codex_usage 0.1.1

Codex and Claude Code telemetry/usage parser, aggregate JSONL events into CodeAnalysis results
Documentation 
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{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":"幫我檢查一下 go.mod 裡面 為何 go版本是 1.23, toolchain 卻是 1.24?\n請幫我統一成1.23並且我希望1.23以上就能使用 並將 .python-version 也改成 3.10\n然後幫我查看一下代碼中 除了 .python-version 以外 哪邊還有無寫死的 python version 字串 \n另外想確認一下這版本是在 go mod init的時候決定的嗎？\n也請你幫我上網找看看 golang 最新版是幾版\n幫我把這段說明更新到兩個 markdown 來告訴其他開發者 一個是繁體中文 一個簡體中文\n最後幫我把 TODO.md 刪除"},"uuid":"d96b2bdf-ce1d-4f0b-a633-7bf43396d1d2","timestamp":"2025-08-28T12:57:08.611Z"}
{"parentUuid":"d96b2bdf-ce1d-4f0b-a633-7bf43396d1d2","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_017UQnB3T6XMaAEm7o9HraMU","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"thinking","thinking":"用戶要求多個任務，我需要使用TodoWrite來追踪這些任務。用戶的要求包括：\n\n1. 檢查go.mod中go版本和toolchain的問題\n2. 統一版本為1.23並且設置1.23以上可用\n3. 將.python-version改成3.10\n4. 查看代碼中除了.python-version外是否有寫死的python版本字串\n5. 確認go版本是在go mod init時決定的嗎\n6. 上網查看golang最新版本\n7. 更新說明到兩個markdown（繁體中文、簡體中文）\n8. 刪除TODO.md\n\n這是一個複雜的多步驟任務，我應該使用TodoWrite工具。","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":3788,"cache_read_input_tokens":11459,"cache_creation":{"ephemeral_5m_input_tokens":3788,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CSa9PyzwBA9TGcg7ZEXHH","type":"assistant","uuid":"11b4107d-7189-4bd2-b52d-19b51acd9d2b","timestamp":"2025-08-28T12:57:13.873Z"}
{"parentUuid":"11b4107d-7189-4bd2-b52d-19b51acd9d2b","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_017UQnB3T6XMaAEm7o9HraMU","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"text","text":"我來幫你處理這些任務。首先讓我創建一個待辦清單來追蹤所有任務。"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":3788,"cache_read_input_tokens":11459,"cache_creation":{"ephemeral_5m_input_tokens":3788,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CSa9PyzwBA9TGcg7ZEXHH","type":"assistant","uuid":"e321c683-9a24-4cd3-ab92-3c0214bb3033","timestamp":"2025-08-28T12:57:14.717Z"}
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{"parentUuid":"1b04159d-d244-411a-9b1f-50b2717d9b01","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0193wZx1r27o8y3UAyXWRJJG","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"uuid":"4f7e3d5a-8120-43db-85bb-57b6a7a5e137","timestamp":"2025-08-28T12:57:19.002Z","toolUseResult":{"oldTodos":[],"newTodos":[{"content":"檢查go.mod文件中的go版本和toolchain設置","status":"pending","activeForm":"檢查go.mod文件中的go版本和toolchain設置"},{"content":"將go.mod統一成1.23版本並設置1.23以上可用","status":"pending","activeForm":"將go.mod統一成1.23版本並設置1.23以上可用"},{"content":"將.python-version改成3.10","status":"pending","activeForm":"將.python-version改成3.10"},{"content":"搜索代碼中除了.python-version外寫死的python版本字串","status":"pending","activeForm":"搜索代碼中除了.python-version外寫死的python版本字串"},{"content":"上網查看golang最新版本","status":"pending","activeForm":"上網查看golang最新版本"},{"content":"創建繁體中文和簡體中文的說明文檔","status":"pending","activeForm":"創建繁體中文和簡體中文的說明文檔"},{"content":"刪除TODO.md文件","status":"pending","activeForm":"刪除TODO.md文件"}]}}
{"parentUuid":"4f7e3d5a-8120-43db-85bb-57b6a7a5e137","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_019pz1PsZ1mmvQ4rHMC9LBqy","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"thinking","thinking":"現在讓我開始執行第一個任務：檢查go.mod文件中的go版本和toolchain設置。","signature":"EogCCkYIBxgCKkB2Plig6v4Lw1QLid9ulFz3WTBFn7TLett8UPAd6KR47Dn9iKwJX1q0FOJ0ji/3STMlrv91de5a31w1MbIt5ldGEgx7Y0vlhSjwSNgNUeUaDCBrZIovLuHpREDtXCIwX0neSMOdpqIo0oQVEOYj2jqsVZmw0zCFb0DN/23N2QLPxJTc/cEJMAmQkofOuBnPKnCIwvOmkFka29FAW+YG2ZAZRcwMF/LGZ+LEca25AiSs3Z5PgbZZhJtyb/ZdfjN1IBVAGyN+LDkmU3ZA3wbF8Mgfhajq9Cz/uUaJIFEVSTwBCK++X/ZJqNCghIVhv2Wcc8dHgdZ5NfXj7B4wtfoIj+DgGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1541,"cache_read_input_tokens":15247,"cache_creation":{"ephemeral_5m_input_tokens":1541,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CSa9QkEj8NnanUK6wtL6x","type":"assistant","uuid":"286adbd7-b191-4cf3-8655-09c9e9e2e8e4","timestamp":"2025-08-28T12:57:22.268Z"}
{"parentUuid":"286adbd7-b191-4cf3-8655-09c9e9e2e8e4","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_019pz1PsZ1mmvQ4rHMC9LBqy","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_018NUVV9yEFRyqXaGNFJw3k3","name":"TodoWrite","input":{"todos":[{"content":"檢查go.mod文件中的go版本和toolchain設置","status":"in_progress","activeForm":"檢查go.mod文件中的go版本和toolchain設置"},{"content":"將go.mod統一成1.23版本並設置1.23以上可用","status":"pending","activeForm":"將go.mod統一成1.23版本並設置1.23以上可用"},{"content":"將.python-version改成3.10","status":"pending","activeForm":"將.python-version改成3.10"},{"content":"搜索代碼中除了.python-version外寫死的python版本字串","status":"pending","activeForm":"搜索代碼中除了.python-version外寫死的python版本字串"},{"content":"上網查看golang最新版本","status":"pending","activeForm":"上網查看golang最新版本"},{"content":"創建繁體中文和簡體中文的說明文檔","status":"pending","activeForm":"創建繁體中文和簡體中文的說明文檔"},{"content":"刪除TODO.md文件","status":"pending","activeForm":"刪除TODO.md文件"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1541,"cache_read_input_tokens":15247,"cache_creation":{"ephemeral_5m_input_tokens":1541,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard"}},"requestId":"req_011CSa9QkEj8NnanUK6wtL6x","type":"assistant","uuid":"fed0240e-6e59-4857-a5e7-5ad23ff0ac3a","timestamp":"2025-08-28T12:57:28.531Z"}
{"parentUuid":"fed0240e-6e59-4857-a5e7-5ad23ff0ac3a","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018NUVV9yEFRyqXaGNFJw3k3","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"uuid":"e8cd7777-9108-42cb-8468-5b6e354c5d03","timestamp":"2025-08-28T12:57:28.576Z","toolUseResult":{"oldTodos":[],"newTodos":[{"content":"檢查go.mod文件中的go版本和toolchain設置","status":"in_progress","activeForm":"檢查go.mod文件中的go版本和toolchain設置"},{"content":"將go.mod統一成1.23版本並設置1.23以上可用","status":"pending","activeForm":"將go.mod統一成1.23版本並設置1.23以上可用"},{"content":"將.python-version改成3.10","status":"pending","activeForm":"將.python-version改成3.10"},{"content":"搜索代碼中除了.python-version外寫死的python版本字串","status":"pending","activeForm":"搜索代碼中除了.python-version外寫死的python版本字串"},{"content":"上網查看golang最新版本","status":"pending","activeForm":"上網查看golang最新版本"},{"content":"創建繁體中文和簡體中文的說明文檔","status":"pending","activeForm":"創建繁體中文和簡體中文的說明文檔"},{"content":"刪除TODO.md文件","status":"pending","activeForm":"刪除TODO.md文件"}]}}
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{"parentUuid":"cb59a162-323f-48a4-8784-41d23a04da34","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014WvJQVXQ9y641v1V4N2v4P","type":"tool_result","content":"     1→module claude-code-helper\n     2→\n     3→go 1.23.0\n     4→\n     5→toolchain go1.24.3\n     6→\n     7→require (\n     8→\tgithub.com/charmbracelet/bubbles v0.21.0\n     9→\tgithub.com/charmbracelet/bubbletea v1.3.6\n    10→\tgithub.com/charmbracelet/lipgloss v1.1.0\n    11→\tgithub.com/denisbrodbeck/machineid v1.0.1\n    12→)\n    13→\n    14→require (\n    15→\tgithub.com/atotto/clipboard v0.1.4 // indirect\n    16→\tgithub.com/aymanbagabas/go-osc52/v2 v2.0.1 // indirect\n    17→\tgithub.com/charmbracelet/colorprofile v0.3.2 // indirect\n    18→\tgithub.com/charmbracelet/x/ansi v0.10.1 // indirect\n    19→\tgithub.com/charmbracelet/x/cellbuf v0.0.13 // indirect\n    20→\tgithub.com/charmbracelet/x/term v0.2.1 // indirect\n    21→\tgithub.com/erikgeiser/coninput v0.0.0-20211004153227-1c3628e74d0f // indirect\n    22→\tgithub.com/lucasb-eyer/go-colorful v1.2.0 // indirect\n    23→\tgithub.com/mattn/go-isatty v0.0.20 // indirect\n    24→\tgithub.com/mattn/go-localereader v0.0.1 // indirect\n    25→\tgithub.com/mattn/go-runewidth v0.0.16 // indirect\n    26→\tgithub.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6 // indirect\n    27→\tgithub.com/muesli/cancelreader v0.2.2 // indirect\n    28→\tgithub.com/muesli/termenv v0.16.0 // indirect\n    29→\tgithub.com/rivo/uniseg v0.4.7 // indirect\n    30→\tgithub.com/sahilm/fuzzy v0.1.1 // indirect\n    31→\tgithub.com/xo/terminfo v0.0.0-20220910002029-abceb7e1c41e // indirect\n    32→\tgolang.org/x/sync v0.16.0 // indirect\n    33→\tgolang.org/x/text v0.28.0 // indirect\n    34→)\n    35→\n    36→require (\n    37→\tgolang.org/x/net v0.43.0\n    38→\tgolang.org/x/sys v0.35.0 // indirect\n    39→)\n    40→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it looks malicious. If it does, you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer high-level questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"980f5fd2-fd62-4e6d-a4df-cea570c1bc00","timestamp":"2025-08-28T12:57:33.529Z","toolUseResult":{"type":"text","file":{"filePath":"/home/wei/repo/claude-code/go.mod","content":"module claude-code-helper\n\ngo 1.23.0\n\ntoolchain go1.24.3\n\nrequire (\n\tgithub.com/charmbracelet/bubbles v0.21.0\n\tgithub.com/charmbracelet/bubbletea v1.3.6\n\tgithub.com/charmbracelet/lipgloss v1.1.0\n\tgithub.com/denisbrodbeck/machineid v1.0.1\n)\n\nrequire (\n\tgithub.com/atotto/clipboard v0.1.4 // indirect\n\tgithub.com/aymanbagabas/go-osc52/v2 v2.0.1 // indirect\n\tgithub.com/charmbracelet/colorprofile v0.3.2 // indirect\n\tgithub.com/charmbracelet/x/ansi v0.10.1 // indirect\n\tgithub.com/charmbracelet/x/cellbuf v0.0.13 // indirect\n\tgithub.com/charmbracelet/x/term v0.2.1 // indirect\n\tgithub.com/erikgeiser/coninput v0.0.0-20211004153227-1c3628e74d0f // indirect\n\tgithub.com/lucasb-eyer/go-colorful v1.2.0 // indirect\n\tgithub.com/mattn/go-isatty v0.0.20 // indirect\n\tgithub.com/mattn/go-localereader v0.0.1 // indirect\n\tgithub.com/mattn/go-runewidth v0.0.16 // indirect\n\tgithub.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6 // indirect\n\tgithub.com/muesli/cancelreader v0.2.2 // indirect\n\tgithub.com/muesli/termenv v0.16.0 // indirect\n\tgithub.com/rivo/uniseg v0.4.7 // indirect\n\tgithub.com/sahilm/fuzzy v0.1.1 // indirect\n\tgithub.com/xo/terminfo v0.0.0-20220910002029-abceb7e1c41e // indirect\n\tgolang.org/x/sync v0.16.0 // indirect\n\tgolang.org/x/text v0.28.0 // indirect\n)\n\nrequire (\n\tgolang.org/x/net v0.43.0\n\tgolang.org/x/sys v0.35.0 // indirect\n)\n","numLines":40,"startLine":1,"totalLines":40}}}
{"parentUuid":"980f5fd2-fd62-4e6d-a4df-cea570c1bc00","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ADafGuxkjzdYjrKMBX5DKE","type":"tool_result","content":"     1→3.12\n     2→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it looks malicious. If it does, you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer high-level questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"fe3e5188-7939-4c80-9a06-e863a19b1d56","timestamp":"2025-08-28T12:57:33.529Z","toolUseResult":{"type":"text","file":{"filePath":"/home/wei/repo/claude-code/.python-version","content":"3.12\n","numLines":2,"startLine":1,"totalLines":2}}}
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{"parentUuid":"b61af956-7cb0-483e-a653-577deb702fdb","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_01NudsoqEJXy7ux27eod4pB9","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_01JamPL6BZWYavh9M2fNEdAS","name":"TodoWrite","input":{"todos":[{"content":"檢查go.mod文件中的go版本和toolchain設置","status":"completed","activeForm":"檢查go.mod文件中的go版本和toolchain設置"},{"content":"將go.mod統一成1.23版本並設置1.23以上可用","status":"in_progress","activeForm":"將go.mod統一成1.23版本並設置1.23以上可用"},{"content":"將.python-version改成3.10","status":"pending","activeForm":"將.python-version改成3.10"},{"content":"搜索代碼中除了.python-version外寫死的python版本字串","status":"pending","activeForm":"搜索代碼中除了.python-version外寫死的python版本字串"},{"content":"上網查看golang最新版本","status":"pending","activeForm":"上網查看golang最新版本"},{"content":"創建繁體中文和簡體中文的說明文檔","status":"pending","activeForm":"創建繁體中文和簡體中文的說明文檔"},{"content":"刪除TODO.md文件","status":"pending","activeForm":"刪除TODO.md文件"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":1120,"cache_read_input_tokens":18116,"cache_creation":{"ephemeral_5m_input_tokens":1120,"ephemeral_1h_input_tokens":0},"output_tokens":992,"service_tier":"standard"}},"requestId":"req_011CSa9RpMJttijHYvn7AJxe","type":"assistant","uuid":"94882252-3178-436d-b568-39549f1c2585","timestamp":"2025-08-28T12:57:45.282Z"}
{"parentUuid":"94882252-3178-436d-b568-39549f1c2585","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JamPL6BZWYavh9M2fNEdAS","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"uuid":"46829194-555b-4ed7-bed2-d0218ee67a65","timestamp":"2025-08-28T12:57:45.333Z","toolUseResult":{"oldTodos":[],"newTodos":[{"content":"檢查go.mod文件中的go版本和toolchain設置","status":"completed","activeForm":"檢查go.mod文件中的go版本和toolchain設置"},{"content":"將go.mod統一成1.23版本並設置1.23以上可用","status":"in_progress","activeForm":"將go.mod統一成1.23版本並設置1.23以上可用"},{"content":"將.python-version改成3.10","status":"pending","activeForm":"將.python-version改成3.10"},{"content":"搜索代碼中除了.python-version外寫死的python版本字串","status":"pending","activeForm":"搜索代碼中除了.python-version外寫死的python版本字串"},{"content":"上網查看golang最新版本","status":"pending","activeForm":"上網查看golang最新版本"},{"content":"創建繁體中文和簡體中文的說明文檔","status":"pending","activeForm":"創建繁體中文和簡體中文的說明文檔"},{"content":"刪除TODO.md文件","status":"pending","activeForm":"刪除TODO.md文件"}]}}
{"parentUuid":"46829194-555b-4ed7-bed2-d0218ee67a65","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_01NBbaXkJVH5BxnLfUazbGiy","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"thinking","thinking":"現在我需要修改go.mod文件，將toolchain從go1.24.3改為go1.23，並且為了支持1.23以上版本，我可以調整go版本為1.23（不指定小版本）。","signature":"EtQCCkYIBxgCKkAw0UZdjE6T5GjYaggQ04RZkTT6ne/EhKHsj+EF6i/poO6oW+oUXOLu4ykGG+247fYVVJsOl3PLKkJsszH+S4YMEgyzusff/73sZG9LgeEaDEkPwT28pfij9wItEyIw/ncc8rXwMHJlxIwdL8XMQStH+lV52UF6zQZjBIPyVXS8tRQZao7CMbwVqPt8rZfhKrsB/3vHKSHDUC+yj5yFnDqvMGGl6FIwtGoJSOGslwKKt3OahF32KFjnd7eI5l+vxcTER1BZ3Jq/1xESGuZ8+rcBzK4ZdV3hCJ2+DxJEnKo07PaviApQQUxf7CkugHvNrWQ4p6s5tmC9/KRxULb+2URGUyKiCPQ2B9zUwkvh9DIt97k7s4tOzNPlkYEfdaqyNjcVMzqeEhsY9R7BUokxYxHRiw8ZpS5NG3qWhphmRa6NUQl0eXqHlsRq1rs3ARgB"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":10,"cache_creation_input_tokens":1396,"cache_read_input_tokens":19236,"cache_creation":{"ephemeral_5m_input_tokens":1396,"ephemeral_1h_input_tokens":0},"output_tokens":15,"service_tier":"standard"}},"requestId":"req_011CSa9SgtNZAWgyE6UERBbX","type":"assistant","uuid":"b79c8ac2-10ba-4a09-a8a9-1c3345301fb2","timestamp":"2025-08-28T12:57:49.230Z"}
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{"parentUuid":"cd5026de-dd0b-4553-bce1-cc456740ad70","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.","is_error":true,"tool_use_id":"toolu_0163MUAqdsdd6qYPyBhPBJqd"}]},"uuid":"49a01b22-ab74-4edd-a1ca-6208f567bbf2","timestamp":"2025-08-28T12:58:11.886Z","toolUseResult":"Error: The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed."}
{"parentUuid":"49a01b22-ab74-4edd-a1ca-6208f567bbf2","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"type":"text","text":"[Request interrupted by user for tool use]"}]},"uuid":"7d6aa57c-731c-4de3-8d09-1472714cd2ff","timestamp":"2025-08-28T12:58:11.910Z"}
{"parentUuid":"7d6aa57c-731c-4de3-8d09-1472714cd2ff","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":"toolchain不用保留吧?"},"uuid":"cde70d7e-125d-4aeb-8d16-75148f51b4a6","timestamp":"2025-08-28T12:58:18.216Z"}
{"parentUuid":"cde70d7e-125d-4aeb-8d16-75148f51b4a6","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_01TcZ9d8AeCgfLAdfQiHnNJH","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"text","text":"你說得對，toolchain 不需要保留。讓我修改 go.mod 文件："}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":5749,"cache_read_input_tokens":14486,"cache_creation":{"ephemeral_5m_input_tokens":5749,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CSa9V7YkKjNei8eg3GHAZ","type":"assistant","uuid":"dbd7f92e-48b4-4923-a8d0-afcf83cfd568","timestamp":"2025-08-28T12:58:21.633Z"}
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{"parentUuid":"544eade4-09fb-4607-9b56-f774325b3d87","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YcDiv1RvM1EkMa56cuE6e7","type":"tool_result","content":"Web search results for query: \"golang latest version 2025 current release\"\n\nLinks: [{\"title\":\"Release History - The Go Programming Language\",\"url\":\"https://go.dev/doc/devel/release\"},{\"title\":\"Go 1.25 Release Notes - The Go Programming Language\",\"url\":\"https://tip.golang.org/doc/go1.25\"},{\"title\":\"Go 1.24 Release Notes - The Go Programming Language\",\"url\":\"https://tip.golang.org/doc/go1.24\"},{\"title\":\"All releases - The Go Programming Language\",\"url\":\"https://go.dev/dl/\"},{\"title\":\"Go | endoflife.date\",\"url\":\"https://endoflife.date/go\"},{\"title\":\"Go 1.23 Release Notes - The Go Programming Language\",\"url\":\"https://tip.golang.org/doc/go1.23\"},{\"title\":\"What’s New in GoLand 2025.2\",\"url\":\"https://www.jetbrains.com/go/whatsnew/\"},{\"title\":\"GoLand 2025.1 Is Out! | The GoLand Blog\",\"url\":\"https://blog.jetbrains.com/go/2025/04/16/goland-2025-1-is-out/\"},{\"title\":\"Go (programming language) - Wikipedia\",\"url\":\"https://en.wikipedia.org/wiki/Go_(programming_language)\"},{\"title\":\"The Future of Golang in 2025 [Top Trends and Predictions] - GeeksforGeeks\",\"url\":\"https://www.geeksforgeeks.org/blogs/future-of-golang/\"}]\n\nBased on the web search results, here's information about the latest Go versions in 2025:\n\n## Current Go Versions (August 2025)\n\nThe latest stable Go release is **Go 1.24.6** (released 2025-08-06), which includes security fixes to the database/sql and os/exec packages, as well as bug fixes to the runtime.\n\n## Go Version Timeline for 2025\n\n**Go 1.24 Series:**\n- Go 1.24 was released in February 2025\n- Latest patch: Go 1.24.6 (released 2025-08-06)\n\n**Go 1.23 Series (Previous Stable):**\n- Go 1.23.12 (released 2025-08-06) - latest patch for the 1.23 series\n\n## Upcoming: Go 1.25\n\nGo 1.25 includes a new, experimental JSON implementation, which can be enabled by setting the environment variable GOEXPERIMENT=jsonv2 at build time. The release notes for Go 1.25 are already available, indicating it's in development.\n\n## Key Features in Recent Versions\n\n**Go 1.24 highlights:**\n- Go 1.24 now fully supports generic type aliases\n- Go modules can now track executable dependencies using tool directives in go.mod\n- Go 1.24 includes Go Cryptographic Module version v1.0.0\n\n**Go 1.25 upcoming features:**\n- The compiler and linker in Go 1.25 now generate debug information using DWARF version 5\n- The default behavior of the GOMAXPROCS has changed\n\nThe current recommendation is to use **Go 1.24.6** for production environments, as it includes the latest security fixes and bug patches."}]},"uuid":"83baa9da-f0d9-4082-8569-64ab89e4f3c9","timestamp":"2025-08-28T12:59:34.526Z","toolUseResult":{"query":"golang latest version 2025 current release","results":[{"tool_use_id":"srvtoolu_01AwHReYKnmpUBfsgteeYRCd","content":[{"title":"Release History - The Go Programming Language","url":"https://go.dev/doc/devel/release"},{"title":"Go 1.25 Release Notes - The Go Programming Language","url":"https://tip.golang.org/doc/go1.25"},{"title":"Go 1.24 Release Notes - The Go Programming Language","url":"https://tip.golang.org/doc/go1.24"},{"title":"All releases - The Go Programming Language","url":"https://go.dev/dl/"},{"title":"Go | endoflife.date","url":"https://endoflife.date/go"},{"title":"Go 1.23 Release Notes - The Go Programming Language","url":"https://tip.golang.org/doc/go1.23"},{"title":"What’s New in GoLand 2025.2","url":"https://www.jetbrains.com/go/whatsnew/"},{"title":"GoLand 2025.1 Is Out! | The GoLand Blog","url":"https://blog.jetbrains.com/go/2025/04/16/goland-2025-1-is-out/"},{"title":"Go (programming language) - Wikipedia","url":"https://en.wikipedia.org/wiki/Go_(programming_language)"},{"title":"The Future of Golang in 2025 [Top Trends and Predictions] - GeeksforGeeks","url":"https://www.geeksforgeeks.org/blogs/future-of-golang/"}]},"Based on the web search results, here's information about the latest Go versions in 2025:\n\n## Current Go Versions (August 2025)\n\nThe latest stable Go release is **Go 1.24.6** (released 2025-08-06), which includes security fixes to the database/sql and os/exec packages, as well as bug fixes to the runtime.\n\n## Go Version Timeline for 2025\n\n**Go 1.24 Series:**\n- Go 1.24 was released in February 2025\n- Latest patch: Go 1.24.6 (released 2025-08-06)\n\n**Go 1.23 Series (Previous Stable):**\n- Go 1.23.12 (released 2025-08-06) - latest patch for the 1.23 series\n\n## Upcoming: Go 1.25\n\nGo 1.25 includes a new, experimental JSON implementation, which can be enabled by setting the environment variable GOEXPERIMENT=jsonv2 at build time. The release notes for Go 1.25 are already available, indicating it's in development.\n\n## Key Features in Recent Versions\n\n**Go 1.24 highlights:**\n- Go 1.24 now fully supports generic type aliases\n- Go modules can now track executable dependencies using tool directives in go.mod\n- Go 1.24 includes Go Cryptographic Module version v1.0.0\n\n**Go 1.25 upcoming features:**\n- The compiler and linker in Go 1.25 now generate debug information using DWARF version 5\n- The default behavior of the GOMAXPROCS has changed\n\nThe current recommendation is to use **Go 1.24.6** for production environments, as it includes the latest security fixes and bug patches."],"durationSeconds":18.563167093999976}}
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{"parentUuid":"9da092b8-9efb-490d-bec0-254c4e86dac1","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_013XjxxcmjSVQAnMX28zFqeq","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_0188khdXCsjDbEJQMAABhtX2","name":"Write","input":{"file_path":"/home/wei/repo/claude-code/版本需求說明-繁體中文.md","content":"# 版本需求說明\n\n## 概述\n\n此項目支援不同的程式語言版本，開發者可根據需求選擇適當版本。\n\n## Go 語言版本需求\n\n### 目前設定\n- **最低支援版本**: Go 1.23\n- **設定檔案**: `go.mod`\n- **版本宣告**: `go 1.23`\n\n### 版本決定機制\nGo 版本是在執行 `go mod init` 時決定的，會根據當前安裝的 Go 版本自動設定。如需變更，可直接編輯 `go.mod` 文件。\n\n### Go 版本現況 (2025年8月)\n- **最新穩定版**: Go 1.24.6 (2025-08-06 發布)\n- **包含安全性修復**: database/sql 和 os/exec 套件\n- **開發中版本**: Go 1.25 (實驗性功能包含新的 JSON 實作)\n- **建議生產環境**: Go 1.24.6\n\n## Python 版本需求\n\n### 目前設定\n- **指定版本**: Python 3.10\n- **設定檔案**: `.python-version`\n\n### 其他發現的 Python 版本引用\n在程式碼中發現以下寫死的 Python 版本字串：\n- `pyproject.toml` 第6行: `requires-python = \">=3.12\"`\n- `uv.lock` 第3行: `requires-python = \">=3.12\"`\n\n**注意**: 上述檔案中仍有 Python 3.12+ 的版本需求，可能需要根據實際需求進行調整。\n\n## 工具鏈說明\n\n- **移除工具鏈指定**: 已從 `go.mod` 中移除 `toolchain` 指定，系統將使用預設的 Go 工具鏈\n- **向後相容性**: Go 1.23+ 版本都可以編譯此項目\n\n## 開發者注意事項\n\n1. 確保本機安裝的 Go 版本不低於 1.23\n2. Python 環境設定為 3.10，但部分相依性可能需要更高版本\n3. 如需更新版本需求，請同時檢查所有相關設定檔案\n4. 建議定期檢查最新版本的安全性修復和功能改進"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1280,"cache_read_input_tokens":35821,"cache_creation":{"ephemeral_5m_input_tokens":1280,"ephemeral_1h_input_tokens":0},"output_tokens":766,"service_tier":"standard"}},"requestId":"req_011CSa9bUbSwS5qbNFuoaPg4","type":"assistant","uuid":"973eff06-7bef-40c9-9cb7-dc0b016c4dc7","timestamp":"2025-08-28T13:00:02.095Z"}
{"parentUuid":"973eff06-7bef-40c9-9cb7-dc0b016c4dc7","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0188khdXCsjDbEJQMAABhtX2","type":"tool_result","content":"File created successfully at: /home/wei/repo/claude-code/版本需求說明-繁體中文.md"}]},"uuid":"fe8d7423-02ae-46c4-a4df-4d6f65adf041","timestamp":"2025-08-28T13:01:39.481Z","toolUseResult":{"type":"create","filePath":"/home/wei/repo/claude-code/版本需求說明-繁體中文.md","content":"# 版本需求說明\n\n## 概述\n\n此項目支援不同的程式語言版本，開發者可根據需求選擇適當版本。\n\n## Go 語言版本需求\n\n### 目前設定\n- **最低支援版本**: Go 1.23\n- **設定檔案**: `go.mod`\n- **版本宣告**: `go 1.23`\n\n### 版本決定機制\nGo 版本是在執行 `go mod init` 時決定的，會根據當前安裝的 Go 版本自動設定。如需變更，可直接編輯 `go.mod` 文件。\n\n### Go 版本現況 (2025年8月)\n- **最新穩定版**: Go 1.24.6 (2025-08-06 發布)\n- **包含安全性修復**: database/sql 和 os/exec 套件\n- **開發中版本**: Go 1.25 (實驗性功能包含新的 JSON 實作)\n- **建議生產環境**: Go 1.24.6\n\n## Python 版本需求\n\n### 目前設定\n- **指定版本**: Python 3.10\n- **設定檔案**: `.python-version`\n\n### 其他發現的 Python 版本引用\n在程式碼中發現以下寫死的 Python 版本字串：\n- `pyproject.toml` 第6行: `requires-python = \">=3.12\"`\n- `uv.lock` 第3行: `requires-python = \">=3.12\"`\n\n**注意**: 上述檔案中仍有 Python 3.12+ 的版本需求，可能需要根據實際需求進行調整。\n\n## 工具鏈說明\n\n- **移除工具鏈指定**: 已從 `go.mod` 中移除 `toolchain` 指定，系統將使用預設的 Go 工具鏈\n- **向後相容性**: Go 1.23+ 版本都可以編譯此項目\n\n## 開發者注意事項\n\n1. 確保本機安裝的 Go 版本不低於 1.23\n2. Python 環境設定為 3.10，但部分相依性可能需要更高版本\n3. 如需更新版本需求，請同時檢查所有相關設定檔案\n4. 建議定期檢查最新版本的安全性修復和功能改進","structuredPatch":[]}}
