoQs_Xf9BEXoE-qTaEcud4TfWY5n4-3tzNPJ3XFjBKiRzbEJVJ5KAVyDziNTj_joYhZZCQQ7p4yjhubeCZspY2ZeE3GMrW4nFkym6bK8pmW-msJQ863AtA1x5ucVD-06CBEPAxbZJbhP8IUJ0gav-0vbdd6YRYK002NzydDuKzA6OQBKwGzxY264g14rdVlwNyEr0AvJDFAVwtYeQmUyrj9A60x7aFNhDtfffJg9KubgcQbpMAXipEoVjabPJrnrvp6A6WIirrLeMqVfeZ4zakq3lgwPNA3PxjmUXvTk4zeSdP6f8HBbSFUlcUNFtFQDwBVqWc4fBo4OrgvRSAlSvAMosHdY63UAagrMX4ffOdO9WSrGeFtff8pG__EwVBcD_BkdEGyK48qrRUpXbTAjtwLlCh3lAhyXZJLQhB4SDNNovlW3JCk0JeKXu-5NrInoNeAbulUu88d2eRyvxxoOGWBB84O94K3DmkDpAhTNfHn6wL2foFDlnDb-o7iUSS9LOfqwc68wYxO4Pw_o0DASX1EbWgj_w3Mw2BtqFMRfw==\",\"summary\":[]}"},{"type":"toolCall","id":"call_BWHCFAu5lok2LyYxAWDctr0R|fc_075031e12bebdd06016a921aa6948887d09beedf978c047a29","name":"bash","arguments":{"command":"python quickstart.py","timeout":300}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":1348,"output":34,"cacheRead":31232,"cacheWrite":0,"totalTokens":32614,"cost":{"input":0.00674,"output":0.00102,"cacheRead":0.015616,"cacheWrite":0,"total":0.023376}},"stopReason":"toolUse","timestamp":1787959973296,"responseId":"resp_075031e12bebdd06016a921aa590b087d0b649e6f427ca0055"}}
{"type":"message","id":"fd8fba50","parentId":"2238e82e","timestamp":"2026-08-28T23:33:04.150Z","message":{"role":"toolResult","toolCallId":"call_BWHCFAu5lok2LyYxAWDctr0R|fc_075031e12bebdd06016a921aa6948887d09beedf978c047a29","toolName":"bash","content":[{"type":"text","text":"None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\nUsing embedding model (dimension 768)\nInserted 3 entities\nFIRST_SEARCH_RESULTS: data: [[{'id': 2, 'distance': 0.5859946012496948, 'entity': {'id': 2, 'text': 'Born in Maida Vale, London, Turing was raised in southern England.', 'subject': 'history'}}, {'id': 1, 'distance': 0.5118256211280823, 'entity': {'id': 1, 'text': 'Alan Turing was the first person to conduct substantial research in AI.', 'subject': 'history'}}]]\nFILTERED_RESULTS: data: [[{'id': 4, 'distance': 0.2703056335449219, 'entity': {'id': 4, 'text': 'Computational synthesis with AI algorithms predicts molecular properties.', 'subject': 'biology'}}, {'id': 3, 'distance': 0.16425907611846924, 'entity': {'id': 3, 'text': 'Machine learning has been used for drug design.', 'subject': 'biology'}}]]\nHISTORY_COUNT: 3\nIDS: [0, 2]\nQuickstart completed successfully\n"}],"isError":false,"timestamp":1787959984150}}