{"parentUuid":"fe8d7423-02ae-46c4-a4df-4d6f65adf041","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","message":{"id":"msg_0192CYLPFpgwxJYdoQTx5Dnw","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_01DG4Q1yEGKcZH1gLEcZLRqE","name":"Write","input":{"file_path":"/home/wei/repo/claude-code/版本需求说明-简体中文.md","content":"# 版本需求说明\n\n## 概述\n\n此项目支持不同的程式语言版本，开发者可根据需求选择适当版本。\n\n## Go 语言版本需求\n\n### 当前设置\n- **最低支持版本**: Go 1.23\n- **配置文件**: `go.mod`\n- **版本声明**: `go 1.23`\n\n### 版本决定机制\nGo 版本是在执行 `go mod init` 时决定的，会根据当前安装的 Go 版本自动设置。如需变更，可直接编辑 `go.mod` 文件。\n\n### Go 版本现状 (2025年8月)\n- **最新稳定版**: Go 1.24.6 (2025-08-06 发布)\n- **包含安全性修复**: database/sql 和 os/exec 包\n- **开发中版本**: Go 1.25 (实验性功能包含新的 JSON 实现)\n- **建议生产环境**: Go 1.24.6\n\n## Python 版本需求\n\n### 当前设置\n- **指定版本**: Python 3.10\n- **配置文件**: `.python-version`\n\n### 其他发现的 Python 版本引用\n在程式代码中发现以下写死的 Python 版本字符串：\n- `pyproject.toml` 第6行: `requires-python = \">=3.12\"`\n- `uv.lock` 第3行: `requires-python = \">=3.12\"`\n\n**注意**: 上述文件中仍有 Python 3.12+ 的版本需求，可能需要根据实际需求进行调整。\n\n## 工具链说明\n\n- **移除工具链指定**: 已从 `go.mod` 中移除 `toolchain` 指定，系统将使用默认的 Go 工具链\n- **向后兼容性**: Go 1.23+ 版本都可以编译此项目\n\n## 开发者注意事项\n\n1. 确保本机安装的 Go 版本不低于 1.23\n2. Python 环境设置为 3.10，但部分依赖可能需要更高版本\n3. 如需更新版本需求，请同时检查所有相关配置文件\n4. 建议定期检查最新版本的安全性修复和功能改进"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":807,"cache_read_input_tokens":37101,"cache_creation":{"ephemeral_5m_input_tokens":807,"ephemeral_1h_input_tokens":0},"output_tokens":33,"service_tier":"standard"}},"requestId":"req_011CSa9jwzSKGNSNafEpsLUo","type":"assistant","uuid":"7f7273a7-c64e-4378-bf44-ca67dec8463e","timestamp":"2025-08-28T13:01:51.557Z"}
{"parentUuid":"7f7273a7-c64e-4378-bf44-ca67dec8463e","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"e9f146fa-3b20-48d0-9be4-d99ca901cae4","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DG4Q1yEGKcZH1gLEcZLRqE","type":"tool_result","content":"File created successfully at: /home/wei/repo/claude-code/版本需求说明-简体中文.md"}]},"uuid":"f6520a96-faf8-4348-ad95-bcdd3c718b44","timestamp":"2025-08-28T13:01:54.669Z","toolUseResult":{"type":"create","filePath":"/home/wei/repo/claude-code/版本需求说明-简体中文.md","content":"# 版本需求说明\n\n## 概述\n\n此项目支持不同的程式语言版本，开发者可根据需求选择适当版本。\n\n## Go 语言版本需求\n\n### 当前设置\n- **最低支持版本**: Go 1.23\n- **配置文件**: `go.mod`\n- **版本声明**: `go 1.23`\n\n### 版本决定机制\nGo 版本是在执行 `go mod init` 时决定的，会根据当前安装的 Go 版本自动设置。如需变更，可直接编辑 `go.mod` 文件。\n\n### Go 版本现状 (2025年8月)\n- **最新稳定版**: Go 1.24.6 (2025-08-06 发布)\n- **包含安全性修复**: database/sql 和 os/exec 包\n- **开发中版本**: Go 1.25 (实验性功能包含新的 JSON 实现)\n- **建议生产环境**: Go 1.24.6\n\n## Python 版本需求\n\n### 当前设置\n- **指定版本**: Python 3.10\n- **配置文件**: `.python-version`\n\n### 其他发现的 Python 版本引用\n在程式代码中发现以下写死的 Python 版本字符串：\n- `pyproject.toml` 第6行: `requires-python = \">=3.12\"`\n- `uv.lock` 第3行: `requires-python = \">=3.12\"`\n\n**注意**: 上述文件中仍有 Python 3.12+ 的版本需求，可能需要根据实际需求进行调整。\n\n## 工具链说明\n\n- **移除工具链指定**: 已从 `go.mod` 中移除 `toolchain` 指定，系统将使用默认的 Go 工具链\n- **向后兼容性**: Go 1.23+ 版本都可以编译此项目\n\n## 开发者注意事项\n\n1. 确保本机安装的 Go 版本不低于 1.23\n2. Python 环境设置为 3.10，但部分依赖可能需要更高版本\n3. 如需更新版本需求，请同时检查所有相关配置文件\n4. 建议定期检查最新版本的安全性修复和功能改进","structuredPatch":[]}}
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{"parentUuid":"562d6805-363d-4bf4-aba6-7c6ec68a291d","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"assistant","timestamp":"2025-08-28T13:08:44.666Z","message":{"id":"msg_015oywKF9VvNjAdXTg7S4ZDm","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"text","text":"## 完成總結\n\n### 1. Go 版本問題分析\n- **原因**: go.mod 中 Go 版本設定為 1.23.0，但 toolchain 卻指向 1.24.3，造成版本不一致\n- **解決**: 統一為 `go 1.23` 並移除 toolchain 指定\n\n### 2. 版本更新完成\n- **go.mod**: go.mod:3 - 設定為 `go 1.23`\n- **.python-version**: .python-version:1 - 改為 `3.10`\n\n### 3. 版本字串搜尋結果\n發現其他寫死的 Python 版本：\n- **pyproject.toml:6** - `requires-python = \">=3.12\"`\n- **uv.lock:3** - `requires-python = \">=3.12\"`\n\n### 4. Go 版本決定機制\n是的，Go 版本在 `go mod init` 時決定，根據當前安裝的 Go 版本自動設定。\n\n### 5. Golang 最新版本 (2025年8月)\n- **最新穩定版**: Go 1.24.6 (2025-08-06)\n- **包含**: database/sql 和 os/exec 安全修復\n- **生產建議**: 使用 Go 1.24.6\n\n### 6. 文檔建立\n- **版本需求說明-繁體中文.md** \n- **版本需求说明-简体中文.md**\n\n### 7. 清理\n- TODO.md 已刪除"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1004,"cache_read_input_tokens":40088,"cache_creation":{"ephemeral_5m_input_tokens":1004,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CSa9oak7dP4p3jW2Tpkq6","uuid":"186a3b1b-3a80-453d-b1ee-c7d5ac930721"}
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{"parentUuid":"23de496f-a472-491b-9902-de1a4c526c01","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ttviy6d8MdtsYw4PkmFErg","type":"tool_result","content":[{"type":"text","text":"Available Libraries (top matches):\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Trust Score: Authority indicator\n- Versions: List of versions if available. Use one of those versions if and only if the user explicitly provides a version in their query.\n\nFor best results, select libraries based on name match, trust score, snippet coverage, and relevance to your use case.\n\n----------\n\n- Title: Geo Golang\n- Context7-compatible library ID: /codingsince1985/geo-golang\n- Description: A Go geocoding service that supports multiple geocoding providers, allowing easy switching and concurrent usage.\n- Code Snippets: 11\n- Trust Score: 8.2\n----------\n- Title: GoS7\n- Context7-compatible library ID: /robinson/gos7\n- Description: Implementation of Siemens S7 protocol in golang\n- Code Snippets: 1\n- Trust Score: 7.8\n----------\n- Title: Go Tree-sitter\n- Context7-compatible library ID: /smacker/go-tree-sitter\n- Description: Golang bindings for tree-sitter, a parser generator tool and an incremental parsing library.\n- Code Snippets: 14\n----------\n- Title: H3-Go\n- Context7-compatible library ID: /uber/h3-go\n- Description: Golang bindings for the H3 Core Library, providing geospatial indexing capabilities for efficient spatial analysis and data management.\n- Code Snippets: 7\n- Trust Score: 8.4\n----------\n- Title: Apache SkyWalking Go\n- Context7-compatible library ID: /apache/skywalking-go\n- Description: The Golang auto-instrument Agent for Apache SkyWalking, providing native tracing, metrics, and logging for Golang projects.\n- Code Snippets: 74\n- Trust Score: 9.1\n----------\n- Title: Socket.IO for Golang\n- Context7-compatible library ID: /zishang520/socket.io\n- Description: socket.io for golang, Start your pleasant journey! Support Socket.IO v4+😀\n- Code Snippets: 11\n- Trust Score: 8.6\n----------\n- Title: go-u2u\n- Context7-compatible library ID: /unicornultrafoundation/go-u2u\n- Description: Golang implementation of Unicorn Ultra Distributed Network, based on various open-source projects and research papers related to distributed ledger technology and consensus mechanisms.\n- Code Snippets: 75\n- Trust Score: 5.3\n----------\n- Title: Golang Wayland\n- Context7-compatible library ID: /neurlang/wayland\n- Description: A pure Go implementation of Wayland with sample applications demonstrating rendering into shared memory, including a cube demo and a multiplatform text editor.\n- Code Snippets: 5\n- Trust Score: 7.3\n----------\n- Title: Mail.ru IM Bot Golang\n- Context7-compatible library ID: /mail-ru-im/bot-golang\n- Description: A Golang SDK for interacting with the VK Teams Bot API, enabling developers to create and manage bots for the VK Teams platform.\n- Code Snippets: 6\n- Trust Score: 6\n----------\n- Title: Go Swagger\n- Context7-compatible library ID: /go-swagger/go-swagger\n- Description: A golang implementation of Swagger 2.0 (OpenAPI 2.0) providing tools to generate servers, clients, and CLIs from swagger specifications, and generate specifications from annotated Go code.\n- Code Snippets: 458\n- Trust Score: 6.7\n----------\n- Title: FFmpeg Go\n- Context7-compatible library ID: /u2takey/ffmpeg-go\n- Description: golang binding for ffmpeg\n- Code Snippets: 14\n- Trust Score: 9.4\n----------\n- Title: GooseLang\n- Context7-compatible library ID: /gooselang-dev/gooselang-dev.github.io\n- Description: This website is built using Docusaurus, a modern static website generator, providing instructions for installation, local development, building, and deployment.\n- Code Snippets: 78\n- Trust Score: 2\n----------\n- Title: Postmark\n- Context7-compatible library ID: /mrz1836/postmark\n- Description: A Golang package for using the Postmark API to send emails, manage bounces, templates, and more.\n- Code Snippets: 9\n- Trust Score: 7.5\n----------\n- Title: Sentinel Go\n- Context7-compatible library ID: /alibaba/sentinel-golang\n- Description: Sentinel Golang is a powerful Go library for protecting microservices with flow control, traffic shaping, concurrency limiting, circuit breaking, and overload protection.\n- Code Snippets: 45\n- Trust Score: 9.1\n----------\n- Title: PA-API\n- Context7-compatible library ID: /goark/pa-api\n- Description: A Golang package providing APIs for Amazon Product Advertising API v5, enabling developers to interact with Amazon's product data.\n- Code Snippets: 8\n- Trust Score: 6.8\n----------\n- Title: UniPDF\n- Context7-compatible library ID: /unidoc/unipdf\n- Description: Golang PDF library for creating and processing PDF files (pure go)\n- Code Snippets: 3\n- Trust Score: 9\n----------\n- Title: FalkorDB Go\n- Context7-compatible library ID: /falkordb/falkordb-go\n- Description: A Golang client for FalkorDB\n- Code Snippets: 3\n- Trust Score: 8.5\n----------\n- Title: Librespot Golang\n- Context7-compatible library ID: /davidroman0o/librespot-golang\n- Description: Librespot-golang is an open-source Golang library for controlling Spotify Connect devices, retrieving metadata, and playing music, serving as a replacement for the defunct libspotify.\n- Code Snippets: 4\n- Trust Score: 8.1\n----------\n- Title: Gateway-Websocket\n- Context7-compatible library ID: /shawn-golang/gateway-websocket\n- Description: Gateway-websocket is a high-efficiency, high-stability, and zero-intrusion open-source websocket server developed in Golang, providing a robust platform for real-time communication.\n- Code Snippets: 11\n- Trust Score: 4.6\n----------\n- Title: demoinfocs-golang\n- Context7-compatible library ID: /markus-wa/demoinfocs-golang\n- Description: A fast, feature-complete, and production-ready Go library for parsing and analyzing Counter-Strike 2 and CS:GO demos.\n- Code Snippets: 133\n- Trust Score: 9.8\n----------\n- Title: Telegram Mini Apps Init Data Golang\n- Context7-compatible library ID: /telegram-mini-apps/init-data-golang\n- Description: Golang library to make work with Telegram Mini Apps init data easier.\n- Code Snippets: 5\n- Trust Score: 7.5\n----------\n- Title: SuperTokens GoLang SDK\n- Context7-compatible library ID: /supertokens/supertokens-golang\n- Description: A Golang library for interfacing Go API processes with the SuperTokens HTTP service, enabling seamless integration of authentication and session management.\n- Code Snippets: 62\n- Trust Score: 9.3\n----------\n- Title: Gopay\n- Context7-compatible library ID: /go-pay/gopay\n- Description: GoPay is a Golang SDK that supports WeChat, Alipay, QQ, Allinpay, Lakala, PayPal, Saobei, and Apple Pay, providing a unified interface for various payment gateways.\n- Code Snippets: 41\n- Trust Score: 8.1\n----------\n- Title: Gadb\n- Context7-compatible library ID: /electricbubble/gadb\n- Description: gadb is an ADB client implemented entirely in Golang, allowing developers to interact with Android devices programmatically.\n- Code Snippets: 2\n- Trust Score: 9.2\n----------\n- Title: Open Platform Golang Sdk\n- Context7-compatible library ID: /cleargrass/openapisdkgo\n- Description: This project provides a Golang SDK for interacting with an open platform, simplifying API calls and integration for developers.\n- Code Snippets: 2\n- Trust Score: 4.4\n----------\n- Title: MCP Golang\n- Context7-compatible library ID: /metoro-io/mcp-golang\n- Description: An unofficial implementation of the Model Context Protocol in Go, enabling developers to easily write MCP servers and clients with type safety, custom transports, and low boilerplate.\n- Code Snippets: 79\n- Trust Score: 7.8\n----------\n- Title: Gobble FM\n- Context7-compatible library ID: /twoscott/gobble-fm\n- Description: A Go (Golang) library for the Last.fm API\n- Code Snippets: 4\n- Trust Score: 6.5\n----------\n- Title: PayOS Lib Golang\n- Context7-compatible library ID: /payoshq/payos-lib-golang\n- Description: \n- Code Snippets: 10\n- Trust Score: 4.9\n----------\n- Title: Rollbar Go\n- Context7-compatible library ID: /rollbar/rollbar-go\n- Description: Rollbar Go is a Golang client for Rollbar, a real-time exception reporting service that helps developers track and fix errors in their Go applications.\n- Code Snippets: 3\n- Trust Score: 9.4\n----------\n- Title: Pgxmock\n- Context7-compatible library ID: /pashagolub/pgxmock\n- Description: pgx mock driver for golang to test database interactions \n- Code Snippets: 6\n- Trust Score: 8.9"}]}]},"uuid":"d23a9203-fdc7-4997-a595-b27bdebdb3b0","timestamp":"2025-08-28T13:09:50.943Z","toolUseResult":[{"type":"text","text":"Available Libraries (top matches):\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Trust Score: Authority indicator\n- Versions: List of versions if available. Use one of those versions if and only if the user explicitly provides a version in their query.\n\nFor best results, select libraries based on name match, trust score, snippet coverage, and relevance to your use case.\n\n----------\n\n- Title: Geo Golang\n- Context7-compatible library ID: /codingsince1985/geo-golang\n- Description: A Go geocoding service that supports multiple geocoding providers, allowing easy switching and concurrent usage.\n- Code Snippets: 11\n- Trust Score: 8.2\n----------\n- Title: GoS7\n- Context7-compatible library ID: /robinson/gos7\n- Description: Implementation of Siemens S7 protocol in golang\n- Code Snippets: 1\n- Trust Score: 7.8\n----------\n- Title: Go Tree-sitter\n- Context7-compatible library ID: /smacker/go-tree-sitter\n- Description: Golang bindings for tree-sitter, a parser generator tool and an incremental parsing library.\n- Code Snippets: 14\n----------\n- Title: H3-Go\n- Context7-compatible library ID: /uber/h3-go\n- Description: Golang bindings for the H3 Core Library, providing geospatial indexing capabilities for efficient spatial analysis and data management.\n- Code Snippets: 7\n- Trust Score: 8.4\n----------\n- Title: Apache SkyWalking Go\n- Context7-compatible library ID: /apache/skywalking-go\n- Description: The Golang auto-instrument Agent for Apache SkyWalking, providing native tracing, metrics, and logging for Golang projects.\n- Code Snippets: 74\n- Trust Score: 9.1\n----------\n- Title: Socket.IO for Golang\n- Context7-compatible library ID: /zishang520/socket.io\n- Description: socket.io for golang, Start your pleasant journey! Support Socket.IO v4+😀\n- Code Snippets: 11\n- Trust Score: 8.6\n----------\n- Title: go-u2u\n- Context7-compatible library ID: /unicornultrafoundation/go-u2u\n- Description: Golang implementation of Unicorn Ultra Distributed Network, based on various open-source projects and research papers related to distributed ledger technology and consensus mechanisms.\n- Code Snippets: 75\n- Trust Score: 5.3\n----------\n- Title: Golang Wayland\n- Context7-compatible library ID: /neurlang/wayland\n- Description: A pure Go implementation of Wayland with sample applications demonstrating rendering into shared memory, including a cube demo and a multiplatform text editor.\n- Code Snippets: 5\n- Trust Score: 7.3\n----------\n- Title: Mail.ru IM Bot Golang\n- Context7-compatible library ID: /mail-ru-im/bot-golang\n- Description: A Golang SDK for interacting with the VK Teams Bot API, enabling developers to create and manage bots for the VK Teams platform.\n- Code Snippets: 6\n- Trust Score: 6\n----------\n- Title: Go Swagger\n- Context7-compatible library ID: /go-swagger/go-swagger\n- Description: A golang implementation of Swagger 2.0 (OpenAPI 2.0) providing tools to generate servers, clients, and CLIs from swagger specifications, and generate specifications from annotated Go code.\n- Code Snippets: 458\n- Trust Score: 6.7\n----------\n- Title: FFmpeg Go\n- Context7-compatible library ID: /u2takey/ffmpeg-go\n- Description: golang binding for ffmpeg\n- Code Snippets: 14\n- Trust Score: 9.4\n----------\n- Title: GooseLang\n- Context7-compatible library ID: /gooselang-dev/gooselang-dev.github.io\n- Description: This website is built using Docusaurus, a modern static website generator, providing instructions for installation, local development, building, and deployment.\n- Code Snippets: 78\n- Trust Score: 2\n----------\n- Title: Postmark\n- Context7-compatible library ID: /mrz1836/postmark\n- Description: A Golang package for using the Postmark API to send emails, manage bounces, templates, and more.\n- Code Snippets: 9\n- Trust Score: 7.5\n----------\n- Title: Sentinel Go\n- Context7-compatible library ID: /alibaba/sentinel-golang\n- Description: Sentinel Golang is a powerful Go library for protecting microservices with flow control, traffic shaping, concurrency limiting, circuit breaking, and overload protection.\n- Code Snippets: 45\n- Trust Score: 9.1\n----------\n- Title: PA-API\n- Context7-compatible library ID: /goark/pa-api\n- Description: A Golang package providing APIs for Amazon Product Advertising API v5, enabling developers to interact with Amazon's product data.\n- Code Snippets: 8\n- Trust Score: 6.8\n----------\n- Title: UniPDF\n- Context7-compatible library ID: /unidoc/unipdf\n- Description: Golang PDF library for creating and processing PDF files (pure go)\n- Code Snippets: 3\n- Trust Score: 9\n----------\n- Title: FalkorDB Go\n- Context7-compatible library ID: /falkordb/falkordb-go\n- Description: A Golang client for FalkorDB\n- Code Snippets: 3\n- Trust Score: 8.5\n----------\n- Title: Librespot Golang\n- Context7-compatible library ID: /davidroman0o/librespot-golang\n- Description: Librespot-golang is an open-source Golang library for controlling Spotify Connect devices, retrieving metadata, and playing music, serving as a replacement for the defunct libspotify.\n- Code Snippets: 4\n- Trust Score: 8.1\n----------\n- Title: Gateway-Websocket\n- Context7-compatible library ID: /shawn-golang/gateway-websocket\n- Description: Gateway-websocket is a high-efficiency, high-stability, and zero-intrusion open-source websocket server developed in Golang, providing a robust platform for real-time communication.\n- Code Snippets: 11\n- Trust Score: 4.6\n----------\n- Title: demoinfocs-golang\n- Context7-compatible library ID: /markus-wa/demoinfocs-golang\n- Description: A fast, feature-complete, and production-ready Go library for parsing and analyzing Counter-Strike 2 and CS:GO demos.\n- Code Snippets: 133\n- Trust Score: 9.8\n----------\n- Title: Telegram Mini Apps Init Data Golang\n- Context7-compatible library ID: /telegram-mini-apps/init-data-golang\n- Description: Golang library to make work with Telegram Mini Apps init data easier.\n- Code Snippets: 5\n- Trust Score: 7.5\n----------\n- Title: SuperTokens GoLang SDK\n- Context7-compatible library ID: /supertokens/supertokens-golang\n- Description: A Golang library for interfacing Go API processes with the SuperTokens HTTP service, enabling seamless integration of authentication and session management.\n- Code Snippets: 62\n- Trust Score: 9.3\n----------\n- Title: Gopay\n- Context7-compatible library ID: /go-pay/gopay\n- Description: GoPay is a Golang SDK that supports WeChat, Alipay, QQ, Allinpay, Lakala, PayPal, Saobei, and Apple Pay, providing a unified interface for various payment gateways.\n- Code Snippets: 41\n- Trust Score: 8.1\n----------\n- Title: Gadb\n- Context7-compatible library ID: /electricbubble/gadb\n- Description: gadb is an ADB client implemented entirely in Golang, allowing developers to interact with Android devices programmatically.\n- Code Snippets: 2\n- Trust Score: 9.2\n----------\n- Title: Open Platform Golang Sdk\n- Context7-compatible library ID: /cleargrass/openapisdkgo\n- Description: This project provides a Golang SDK for interacting with an open platform, simplifying API calls and integration for developers.\n- Code Snippets: 2\n- Trust Score: 4.4\n----------\n- Title: MCP Golang\n- Context7-compatible library ID: /metoro-io/mcp-golang\n- Description: An unofficial implementation of the Model Context Protocol in Go, enabling developers to easily write MCP servers and clients with type safety, custom transports, and low boilerplate.\n- Code Snippets: 79\n- Trust Score: 7.8\n----------\n- Title: Gobble FM\n- Context7-compatible library ID: /twoscott/gobble-fm\n- Description: A Go (Golang) library for the Last.fm API\n- Code Snippets: 4\n- Trust Score: 6.5\n----------\n- Title: PayOS Lib Golang\n- Context7-compatible library ID: /payoshq/payos-lib-golang\n- Description: \n- Code Snippets: 10\n- Trust Score: 4.9\n----------\n- Title: Rollbar Go\n- Context7-compatible library ID: /rollbar/rollbar-go\n- Description: Rollbar Go is a Golang client for Rollbar, a real-time exception reporting service that helps developers track and fix errors in their Go applications.\n- Code Snippets: 3\n- Trust Score: 9.4\n----------\n- Title: Pgxmock\n- Context7-compatible library ID: /pashagolub/pgxmock\n- Description: pgx mock driver for golang to test database interactions \n- Code Snippets: 6\n- Trust Score: 8.9"}]}
{"parentUuid":"d23a9203-fdc7-4997-a595-b27bdebdb3b0","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","message":{"id":"msg_011iyjWTA1vpKssgTSBw43Vp","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"text","text":"我需要搜尋更準確的 Go 官方文檔。讓我嘗試搜尋 \"go\" 關鍵詞："}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":2468,"cache_read_input_tokens":39631,"cache_creation":{"ephemeral_5m_input_tokens":2468,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CSaANBGuSpsGa8haatJdy","type":"assistant","uuid":"84b950eb-ef45-41bb-aec5-969d8c76d282","timestamp":"2025-08-28T13:09:54.839Z"}
{"parentUuid":"84b950eb-ef45-41bb-aec5-969d8c76d282","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","message":{"id":"msg_011iyjWTA1vpKssgTSBw43Vp","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_01Sa7wKVvRwjRq615Ye7Lc8p","name":"mcp__context7__resolve-library-id","input":{"libraryName":"go standard library"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":2468,"cache_read_input_tokens":39631,"cache_creation":{"ephemeral_5m_input_tokens":2468,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CSaANBGuSpsGa8haatJdy","type":"assistant","uuid":"0c3d38e4-7278-4c51-8bb9-94eb48b776e5","timestamp":"2025-08-28T13:09:55.321Z"}
{"parentUuid":"0c3d38e4-7278-4c51-8bb9-94eb48b776e5","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Sa7wKVvRwjRq615Ye7Lc8p","type":"tool_result","content":[{"type":"text","text":"Available Libraries (top matches):\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Trust Score: Authority indicator\n- Versions: List of versions if available. Use one of those versions if and only if the user explicitly provides a version in their query.\n\nFor best results, select libraries based on name match, trust score, snippet coverage, and relevance to your use case.\n\n----------\n\n- Title: Gleam Standard Library\n- Context7-compatible library ID: /websites/hexdocs_pm-gleam_stdlib\n- Description: Gleam's standard library provides essential modules for common programming tasks, supporting both Erlang and JavaScript targets. It includes utilities for data structures, I/O, strings, and more.\n- Code Snippets: 364\n- Trust Score: 7.5\n----------\n- Title: Gleam Standard Library\n- Context7-compatible library ID: /gleam-lang/stdlib\n- Description: Gleam's standard library provides essential functions and modules for Gleam programming, supporting both Erlang and JavaScript targets.\n- Code Snippets: 2\n- Trust Score: 8\n----------\n- Title: ModelingToolkit Standard Library\n- Context7-compatible library ID: /sciml/modelingtoolkitstandardlibrary.jl\n- Description: A standard library of components to model the world and beyond\n- Code Snippets: 75\n- Trust Score: 9.7\n----------\n- Title: Gaubee Standard Library\n- Context7-compatible library ID: /gaubee/std\n- Description: Gaubee Standard Library is a collection of reusable packages for various functionalities, including utilities, flow control, and Node.js/Deno specific tools.\n- Code Snippets: 125\n- Trust Score: 8.4\n----------\n- Title: Modelica Standard Library\n- Context7-compatible library ID: /modelica/modelicastandardlibrary\n- Description: The Modelica Standard Library is a free library from the Modelica Association providing model components and standard interfaces for various engineering domains, including mechanical, electrical, thermal, and fluid systems.\n- Code Snippets: 636\n- Trust Score: 8.4\n----------\n- Title: C++ Standard Library\n- Context7-compatible library ID: /p12tic/cppreference-doc\n- Description: C++ standard library reference\n- Code Snippets: 151\n- Trust Score: 9.3\n----------\n- Title: Lo\n- Context7-compatible library ID: /samber/lo\n- Description: 💥  A Lodash-style Go library based on Go 1.18+ Generics (map, filter, contains, find...)\n- Code Snippets: 228\n- Trust Score: 9.6\n- Versions: v1_51_0\n----------\n- Title: go-cmb\n- Context7-compatible library ID: /ahkevinxy/go-cmb\n- Description: A Go library for integrating with China Merchants Bank (CMB) corporate banking direct connection services, supporting various financial operations like account management, payments, and transaction inquiries.\n- Code Snippets: 9\n- Trust Score: 5\n----------\n- Title: Go Atlassian\n- Context7-compatible library ID: /ctreminiom/go-atlassian\n- Description: go-atlassian is a Go library simplifying interaction with Atlassian products' REST APIs, offering functions for Jira, Confluence, and more.\n- Code Snippets: 11\n- Trust Score: 8.4\n----------\n- Title: Microsoft Authentication Library for Go\n- Context7-compatible library ID: /azuread/microsoft-authentication-library-for-go\n- Description: The Microsoft Authentication Library (MSAL) for Go enables signing in users and apps with Microsoft identities and obtaining tokens to call Microsoft APIs using standard OAuth2 and OpenID Connect protocols.\n- Code Snippets: 74\n- Trust Score: 8\n----------\n- Title: XLSX\n- Context7-compatible library ID: /tealeg/xlsx\n- Description: Go library for reading and writing XLSX files. \n- Code Snippets: 31\n- Trust Score: 9.3\n----------\n- Title: GNU C Library\n- Context7-compatible library ID: /bminor/glibc\n- Description: The GNU C Library is the standard C system library for GNU systems, providing the system API for C and C-compatible languages and working with kernels to implement operating system behavior.\n- Code Snippets: 4\n- Trust Score: 7.3\n----------\n- Title: Going.Plaid\n- Context7-compatible library ID: /viceroypenguin/going.plaid\n- Description: Going.Plaid is a .NET library for interacting with Plaid's banking APIs, supporting .NET Standard 2.0, .NET Core 3.1, and .NET 5.0+.\n- Code Snippets: 3\n- Trust Score: 8.5\n----------\n- Title: go-gh v2\n- Context7-compatible library ID: /websites/pkg_go_dev-github.com-cli-go-gh-v2\n- Description: A Go library for the GitHub CLI, making it easier to author GitHub CLI extensions and interact with the GitHub API.\n- Code Snippets: 269\n- Trust Score: 7.5\n----------\n- Title: Standard Schema\n- Context7-compatible library ID: /standard-schema/standard-schema\n- Description: Standard Schema is a specification for a common interface for JavaScript and TypeScript schema validation libraries, enabling easier integration for ecosystem tools without runtime dependencies.\n- Code Snippets: 26\n- Trust Score: 6.7\n----------\n- Title: Stdlib\n- Context7-compatible library ID: /stdlib-js/stdlib\n- Description: stdlib is a standard library for JavaScript, emphasizing numerical and scientific computation for browsers and Node.js, featuring a decomposable architecture and high-quality, well-tested code.\n- Code Snippets: 45645\n----------\n- Title: Go Flags\n- Context7-compatible library ID: /jessevdk/go-flags\n- Description: go-flags is a Go library for parsing command-line arguments, offering more functionality and better formatting than the built-in flag library using reflection and struct tags.\n- Code Snippets: 2\n- Trust Score: 8.7\n----------\n- Title: Go IAP\n- Context7-compatible library ID: /awa/go-iap\n- Description: go-iap is a Go library for verifying purchase receipts across various app stores including AppStore, GooglePlayStore, Amazon AppStore, HMS, and MicrosoftStore.\n- Code Snippets: 12\n- Trust Score: 8\n----------\n- Title: Deno Standard Library\n- Context7-compatible library ID: /denoland/std\n- Description: High-quality APIs for Deno and the web, offering a collection of reliable and well-tested modules for various functionalities.\n- Code Snippets: 8\n- Trust Score: 9.7\n----------\n- Title: Go Redis\n- Context7-compatible library ID: /redis/go-redis\n- Description: go-redis is the official Redis client library for the Go programming language, offering a straightforward interface for interacting with Redis servers, including features like connection pooling, Pub/Sub, pipelines, transactions, and support for Redis Sentinel and Cluster.\n- Code Snippets: 67\n- Trust Score: 9\n----------\n- Title: Go Shopify\n- Context7-compatible library ID: /bold-commerce/go-shopify\n- Description: A Go library for interacting with the Shopify API, supporting OAuth flows, private app authentication, and various client options for API versioning and retries.\n- Code Snippets: 18\n- Trust Score: 7.6\n----------\n- Title: GritQL\n- Context7-compatible library ID: /honeycombio/stdlib\n- Description: The GritQL standard library provides common patterns for refactoring and upgrading code.\n- Code Snippets: 50\n- Trust Score: 10\n----------\n- Title: Go-GitHub\n- Context7-compatible library ID: /google/go-github\n- Description: go-github is a Go client library for accessing the GitHub API v3, providing services to interact with various parts of the GitHub API.\n- Code Snippets: 24\n- Trust Score: 8.9\n----------\n- Title: Open Library RESTful API\n- Context7-compatible library ID: /websites/openlibrary-dev\n- Description: Open Library provides a RESTful API for accessing and querying its vast collection of books and author data in various formats like JSON and RDF. It allows programmatic access to library resources, including editions, works, and recent changes.\n- Code Snippets: 5\n- Trust Score: 7.5\n----------\n- Title: Libpostal\n- Context7-compatible library ID: /openvenues/libpostal\n- Description: A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.\n- Code Snippets: 132\n- Trust Score: 6.8\n----------\n- Title: Go Native Syscall\n- Context7-compatible library ID: /carved4/go-native-syscall\n- Description: A Go library for directly calling native Windows API syscalls, offering both direct and indirect methods for system-level operations.\n- Code Snippets: 12\n- Trust Score: 7\n----------\n- Title: Couchbase Go SDK\n- Context7-compatible library ID: /couchbase/gocb\n- Description: The Go SDK library allows you to connect to a Couchbase cluster from Go, using the included gocbcore library to handle communication over the Couchbase binary protocol.\n- Code Snippets: 8\n- Trust Score: 7\n----------\n- Title: go-tiled\n- Context7-compatible library ID: /lafriks/go-tiled\n- Description: A Go library for parsing Tiled map editor files (TMX) and rendering maps to images, with support for orthogonal finite maps.\n- Code Snippets: 4\n- Trust Score: 9\n----------\n- Title: Cubical Agda\n- Context7-compatible library ID: /agda/cubical\n- Description: A standard library for Cubical Agda, providing a dependently typed programming language with univalence and higher inductive types.\n- Code Snippets: 26\n- Trust Score: 8.6\n----------\n- Title: Go Functional\n- Context7-compatible library ID: /booleancat/go-functional\n- Description: A Go library providing iterators and consumers for functional programming patterns, compatible with Go 1.23+ iterators.\n- Code Snippets: 40\n- Trust Score: 9.2"}]}]},"uuid":"6f320b9a-df1a-4179-bbc7-e7fabb40168e","timestamp":"2025-08-28T13:10:16.141Z","toolUseResult":[{"type":"text","text":"Available Libraries (top matches):\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Trust Score: Authority indicator\n- Versions: List of versions if available. Use one of those versions if and only if the user explicitly provides a version in their query.\n\nFor best results, select libraries based on name match, trust score, snippet coverage, and relevance to your use case.\n\n----------\n\n- Title: Gleam Standard Library\n- Context7-compatible library ID: /websites/hexdocs_pm-gleam_stdlib\n- Description: Gleam's standard library provides essential modules for common programming tasks, supporting both Erlang and JavaScript targets. It includes utilities for data structures, I/O, strings, and more.\n- Code Snippets: 364\n- Trust Score: 7.5\n----------\n- Title: Gleam Standard Library\n- Context7-compatible library ID: /gleam-lang/stdlib\n- Description: Gleam's standard library provides essential functions and modules for Gleam programming, supporting both Erlang and JavaScript targets.\n- Code Snippets: 2\n- Trust Score: 8\n----------\n- Title: ModelingToolkit Standard Library\n- Context7-compatible library ID: /sciml/modelingtoolkitstandardlibrary.jl\n- Description: A standard library of components to model the world and beyond\n- Code Snippets: 75\n- Trust Score: 9.7\n----------\n- Title: Gaubee Standard Library\n- Context7-compatible library ID: /gaubee/std\n- Description: Gaubee Standard Library is a collection of reusable packages for various functionalities, including utilities, flow control, and Node.js/Deno specific tools.\n- Code Snippets: 125\n- Trust Score: 8.4\n----------\n- Title: Modelica Standard Library\n- Context7-compatible library ID: /modelica/modelicastandardlibrary\n- Description: The Modelica Standard Library is a free library from the Modelica Association providing model components and standard interfaces for various engineering domains, including mechanical, electrical, thermal, and fluid systems.\n- Code Snippets: 636\n- Trust Score: 8.4\n----------\n- Title: C++ Standard Library\n- Context7-compatible library ID: /p12tic/cppreference-doc\n- Description: C++ standard library reference\n- Code Snippets: 151\n- Trust Score: 9.3\n----------\n- Title: Lo\n- Context7-compatible library ID: /samber/lo\n- Description: 💥  A Lodash-style Go library based on Go 1.18+ Generics (map, filter, contains, find...)\n- Code Snippets: 228\n- Trust Score: 9.6\n- Versions: v1_51_0\n----------\n- Title: go-cmb\n- Context7-compatible library ID: /ahkevinxy/go-cmb\n- Description: A Go library for integrating with China Merchants Bank (CMB) corporate banking direct connection services, supporting various financial operations like account management, payments, and transaction inquiries.\n- Code Snippets: 9\n- Trust Score: 5\n----------\n- Title: Go Atlassian\n- Context7-compatible library ID: /ctreminiom/go-atlassian\n- Description: go-atlassian is a Go library simplifying interaction with Atlassian products' REST APIs, offering functions for Jira, Confluence, and more.\n- Code Snippets: 11\n- Trust Score: 8.4\n----------\n- Title: Microsoft Authentication Library for Go\n- Context7-compatible library ID: /azuread/microsoft-authentication-library-for-go\n- Description: The Microsoft Authentication Library (MSAL) for Go enables signing in users and apps with Microsoft identities and obtaining tokens to call Microsoft APIs using standard OAuth2 and OpenID Connect protocols.\n- Code Snippets: 74\n- Trust Score: 8\n----------\n- Title: XLSX\n- Context7-compatible library ID: /tealeg/xlsx\n- Description: Go library for reading and writing XLSX files. \n- Code Snippets: 31\n- Trust Score: 9.3\n----------\n- Title: GNU C Library\n- Context7-compatible library ID: /bminor/glibc\n- Description: The GNU C Library is the standard C system library for GNU systems, providing the system API for C and C-compatible languages and working with kernels to implement operating system behavior.\n- Code Snippets: 4\n- Trust Score: 7.3\n----------\n- Title: Going.Plaid\n- Context7-compatible library ID: /viceroypenguin/going.plaid\n- Description: Going.Plaid is a .NET library for interacting with Plaid's banking APIs, supporting .NET Standard 2.0, .NET Core 3.1, and .NET 5.0+.\n- Code Snippets: 3\n- Trust Score: 8.5\n----------\n- Title: go-gh v2\n- Context7-compatible library ID: /websites/pkg_go_dev-github.com-cli-go-gh-v2\n- Description: A Go library for the GitHub CLI, making it easier to author GitHub CLI extensions and interact with the GitHub API.\n- Code Snippets: 269\n- Trust Score: 7.5\n----------\n- Title: Standard Schema\n- Context7-compatible library ID: /standard-schema/standard-schema\n- Description: Standard Schema is a specification for a common interface for JavaScript and TypeScript schema validation libraries, enabling easier integration for ecosystem tools without runtime dependencies.\n- Code Snippets: 26\n- Trust Score: 6.7\n----------\n- Title: Stdlib\n- Context7-compatible library ID: /stdlib-js/stdlib\n- Description: stdlib is a standard library for JavaScript, emphasizing numerical and scientific computation for browsers and Node.js, featuring a decomposable architecture and high-quality, well-tested code.\n- Code Snippets: 45645\n----------\n- Title: Go Flags\n- Context7-compatible library ID: /jessevdk/go-flags\n- Description: go-flags is a Go library for parsing command-line arguments, offering more functionality and better formatting than the built-in flag library using reflection and struct tags.\n- Code Snippets: 2\n- Trust Score: 8.7\n----------\n- Title: Go IAP\n- Context7-compatible library ID: /awa/go-iap\n- Description: go-iap is a Go library for verifying purchase receipts across various app stores including AppStore, GooglePlayStore, Amazon AppStore, HMS, and MicrosoftStore.\n- Code Snippets: 12\n- Trust Score: 8\n----------\n- Title: Deno Standard Library\n- Context7-compatible library ID: /denoland/std\n- Description: High-quality APIs for Deno and the web, offering a collection of reliable and well-tested modules for various functionalities.\n- Code Snippets: 8\n- Trust Score: 9.7\n----------\n- Title: Go Redis\n- Context7-compatible library ID: /redis/go-redis\n- Description: go-redis is the official Redis client library for the Go programming language, offering a straightforward interface for interacting with Redis servers, including features like connection pooling, Pub/Sub, pipelines, transactions, and support for Redis Sentinel and Cluster.\n- Code Snippets: 67\n- Trust Score: 9\n----------\n- Title: Go Shopify\n- Context7-compatible library ID: /bold-commerce/go-shopify\n- Description: A Go library for interacting with the Shopify API, supporting OAuth flows, private app authentication, and various client options for API versioning and retries.\n- Code Snippets: 18\n- Trust Score: 7.6\n----------\n- Title: GritQL\n- Context7-compatible library ID: /honeycombio/stdlib\n- Description: The GritQL standard library provides common patterns for refactoring and upgrading code.\n- Code Snippets: 50\n- Trust Score: 10\n----------\n- Title: Go-GitHub\n- Context7-compatible library ID: /google/go-github\n- Description: go-github is a Go client library for accessing the GitHub API v3, providing services to interact with various parts of the GitHub API.\n- Code Snippets: 24\n- Trust Score: 8.9\n----------\n- Title: Open Library RESTful API\n- Context7-compatible library ID: /websites/openlibrary-dev\n- Description: Open Library provides a RESTful API for accessing and querying its vast collection of books and author data in various formats like JSON and RDF. It allows programmatic access to library resources, including editions, works, and recent changes.\n- Code Snippets: 5\n- Trust Score: 7.5\n----------\n- Title: Libpostal\n- Context7-compatible library ID: /openvenues/libpostal\n- Description: A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.\n- Code Snippets: 132\n- Trust Score: 6.8\n----------\n- Title: Go Native Syscall\n- Context7-compatible library ID: /carved4/go-native-syscall\n- Description: A Go library for directly calling native Windows API syscalls, offering both direct and indirect methods for system-level operations.\n- Code Snippets: 12\n- Trust Score: 7\n----------\n- Title: Couchbase Go SDK\n- Context7-compatible library ID: /couchbase/gocb\n- Description: The Go SDK library allows you to connect to a Couchbase cluster from Go, using the included gocbcore library to handle communication over the Couchbase binary protocol.\n- Code Snippets: 8\n- Trust Score: 7\n----------\n- Title: go-tiled\n- Context7-compatible library ID: /lafriks/go-tiled\n- Description: A Go library for parsing Tiled map editor files (TMX) and rendering maps to images, with support for orthogonal finite maps.\n- Code Snippets: 4\n- Trust Score: 9\n----------\n- Title: Cubical Agda\n- Context7-compatible library ID: /agda/cubical\n- Description: A standard library for Cubical Agda, providing a dependently typed programming language with univalence and higher inductive types.\n- Code Snippets: 26\n- Trust Score: 8.6\n----------\n- Title: Go Functional\n- Context7-compatible library ID: /booleancat/go-functional\n- Description: A Go library providing iterators and consumers for functional programming patterns, compatible with Go 1.23+ iterators.\n- Code Snippets: 40\n- Trust Score: 9.2"}]}
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{"parentUuid":"b42793d3-b228-4992-8424-b0e9edc85624","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QBgZg2nZd8qZa5MzDhgf2k","type":"tool_result","content":[{"type":"text","text":"Available Libraries (top matches):\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Trust Score: Authority indicator\n- Versions: List of versions if available. 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It uses type hints to define data schemas, enabling powerful validation and serialization.\n- Code Snippets: 2479\n- Trust Score: 7.5\n----------\n- Title: Pydantic AI\n- Context7-compatible library ID: /pydantic/pydantic-ai\n- Description: Pydantic AI is a Python agent framework designed to simplify building production-grade applications with Generative AI, bringing a FastAPI-like developer experience to GenAI development.\n- Code Snippets: 402\n- Trust Score: 9.6\n- Versions: v0.0.49, v0_7_0\n----------\n- Title: Pydantic (llmstxt)\n- Context7-compatible library ID: /llmstxt/pydantic_dev-llms-full.txt\n- Description: Pydantic is a Python library for data validation using type hints, offering fast and extensible validation for Python 3.9+.\n- Code Snippets: 3387\n- Trust Score: 8\n----------\n- Title: Pydantic\n- Context7-compatible library ID: /websites/pydantic_dev_1_10\n- Description: Pydantic enforces type hints at runtime and provides user-friendly errors when data is invalid, enabling data validation and settings management using Python type annotations.\n- Code Snippets: 882\n- Trust Score: 7.5\n----------\n- Title: Pydantic AI\n- Context7-compatible library ID: /websites/ai_pydantic_dev\n- Description: PydanticAI is a Python agent framework that brings the ergonomic design of FastAPI to Generative AI application development. It leverages Pydantic for type-safe, structured responses and offers model-agnostic support for building robust LLM-powered applications.\n- Code Snippets: 3069\n- Trust Score: 7.5\n----------\n- Title: Pydantic AI (llmstxt)\n- Context7-compatible library ID: /llmstxt/ai_pydantic_dev-llms-full.txt\n- Description: Pydantic AI is a Python agent framework that simplifies building production-grade applications with Generative AI by providing an agent interface for interacting with LLMs.\n- Code Snippets: 8735\n- Trust Score: 8\n----------\n- Title: Pydantic Settings\n- Context7-compatible library ID: /pydantic/pydantic-settings\n- Description: Pydantic-settings is a library for managing application settings using Pydantic, enabling easy configuration loading from various sources.\n- Code Snippets: 75\n- Trust Score: 9.6\n----------\n- Title: Pydantic RDF\n- Context7-compatible library ID: /omegaice/pydantic-rdf\n- Description: PydanticRDF is a Python library that bridges Pydantic V2 models 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and RDF graphs, enabling seamless bidirectional conversion between typed data models and semantic web data.\n- Code Snippets: 24\n- Trust Score: 7.2\n----------\n- Title: Pydantic XML\n- Context7-compatible library ID: /dapper91/pydantic-xml\n- Description: python xml for humans\n- Code Snippets: 104\n- Trust Score: 8.1\n----------\n- Title: OpenAPI Pydantic\n- Context7-compatible library ID: /mike-oakley/openapi-pydantic\n- Description: A Python library for generating modern, type-safe OpenAPI schemas using Pydantic, supporting both Pydantic v1.8+ and v2.x.\n- Code Snippets: 11\n- Trust Score: 5.7\n----------\n- Title: Argdantic\n- Context7-compatible library ID: /edornd/argdantic\n- Description: Argdantic creates typed command-line interfaces using argparse and pydantic, supporting nested models, commands, validation, and multiple argument sources like environment variables and files.\n- Code Snippets: 57\n- Trust Score: 7.9\n----------\n- Title: Firedantic\n- Context7-compatible library ID: /ioxiocom/firedantic\n- Description: Firedantic provides database models for Firestore using Pydantic base models, enabling seamless integration with Google Cloud Firestore for data storage and querying.\n- Code Snippets: 15\n- Trust Score: 6.3\n----------\n- Title: Pydantic Settings Vault\n- Context7-compatible library ID: /aleksey925/pydantic-settings-vault\n- Description: A Pydantic extension that retrieves secrets from Hashicorp Vault, integrating seamlessly with pydantic-settings for type-hinted configuration loading.\n- Code Snippets: 22\n- Trust Score: 5.8\n----------\n- Title: Pydantic AI Research Agent\n- Context7-compatible library ID: /coleam00/pydanticai-research-agent\n- Description: A multi-agent system built with Pydantic AI that combines web search capabilities with automated email generation, featuring intelligent research agents and specialized email agents with Gmail integration.\n- Code Snippets: 89\n- Trust Score: 8.5\n----------\n- Title: ODMantic\n- Context7-compatible library ID: /websites/art049_github_io_odmantic\n- Description: ODMantic is a Python Object Document Mapper for MongoDB, built on Pydantic for model definition and validation, offering a simple, typed, and async-friendly way to interact with databases.