{"type":"message","id":"91b54b7a","parentId":"fd8fba50","timestamp":"2026-08-28T23:33:06.320Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Verifying absence of database and files**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921ab119c087d0a99153ffb3dff77e\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhqxB0MI4P8YExHbDUgivqUUjpFxzNMRoR_RKwBP2Y0m_XXFsF2Zz7ZD_1ZDipikIZLbR3rVQDXGx4Lc34MeJrxvDcnuMI0TgvjmULhiejZRezp-3pRDecFy1WdTYJg56V0IsNf-1r8jry76vPguSbUN3hUHhrS8G9MLgkfl4BZJgAMMGJnOCWs8OB7mTahN2J4w-u8TFlSzXZZb2xxr4XMXFbNSTKyajPYjTPU2beBn3KUn8_zfpagZCSblN9d3IvIToBo0741DIUFaOfc7lA91FIsF6-Um4doO_yT4BtcdqNGhpewAJb70tfYyweldlPaYfTMPqqFATjrRHNac3w0P5q-b-3HUVzzHTGgb-l0rCLzvda-F4gn9Gbx7-NQqFn98GTO-sob7Rp9bPAc52wA6mNGmtKj4khhnk0G1ufniqsWuqFR78SnUkN0QbHjl_JVCw4AyqI2s6iGFcxY-UUQgA9y-I-HhoVJBZqosyuYgv_hEP7VJ_OuJitrL3MHXGvYpDNElDCjuWGQJm3UgwS2hXa9Uakn04gVlVm15cDUif70ltnUAenHJOyfLGeFfluRlkx-xEaoCWK3Yg9qQYXI5ip4EMlOyPDCd1Uxh1cCVAyXp2p9Jz1oITYZ2CGv5olMHxyjy-BRq5MoC_MnPOww7ZaNyW26BpV5OPEs9ziGl8uH8B8FfctABqVfOjPuQWkSuvewbY-pnBdvo-f8WIOxKUN--BFgNuwEvMsuIyuzC8o7-cWpN_vNLKWF0jYAaJvL1TkX--yVjfjcVeE29RW0i6lgIVH2gQq_PKoBPY8TFypkOWHsu6JpF86di6bqo1XA9evNTPZxZL3kSThKSInVrY7E-BvnNkOv9euUffH0idIIoNpJoVpiFJ6Z6SNftYPjG-hYSQhKuxAZRiv1RhpYkLBwG51tx-q_DvL0cZyzFXyRB_1yfEmUG8PdR-taXnrGFQksNzDMZdSIUXzBECE5pWhzfK8ecCjQ8bquX5-u83K_09WJGZeI-Py0Jhw8puJ47Bg16NVeqPIswJmQ4zJXB16nyoYy2VBCZryEhWIPIfLa23zBxYHqWIVdgeLiX6hH7nNkbw6l_kja3jY0fut_XyTiIgYjyAUEJwICxgzcDTVFu_ZPG75G5hH9BNkk4EipNR8MmUSzDGERzLrZKpxxYqqcdHpRn83xxqFvM6BkXazjZeEvAOmVFW7VJdY8Eot4MbJLRSjax3Mt-thkgXoWKJpjfroGv6PSZt60iVpnZkxUHW5jhRN0Zsl1Yb1D1RiYyUVSwVE8_AR8E9Z4h-iFcuVmG-d-LytcglrRFSFYg57fxDJ7rQjt3WRK6F2y0UZzc\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Verifying absence of database and files**\"}]}"},{"type":"toolCall","id":"call_9pPuELbyjsZCPRib9rKOpB3R|fc_075031e12bebdd06016a921ab1cc0087d097438ed5215aa2f9","name":"bash","arguments":{"command":"ls -la /app && python -m py_compile quickstart.py && git diff -- quickstart.py requirements.txt 2>/dev/null || true","timeout":30}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":659,"output":65,"cacheRead":32256,"cacheWrite":0,"totalTokens":32980,"cost":{"input":0.003295,"output":0.0019500000000000001,"cacheRead":0.016128,"cacheWrite":0,"total":0.021373}},"stopReason":"toolUse","timestamp":1787959984151,"responseId":"resp_075031e12bebdd06016a921ab082e087d091c14bf401c256a9"}}
{"type":"message","id":"c8a4f574","parentId":"91b54b7a","timestamp":"2026-08-28T23:33:06.386Z","message":{"role":"toolResult","toolCallId":"call_9pPuELbyjsZCPRib9rKOpB3R|fc_075031e12bebdd06016a921ab1cc0087d097438ed5215aa2f9","toolName":"bash","content":[{"type":"text","text":"total 32\ndrwxrwxr-x+ 1 root root 122 Aug 28 16:33 .\ndrwxr-xr-x 15 root root 340 Aug 28 16:29 ..