\n- Code Snippets: 366\n- Trust Score: 7.5\n----------\n- Title: Pydantic SQLAlchemy\n- Context7-compatible library ID: /tiangolo/pydantic-sqlalchemy\n- Description: Tools to convert SQLAlchemy models to Pydantic models\n- Code Snippets: 3\n- Trust Score: 9.9\n----------\n- Title: Pydantic Kedro\n- Context7-compatible library ID: /nowanilfideme/pydantic-kedro\n- Description: Pydantic Kedro provides advanced serialization for Pydantic models using Kedro and fsspec, enabling custom Kedro datasets for pure and arbitrary Pydantic models.\n- Code Snippets: 13\n- Trust Score: 8.5\n----------\n- Title: Json Schema To Pydantic\n- Context7-compatible library ID: /richard-gyiko/json-schema-to-pydantic\n- Description: A Python library for automatically generating Pydantic v2 models from JSON Schema definitions, supporting complex schema features and full type hinting.\n- Code Snippets: 13\n- Trust Score: 6.2\n----------\n- Title: Pydantic AI GitHub Agent\n- Context7-compatible library ID: /coleam00/pydantic-ai-github-agent\n- Description: An intelligent agent that analyzes GitHub repositories using Pydantic AI, capable of fetching repository information, exploring directory structures, and analyzing file contents via the GitHub API.\n- Code Snippets: 9\n- Trust Score: 8.5\n----------\n- Title: Django Pydantic Field\n- Context7-compatible library ID: /surenkov/django-pydantic-field\n- Description: Django Pydantic Field provides a Django JSONField that uses Pydantic models as its schema, supporting both Pydantic v1 and v2 for robust data validation and serialization.\n- Code Snippets: 9\n- Trust Score: 8.3\n----------\n- Title: Logfire\n- Context7-compatible library ID: /pydantic/logfire\n- Description: Pydantic Logfire is an observability platform that provides Python-centric insights, SQL querying, and OpenTelemetry integration for faster and easier application development.\n- Code Snippets: 419\n- Trust Score: 9.6\n----------\n- Title: Pydantic to TypeScript\n- Context7-compatible library ID: /phillipdupuis/pydantic-to-typescript\n- Description: CLI Tool for converting pydantic models into typescript definitions\n- Code Snippets: 7\n- Trust Score: 6.7\n----------\n- Title: Flask-OpenAPI3\n- Context7-compatible library ID: /luolingchun/flask-openapi3\n- Description: Flask OpenAPI3 is a web API framework for Flask that uses Pydantic for data validation and automatic generation of interactive OpenAPI documentation.\n- Code Snippets: 130\n- Trust Score: 7.7\n----------\n- Title: Patito\n- Context7-compatible library ID: /jakobgm/patito\n- Description: Patito combines pydantic and polars to enable modern, type-annotated data frame logic, offering validation, mock data generation, and object-oriented row representation.\n- Code Snippets: 43\n- Trust Score: 9.6\n----------\n- Title: PyOrthanc\n- Context7-compatible library ID: /gacou54/pyorthanc\n- Description: PyOrthanc is a Python client for the Orthanc PACS server, offering a comprehensive wrapper for its REST API, high-level DICOM utilities, asynchronous support, and integration with the Orthanc Python plugin.\n- Code Snippets: 181\n- Trust Score: 7.8\n----------\n- Title: Py3xui\n- Context7-compatible library ID: /iwatkot/py3xui\n- Description: A synchronous and asynchronous object-oriented Python SDK for interacting with the 3x-ui API, utilizing Pydantic for data validation.\n- Code Snippets: 111\n- Trust Score: 7.4\n----------\n- Title: Pyadomd\n- Context7-compatible library ID: /s-c-o-u-t/pyadomd\n- Description: Pyadomd provides a Pythonic way to query SSAS (SQL Server Analysis Services) data models, integrating seamlessly with pandas for data manipulation.\n- Code Snippets: 8\n- Trust Score: 4.8\n----------\n- Title: Mihomo\n- Context7-compatible library ID: /metacubex/mihomo\n- Description: A Python Pydantic model for Honkai: Star Rail parsed data from the Mihomo API, offering type hints and autocompletion support for easy data handling.\n- Code Snippets: 5\n- Trust Score: 6.8\n- Versions: v1.19.10\n----------\n- Title: Mihomo\n- Context7-compatible library ID: /epidnkkeu/mihomo\n- Description: A simple Python Pydantic model for Honkai: Star Rail parsed data from the Mihomo API.\n- Code Snippets: 5\n- Trust Score: 1.7"}]}
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{"parentUuid":"3fa0a9ab-1857-4db0-8bd7-1ab0d91a8bfc","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JT8KPFatT9ZnyEhK2T8add","type":"tool_result","content":[{"type":"text","text":"========================\nCODE SNIPPETS\n========================\nTITLE: Pydantic Field Customization with Field()\nDESCRIPTION: Demonstrates basic customization of Pydantic model fields using the Field() function for required fields and frozen attributes.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_0\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    name: str = Field(frozen=True)\n```\n\n----------------------------------------\n\nTITLE: Field Exclusion at Field Level\nDESCRIPTION: Shows how to exclude fields during serialization directly at the field definition using `Field(exclude=True)` or `Field(exclude_if=...)`. This provides a declarative way to manage serialization output.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_19\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Transaction(BaseModel):\n    id: int\n    private_id: int = Field(exclude=True)\n    value: int = Field(ge=0, exclude_if=lambda v: v == 0)\n\n\nprint(Transaction(id=1, private_id=2, value=0).model_dump())\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field vs. Type Metadata with Annotated\nDESCRIPTION: Highlights the difference in applying metadata to fields versus types using Annotated and Field(), and the impact on features like deprecation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_4\n\nLANGUAGE: python\nCODE:\n```\nclass Model(BaseModel):\n    field_bad: Annotated[int, Field(deprecated=True)] | None = None  # (1)!\n    field_ok: Annotated[int | None, Field(deprecated=True)] = None  # (2)!\n\n      # 1. The [`Field()`][pydantic.fields.Field] function is applied to `int` type, hence the\n      #    `deprecated` flag won't have any effect. While this may be confusing given that the name of\n      #    the [`Field()`][pydantic.fields.Field] function would imply it should apply to the field, \n      #    the API was designed when this function was the only way to provide metadata. You can\n      #    alternatively make use of the [`annotated_types`](https://github.com/annotated-types/annotated-types)\n      #    library which is now supported by Pydantic.\n      #\n      # 2. The [`Field()`][pydantic.fields.Field] function is applied to the \"top-level\" union type, \n      #    hence the `deprecated` flag will be applied to the field.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Annotated Pattern for Field Metadata\nDESCRIPTION: Illustrates using the Annotated typing construct to attach metadata like Field() and WithJsonSchema to model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_2\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field, WithJsonSchema\n\n\nclass Model(BaseModel):\n    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]\n```\n\n----------------------------------------\n\nTITLE: Pydantic Dataclass Field Declaration\nDESCRIPTION: Enhances Pydantic dataclass field declarations by allowing the use of Pydantic's `Field` or the `'metadata'` keyword argument from `dataclasses.field`. This provides more flexibility in defining fields within Pydantic-integrated dataclasses.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_142\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic Dataclass Field Declaration:\n\nAllows using `pydantic.Field` or `dataclasses.field(metadata=...)` for field declarations in Pydantic dataclasses.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization with Ellipsis\nDESCRIPTION: Shows how to use the ellipsis (...) with Field() to explicitly mark a field as required, even when a value is assigned.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_1\n\nLANGUAGE: python\nCODE:\n```\nclass Model(BaseModel):\n    name: str = Field(..., frozen=True)\n```\n\n----------------------------------------\n\nTITLE: Field Representation Control with Pydantic\nDESCRIPTION: Explains how to use the `repr` parameter in Pydantic's Field to include or exclude fields from the model's string representation. Provides an example demonstrating this functionality.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_24\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(repr=True)  # (1)!\n    age: int = Field(repr=False)\n\n\nuser = User(name='John', age=42)\nprint(user)\n#> name='John'\n```\n\n----------------------------------------\n\nTITLE: Immutable Fields (Frozen)\nDESCRIPTION: Demonstrates the use of the `frozen=True` parameter on Pydantic fields to make them immutable. Once a model with frozen fields is created, these fields cannot be reassigned, emulating frozen dataclass behavior.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_28\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass User(BaseModel):\n    name: str = Field(frozen=True)\n    age: int\n\n\nuser = User(name='John', age=42)\n\ntry:\n    user.name = 'Jane'  # (1)!\nexcept ValidationError as e:\n    print(e)\n    \"\"\"\n    1 validation error for User\n    name\n      Field is frozen [type=frozen_field, input_value='Jane', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Field repr parameter to hide fields\nDESCRIPTION: Introduces a `repr` parameter to `Field`, defaulting to `True`. This parameter allows users to hide specific fields from the default string representation of a `BaseModel`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_125\n\nLANGUAGE: python\nCODE:\n```\nAdd `repr` (defaults to `True`) parameter to `Field`, to hide it from the default representation of the `BaseModel`, [#1831](https://github.com/pydantic/pydantic/pull/1831) by @fnep\n```\n\n----------------------------------------\n\nTITLE: Pydantic Annotated Field Constraints\nDESCRIPTION: Demonstrates using `Annotated` with Pydantic `Field` for constraints, highlighting potential issues with compound types and recommending `Annotated` for safety.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_19\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated, Optional\n\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    positive: Optional[Annotated[int, Field(gt=0)]]\n    # Can error in some cases, not recommended:\n    non_negative: Optional[int] = Field(ge=0)\n\n```\n\n----------------------------------------\n\nTITLE: Field Strictness for Individual Model Fields\nDESCRIPTION: Demonstrates how to apply strict=True to individual fields using pydantic.Field. This ensures strict validation for that specific field, even when the validation method is called without strict=True.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_7\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass User(BaseModel):\n    name: str\n    age: int\n    n_pets: int\n\n\nuser = User(name='John', age='42', n_pets='1')\nprint(user)\n#> name='John' age=42 n_pets=1\n\n\nclass AnotherUser(BaseModel):\n    name: str\n    age: int = Field(strict=True)\n    n_pets: int\n\n\ntry:\n    anotheruser = AnotherUser(name='John', age='42', n_pets='1')\nexcept ValidationError as e:\n    print(e)\n    \"\"\"\n    1 validation error for AnotherUser\n    age\n      Input should be a valid integer [type=int_type, input_value='42', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Discriminator with Field Name\nDESCRIPTION: Demonstrates using the `discriminator` parameter with a field name to control discriminated unions in Pydantic models. This is useful when a single field determines the type within a union.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_25\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Literal, Union\n\nfrom pydantic import BaseModel, Field\n\n\nclass Cat(BaseModel):\n    pet_type: Literal['cat']\n    age: int\n\n\nclass Dog(BaseModel):\n    pet_type: Literal['dog']\n    age: int\n\n\nclass Model(BaseModel):\n    pet: Union[Cat, Dog] = Field(discriminator='pet_type')\n\n\nprint(Model.model_validate({'pet': {'pet_type': 'cat', 'age': 12}}))  # (1)!\n#> pet=Cat(pet_type='cat', age=12)\n```\n\n----------------------------------------\n\nTITLE: Pydantic `Field()` in Dataclasses\nDESCRIPTION: Refactors logic to better support Pydantic's `Field()` function when used within Python dataclasses. This allows for more consistent application of Pydantic's validation and configuration features to fields defined in dataclasses.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom dataclasses import dataclass\nfrom pydantic import Field\n\n@dataclass\nclass Product:\n    name: str = Field(..., description=\"The name of the product\")\n    price: float = Field(gt=0, description=\"The price of the product\")\n\n# This dataclass can now be used with Pydantic's validation and schema generation.\n# Example:\n# product = Product(name=\"Laptop\", price=1200.50)\n# print(product.model_dump())\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Alias with Static Type Checking (Field)\nDESCRIPTION: Explains how Pydantic's `Field(alias='...')` affects static type checking, making the alias the expected identifier for initialization. It highlights potential type checker errors when `validate_by_name=True` and the actual field name is used.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(alias='username')\n\n\nuser = User(username='johndoe')  # (1)!\n```\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\nclass User(BaseModel):\n    model_config = ConfigDict(validate_by_name=True)\n\n    name: str = Field(alias='username')\n\n\nuser = User(name='johndoe')  # (1)!\n```\n\n----------------------------------------\n\nTITLE: String Constraints with Pydantic\nDESCRIPTION: Demonstrates how to use Pydantic's Field to enforce minimum length, maximum length, and regular expression patterns on string fields within a BaseModel. Includes an example of model instantiation and output.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_20\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    short: str = Field(min_length=3)\n    long: str = Field(max_length=10)\n    regex: str = Field(pattern=r'^\\d*$')  # (1)!\n\n\nfoo = Foo(short='foo', long='foobarbaz', regex='123')\nprint(foo)\n#> short='foo' long='foobarbaz' regex='123'\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization API\nDESCRIPTION: Provides an overview of the pydantic.fields.Field function for customizing Pydantic model fields, including default values, JSON Schema metadata, and constraints.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_5\n\nLANGUAGE: APIDOC\nCODE:\n```\npydantic.fields.Field\n\nUsed to customize Pydantic model fields, providing mechanisms for default values, JSON Schema metadata, constraints, and more. It behaves similarly to dataclasses.field.\n\nUsage:\n```python\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    name: str = Field(frozen=True)\n    # Or to explicitly mark as required:\n    # name: str = Field(..., frozen=True)\n```\n\nKey Parameters and Concepts:\n- `default`: Sets a default value for the field. If not provided and the field is not marked with `...` (ellipsis), it's considered optional.\n- `default_factory`: A callable that returns a default value. Useful for mutable defaults like lists or dictionaries.\n- `alias`: Specifies a different name for the field when parsing JSON.\n- `frozen`: If True, the field cannot be changed after initialization.\n- `...` (Ellipsis): Used to explicitly mark a field as required, even if a default value is assigned.\n- `Annotated`: Can be used with `Field` to attach metadata to a field's type, allowing for more complex validation and customization.\n\nExample with Annotated:\n```python\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field, WithJsonSchema\n\nclass Model(BaseModel):\n    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]\n```\n\nConsiderations:\n- Arguments like `default`, `default_factory`, and `alias` are recognized by static type checkers for `__init__` synthesis.\n- The `Annotated` pattern is not directly understood by static type checkers for `__init__` synthesis, so use the normal assignment form when type checker compatibility is critical for these arguments.\n- Metadata applied via `Annotated` can be specific to the type or the field. Ensure correct placement to achieve the desired effect (e.g., `deprecated` flag applies to the field when applied to the top-level type).\n```\n\n----------------------------------------\n\nTITLE: Deprecate Field with deprecated=True in Field\nDESCRIPTION: Shows how to mark a Pydantic field as deprecated by setting the `deprecated` argument to `True` within the `Field` function. This method also illustrates how to inspect the generated JSON schema to verify the deprecation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_32\n\nLANGUAGE: Python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, Field(deprecated=True)]\n\n\nprint(Model.model_json_schema()['properties']['deprecated_field'])\n#> {'deprecated': True, 'title': 'Deprecated Field', 'type': 'integer'}\n\n```\n\n----------------------------------------\n\nTITLE: Required Optional Fields\nDESCRIPTION: Introduces support for defining fields as required Optional types using `name: Optional[AnyType] = Field(...)`. This change refactors `ModelField` creation to correctly preserve the `required` parameter value, enhancing flexibility in model definitions.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_247\n\nLANGUAGE: APIDOC\nCODE:\n```\nname: Optional[AnyType] = Field(...)\n\n# Example usage:\nfrom typing import Optional\nfrom pydantic import BaseModel, Field\n\nclass MyModel(BaseModel):\n    my_field: Optional[str] = Field(None)\n    required_optional_field: Optional[int] = Field(...)\n\n```\n\n----------------------------------------\n\nTITLE: Excluding Fields During Export\nDESCRIPTION: Explains how to use the `exclude=True` parameter on a Pydantic `Field` to prevent that field from being included when exporting the model, for example, using `model_dump()`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_29\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str\n    age: int = Field(exclude=True)\n\n\nuser = User(name='John', age=42)\nprint(user.model_dump())  # (1)!\n#> {'name': 'John'}\n```\n\n----------------------------------------\n\nTITLE: Custom Field Serializer with Context\nDESCRIPTION: Demonstrates using a custom field serializer that accesses context information for conditional data processing. The serializer removes specified stopwords from the text field based on the provided context.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, FieldSerializationInfo, field_serializer\n\n\nclass Model(BaseModel):\n    text: str\n\n    @field_serializer('text', mode='plain')\n    @classmethod\n    def remove_stopwords(cls, v: str, info: FieldSerializationInfo) -> str:\n        if isinstance(info.context, dict):\n            stopwords = info.context.get('stopwords', set())\n            v = ' '.join(w for w in v.split() if w.lower() not in stopwords)\n        return v\n\n\nmodel = Model(text='This is an example document')\nprint(model.model_dump())\nprint(model.model_dump(context={'stopwords': ['this', 'is', 'an']}))\n```\n\n----------------------------------------\n\nTITLE: Decorator Pattern for Field Serialization\nDESCRIPTION: Illustrates using the @field_serializer decorator to apply a serialization function to multiple fields. It covers options like applying to all fields ('*') and disabling field existence checks.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_10\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, field_serializer\n\n\nclass Model(BaseModel):\n    f1: str\n    f2: str\n\n    @field_serializer('f1', 'f2', mode='plain')\n    def capitalize(self, value: str) -> str:\n        return value.capitalize()\n```\n\n----------------------------------------\n\nTITLE: Overriding Strict Mode with Field(strict=False)\nDESCRIPTION: Demonstrates how to selectively disable strict mode for individual fields within a Pydantic model that has global strict mode enabled via `ConfigDict(strict=True)`. This is achieved by using `Field(strict=False)` on the specific field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\nclass User(BaseModel):\n    model_config = ConfigDict(strict=True)\n\n    name: str\n    age: int = Field(strict=False)\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization with Field Parameters\nDESCRIPTION: Demonstrates using Pydantic's Field function to customize model fields with parameters like description, examples, title, and json_schema_extra. This allows for richer metadata and control over the generated JSON schema.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_5\n\nLANGUAGE: python\nCODE:\n```\nimport json\n\nfrom pydantic import BaseModel, EmailStr, Field, SecretStr\n\n\nclass User(BaseModel):\n    age: int = Field(description='Age of the user')\n    email: EmailStr = Field(examples=['marcelo@mail.com'])\n    name: str = Field(title='Username')\n    password: SecretStr = Field(\n        json_schema_extra={\n            'title': 'Password',\n            'description': 'Password of the user',\n            'examples': ['123456'],\n        }\n    )\n\n\nprint(json.dumps(User.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"age\": {\n      \"description\": \"Age of the user\",\n      \"title\": \"Age\",\n      \"type\": \"integer\"\n    },\n    \"email\": {\n      \"examples\": [\n        \"marcelo@mail.com\"\n      ],\n      \"format\": \"email\",\n      \"title\": \"Email\",\n      \"type\": \"string\"\n    },\n    \"name\": {\n      \"title\": \"Username\",\n      \"type\": \"string\"\n    },\n    \"password\": {\n      \"description\": \"Password of the user\",\n      \"examples\": [\n        \"123456\"\n      ],\n      \"format\": \"password\",\n      \"title\": \"Password\",\n      \"type\": \"string\",\n      \"writeOnly\": true\n    }\n  },\n  \"required\": [\n    \"age\",\n    \"email\",\n    \"name\",\n    \"password\"\n  ],\n  \"title\": \"User\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Field Strictness Affecting Model Instantiation\nDESCRIPTION: Shows that setting strict=True on a Field affects validation during direct model instantiation. It demonstrates that a field marked as strict will reject string inputs, while a field marked as strict=False will accept them.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass Model(BaseModel):\n    x: int = Field(strict=True)\n    y: int = Field(strict=False)\n\n\ntry:\n    Model(x='1', y='2')\nexcept ValidationError as exc:\n    print(exc)\n    \"\"\"\n    1 validation error for Model\n    x\n      Input should be a valid integer [type=int_type, input_value='1', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Alias for Validation and Serialization\nDESCRIPTION: Demonstrates using the `alias` parameter in Pydantic's `Field` to specify a name for both validation and serialization. The example shows creating a model instance with the alias and dumping it back to a dictionary using the alias.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_11\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(alias='username')\n\n\nuser = User(username='johndoe')  # (1)!\nprint(user)\n#> name='johndoe'\nprint(user.model_dump(by_alias=True))  # (2)!\n#>\n#> {'username': 'johndoe'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic Serialization Alias Only\nDESCRIPTION: Shows how to use `serialization_alias` in Pydantic's `Field` for an alias used exclusively during serialization. The example demonstrates validation using the field name and serialization using the specified alias.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_13\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(serialization_alias='username')\n\n\nuser = User(name='johndoe')  # (1)!\nprint(user)\n#> name='johndoe'\nprint(user.model_dump(by_alias=True))  # (2)!\n#>\n#> {'username': 'johndoe'}\n```\n\n----------------------------------------\n\nTITLE: Update Config.field to update Field elements\nDESCRIPTION: Allows elements defined in `Config.field` to correctly update corresponding elements of a `Field` definition. This enhances flexibility in configuring model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_133\n\nLANGUAGE: python\nCODE:\n```\nallow elements of `Config.field` to update elements of a `Field`, [#2461](https://github.com/pydantic/pydantic/pull/2461) by @samuelcolvin\n```\n\n----------------------------------------\n\nTITLE: Pydantic Dataclass with Fields and Metadata\nDESCRIPTION: Shows how to use `dataclasses.field` and Pydantic's `Field` for default values, factories, and metadata like titles and constraints.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/dataclasses.md#_snippet_1\n\nLANGUAGE: python\nCODE:\n```\nimport dataclasses\nfrom typing import Optional\n\nfrom pydantic import Field\nfrom pydantic.dataclasses import dataclass\n\n\n@dataclass\nclass User:\n    id: int\n    name: str = 'John Doe'\n    friends: list[int] = dataclasses.field(default_factory=lambda: [0])\n    age: Optional[int] = dataclasses.field(\n        default=None,\n        metadata={'title': 'The age of the user', 'description': 'do not lie!'},\n    )\n    height: Optional[int] = Field(\n        default=None, title='The height in cm', ge=50, le=300\n    )\n\n\nuser = User(id='42', height='250')\nprint(user)\n```\n\n----------------------------------------\n\nTITLE: Ellipsis on Field in Annotated for TypedDicts\nDESCRIPTION: Allows the use of ellipsis (`...`) on `Field`s within `Annotated` for `TypedDict`s, marking fields as required and improving type hint clarity.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_109\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated, TypedDict\nfrom pydantic import Field\n\nclass UserInfo(TypedDict):\n    # Ellipsis indicates 'name' is a required field\n    name: Annotated[str, Field(required=True)]\n    age: int\n\n# Example:\n# user_data: UserInfo = {'name': 'Alice', 'age': 30}\n# This syntax helps Pydantic and type checkers understand required fields.\n```\n\n----------------------------------------\n\nTITLE: Field Strictness as Annotation with TypedDict\nDESCRIPTION: Illustrates using Field(strict=True) as an annotation, particularly useful with TypedDict. This allows applying strict validation rules to fields within a TypedDict structure when used with Pydantic's TypeAdapter.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_9\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom pydantic import Field, TypeAdapter, ValidationError\n\n\nclass MyDict(TypedDict):\n    x: Annotated[int, Field(strict=True)]\n\n\ntry:\n    TypeAdapter(MyDict).validate_python({'x': '1'})\nexcept ValidationError as exc:\n    print(exc)\n    \"\"\"\n    1 validation error for MyDict\n    x\n      Input should be a valid integer [type=int_type, input_value='1', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Annotated Pattern for Type Constraints\nDESCRIPTION: Demonstrates applying validation constraints to elements within a list using the Annotated pattern with Field().\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_3\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    int_list: list[Annotated[int, Field(gt=0)]]\n    # Valid: [1, 3]\n    # Invalid: [-1, 2]\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field allow_mutation Constraint\nDESCRIPTION: Introduces the `allow_mutation` constraint to the `Field` function. This allows explicit control over whether a field's value can be mutated after model instantiation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_156\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic Field allow_mutation Constraint:\n\nAdds `allow_mutation` constraint to `Field`.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Types: Deque and Enums\nDESCRIPTION: Adds support for `collections.deque` as a valid field type and allows `Enum` and `IntEnum` types to be used directly for fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_200\n\nLANGUAGE: python\nCODE:\n```\nfrom collections import deque\nfrom enum import Enum, IntEnum\nfrom pydantic import BaseModel\n\nclass Status(Enum):\n    PENDING = 'pending'\n    COMPLETE = 'complete'\n\nclass IntStatus(IntEnum):\n    ONE = 1\n    TWO = 2\n\nclass DataModel(BaseModel):\n    my_deque: deque[int]\n    status: Status\n    int_status: IntStatus\n```\n\n----------------------------------------\n\nTITLE: Use Pydantic `computed_field` with `model_dump`\nDESCRIPTION: This snippet illustrates how a `computed_field` is automatically included in the dictionary representation when `model_dump()` is called on a Pydantic model. It shows that the computed `volume` attribute is present alongside the regular fields, reflecting the model's serialized state.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_40\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nb = Box(width=1, height=2, depth=3)\nprint(b.model_dump())\n#> {'width': 1.0, 'height': 2.0, 'depth': 3.0, 'volume': 6.0}\n```\n\n----------------------------------------\n\nTITLE: Basic Default Values\nDESCRIPTION: Demonstrates setting default values for fields using direct assignment and the `Field` function with `default`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    # Both fields aren't required:\n    name: str = 'John Doe'\n    age: int = Field(default=20)\n```\n\n----------------------------------------\n\nTITLE: Alias Precedence with Field Alias\nDESCRIPTION: Illustrates how a specific `Field` alias (`lang`) takes precedence over the model-level `alias_generator` for serialization. Shows accessing fields by attribute name and by alias.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/alias.md#_snippet_5\n\nLANGUAGE: Python\nCODE:\n```\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\ndef to_camel(string: str) -> str:\n    return ''.join(word.capitalize() for word in string.split('_'))\n\n\nclass Voice(BaseModel):\n    model_config = ConfigDict(alias_generator=to_camel)\n\n    name: str\n    language_code: str = Field(alias='lang')\n\n\nvoice = Voice(Name='Filiz', lang='tr-TR')\nprint(voice.language_code)\n#> tr-TR\nprint(voice.model_dump(by_alias=True))\n#> {'Name': 'Filiz', 'lang': 'tr-TR'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Serializer API Reference\nDESCRIPTION: API documentation for Pydantic's field serializer components, including PlainSerializer, WrapSerializer, and the field_serializer decorator.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_8\n\nLANGUAGE: APIDOC\nCODE:\n```\npydantic.functional_serializers.PlainSerializer\n  - Serializer that is called unconditionally.\n  - The serialization logic for types supported by Pydantic will not be called.\n\npydantic.functional_serializers.WrapSerializer\n  - Serializer that provides more flexibility by allowing custom logic before or after Pydantic's default serialization.\n  - Requires a mandatory 'handler' parameter.\n\npydantic.functional_serializers.field_serializer\n  - Decorator to apply custom serialization logic to fields.\n  - Can be used with mode='plain' or mode='wrap'.\n  - Applied on instance or static methods.\n  - Signature: @field_serializer(field_name, *, mode='plain', return_type=None)\n    - field_name: The name of the field to serialize.\n    - mode: 'plain' or 'wrap'. Defaults to 'plain'.\n    - return_type: Optional return type annotation for the serialized value.\n```\n\n----------------------------------------\n\nTITLE: Dataclass Constraints with Pydantic\nDESCRIPTION: Demonstrates using Pydantic's Field within dataclasses to control initialization behavior (`init`, `init_var`) and keyword-only arguments (`kw_only`). Includes an example of a nested model structure and its output.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_23\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\nfrom pydantic.dataclasses import dataclass\n\n\n@dataclass\nclass Foo:\n    bar: str\n    baz: str = Field(init_var=True)\n    qux: str = Field(kw_only=True)\n\n\nclass Model(BaseModel):\n    foo: Foo\n\n\nmodel = Model(foo=Foo('bar', baz='baz', qux='qux'))\nprint(model.model_dump())  # (1)!\n#> {'foo': {'bar': 'bar', 'qux': 'qux'}}\n```\n\n----------------------------------------\n\nTITLE: Reworked create_model Field Definitions\nDESCRIPTION: The format for defining fields within `create_model` has been refactored. This change aims to improve consistency and maintainability of dynamically created models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_26\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import create_model\n\n# The structure for field definitions in create_model has been updated.\n# For example, the way default values or FieldInfo are passed might have changed.\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Correct validator fields as arguments\nDESCRIPTION: Shows the correct method for applying a validator to multiple fields by passing each field name as a separate string argument to the `@field_validator` decorator. This avoids the 'invalid validator fields' error.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_29\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, field_validator\n\n\nclass Model(BaseModel):\n    a: str\n    b: str\n\n    @field_validator('a', 'b')\n    def check_fields(cls, v):\n        return v\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Constraints and Schema Generation\nDESCRIPTION: Illustrates how Pydantic handles field constraints, including cases where constraints might not be enforced by default and how to explicitly include them in the JSON schema using Field arguments. It shows how to use Field with raw schema attributes for unenforced constraints.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, PositiveInt\n\ntry:\n    # this won't work since `PositiveInt` takes precedence over the\n    # constraints defined in `Field`, meaning they're ignored\n    class Model(BaseModel):\n        foo: PositiveInt = Field(lt=10)\n\nexcept ValueError as e:\n    print(e)\n\n\n# if you find yourself needing this, an alternative is to declare\n# the constraints in `Field` (or you could use `conint()`)\n# here both constraints will be enforced:\nclass ModelB(BaseModel):\n    # Here both constraints will be applied and the schema\n    # will be generated correctly\n    foo: int = Field(gt=0, lt=10)\n\n\nprint(ModelB.model_json_schema())\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n    \"properties\": {\n        \"foo\": {\n            \"exclusiveMaximum\": 10,\n            \"exclusiveMinimum\": 0,\n            \"title\": \"Foo\",\n            \"type\": \"integer\"\n        }\n    },\n    \"required\": [\"foo\"],\n    \"title\": \"ModelB\",\n    \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Decorator on Missing Field: Check Fields False\nDESCRIPTION: Demonstrates how to prevent the 'decorator-missing-field' error when using `field_validator` on a field that might not exist, by setting `check_fields=False`. This is useful in inheritance scenarios.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Any\n\nfrom pydantic import BaseModel, create_model, field_validator\n\n\nclass Model(BaseModel):\n    @field_validator('a', check_fields=False)\n    def check_a(cls, v: Any):\n        return v\n\n\nmodel = create_model('FooModel', a=(str, 'cake'), __base__=Model)\n```\n\n----------------------------------------\n\nTITLE: Model Field Missing Annotation (ClassVar): Python Example\nDESCRIPTION: Demonstrates resolving the 'model-field-missing-annotation' error by annotating a field as a ClassVar, indicating it's not intended to be a model field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_16\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import ClassVar\n\nfrom pydantic import BaseModel\n\n\nclass Model(BaseModel):\n    a: ClassVar[str]\n```\n\n----------------------------------------\n\nTITLE: Pydantic Model with Strict Boolean Field\nDESCRIPTION: Illustrates defining a Pydantic `BaseModel` with a boolean field (`foo`) configured to be strictly validated using `Field(strict=True)`. This demonstrates how Python model definitions translate into core schema configurations.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/internals/architecture.md#_snippet_1\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    foo: bool = Field(strict=True)\n```\n\n----------------------------------------\n\nTITLE: Generate JSON Schema with Pydantic `computed_field`\nDESCRIPTION: This example demonstrates how the `computed_field` decorator influences the JSON Schema generated for a Pydantic model. It shows that computed properties like `volume` are included in the schema as read-only fields, ensuring they are part of the model's contract during serialization.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_39\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property  # (1)!\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nprint(Box.model_json_schema(mode='serialization'))\n\"\"\"\n{\n    'properties': {\n        'width': {'title': 'Width', 'type': 'number'},\n        'height': {'title': 'Height', 'type': 'number'},\n        'depth': {'title': 'Depth', 'type': 'number'},\n        'volume': {'readOnly': True, 'title': 'Volume', 'type': 'number'}\n    },\n    'required': ['width', 'height', 'depth', 'volume'],\n    'title': 'Box',\n    'type': 'object'\n}\n\"\"\"\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Generate Field Titles with `field_title_generator`\nDESCRIPTION: Demonstrates how to use the `field_title_generator` within `ConfigDict` to programmatically set titles for individual fields in a Pydantic model's JSON schema. This allows for dynamic title generation based on field name and info, affecting all fields in the class.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_19\n\nLANGUAGE: python\nCODE:\n```\nimport json\n\nfrom pydantic import BaseModel, ConfigDict\n\n\nclass Person(BaseModel):\n    model_config = ConfigDict(\n        field_title_generator=lambda field_name, field_info: field_name.upper()\n    )\n    name: str\n    age: int\n\n\nprint(json.dumps(Person.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"name\": {\n      \"title\": \"NAME\",\n      \"type\": \"string\"\n    },\n    \"age\": {\n      \"title\": \"AGE\",\n      \"type\": \"integer\"\n    }\n  },\n  \"required\": [\n    \"name\",\n    \"age\"\n  ],\n  \"title\": \"Person\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Basic Include in Pydantic\nDESCRIPTION: Demonstrates how to use the `include` parameter to specify which fields to serialize. It shows including specific fields and nested fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_21\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel\n\nclass User(BaseModel):\n    id: str\n    name: str\n    user: dict\n\nt = User(id='1234567890', name='John Doe', user={'id': 42})\nprint(t.model_dump(include={'id': True, 'user': {'id'}}))\n#> {'id': '1234567890', 'user': {'id': 42}}\n```\n\n----------------------------------------\n\nTITLE: Access Field Name with `Annotated` and `AfterValidator`\nDESCRIPTION: Demonstrates accessing the field name via `ValidationInfo` when using `Annotated` with validators like `AfterValidator`. This provides context to the validator about the field it's processing, leveraging the `handler.field_name` capability.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/types.md#_snippet_26\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import AfterValidator, BaseModel, ValidationInfo\n\n\ndef my_validators(value: int, info: ValidationInfo):\n    return f'<{value} {info.field_name!r}>'\n\n\nclass MyModel(BaseModel):\n    my_field: Annotated[int, AfterValidator(my_validators)]\n\n\nm = MyModel(my_field=1)\nprint(m.my_field)\n#> <1 'my_field'>\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Alias with Static Type Checking (Annotated)\nDESCRIPTION: Demonstrates the `Annotated` pattern with `Field(alias='...')` in Pydantic for static type checking. This approach allows type checkers to recognize the alias while Pydantic can validate using both the field name and the alias when `validate_by_name=True` and `validate_by_alias=True`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_15\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\nclass User(BaseModel):\n    model_config = ConfigDict(validate_by_name=True, validate_by_alias=True)\n\n    name: Annotated[str, Field(alias='username')]\n\n\nuser = User(name='johndoe')  # (1)!\nuser = User(username='johndoe')  # (2)!\n```\n\n----------------------------------------\n\nTITLE: Pydantic Schema `Union` and `Field` Combination\nDESCRIPTION: Removes redundant `allOf` constructs from schemas generated for fields that use `Union` types in conjunction with custom `Field` configurations. This simplifies the generated OpenAPI/JSON schema.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_244\n\nLANGUAGE: python\nCODE:\n```\nRemove extra `allOf` from schema for fields with `Union` and custom `Field`\nRelated Issue: [#1209](https://github.com/pydantic/pydantic/pull/1209)\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Serialization Alias\nDESCRIPTION: Demonstrates how `serialization_alias` overrides `alias` during serialization in Pydantic models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_16\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass MyModel(BaseModel):\n    my_field: int = Field(validation_alias='myValidationAlias')\n\n```\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass MyModel(BaseModel):\n    my_field: int = Field(\n        alias='myValidationAlias',\n        serialization_alias='my_field',\n    )\n\n\nm = MyModel(myValidationAlias=1)\nprint(m.model_dump(by_alias=True))\n#> {'my_field': 1}\n\n```\n\n----------------------------------------\n\nTITLE: Discriminator No Field: Union Example\nDESCRIPTION: Illustrates the 'discriminator-no-field' error, which arises when a model within a discriminated union (using `typing.Union` and `pydantic.Field(discriminator=...)`) does not define the specified discriminator field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_7\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Literal, Union\n\nfrom pydantic import BaseModel, Field, PydanticUserError\n\n\nclass Cat(BaseModel):\n    c: str\n\n\nclass Dog(BaseModel):\n    pet_type: Literal['dog']\n    d: str\n\n\ntry:\n\n    class Model(BaseModel):\n        pet: Union[Cat, Dog] = Field(discriminator='pet_type')\n        number: int\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'discriminator-no-field'\n```\n\n----------------------------------------\n\nTITLE: Mark `computed_field` as Deprecated\nDESCRIPTION: Illustrates how to mark a computed field, decorated with `@computed_field` and `@property`, as deprecated using the `deprecated` decorator from `typing_extensions`. This provides a warning when the computed field is accessed.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_37\n\nLANGUAGE: python\nCODE:\n```\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n----------------------------------------\n\nTITLE: Pydantic Generic Field Schema Generation\nDESCRIPTION: Adds support for generating schemas for generic fields. This allows Pydantic to correctly represent and validate fields that use generic types.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_159\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic Generic Field Schema Generation:\n\nSupports generating schemas for Generic fields.\n```\n\n----------------------------------------\n\nTITLE: Deprecated Config Fields for Extra\nDESCRIPTION: Deprecated `ignore_extra` and `allow_extra` configuration fields in favor of a unified `extra` field for managing extra data.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_271\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Extra\n\nclass ModelWithExtra(BaseModel):\n    class Config:\n        extra = Extra.allow # or Extra.ignore, Extra.forbid\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Correct validator usage with fields\nDESCRIPTION: Demonstrates the correct way to define a validator using `@field_validator` by explicitly passing the field name(s) as arguments. This ensures the validator is correctly applied to the intended model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_27\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, field_validator\n\n\nclass Model(BaseModel):\n    a: str\n\n    @field_validator('a')\n    def checker(cls, v):\n        return v\n```\n\n----------------------------------------\n\nTITLE: Strict Mode Validation\nDESCRIPTION: Shows how to use the `strict=True` parameter on a Pydantic `Field` to enforce strict validation, preventing data coercion. When `strict=True`, Pydantic will raise an error instead of attempting to convert the data type.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_27\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(strict=True)\n    age: int = Field(strict=False)  # (1)!\n\n\nuser = User(name='John', age='42')  # (2)!\nprint(user)\n#> name='John' age=42\n```\n\n----------------------------------------\n\nTITLE: Pydantic BaseModel.construct() Field Order\nDESCRIPTION: Ensures that the order of fields is preserved when creating models using `BaseModel.construct()`. This is important for maintaining predictable field ordering, especially in scenarios where order matters.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_148\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic BaseModel.construct() Field Order:\n\nPreserves the order of fields when using `BaseModel.construct()`.\n```\n\n----------------------------------------\n\nTITLE: Decimal Constraints with Pydantic\nDESCRIPTION: Shows how to use Pydantic's Field to constrain Decimal types, specifying the maximum number of digits and decimal places allowed. Includes a model definition and instantiation example.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_22\n\nLANGUAGE: python\nCODE:\n```\nfrom decimal import Decimal\n\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    precise: Decimal = Field(max_digits=5, decimal_places=2)\n\n\nfoo = Foo(precise=Decimal('123.45'))\nprint(foo)\n#> precise=Decimal('123.45')\n```\n\n----------------------------------------\n\nTITLE: Pydantic Validation Alias Only\nDESCRIPTION: Illustrates using `validation_alias` in Pydantic's `Field` to specify an alias used only during model validation. The example shows instance creation with the validation alias and serialization using the original field name.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_12\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(validation_alias='username')\n\n\nuser = User(username='johndoe')  # (1)!\nprint(user)\n#> name='johndoe'\nprint(user.model_dump(by_alias=True))  # (2)!\n#>\n#> {'name': 'johndoe'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic AliasPath for Nested Field Aliases\nDESCRIPTION: Demonstrates using AliasPath to specify a path to a field, including array indices, for validation. Shows how to access nested data using aliases.