\n-rw-rw-r--+ 1 root root 13820 Aug 28 16:13 quickstart.md\n-rw-rw-r--+ 1 root root 5216 Aug 28 16:32 quickstart.py\n-rw-rw-r--+ 1 root root 124 Aug 28 16:32 requirements.txt\n-rw-rw-r--+ 1 root root 515 Aug 28 16:13 source-receipt.json\ndiff --git a/quickstart.py b/requirements.txt\nindex 577a39a..fc5dbbc 100644\n--- a/quickstart.py\n+++ b/requirements.txt\n@@ -1,155 +1,3 @@\n-\"\"\"Milvus Lite quickstart.\n-\n-Run with::\n-\n- python quickstart.py\n-\n-The embedding model is used when it is available. If its download is not\n-available, the documented random-vector fallback keeps this example runnable.\n-\"\"\"\n-\n-from __future__ import annotations\n-\n-import random\n-import shutil\n-from pathlib import Path\n-from typing import Any\n-\n-from pymilvus import MilvusClient\n-\n-COLLECTION_NAME = \"demo_collection\"\n-DIMENSION = 768\n-DATABASE_PATH = Path(__file__).with_name(\"milvus_demo.db\")\n-\n-DOCS = [\n- \"Artificial intelligence was founded as an academic discipline in 1956.\",\n- \"Alan Turing was the first person to conduct substantial research in AI.\",\n- \"Born in Maida Vale, London, Turing was raised in southern England.\",\n-]\n-\n-\n-def random_vectors(texts: list[str]) -> list[list[float]]:\n- \"\"\"Create the fallback representation described in the quickstart.\"\"\"\n- rng = random.Random(42)\n- return [\n- [rng.uniform(-1, 1) for _ in range(DIMENSION)]\n- for _ in texts\n- ]\n-\n-\n-def make_embeddings(texts: list[str], embedding_fn: Any | None, *, query: bool = False):\n- if embedding_fn is not None:\n- if query:\n- return embedding_fn.encode_queries(texts)\n- return embedding_fn.encode_documents(texts)\n- return random_vectors(texts)\n-\n-\n-def main() -> None:\n- # Milvus Lite stores the database in this local file.\n- client = MilvusClient(str(DATABASE_PATH))\n- if client.has_collection(collection_name=COLLECTION_NAME):\n- client.drop_collection(collection_name=COLLECTION_NAME)\n- client.create_collection(\n- collection_name=COLLECTION_NAME,\n- dimension=DIMENSION,\n- )\n-\n- # The quickstart's default model is small (~50 MB), but network access is\n- # not required to run the example because the docs provide a fallback.\n- embedding_fn = None\n- try:\n- from pymilvus import model\n-\n- embedding_fn = model.DefaultEmbeddingFunction()\n- print(f\"Using embedding model (dimension {embedding_fn.dim})\")\n- except Exception as exc:\n- print(f\"Embedding model unavailable ({exc}); using random vectors\")\n-\n- try:\n- vectors = make_embeddings(DOCS, embedding_fn)\n- except Exception as exc:\n- print(f\"Embedding failed ({exc}); using random vectors\")\n- embedding_fn = None\n- vectors = make_embeddings(DOCS, embedding_fn)\n- if