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/alias.md#_snippet_0\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, AliasPath\n\n\nclass User(BaseModel):\n    first_name: str = Field(validation_alias=AliasPath('names', 0))\n    last_name: str = Field(validation_alias=AliasPath('names', 1))\n\nuser = User.model_validate({'names': ['John', 'Doe']})  # (1)!\nprint(user)\n#> first_name='John' last_name='Doe'\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Invalid `create_model` field definitions\nDESCRIPTION: This error occurs when invalid field definitions are provided to the `create_model()` function. It typically arises from incorrect tuple structures for field arguments.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_25\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import PydanticUserError, create_model\n\ntry:\n    create_model('FooModel', foo=(str, 'default value', 'more'))\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'create-model-field-definitions'\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization with typing.Annotated\nDESCRIPTION: Shows how to specify JSON schema modifications via the Field constructor using typing.Annotated. This approach allows for cleaner integration of field metadata and constraints directly within the type hint.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_7\n\nLANGUAGE: python\nCODE:\n```\nimport json\nfrom typing import Annotated\nfrom uuid import uuid4\n\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    id: Annotated[str, Field(default_factory=lambda: uuid4().hex)]\n    name: Annotated[str, Field(max_length=256)] = Field(\n        'Bar', title='CustomName'\n    )\n\n\nprint(json.dumps(Foo.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"id\": {\n      \"title\": \"Id\",\n      \"type\": \"string\"\n    },\n    \"name\": {\n      \"default\": \"Bar\",\n      \"maxLength\": 256,\n      \"title\": \"CustomName\",\n      \"type\": \"string\"\n    }\n  },\n  \"title\": \"Foo\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Field Inclusion/Exclusion with __all__\nDESCRIPTION: Corrects the behavior of the `__all__` key when used with index keys in advanced field inclusion/exclusion logic for sequences. This ensures more predictable handling of complex field selection in models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_237\n\nLANGUAGE: python\nCODE:\n```\n# Internal Pydantic logic fix related to field inclusion/exclusion.\n# Affects models with complex field configurations, especially sequences.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Numeric Constraints\nDESCRIPTION: Illustrates the use of numeric constraints like `gt`, `lt`, `ge`, `le`, `multiple_of`, and `allow_inf_nan` in Pydantic fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_17\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    positive: int = Field(gt=0)\n    non_negative: int = Field(ge=0)\n    negative: int = Field(lt=0)\n    non_positive: int = Field(le=0)\n    even: int = Field(multiple_of=2)\n    love_for_pydantic: float = Field(allow_inf_nan=True)\n\n\nfoo = Foo(\n    positive=1,\n    non_negative=0,\n    negative=-1,\n    non_positive=0,\n    even=2,\n    love_for_pydantic=float('inf'),\n)\nprint(foo)\n\"\"\"\npositive=1 non_negative=0 negative=-1 non_positive=0 even=2 love_for_pydantic=inf\n\"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Demonstrate `frozen_field` Pydantic Error in Python\nDESCRIPTION: This error is raised when attempting to assign a new value to a field marked with `frozen=True` or to delete such a field. It enforces immutability for specific model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/validation_errors.md#_snippet_36\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass Model(BaseModel):\n    x: str = Field('test', frozen=True)\n\n\nmodel = Model()\n\ntry:\n    model.x = 'test1'\nexcept ValidationError as exc:\n    print(repr(exc.errors()[0]['type']))\n    #> 'frozen_field'\n\ntry:\n    del model.x\nexcept ValidationError as exc:\n    print(repr(exc.errors()[0]['type']))\n    #> 'frozen_field'\n```\n\n----------------------------------------\n\nTITLE: Pydantic V2 Field Requirements and Nullability\nDESCRIPTION: Illustrates Pydantic V2's updated logic for field requirements and nullability, which now more closely matches Python dataclasses. It clarifies how `Optional` and `Any` annotations behave and how default values impact whether a field is required.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/migration.md#_snippet_24\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Optional\n\nfrom pydantic import BaseModel, ValidationError\n\n\nclass Foo(BaseModel):\n    f1: str  # required, cannot be None\n    f2: Optional[str]  # required, can be None - same as str | None\n    f3: Optional[str] = None  # not required, can be None\n    f4: str = 'Foobar'  # not required, but cannot be None\n\n\ntry:\n    # Example demonstrating validation errors for f1 being None\n    Foo(f1=None, f2=None, f4='b')\nexcept ValidationError as e:\n    print(e)\n    # Output will show validation error for f1\n    # 1 validation error for Foo\n    # f1\n    #   Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]\n```\n\n----------------------------------------\n\nTITLE: Decorator on Missing Field: FieldValidator\nDESCRIPTION: Shows the 'decorator-missing-field' error that occurs when a `field_validator` is applied to a field name ('b') that does not exist on the Pydantic model.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_5\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Any\n\nfrom pydantic import BaseModel, PydanticUserError, field_validator\n\ntry:\n\n    class Model(BaseModel):\n        a: str\n\n        @field_validator('b')\n        def check_b(cls, v: Any):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'decorator-missing-field'\n```\n\n----------------------------------------\n\nTITLE: Programmatic Field Title Generation in Pydantic\nDESCRIPTION: Demonstrates using the `field_title_generator` parameter in Pydantic's Field function to programmatically set a field's title based on its name and information. This is useful for consistent naming conventions across models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nimport json\n\nfrom pydantic import BaseModel, Field\nfrom pydantic.fields import FieldInfo\n\n\ndef make_title(field_name: str, field_info: FieldInfo) -> str:\n    return field_name.upper()\n\n\nclass Person(BaseModel):\n    name: str = Field(field_title_generator=make_title)\n    age: int = Field(field_title_generator=make_title)\n\n\nprint(json.dumps(Person.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"name\": {\n      \"title\": \"NAME\",\n      \"type\": \"string\"\n    },\n    \"age\": {\n      \"title\": \"AGE\",\n      \"type\": \"integer\"\n    }\n  },\n  \"required\": [\n    \"name\",\n    \"age\"\n  ],\n  \"title\": \"Person\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Ordering and Validation\nDESCRIPTION: Explains that Pydantic preserves field order in JSON Schema, validation errors, and serialization. The example shows how fields are ordered in `model_dump()` and how validation errors report locations based on this order.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/models.md#_snippet_52\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, ValidationError\n\n\nclass Model(BaseModel):\n    a: int\n    b: int = 2\n    c: int = 1\n    d: int = 0\n    e: float\n\n\nprint(Model.model_fields.keys())\n#> dict_keys(['a', 'b', 'c', 'd', 'e'])\nm = Model(e=2, a=1)\nprint(m.model_dump())\n#> {'a': 1, 'b': 2, 'c': 1, 'd': 0, 'e': 2.0}\ntry:\n    Model(a='x', b='x', c='x', d='x', e='x')\nexcept ValidationError as err:\n    error_locations = [e['loc'] for e in err.errors()]\n\nprint(error_locations)\n#> [('a',), ('b',), ('c',), ('d',), ('e',)]\n```\n\n----------------------------------------\n\nTITLE: Marking Fields as Deprecated\nDESCRIPTION: Shows how to use the `deprecated` parameter with a string message to mark Pydantic fields as deprecated. This generates a runtime deprecation warning upon access and sets the `deprecated` keyword in the JSON schema.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_30\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, Field(deprecated='This is deprecated')]\n\n\nprint(Model.model_json_schema()['properties']['deprecated_field'])\n#> {'deprecated': True, 'title': 'Deprecated Field', 'type': 'integer'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Default Values with Field (Python)\nDESCRIPTION: Illustrates using `pydantic.Field` to provide default values for model attributes. It highlights the requirement for `default` to be a keyword argument for type checkers like Pyright to correctly infer optional fields, and explains a limitation related to runtime vs. type-checker behavior.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/integrations/visual_studio_code.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Knight(BaseModel):\n    title: str = Field(default='Sir Lancelot')  # this is okay\n    age: int = Field(\n        23\n    )  # this works fine at runtime but will case an error for pyright\n\n\nlance = Knight()  # error: Argument missing for parameter \"age\"\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Invalid validator fields\nDESCRIPTION: This error occurs when a validator is applied to fields that are not specified as separate string arguments. Fields should be passed individually or as a list of strings to the validator decorator.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_28\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError, field_validator\n\ntry:\n\n    class Model(BaseModel):\n        a: str\n        b: str\n\n        @field_validator(['a', 'b'])\n        def check_fields(cls, v):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'validator-invalid-fields'\n```\n\n----------------------------------------\n\nTITLE: Pydantic Dataclass Fields with `doc` Attribute\nDESCRIPTION: Provides support for the `doc` attribute on dataclass fields within Pydantic models. This allows docstrings to be associated directly with individual fields, improving code clarity and enabling better introspection.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_3\n\nLANGUAGE: python\nCODE:\n```\nfrom dataclasses import dataclass, field\nfrom pydantic import BaseModel\n\n@dataclass\nclass UserInfo(BaseModel):\n    name: str = field(metadata={\"doc\": \"The user's full name.\"})\n    age: int = field(metadata={\"doc\": \"The user's age in years.\"})\n\n# The 'doc' metadata can be accessed via model introspection or schema generation.\n# Example access:\n# print(UserInfo.model_fields['name'].json_schema_extra.get('doc'))\n\n```\n\n----------------------------------------\n\nTITLE: Validate JSON with Invalid/Omitted Partial Field\nDESCRIPTION: Illustrates validating a JSON string where a field ('c') is incomplete and fails validation (MinLen(5)). The 'experimental_allow_partial=True' flag allows processing to continue, omitting the invalid field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/experimental.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom annotated_types import MinLen\nfrom typing_extensions import NotRequired, TypedDict\n\nfrom pydantic import TypeAdapter\n\n\nclass Foobar(TypedDict):\n    a: int\n    b: NotRequired[float]\n    c: NotRequired[Annotated[str, MinLen(5)]]\n\n\nta = TypeAdapter(list[Foobar])\n\nv = ta.validate_json(\n    '[{\"a\": 1, \"b\": 1.0, \"c\": \"abcd\"', experimental_allow_partial=True\n)\nprint(v)\n#> [{'a': 1, 'b': 1.0}]\n```\n\n----------------------------------------\n\nTITLE: Annotated Pattern for Field Serialization\nDESCRIPTION: Demonstrates using Annotated with PlainSerializer to apply custom serialization logic to fields. This pattern allows for reusable serializers defined directly within type annotations.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_9\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field, PlainSerializer\n\nDoubleNumber = Annotated[int, PlainSerializer(lambda v: v * 2)]\n\n\nclass Model1(BaseModel):\n    my_number: DoubleNumber\n\n\nclass Model2(BaseModel):\n    other_number: Annotated[DoubleNumber, Field(description='My other number')]\n\n\nclass Model3(BaseModel):\n    list_of_even_numbers: list[DoubleNumber]  # (1)!\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Support typing.Annotated for Field hints\nDESCRIPTION: Demonstrates how to use `typing.Annotated` to specify Pydantic `Field` configurations directly within type hints. Other annotations are ignored but preserved.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_169\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field\n\nclass MyModel(BaseModel):\n    my_field: Annotated[int, Field(gt=0, description=\"A positive integer\")]\n\n# get_type_hints(MyModel, include_extras=True) would show the Field details.\n```\n\n----------------------------------------\n\nTITLE: Deprecate Field with @warnings.deprecated Decorator\nDESCRIPTION: Demonstrates marking a Pydantic field as deprecated using the `deprecated` decorator from `typing_extensions` or `warnings`. This method is compatible with Python 3.9 and above, with a specific version for Python 3.13+ using the built-in `warnings.deprecated`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_31\n\nLANGUAGE: Python\nCODE:\n```\nfrom typing import Annotated\n\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, deprecated('This is deprecated')]\n\n    # Or explicitly using `Field`:\n    alt_form: Annotated[int, Field(deprecated=deprecated('This is deprecated'))]\n\n```\n\nLANGUAGE: Python\nCODE:\n```\nfrom typing import Annotated\nfrom warnings import deprecated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, deprecated('This is deprecated')]\n\n    # Or explicitly using `Field`:\n    alt_form: Annotated[int, Field(deprecated=deprecated('This is deprecated'))]\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic `computed_field` API\nDESCRIPTION: The `computed_field` decorator from Pydantic is used to include `property` or `cached_property` attributes when serializing a model or dataclass. It also ensures the property is considered in the JSON Schema. While `computed_field` can implicitly convert a method to a property, explicitly using `@property` is preferred for type checking.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_38\n\nLANGUAGE: APIDOC\nCODE:\n```\ncomputed_field\n\nDecorator to include properties in Pydantic models.\n\nUse Cases:\n- Include computed properties in model serialization.\n- Include computed properties in JSON Schema generation.\n- Useful for fields computed from other fields or expensive computations (with caching).\n\nNotes:\n- Pydantic does not perform additional logic (validation, cache invalidation) on the wrapped property.\n- Can be used with `@property` or `@cached_property`.\n- Explicitly using `@property` is recommended for type checking.\n\nExample:\n```python\nfrom pydantic import BaseModel, computed_field\n\nclass MyModel(BaseModel):\n    a: int\n    b: int\n\n    @computed_field\n    @property\n    def sum_ab(self) -> int:\n        return self.a + self.b\n```\n\nRelated:\n- `pydantic.fields.computed_field`\n- `property`\n- `functools.cached_property`\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Validator with no fields specified\nDESCRIPTION: This error is raised when a validator method is defined without specifying any fields it should apply to, either directly or via keyword arguments. Validators must be associated with fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_26\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError, field_validator\n\ntry:\n\n    class Model(BaseModel):\n        a: str\n\n        @field_validator\n        def checker(cls, v):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'validator-no-fields'\n```\n\n----------------------------------------\n\nTITLE: Pydantic computed_field with Property for Volume Calculation\nDESCRIPTION: Demonstrates using the computed_field decorator with a property to calculate the volume of a Box model. Includes JSON schema generation and model dumping.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_34\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property  # (1)!\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nprint(Box.model_json_schema(mode='serialization'))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n    'properties': {\n        'width': {'title': 'Width', 'type': 'number'},\n        'height': {'title': 'Height', 'type': 'number'},\n        'depth': {'title': 'Depth', 'type': 'number'},\n        'volume': {'readOnly': True, 'title': 'Volume', 'type': 'number'},\n    },\n    'required': ['width', 'height', 'depth', 'volume'],\n    'title': 'Box',\n    'type': 'object',\n}\n```\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nb = Box(width=1, height=2, depth=3)\nprint(b.model_dump())\n```\n\n----------------------------------------\n\nTITLE: Model Field Overridden: Python Example\nDESCRIPTION: This error is raised when a field defined on a base class was overridden by a non-annotated attribute. The example demonstrates catching this specific PydanticUserError.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError\n\n\nclass Foo(BaseModel):\n    a: float\n\n\ntry:\n\n    class Bar(Foo):\n        x: float = 12.3\n        a = 123.0\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'model-field-overridden'\n```\n\n----------------------------------------\n\nTITLE: Pydantic `Field` Default Value Mutation\nDESCRIPTION: Prevents accidental mutation of default values associated with `Field` instances. This ensures that default values remain unchanged across multiple uses or validations.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_246\n\nLANGUAGE: python\nCODE:\n```\nAvoid mutating `Field` default value\nRelated Issue: [#1412](https://github.com/pydantic/pydantic/pull/1412)\n```\n\n----------------------------------------\n\nTITLE: Pydantic computed_field with Deprecated Property\nDESCRIPTION: Shows how to mark a computed field as deprecated using the @deprecated decorator from typing_extensions.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_35\n\nLANGUAGE: python\nCODE:\n```\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n----------------------------------------\n\nTITLE: Multiple Field Serializers in Pydantic\nDESCRIPTION: This error is raised when multiple `model_serializer` functions are defined for a single field. It prevents ambiguity in serialization logic.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_41\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError, field_serializer\n\ntry:\n\n    class MyModel(BaseModel):\n        x: int\n        y: int\n\n        @field_serializer('x', 'y')\n        def serializer1(v):\n            return f'{v:,}'\n\n        @field_serializer('x')\n        def serializer2(v):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'multiple-field-serializers'\n```\n\n----------------------------------------\n\nTITLE: Schema Generation for Optional Fields\nDESCRIPTION: Fixed schema generation logic to correctly handle fields annotated with `Optional`, ensuring accurate schema representation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_275\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel\nfrom typing import Optional\n\nclass ModelWithOptional(BaseModel):\n    name: Optional[str] = None\n    age: int\n```\n\n----------------------------------------\n\nTITLE: Mark Pydantic `computed_field` as Deprecated\nDESCRIPTION: This example demonstrates how to mark a `computed_field` as deprecated using the `@deprecated` decorator from `typing_extensions`. This allows developers to signal that a computed property is no longer recommended for use, providing clear guidance for API evolution.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_41\n\nLANGUAGE: python\nCODE:\n```\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n----------------------------------------\n\nTITLE: Pydantic Forward Refs and Optional Fields\nDESCRIPTION: Fixes behavior with forward references and optional fields in nested Pydantic models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_210\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel\nfrom typing import Optional, TYPE_CHECKING\n\nif TYPE_CHECKING:\n    # Forward reference to a model defined later or in another module\n    class ParentModel(BaseModel):\n        child: Optional['ChildModel']\nelse:\n    # Actual definition for runtime\n    class ChildModel(BaseModel):\n        value: int\n\n    class ParentModel(BaseModel):\n        child: Optional[ChildModel]\n\n# This ensures that optional fields with forward references are handled correctly.\n```\n\n----------------------------------------\n\nTITLE: Model Field Missing Annotation: Python Example\nDESCRIPTION: This error occurs when a field within a Pydantic model lacks a type annotation. The example shows how to catch this error and demonstrates resolving it using ClassVar or ignored_types.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_15\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, PydanticUserError\n\ntry:\n\n    class Model(BaseModel):\n        a = Field('foobar')\n        b = None\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'model-field-missing-annotation'\n```\n\n----------------------------------------\n\nTITLE: Default Factory with Data Argument\nDESCRIPTION: Illustrates using `default_factory` with a lambda that accepts the validated data dictionary as an argument to derive a default value.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, EmailStr, Field\n\n\nclass User(BaseModel):\n    email: EmailStr\n    username: str = Field(default_factory=lambda data: data['email'])\n\n\nuser = User(email='user@example.com')\nprint(user.username)\n#> user@example.com\n```\n\n----------------------------------------\n\nTITLE: JSON Schema for String Constraints\nDESCRIPTION: Illustrates the JSON schema generated by Pydantic for string constraints, showing how 'min_length', 'max_length', and 'pattern' are translated to 'minLength', 'maxLength', and 'pattern' respectively.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_21\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"title\": \"Foo\",\n  \"type\": \"object\",\n  \"properties\": {\n    \"short\": {\n      \"title\": \"Short\",\n      \"type\": \"string\",\n      \"minLength\": 3\n    },\n    \"long\": {\n      \"title\": \"Long\",\n      \"type\": \"string\",\n      \"maxLength\": 10\n    },\n    \"regex\": {\n      \"title\": \"Regex\",\n      \"type\": \"string\",\n      \"pattern\": \"^\\\\d*$\"\n    }\n  },\n  \"required\": [\n    \"short\",\n    \"long\",\n    \"regex\"\n  ]\n}\n```\n\n----------------------------------------\n\nTITLE: Support typing.Counter as model field\nDESCRIPTION: Adds support for using `typing.Counter` as a valid type for model fields. This allows Pydantic models to directly incorporate `collections.Counter` objects.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_120\n\nLANGUAGE: python\nCODE:\n```\nAdd ability to use `typing.Counter` as a model field type, [#2060](https://github.com/pydantic/pydantic/pull/2060) by @uriyyo\n```\n\n----------------------------------------\n\nTITLE: SerializeAsAny Annotation Example\nDESCRIPTION: Demonstrates using the SerializeAsAny annotation to achieve duck typing serialization behavior for a Pydantic model field. This allows a field to be serialized as if its type hint was Any.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_17\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, SerializeAsAny\n\n\nclass User(BaseModel):\n    name: str\n\n\nclass UserLogin(User):\n    password: str\n\n\nclass OuterModel(BaseModel):\n    as_any: SerializeAsAny[User]\n    as_user: User\n\n\nuser = UserLogin(name='pydantic', password='password')\n\nprint(OuterModel(as_any=user, as_user=user).model_dump())\n```\n\n========================\nQUESTIONS AND ANSWERS\n========================\nTOPIC: Pydantic Field Customization\nQ: What is the difference between applying `Field()` to a type versus a union type in Pydantic with `Annotated`?\nA: When `Field()` is applied to a specific type within an `Annotated` union (e.g., `Annotated[int, Field(deprecated=True)] | None`), the metadata might not affect the field as intended. Applying `Field()` to the top-level union type (e.g., `Annotated[int | None, Field(deprecated=True)]`) ensures the metadata is applied to the field itself.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_6\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: What is the purpose of the `Field()` function in Pydantic?\nA: The `Field()` function in Pydantic is used to customize model fields by specifying default values, JSON Schema metadata, constraints, and other attributes. It functions similarly to the `field()` function in Python's standard library for dataclasses.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_0\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: Can Pydantic's `Field()` function be used to make a field immutable?\nA: Yes, the `Field()` function in Pydantic can be used with the `frozen=True` argument to make a field immutable, meaning its value cannot be changed after initialization.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_8\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: How can Pydantic fields be customized?\nA: Pydantic fields can be customized using the `Field()` function to set default values, JSON Schema metadata, and constraints. The `Annotated` typing construct can also be used to attach metadata like `Field()` or `WithJsonSchema` to model fields.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_1\n\n----------------------------------------\n\nTOPIC: Pydantic Dataclasses\nQ: Can both Pydantic's `Field()` and stdlib's `field()` be used with Pydantic dataclasses?\nA: Yes, you can use both Pydantic's `Field()` and the standard library's `field()` functions when defining fields in Pydantic dataclasses. This allows for customization and validation using Pydantic's features.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/dataclasses.md#_qa_6\n\n----------------------------------------\n\nTOPIC: \nQ: What is the purpose of the `pydantic.fields` module?\nA: The `pydantic.fields` module in Pydantic provides core components for defining and managing fields within data models. It includes classes like `Field` and `FieldInfo` for detailed field configuration and metadata.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/api/fields.md#_qa_0\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: What is the role of `typing.Annotated` when customizing Pydantic fields?\nA: The `Annotated` typing construct in Pydantic allows attaching metadata, such as `Field()` or `WithJsonSchema`, to a model field's annotation. This provides a way to add validation logic and type constraints without altering the field's base type from a static type checker's perspective.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_2\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: What are the advantages of using the `Annotated` pattern for Pydantic fields?\nA: The `Annotated` pattern avoids confusion about whether a field is required or has a default value, unlike the `f: <type> = Field(...)` form. It also allows for an arbitrary amount of metadata elements to be provided for a field, enabling the use of different Pydantic utilities like `WithJsonSchema`.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_3\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: Can Pydantic fields be made immutable?\nA: Yes, Pydantic fields can be made immutable by setting the `frozen=True` argument within the `Field()` function.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_7\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: When should the standard assignment form be preferred over the `Annotated` pattern in Pydantic?\nA: The standard assignment form (`f: <type> = Field(...)`) should be used when arguments like `default`, `default_factory`, or `alias` are provided to `Field()`. This is because static type checkers do not understand the `Annotated` pattern for synthesizing `__init__` methods.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_4"}]}]},"uuid":"972be571-a89c-4422-a6da-a93338489301","timestamp":"2025-08-28T13:11:58.966Z","toolUseResult":[{"type":"text","text":"========================\nCODE SNIPPETS\n========================\nTITLE: Pydantic Field Customization with Field()\nDESCRIPTION: Demonstrates basic customization of Pydantic model fields using the Field() function for required fields and frozen attributes.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_0\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    name: str = Field(frozen=True)\n```\n\n----------------------------------------\n\nTITLE: Field Exclusion at Field Level\nDESCRIPTION: Shows how to exclude fields during serialization directly at the field definition using `Field(exclude=True)` or `Field(exclude_if=...)`. This provides a declarative way to manage serialization output.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_19\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Transaction(BaseModel):\n    id: int\n    private_id: int = Field(exclude=True)\n    value: int = Field(ge=0, exclude_if=lambda v: v == 0)\n\n\nprint(Transaction(id=1, private_id=2, value=0).model_dump())\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field vs. Type Metadata with Annotated\nDESCRIPTION: Highlights the difference in applying metadata to fields versus types using Annotated and Field(), and the impact on features like deprecation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_4\n\nLANGUAGE: python\nCODE:\n```\nclass Model(BaseModel):\n    field_bad: Annotated[int, Field(deprecated=True)] | None = None  # (1)!\n    field_ok: Annotated[int | None, Field(deprecated=True)] = None  # (2)!\n\n      # 1. The [`Field()`][pydantic.fields.Field] function is applied to `int` type, hence the\n      #    `deprecated` flag won't have any effect. While this may be confusing given that the name of\n      #    the [`Field()`][pydantic.fields.Field] function would imply it should apply to the field, \n      #    the API was designed when this function was the only way to provide metadata. You can\n      #    alternatively make use of the [`annotated_types`](https://github.com/annotated-types/annotated-types)\n      #    library which is now supported by Pydantic.\n      #\n      # 2. The [`Field()`][pydantic.fields.Field] function is applied to the \"top-level\" union type, \n      #    hence the `deprecated` flag will be applied to the field.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Annotated Pattern for Field Metadata\nDESCRIPTION: Illustrates using the Annotated typing construct to attach metadata like Field() and WithJsonSchema to model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_2\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field, WithJsonSchema\n\n\nclass Model(BaseModel):\n    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]\n```\n\n----------------------------------------\n\nTITLE: Pydantic Dataclass Field Declaration\nDESCRIPTION: Enhances Pydantic dataclass field declarations by allowing the use of Pydantic's `Field` or the `'metadata'` keyword argument from `dataclasses.field`. This provides more flexibility in defining fields within Pydantic-integrated dataclasses.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_142\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic Dataclass Field Declaration:\n\nAllows using `pydantic.Field` or `dataclasses.field(metadata=...)` for field declarations in Pydantic dataclasses.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization with Ellipsis\nDESCRIPTION: Shows how to use the ellipsis (...) with Field() to explicitly mark a field as required, even when a value is assigned.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_1\n\nLANGUAGE: python\nCODE:\n```\nclass Model(BaseModel):\n    name: str = Field(..., frozen=True)\n```\n\n----------------------------------------\n\nTITLE: Field Representation Control with Pydantic\nDESCRIPTION: Explains how to use the `repr` parameter in Pydantic's Field to include or exclude fields from the model's string representation. Provides an example demonstrating this functionality.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_24\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(repr=True)  # (1)!\n    age: int = Field(repr=False)\n\n\nuser = User(name='John', age=42)\nprint(user)\n#> name='John'\n```\n\n----------------------------------------\n\nTITLE: Immutable Fields (Frozen)\nDESCRIPTION: Demonstrates the use of the `frozen=True` parameter on Pydantic fields to make them immutable. Once a model with frozen fields is created, these fields cannot be reassigned, emulating frozen dataclass behavior.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_28\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass User(BaseModel):\n    name: str = Field(frozen=True)\n    age: int\n\n\nuser = User(name='John', age=42)\n\ntry:\n    user.name = 'Jane'  # (1)!\nexcept ValidationError as e:\n    print(e)\n    \"\"\"\n    1 validation error for User\n    name\n      Field is frozen [type=frozen_field, input_value='Jane', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Field repr parameter to hide fields\nDESCRIPTION: Introduces a `repr` parameter to `Field`, defaulting to `True`. This parameter allows users to hide specific fields from the default string representation of a `BaseModel`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_125\n\nLANGUAGE: python\nCODE:\n```\nAdd `repr` (defaults to `True`) parameter to `Field`, to hide it from the default representation of the `BaseModel`, [#1831](https://github.com/pydantic/pydantic/pull/1831) by @fnep\n```\n\n----------------------------------------\n\nTITLE: Pydantic Annotated Field Constraints\nDESCRIPTION: Demonstrates using `Annotated` with Pydantic `Field` for constraints, highlighting potential issues with compound types and recommending `Annotated` for safety.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_19\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated, Optional\n\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    positive: Optional[Annotated[int, Field(gt=0)]]\n    # Can error in some cases, not recommended:\n    non_negative: Optional[int] = Field(ge=0)\n\n```\n\n----------------------------------------\n\nTITLE: Field Strictness for Individual Model Fields\nDESCRIPTION: Demonstrates how to apply strict=True to individual fields using pydantic.Field. This ensures strict validation for that specific field, even when the validation method is called without strict=True.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_7\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass User(BaseModel):\n    name: str\n    age: int\n    n_pets: int\n\n\nuser = User(name='John', age='42', n_pets='1')\nprint(user)\n#> name='John' age=42 n_pets=1\n\n\nclass AnotherUser(BaseModel):\n    name: str\n    age: int = Field(strict=True)\n    n_pets: int\n\n\ntry:\n    anotheruser = AnotherUser(name='John', age='42', n_pets='1')\nexcept ValidationError as e:\n    print(e)\n    \"\"\"\n    1 validation error for AnotherUser\n    age\n      Input should be a valid integer [type=int_type, input_value='42', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Discriminator with Field Name\nDESCRIPTION: Demonstrates using the `discriminator` parameter with a field name to control discriminated unions in Pydantic models. This is useful when a single field determines the type within a union.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_25\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Literal, Union\n\nfrom pydantic import BaseModel, Field\n\n\nclass Cat(BaseModel):\n    pet_type: Literal['cat']\n    age: int\n\n\nclass Dog(BaseModel):\n    pet_type: Literal['dog']\n    age: int\n\n\nclass Model(BaseModel):\n    pet: Union[Cat, Dog] = Field(discriminator='pet_type')\n\n\nprint(Model.model_validate({'pet': {'pet_type': 'cat', 'age': 12}}))  # (1)!\n#> pet=Cat(pet_type='cat', age=12)\n```\n\n----------------------------------------\n\nTITLE: Pydantic `Field()` in Dataclasses\nDESCRIPTION: Refactors logic to better support Pydantic's `Field()` function when used within Python dataclasses. This allows for more consistent application of Pydantic's validation and configuration features to fields defined in dataclasses.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom dataclasses import dataclass\nfrom pydantic import Field\n\n@dataclass\nclass Product:\n    name: str = Field(..., description=\"The name of the product\")\n    price: float = Field(gt=0, description=\"The price of the product\")\n\n# This dataclass can now be used with Pydantic's validation and schema generation.\n# Example:\n# product = Product(name=\"Laptop\", price=1200.50)\n# print(product.model_dump())\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Alias with Static Type Checking (Field)\nDESCRIPTION: Explains how Pydantic's `Field(alias='...')` affects static type checking, making the alias the expected identifier for initialization. It highlights potential type checker errors when `validate_by_name=True` and the actual field name is used.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(alias='username')\n\n\nuser = User(username='johndoe')  # (1)!\n```\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\nclass User(BaseModel):\n    model_config = ConfigDict(validate_by_name=True)\n\n    name: str = Field(alias='username')\n\n\nuser = User(name='johndoe')  # (1)!\n```\n\n----------------------------------------\n\nTITLE: String Constraints with Pydantic\nDESCRIPTION: Demonstrates how to use Pydantic's Field to enforce minimum length, maximum length, and regular expression patterns on string fields within a BaseModel. Includes an example of model instantiation and output.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_20\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    short: str = Field(min_length=3)\n    long: str = Field(max_length=10)\n    regex: str = Field(pattern=r'^\\d*$')  # (1)!\n\n\nfoo = Foo(short='foo', long='foobarbaz', regex='123')\nprint(foo)\n#> short='foo' long='foobarbaz' regex='123'\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization API\nDESCRIPTION: Provides an overview of the pydantic.fields.Field function for customizing Pydantic model fields, including default values, JSON Schema metadata, and constraints.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_5\n\nLANGUAGE: APIDOC\nCODE:\n```\npydantic.fields.Field\n\nUsed to customize Pydantic model fields, providing mechanisms for default values, JSON Schema metadata, constraints, and more. It behaves similarly to dataclasses.field.\n\nUsage:\n```python\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    name: str = Field(frozen=True)\n    # Or to explicitly mark as required:\n    # name: str = Field(..., frozen=True)\n```\n\nKey Parameters and Concepts:\n- `default`: Sets a default value for the field. If not provided and the field is not marked with `...` (ellipsis), it's considered optional.\n- `default_factory`: A callable that returns a default value. Useful for mutable defaults like lists or dictionaries.\n- `alias`: Specifies a different name for the field when parsing JSON.\n- `frozen`: If True, the field cannot be changed after initialization.\n- `...` (Ellipsis): Used to explicitly mark a field as required, even if a default value is assigned.\n- `Annotated`: Can be used with `Field` to attach metadata to a field's type, allowing for more complex validation and customization.\n\nExample with Annotated:\n```python\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field, WithJsonSchema\n\nclass Model(BaseModel):\n    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]\n```\n\nConsiderations:\n- Arguments like `default`, `default_factory`, and `alias` are recognized by static type checkers for `__init__` synthesis.\n- The `Annotated` pattern is not directly understood by static type checkers for `__init__` synthesis, so use the normal assignment form when type checker compatibility is critical for these arguments.\n- Metadata applied via `Annotated` can be specific to the type or the field. Ensure correct placement to achieve the desired effect (e.g., `deprecated` flag applies to the field when applied to the top-level type).\n```\n\n----------------------------------------\n\nTITLE: Deprecate Field with deprecated=True in Field\nDESCRIPTION: Shows how to mark a Pydantic field as deprecated by setting the `deprecated` argument to `True` within the `Field` function. This method also illustrates how to inspect the generated JSON schema to verify the deprecation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_32\n\nLANGUAGE: Python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, Field(deprecated=True)]\n\n\nprint(Model.model_json_schema()['properties']['deprecated_field'])\n#> {'deprecated': True, 'title': 'Deprecated Field', 'type': 'integer'}\n\n```\n\n----------------------------------------\n\nTITLE: Required Optional Fields\nDESCRIPTION: Introduces support for defining fields as required Optional types using `name: Optional[AnyType] = Field(...)`. This change refactors `ModelField` creation to correctly preserve the `required` parameter value, enhancing flexibility in model definitions.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_247\n\nLANGUAGE: APIDOC\nCODE:\n```\nname: Optional[AnyType] = Field(...)\n\n# Example usage:\nfrom typing import Optional\nfrom pydantic import BaseModel, Field\n\nclass MyModel(BaseModel):\n    my_field: Optional[str] = Field(None)\n    required_optional_field: Optional[int] = Field(...)\n\n```\n\n----------------------------------------\n\nTITLE: Excluding Fields During Export\nDESCRIPTION: Explains how to use the `exclude=True` parameter on a Pydantic `Field` to prevent that field from being included when exporting the model, for example, using `model_dump()`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_29\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str\n    age: int = Field(exclude=True)\n\n\nuser = User(name='John', age=42)\nprint(user.model_dump())  # (1)!\n#> {'name': 'John'}\n```\n\n----------------------------------------\n\nTITLE: Custom Field Serializer with Context\nDESCRIPTION: Demonstrates using a custom field serializer that accesses context information for conditional data processing. The serializer removes specified stopwords from the text field based on the provided context.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, FieldSerializationInfo, field_serializer\n\n\nclass Model(BaseModel):\n    text: str\n\n    @field_serializer('text', mode='plain')\n    @classmethod\n    def remove_stopwords(cls, v: str, info: FieldSerializationInfo) -> str:\n        if isinstance(info.context, dict):\n            stopwords = info.context.get('stopwords', set())\n            v = ' '.join(w for w in v.split() if w.lower() not in stopwords)\n        return v\n\n\nmodel = Model(text='This is an example document')\nprint(model.model_dump())\nprint(model.model_dump(context={'stopwords': ['this', 'is', 'an']}))\n```\n\n----------------------------------------\n\nTITLE: Decorator Pattern for Field Serialization\nDESCRIPTION: Illustrates using the @field_serializer decorator to apply a serialization function to multiple fields. It covers options like applying to all fields ('*') and disabling field existence checks.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_10\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, field_serializer\n\n\nclass Model(BaseModel):\n    f1: str\n    f2: str\n\n    @field_serializer('f1', 'f2', mode='plain')\n    def capitalize(self, value: str) -> str:\n        return value.capitalize()\n```\n\n----------------------------------------\n\nTITLE: Overriding Strict Mode with Field(strict=False)\nDESCRIPTION: Demonstrates how to selectively disable strict mode for individual fields within a Pydantic model that has global strict mode enabled via `ConfigDict(strict=True)`. This is achieved by using `Field(strict=False)` on the specific field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\nclass User(BaseModel):\n    model_config = ConfigDict(strict=True)\n\n    name: str\n    age: int = Field(strict=False)\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization with Field Parameters\nDESCRIPTION: Demonstrates using Pydantic's Field function to customize model fields with parameters like description, examples, title, and json_schema_extra. This allows for richer metadata and control over the generated JSON schema.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_5\n\nLANGUAGE: python\nCODE:\n```\nimport json\n\nfrom pydantic import BaseModel, EmailStr, Field, SecretStr\n\n\nclass User(BaseModel):\n    age: int = Field(description='Age of the user')\n    email: EmailStr = Field(examples=['marcelo@mail.com'])\n    name: str = Field(title='Username')\n    password: SecretStr = Field(\n        json_schema_extra={\n            'title': 'Password',\n            'description': 'Password of the user',\n            'examples': ['123456'],\n        }\n    )\n\n\nprint(json.dumps(User.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"age\": {\n      \"description\": \"Age of the user\",\n      \"title\": \"Age\",\n      \"type\": \"integer\"\n    },\n    \"email\": {\n      \"examples\": [\n        \"marcelo@mail.com\"\n      ],\n      \"format\": \"email\",\n      \"title\": \"Email\",\n      \"type\": \"string\"\n    },\n    \"name\": {\n      \"title\": \"Username\",\n      \"type\": \"string\"\n    },\n    \"password\": {\n      \"description\": \"Password of the user\",\n      \"examples\": [\n        \"123456\"\n      ],\n      \"format\": \"password\",\n      \"title\": \"Password\",\n      \"type\": \"string\",\n      \"writeOnly\": true\n    }\n  },\n  \"required\": [\n    \"age\",\n    \"email\",\n    \"name\",\n    \"password\"\n  ],\n  \"title\": \"User\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Field Strictness Affecting Model Instantiation\nDESCRIPTION: Shows that setting strict=True on a Field affects validation during direct model instantiation. It demonstrates that a field marked as strict will reject string inputs, while a field marked as strict=False will accept them.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass Model(BaseModel):\n    x: int = Field(strict=True)\n    y: int = Field(strict=False)\n\n\ntry:\n    Model(x='1', y='2')\nexcept ValidationError as exc:\n    print(exc)\n    \"\"\"\n    1 validation error for Model\n    x\n      Input should be a valid integer [type=int_type, input_value='1', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Alias for Validation and Serialization\nDESCRIPTION: Demonstrates using the `alias` parameter in Pydantic's `Field` to specify a name for both validation and serialization. The example shows creating a model instance with the alias and dumping it back to a dictionary using the alias.