len(vectors[0]) != DIMENSION:\n- raise ValueError(f\"Expected {DIMENSION}-dimensional vectors\")\n-\n- data = [\n- {\"id\": i, \"vector\": vectors[i], \"text\": text, \"subject\": \"history\"}\n- for i, text in enumerate(DOCS)\n- ]\n- insert_result = client.insert(collection_name=COLLECTION_NAME, data=data)\n- print(f\"Inserted {insert_result['insert_count']} entities\")\n-\n- # First vector search from the quickstart.\n- query_vectors = make_embeddings([\"Who is Alan Turing?\"], embedding_fn, query=True)\n- results = client.search(\n- collection_name=COLLECTION_NAME,\n- data=query_vectors,\n- limit=2,\n- output_fields=[\"text\", \"subject\"],\n- )\n- if not results or not results[0]:\n- raise RuntimeError(\"The first vector search returned no results\")\n- print(\"FIRST_SEARCH_RESULTS:\", results)\n-\n- # Search with a scalar metadata filter.\n- biology_docs = [\n- \"Machine learning has been used for drug design.\",\n- \"Computational synthesis with AI algorithms predicts molecular properties.\",\n- \"DDR1 is involved in cancers and fibrosis.\",\n- ]\n- biology_vectors = make_embeddings(biology_docs, embedding_fn)\n- client.insert(\n- collection_name=COLLECTION_NAME,\n- data=[\n- {\n- \"id\": 3 + i,\n- \"vector\": biology_vectors[i],\n- \"text\": text,\n- \"subject\": \"biology\",\n- }\n- for i, text in enumerate(biology_docs)\n- ],\n- )\n- filtered = client.search(\n- collection_name=COLLECTION_NAME,\n- data=make_embeddings([\"tell me AI related information\"], embedding_fn, query=True),\n- filter=\"subject == 'biology'\",\n- limit=2,\n- output_fields=[\"text\", \"subject\"],\n- )\n- print(\"FILTERED_RESULTS:\", filtered)\n-\n- print(\"HISTORY_COUNT:\", len(client.query(\n- collection_name=COLLECTION_NAME,\n- filter=\"subject == 'history'\",\n- output_fields=[\"text\", \"subject\"],\n- )))\n- ids = client.query(\n- collection_name=COLLECTION_NAME,\n- ids=[0, 2],\n- output_fields=[\"vector\", \"text\", \"subject\"],\n- )\n- print(\"IDS:\", [row[\"id\"] for row in ids])\n-\n- client.delete(collection_name=COLLECTION_NAME, ids=[0, 2])\n- client.delete(collection_name=COLLECTION_NAME, filter=\"subject == 'biology'\")\n-\n- # Verify the file-backed database can be opened again, then clean up.\n- client.close()\n- reopened = MilvusClient(str(DATABASE_PATH))\n- if not reopened.has_collection(collection_name=COLLECTION_NAME):\n- raise RuntimeError(\"Collection was not persisted to the Lite database\")\n- reopened.drop_collection(collection_name=COLLECTION_NAME)\n- reopened.close()\n- if DATABASE_PATH.is_dir():\n- shutil.rmtree(DATABASE_PATH)\n- else:\n- DATABASE_PATH.unlink(missing_ok=True)\n- print(\"Quickstart completed successfully\")\n-\n-\n-if __name__ == \"__main__\":\n- main()\n+pymilvus[model,milvus_lite]>=3.0.1\n+# pymilvus-model currently uses tokenizer APIs removed by Transformers 5.\n+transformers<5\n"}],"isError":false,"timestamp":1787959986386}}
{"type":"message","id":"d1448d7c","parentId":"c8a4f574","timestamp":"2026-08-28T23:33:11.034Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Assessing git diff behavior with untracked files**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921ab526c087d097dd583efac89a1c\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhq2HyTmny8jBh1Lfxz-8el3RRB1HQipaUjdgeld6RSMPAMNwsdAIrFGvwOnsOakIT0rTy1nAFhHu_PPOkFdPSd38G29064Me4hP8oXUdkc4JnuO-ZRkiq1uiJBxzE_6BdpM7NZv88PgZkE7tG6EW6WdTIJxYoFYAzvWOakDc-zI-Gy83K8Nq7yv-lCHq2SHHUr77B8ndhBMvHvcDT9nvbLo_YtsDCag0qyvRw--DIaBHMtL_VXwa6iitEwbjksBanlgN2BPNko47fbPbwF-9oOejpf28bHG0LB5u-og4p2HeDpn32bLTocNvsBXbA3c_S0DLWiFWOjkVHa2pA7qx5Om9gGRGoCF4q097IxJzCERyfPZENMNO8vIXVK5ChfAinJKogcNUVWZPla6BEk18NmQJcOPgDvwFjfw03E9pt9scB3S5moYFDNtdbEtfczPGyKfhhigXckhnXTu2DzMhV4wvZmRwvlOyPQWCPsLl9CatafBDWBHdDEw801JAZInpbsHRVvY0Ti17jvGSy8jyAENU8x1ASxkkingnn0_SqtDr5TvxYYLGLROiZXW3QatiMtSD4Etp5mF1Co_m566hmavnadKMF4tO9s0VlnoFdbowE97c_993a0M1vsrXKKH-kQhZogLxT3WG57lNmzoSCUStEpWZscbMKGD4XXxThfdyXgWQCG1Tug2NHAc9EnOfphukvU-b1fyHy-pjqansHs6ZepmwnnoG-LjxC44OqIuvGtzc1QjYIvUrFLk6O0gjgaC-DLiMnvUohZfmrzLj13BRQy97TP0-siJDr_1nVI02BtnnDNLhAtvFpM5WE-F11xDSa__MqEXiCI41h_CblIbYOcL5fp7BSpt3PI2kLJe3s_dgirTDKgvTW2JfvIDmDHMTVyw93LqoH0o4iXe5J_KTRRV9JVd6LYchxHrgQPMxGt7O8WFKCDIYn3YtsST1Ac3Bksl3B-iK2eUhaXkPIxu2n_DxYqAsH4E0vZIJuu477TRmIID-dTbd8iNmLiylopXVnvR8sHqT5GVBu98IJdIkM8g3zUp0PMuAplxz1p6NXsSnlKZMJn7dzM4mExNwtL6y3qxBJODhCdRwbVDrj6a1fpvnTziyk2V4i6xHYongcIdEIKo7pkJ_fzdmgHqsGAmPMyLKSkTpYC1yYJtzmGUBhmKF7dC6xdWiKs8N_iau2Ss2wbG9amlx2rSZu3NWvNblUbUmP5qynh2BXCHnR-EBiC3sH16n2BZpIM7_7PbMFNHXnZq75nH-ztLwohfcz5aocEYV5-8yrMkrA30zv5GK6ddpOb4QjhQ99Vrc2IAyw9PbIXS9Ngz-Rhb8VcUEYnvLuxhO40t2agSm0fuZSP2WFbqk6OeESapSKZimixB7ye9ToHYQ_5OUrFhDHAQZxcKGvNU1N1vI29eqeQLEXT1DN4yrX_EOY4Uuh_Q0CNegebUpksz5Y4KTkLIdMFtSFKAwSTXRomrR_WcVBzqtpgSZpw9SORUGa7x9FNIMifzBHUfSD5-KN5FrLCyLh79bZDAXeUvbZFdc_NLfsz_Emx7p1J4uQFQQ_MwWHny6j5xrQmmWbS__HeV13e2anfZVAdBKk26J29n3gCRMrONaArZp06M_-CHpMe_6Gkcqx8rGJ2y--wzafarV9qQf0Ge7OfVUVeruwhwzd7x_Cmn-TRVfbdNy1V8x9PVzJbZXfNQTqoSZHdqcKWEgcS-rSajjXdqBnNipcKBPHJT9-Wou37gLA==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Assessing git diff behavior with untracked files**\"}]}"},{"type":"toolCall","id":"call_FCOE0aJX5mPkqA9ngDSBxexu|fc_075031e12bebdd06016a921ab6ef6087d0b731f46b9ce757e3","name":"bash","arguments":{"command":"git status --short && git ls-files"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":2260,"output":96,"cacheRead":32256,"cacheWrite":0,"totalTokens":34612,"cost":{"input":0.011300000000000001,"output":0.00288,"cacheRead":0.016128,"cacheWrite":0,"total":0.030308}},"stopReason":"toolUse","timestamp":1787959986387,"responseId":"resp_075031e12bebdd06016a921ab4a5cc87d08912b306d6f061c9"}}
{"type":"message","id":"2fb3fa24","parentId":"d1448d7c","timestamp":"2026-08-28T23:33:11.044Z","message":{"role":"toolResult","toolCallId":"call_FCOE0aJX5mPkqA9ngDSBxexu|fc_075031e12bebdd06016a921ab6ef6087d0b731f46b9ce757e3","toolName":"bash","content":[{"type":"text","text":"fatal: not a git repository (or any parent up to mount point /)\nStopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\n\n\nCommand exited with code 128"}],"details":{},"isError":true,"timestamp":1787959991044}}
{"type":"message","id":"1aa84424","parentId":"2fb3fa24","timestamp":"2026-08-28T23:33:25.349Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Assessing database cleanup approach**\n\n**Planning model fallback and vector search verification**\n\n**Confirming database path and search 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database cleanup approach**\"},{\"type\":\"summary_text\",\"text\":\"**Planning model fallback and vector search verification**\"},{\"type\":\"summary_text\",\"text\":\"**Confirming database path and search behavior**\"}]}"},{"type":"thinking","thinking":"**Planning fallback embedding handling**\n\n**Confirming pymilvus lite inclusion**","thinkingSignature":"{\"id\":\"rs_075031e12bebdd06016a921ac173c487d0ae935f72e8d45935\",\"type\":\"reasoning\",\"content\":[],\"encrypted_content\":\"gAAAAABqkhrEOl3KnnucDqXzsJ3eSiYGg9PrayrOK8pZQM7MbPPMIriEyfUvcdto-naEWjFP6Mw_MzbUsOhrFVZCa564-VyCh-170jVGwSu1R3JCBgdEo5Fg_i1hCYSV1gk05JWaxCr87CDPHvXI8REWw4Eqk7oj0EfAwDs1hejddKoyVzGWJzJh7ncPq7D_i62Y9AGl7SUrJyILyRKbDBwKSc3LVDzGJTPxOQYsH4yK1FJqP2zC4n6MJTpqcRjQzgCRIxnuR7_xAKDAzY8hnKUTjAEOZU-8JfUuVukuc_cMkYeDsKrHSii0ReWA8r7R5ocTVqqIRAWPr2fJosYKcq69qeXriSorjzCTkwj3v7Is0WV_FIprRB9lUBrnMKTWfI_MukxmbFZ1XjznNbWPyzxF_ymdYFK8JQI4jdChsrgXaAsqcegx_KeWg0A_p-aBXXvl_HEAeDNPmE-nHMGODmYXtJUwmYSOxXrgt4x6r7Z8DiwJ4tMqJaUj5EQFWysMSF4DkRfPBxWvmREMuhzqX_ceAZz-q9_lUKLWFnQQisWxTJn-zTSecBURV4BI1zVr0s6oOq45f0VBHhpmFMY2zMEAz3a7EaPV1DC3yYQemx72Z6KoSmx7QL4dKtLr2rpAZ4medRJmfxJoAWs-8Z0VF0ZSQd8vu2B_Cz3QALAd1j_8hJIi6KIgN54q1H5rIOYFNZng0r2DVnfZQQYhg8SoEKlcmiIV4VUkII0RyZMfxK69WHFHcUtJNujs5mkXxDcYwmwmBA8n_MOI4Zz_TeLIv5lZDkpAMHB4jqC_5xpNyHeGHSyVYRC7IB0fexXq_cl53y0sLuE9aDSEgRymWx8f6zGHzZxM3gs7R8UOiFJnQ-aFuMAnMlN1hCUVwcM-59sIwTvrbA2JlXpf7OEc-p2UzSTJlsWBFd1SPqQzwcuyMhE6uB-HYhW2srpBKYZgDFN6eyjAI36pPcGPKsluomGJ8pv3oQTQoq5GX_szozLKfjLza1-ylnxNMQV_ByCvJxyU-fMkRsUspLOJeDiG0S7bOqrsfWBlkjb0dWSfzrzE0Gt0s3VFmKl6r1AwRU48jOZi51_vLIhXrG6FDaJloXHqvYDYmb0BoQBIx1ykLgUSllIqaziP9jwqhV0KNjprCEfQdzOSNkV3wlTFbuoiZyECpQFvW8T4V8xAeYwF1fc6Sv1BAkfgx6siQgE5eejkJWMQ4ZzxT29L40NLUMZAv0NPHY-8Y7_IV4hpTg9MmSmPK-yQegGtZyaQsAQCZQ4z2dD1D9HCZnGlmlajwZhPn1GO9bA1zn7B-09U1ohFhJTGxSHMfWxcdzGdUQ43F1lkHQDFfyY6yEtDjHuLmRP7GZOf88NVxYkaQpUbRVNcBxAc-2Cp-ahSDRyya-glMYk4fVj37oDgM2TDN1Ky6E51Y1Ffzh7hQ6aFV6czytfxS7oEivCvhSVL0dk-Zqa-z5WI8qoyP5U4j_lpqxX53so46PALhITiq4ZktFIFrjSL2Wr7PLfmSKRaP-diO3nu9xNXSa64-EKrEE5QhFE1jXWZcTnf206RS4f52ph1gkzOyvNqqv0P7ce-D6a3pOjcP3sBzAA57oSv9TmYvUxED-dJ3viNV71b_ef5washS3geAzqMQMbbYGW1xkps2T2d6DL755gCP0-Pj6vaYiNQfEI_ob5QKUfMhPVr1c4cLRDVovR3gvDxY6TIv6jrrK5iVVBTLSVW7fpY6zZQ01_9I500mwx4c46NNB1iA3yastTqbLWn_7N1BiU0pohXwkPLI0ZYbo898XANbbqgi9gqM7YShfUac36b9BAea9H9TG1PWMI3TqE_uz1I9lm1s1Q_ONOu8KSpLRpmyMLCCD-Q1wsbgMb8baQYSOTayIRVASq9MaWiZhX9bWKNAFjhsNYHcnnuVUspC8GhEbdV7M9ARvShH3sGX_qUcJNIufyGKFURcWQJzCJRJG0fWprQsRhi0LgXPq_uOXRUcsQh_2HMATXMzjCyE43dPctV22YDcOBfYFNN5dH30l0Jsvcjsu4wD1b0-0W09xHc3UhLk9opnZRezyy1PK_mB4Q5Qfj3qCN-cekxwbOno9zkFhh5kbJ-1Gm4FdxmUXzynf0tXiiucbEFuavvqGLCzfLrwwn_aDlDinRv4IQyv_rfJNy-cpYv8J751jMQWWUohtYVbfYHW7OEKD3DdG43MeLoQ9aJmp32rPJPY2b8PIf7kbp8Pl-Q_voUu_zTHP70XDJMOlx3KVEz4T02S3S9vjlO0lFGF0lcFZlLN8-BL-16O57ApPJlQrPg_AtU2s91IbWPWl_vgVJUerBcw7tBMM3gSDdZzGyNNzLOFnY1hXfSlh4Wqn8R97rYcJyedFgLfv6PSoysplh79_v3L8N8dbaZem_VXtTClb6O84pqjLBvy_fKlMDwMyqmNtY2xnsLBkfiei5c\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Planning fallback embedding handling**\"},{\"type\":\"summary_text\",\"text\":\"**Confirming pymilvus lite inclusion**\"}]}"},{"type":"text","text":"Implemented:\n\n- `/app/quickstart.py`\n- `/app/requirements.txt`\n\nRan successfully with:\n\n```text\nInserted 3 entities\nFIRST_SEARCH_RESULTS: ... id 2 ... id 1 ...\nQuickstart completed successfully\n```\n\nThe first vector search returned two results, including the Alan Turing document.","textSignature":"{\"v\":1,\"id\":\"msg_075031e12bebdd06016a921ac4645c87d0b6096f1ea86cd151\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-5.6-luna","usage":{"input":357,"output":751,"cacheRead":34304,"cacheWrite":0,"totalTokens":35412,"cost":{"input":0.0017850000000000001,"output":0.02253,"cacheRead":0.017152,"cacheWrite":0,"total":0.041467000000000004}},"stopReason":"stop","timestamp":1787959991045,"responseId":"resp_075031e12bebdd06016a921ab74aa487d0975040da89089467"}}