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_11\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(alias='username')\n\n\nuser = User(username='johndoe')  # (1)!\nprint(user)\n#> name='johndoe'\nprint(user.model_dump(by_alias=True))  # (2)!\n#>\n#> {'username': 'johndoe'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic Serialization Alias Only\nDESCRIPTION: Shows how to use `serialization_alias` in Pydantic's `Field` for an alias used exclusively during serialization. The example demonstrates validation using the field name and serialization using the specified alias.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_13\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(serialization_alias='username')\n\n\nuser = User(name='johndoe')  # (1)!\nprint(user)\n#> name='johndoe'\nprint(user.model_dump(by_alias=True))  # (2)!\n#>\n#> {'username': 'johndoe'}\n```\n\n----------------------------------------\n\nTITLE: Update Config.field to update Field elements\nDESCRIPTION: Allows elements defined in `Config.field` to correctly update corresponding elements of a `Field` definition. This enhances flexibility in configuring model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_133\n\nLANGUAGE: python\nCODE:\n```\nallow elements of `Config.field` to update elements of a `Field`, [#2461](https://github.com/pydantic/pydantic/pull/2461) by @samuelcolvin\n```\n\n----------------------------------------\n\nTITLE: Pydantic Dataclass with Fields and Metadata\nDESCRIPTION: Shows how to use `dataclasses.field` and Pydantic's `Field` for default values, factories, and metadata like titles and constraints.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/dataclasses.md#_snippet_1\n\nLANGUAGE: python\nCODE:\n```\nimport dataclasses\nfrom typing import Optional\n\nfrom pydantic import Field\nfrom pydantic.dataclasses import dataclass\n\n\n@dataclass\nclass User:\n    id: int\n    name: str = 'John Doe'\n    friends: list[int] = dataclasses.field(default_factory=lambda: [0])\n    age: Optional[int] = dataclasses.field(\n        default=None,\n        metadata={'title': 'The age of the user', 'description': 'do not lie!'},\n    )\n    height: Optional[int] = Field(\n        default=None, title='The height in cm', ge=50, le=300\n    )\n\n\nuser = User(id='42', height='250')\nprint(user)\n```\n\n----------------------------------------\n\nTITLE: Ellipsis on Field in Annotated for TypedDicts\nDESCRIPTION: Allows the use of ellipsis (`...`) on `Field`s within `Annotated` for `TypedDict`s, marking fields as required and improving type hint clarity.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_109\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated, TypedDict\nfrom pydantic import Field\n\nclass UserInfo(TypedDict):\n    # Ellipsis indicates 'name' is a required field\n    name: Annotated[str, Field(required=True)]\n    age: int\n\n# Example:\n# user_data: UserInfo = {'name': 'Alice', 'age': 30}\n# This syntax helps Pydantic and type checkers understand required fields.\n```\n\n----------------------------------------\n\nTITLE: Field Strictness as Annotation with TypedDict\nDESCRIPTION: Illustrates using Field(strict=True) as an annotation, particularly useful with TypedDict. This allows applying strict validation rules to fields within a TypedDict structure when used with Pydantic's TypeAdapter.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/strict_mode.md#_snippet_9\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom pydantic import Field, TypeAdapter, ValidationError\n\n\nclass MyDict(TypedDict):\n    x: Annotated[int, Field(strict=True)]\n\n\ntry:\n    TypeAdapter(MyDict).validate_python({'x': '1'})\nexcept ValidationError as exc:\n    print(exc)\n    \"\"\"\n    1 validation error for MyDict\n    x\n      Input should be a valid integer [type=int_type, input_value='1', input_type=str]\n    \"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Annotated Pattern for Type Constraints\nDESCRIPTION: Demonstrates applying validation constraints to elements within a list using the Annotated pattern with Field().\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_3\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    int_list: list[Annotated[int, Field(gt=0)]]\n    # Valid: [1, 3]\n    # Invalid: [-1, 2]\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field allow_mutation Constraint\nDESCRIPTION: Introduces the `allow_mutation` constraint to the `Field` function. This allows explicit control over whether a field's value can be mutated after model instantiation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_156\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic Field allow_mutation Constraint:\n\nAdds `allow_mutation` constraint to `Field`.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Types: Deque and Enums\nDESCRIPTION: Adds support for `collections.deque` as a valid field type and allows `Enum` and `IntEnum` types to be used directly for fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_200\n\nLANGUAGE: python\nCODE:\n```\nfrom collections import deque\nfrom enum import Enum, IntEnum\nfrom pydantic import BaseModel\n\nclass Status(Enum):\n    PENDING = 'pending'\n    COMPLETE = 'complete'\n\nclass IntStatus(IntEnum):\n    ONE = 1\n    TWO = 2\n\nclass DataModel(BaseModel):\n    my_deque: deque[int]\n    status: Status\n    int_status: IntStatus\n```\n\n----------------------------------------\n\nTITLE: Use Pydantic `computed_field` with `model_dump`\nDESCRIPTION: This snippet illustrates how a `computed_field` is automatically included in the dictionary representation when `model_dump()` is called on a Pydantic model. It shows that the computed `volume` attribute is present alongside the regular fields, reflecting the model's serialized state.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_40\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nb = Box(width=1, height=2, depth=3)\nprint(b.model_dump())\n#> {'width': 1.0, 'height': 2.0, 'depth': 3.0, 'volume': 6.0}\n```\n\n----------------------------------------\n\nTITLE: Basic Default Values\nDESCRIPTION: Demonstrates setting default values for fields using direct assignment and the `Field` function with `default`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    # Both fields aren't required:\n    name: str = 'John Doe'\n    age: int = Field(default=20)\n```\n\n----------------------------------------\n\nTITLE: Alias Precedence with Field Alias\nDESCRIPTION: Illustrates how a specific `Field` alias (`lang`) takes precedence over the model-level `alias_generator` for serialization. Shows accessing fields by attribute name and by alias.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/alias.md#_snippet_5\n\nLANGUAGE: Python\nCODE:\n```\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\ndef to_camel(string: str) -> str:\n    return ''.join(word.capitalize() for word in string.split('_'))\n\n\nclass Voice(BaseModel):\n    model_config = ConfigDict(alias_generator=to_camel)\n\n    name: str\n    language_code: str = Field(alias='lang')\n\n\nvoice = Voice(Name='Filiz', lang='tr-TR')\nprint(voice.language_code)\n#> tr-TR\nprint(voice.model_dump(by_alias=True))\n#> {'Name': 'Filiz', 'lang': 'tr-TR'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Serializer API Reference\nDESCRIPTION: API documentation for Pydantic's field serializer components, including PlainSerializer, WrapSerializer, and the field_serializer decorator.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_8\n\nLANGUAGE: APIDOC\nCODE:\n```\npydantic.functional_serializers.PlainSerializer\n  - Serializer that is called unconditionally.\n  - The serialization logic for types supported by Pydantic will not be called.\n\npydantic.functional_serializers.WrapSerializer\n  - Serializer that provides more flexibility by allowing custom logic before or after Pydantic's default serialization.\n  - Requires a mandatory 'handler' parameter.\n\npydantic.functional_serializers.field_serializer\n  - Decorator to apply custom serialization logic to fields.\n  - Can be used with mode='plain' or mode='wrap'.\n  - Applied on instance or static methods.\n  - Signature: @field_serializer(field_name, *, mode='plain', return_type=None)\n    - field_name: The name of the field to serialize.\n    - mode: 'plain' or 'wrap'. Defaults to 'plain'.\n    - return_type: Optional return type annotation for the serialized value.\n```\n\n----------------------------------------\n\nTITLE: Dataclass Constraints with Pydantic\nDESCRIPTION: Demonstrates using Pydantic's Field within dataclasses to control initialization behavior (`init`, `init_var`) and keyword-only arguments (`kw_only`). Includes an example of a nested model structure and its output.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_23\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\nfrom pydantic.dataclasses import dataclass\n\n\n@dataclass\nclass Foo:\n    bar: str\n    baz: str = Field(init_var=True)\n    qux: str = Field(kw_only=True)\n\n\nclass Model(BaseModel):\n    foo: Foo\n\n\nmodel = Model(foo=Foo('bar', baz='baz', qux='qux'))\nprint(model.model_dump())  # (1)!\n#> {'foo': {'bar': 'bar', 'qux': 'qux'}}\n```\n\n----------------------------------------\n\nTITLE: Reworked create_model Field Definitions\nDESCRIPTION: The format for defining fields within `create_model` has been refactored. This change aims to improve consistency and maintainability of dynamically created models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_26\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import create_model\n\n# The structure for field definitions in create_model has been updated.\n# For example, the way default values or FieldInfo are passed might have changed.\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Correct validator fields as arguments\nDESCRIPTION: Shows the correct method for applying a validator to multiple fields by passing each field name as a separate string argument to the `@field_validator` decorator. This avoids the 'invalid validator fields' error.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_29\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, field_validator\n\n\nclass Model(BaseModel):\n    a: str\n    b: str\n\n    @field_validator('a', 'b')\n    def check_fields(cls, v):\n        return v\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Constraints and Schema Generation\nDESCRIPTION: Illustrates how Pydantic handles field constraints, including cases where constraints might not be enforced by default and how to explicitly include them in the JSON schema using Field arguments. It shows how to use Field with raw schema attributes for unenforced constraints.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, PositiveInt\n\ntry:\n    # this won't work since `PositiveInt` takes precedence over the\n    # constraints defined in `Field`, meaning they're ignored\n    class Model(BaseModel):\n        foo: PositiveInt = Field(lt=10)\n\nexcept ValueError as e:\n    print(e)\n\n\n# if you find yourself needing this, an alternative is to declare\n# the constraints in `Field` (or you could use `conint()`)\n# here both constraints will be enforced:\nclass ModelB(BaseModel):\n    # Here both constraints will be applied and the schema\n    # will be generated correctly\n    foo: int = Field(gt=0, lt=10)\n\n\nprint(ModelB.model_json_schema())\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n    \"properties\": {\n        \"foo\": {\n            \"exclusiveMaximum\": 10,\n            \"exclusiveMinimum\": 0,\n            \"title\": \"Foo\",\n            \"type\": \"integer\"\n        }\n    },\n    \"required\": [\"foo\"],\n    \"title\": \"ModelB\",\n    \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Decorator on Missing Field: Check Fields False\nDESCRIPTION: Demonstrates how to prevent the 'decorator-missing-field' error when using `field_validator` on a field that might not exist, by setting `check_fields=False`. This is useful in inheritance scenarios.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Any\n\nfrom pydantic import BaseModel, create_model, field_validator\n\n\nclass Model(BaseModel):\n    @field_validator('a', check_fields=False)\n    def check_a(cls, v: Any):\n        return v\n\n\nmodel = create_model('FooModel', a=(str, 'cake'), __base__=Model)\n```\n\n----------------------------------------\n\nTITLE: Model Field Missing Annotation (ClassVar): Python Example\nDESCRIPTION: Demonstrates resolving the 'model-field-missing-annotation' error by annotating a field as a ClassVar, indicating it's not intended to be a model field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_16\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import ClassVar\n\nfrom pydantic import BaseModel\n\n\nclass Model(BaseModel):\n    a: ClassVar[str]\n```\n\n----------------------------------------\n\nTITLE: Pydantic Model with Strict Boolean Field\nDESCRIPTION: Illustrates defining a Pydantic `BaseModel` with a boolean field (`foo`) configured to be strictly validated using `Field(strict=True)`. This demonstrates how Python model definitions translate into core schema configurations.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/internals/architecture.md#_snippet_1\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    foo: bool = Field(strict=True)\n```\n\n----------------------------------------\n\nTITLE: Generate JSON Schema with Pydantic `computed_field`\nDESCRIPTION: This example demonstrates how the `computed_field` decorator influences the JSON Schema generated for a Pydantic model. It shows that computed properties like `volume` are included in the schema as read-only fields, ensuring they are part of the model's contract during serialization.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_39\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property  # (1)!\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nprint(Box.model_json_schema(mode='serialization'))\n\"\"\"\n{\n    'properties': {\n        'width': {'title': 'Width', 'type': 'number'},\n        'height': {'title': 'Height', 'type': 'number'},\n        'depth': {'title': 'Depth', 'type': 'number'},\n        'volume': {'readOnly': True, 'title': 'Volume', 'type': 'number'}\n    },\n    'required': ['width', 'height', 'depth', 'volume'],\n    'title': 'Box',\n    'type': 'object'\n}\n\"\"\"\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Generate Field Titles with `field_title_generator`\nDESCRIPTION: Demonstrates how to use the `field_title_generator` within `ConfigDict` to programmatically set titles for individual fields in a Pydantic model's JSON schema. This allows for dynamic title generation based on field name and info, affecting all fields in the class.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_19\n\nLANGUAGE: python\nCODE:\n```\nimport json\n\nfrom pydantic import BaseModel, ConfigDict\n\n\nclass Person(BaseModel):\n    model_config = ConfigDict(\n        field_title_generator=lambda field_name, field_info: field_name.upper()\n    )\n    name: str\n    age: int\n\n\nprint(json.dumps(Person.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"name\": {\n      \"title\": \"NAME\",\n      \"type\": \"string\"\n    },\n    \"age\": {\n      \"title\": \"AGE\",\n      \"type\": \"integer\"\n    }\n  },\n  \"required\": [\n    \"name\",\n    \"age\"\n  ],\n  \"title\": \"Person\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Basic Include in Pydantic\nDESCRIPTION: Demonstrates how to use the `include` parameter to specify which fields to serialize. It shows including specific fields and nested fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_21\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel\n\nclass User(BaseModel):\n    id: str\n    name: str\n    user: dict\n\nt = User(id='1234567890', name='John Doe', user={'id': 42})\nprint(t.model_dump(include={'id': True, 'user': {'id'}}))\n#> {'id': '1234567890', 'user': {'id': 42}}\n```\n\n----------------------------------------\n\nTITLE: Access Field Name with `Annotated` and `AfterValidator`\nDESCRIPTION: Demonstrates accessing the field name via `ValidationInfo` when using `Annotated` with validators like `AfterValidator`. This provides context to the validator about the field it's processing, leveraging the `handler.field_name` capability.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/types.md#_snippet_26\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import AfterValidator, BaseModel, ValidationInfo\n\n\ndef my_validators(value: int, info: ValidationInfo):\n    return f'<{value} {info.field_name!r}>'\n\n\nclass MyModel(BaseModel):\n    my_field: Annotated[int, AfterValidator(my_validators)]\n\n\nm = MyModel(my_field=1)\nprint(m.my_field)\n#> <1 'my_field'>\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic Alias with Static Type Checking (Annotated)\nDESCRIPTION: Demonstrates the `Annotated` pattern with `Field(alias='...')` in Pydantic for static type checking. This approach allows type checkers to recognize the alias while Pydantic can validate using both the field name and the alias when `validate_by_name=True` and `validate_by_alias=True`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_15\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\nclass User(BaseModel):\n    model_config = ConfigDict(validate_by_name=True, validate_by_alias=True)\n\n    name: Annotated[str, Field(alias='username')]\n\n\nuser = User(name='johndoe')  # (1)!\nuser = User(username='johndoe')  # (2)!\n```\n\n----------------------------------------\n\nTITLE: Pydantic Schema `Union` and `Field` Combination\nDESCRIPTION: Removes redundant `allOf` constructs from schemas generated for fields that use `Union` types in conjunction with custom `Field` configurations. This simplifies the generated OpenAPI/JSON schema.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_244\n\nLANGUAGE: python\nCODE:\n```\nRemove extra `allOf` from schema for fields with `Union` and custom `Field`\nRelated Issue: [#1209](https://github.com/pydantic/pydantic/pull/1209)\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Serialization Alias\nDESCRIPTION: Demonstrates how `serialization_alias` overrides `alias` during serialization in Pydantic models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_16\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass MyModel(BaseModel):\n    my_field: int = Field(validation_alias='myValidationAlias')\n\n```\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass MyModel(BaseModel):\n    my_field: int = Field(\n        alias='myValidationAlias',\n        serialization_alias='my_field',\n    )\n\n\nm = MyModel(myValidationAlias=1)\nprint(m.model_dump(by_alias=True))\n#> {'my_field': 1}\n\n```\n\n----------------------------------------\n\nTITLE: Discriminator No Field: Union Example\nDESCRIPTION: Illustrates the 'discriminator-no-field' error, which arises when a model within a discriminated union (using `typing.Union` and `pydantic.Field(discriminator=...)`) does not define the specified discriminator field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_7\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Literal, Union\n\nfrom pydantic import BaseModel, Field, PydanticUserError\n\n\nclass Cat(BaseModel):\n    c: str\n\n\nclass Dog(BaseModel):\n    pet_type: Literal['dog']\n    d: str\n\n\ntry:\n\n    class Model(BaseModel):\n        pet: Union[Cat, Dog] = Field(discriminator='pet_type')\n        number: int\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'discriminator-no-field'\n```\n\n----------------------------------------\n\nTITLE: Mark `computed_field` as Deprecated\nDESCRIPTION: Illustrates how to mark a computed field, decorated with `@computed_field` and `@property`, as deprecated using the `deprecated` decorator from `typing_extensions`. This provides a warning when the computed field is accessed.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_37\n\nLANGUAGE: python\nCODE:\n```\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n----------------------------------------\n\nTITLE: Pydantic Generic Field Schema Generation\nDESCRIPTION: Adds support for generating schemas for generic fields. This allows Pydantic to correctly represent and validate fields that use generic types.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_159\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic Generic Field Schema Generation:\n\nSupports generating schemas for Generic fields.\n```\n\n----------------------------------------\n\nTITLE: Deprecated Config Fields for Extra\nDESCRIPTION: Deprecated `ignore_extra` and `allow_extra` configuration fields in favor of a unified `extra` field for managing extra data.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_271\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Extra\n\nclass ModelWithExtra(BaseModel):\n    class Config:\n        extra = Extra.allow # or Extra.ignore, Extra.forbid\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Correct validator usage with fields\nDESCRIPTION: Demonstrates the correct way to define a validator using `@field_validator` by explicitly passing the field name(s) as arguments. This ensures the validator is correctly applied to the intended model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_27\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, field_validator\n\n\nclass Model(BaseModel):\n    a: str\n\n    @field_validator('a')\n    def checker(cls, v):\n        return v\n```\n\n----------------------------------------\n\nTITLE: Strict Mode Validation\nDESCRIPTION: Shows how to use the `strict=True` parameter on a Pydantic `Field` to enforce strict validation, preventing data coercion. When `strict=True`, Pydantic will raise an error instead of attempting to convert the data type.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_27\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(strict=True)\n    age: int = Field(strict=False)  # (1)!\n\n\nuser = User(name='John', age='42')  # (2)!\nprint(user)\n#> name='John' age=42\n```\n\n----------------------------------------\n\nTITLE: Pydantic BaseModel.construct() Field Order\nDESCRIPTION: Ensures that the order of fields is preserved when creating models using `BaseModel.construct()`. This is important for maintaining predictable field ordering, especially in scenarios where order matters.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_148\n\nLANGUAGE: APIDOC\nCODE:\n```\nPydantic BaseModel.construct() Field Order:\n\nPreserves the order of fields when using `BaseModel.construct()`.\n```\n\n----------------------------------------\n\nTITLE: Decimal Constraints with Pydantic\nDESCRIPTION: Shows how to use Pydantic's Field to constrain Decimal types, specifying the maximum number of digits and decimal places allowed. Includes a model definition and instantiation example.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_22\n\nLANGUAGE: python\nCODE:\n```\nfrom decimal import Decimal\n\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    precise: Decimal = Field(max_digits=5, decimal_places=2)\n\n\nfoo = Foo(precise=Decimal('123.45'))\nprint(foo)\n#> precise=Decimal('123.45')\n```\n\n----------------------------------------\n\nTITLE: Pydantic Validation Alias Only\nDESCRIPTION: Illustrates using `validation_alias` in Pydantic's `Field` to specify an alias used only during model validation. The example shows instance creation with the validation alias and serialization using the original field name.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_12\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass User(BaseModel):\n    name: str = Field(validation_alias='username')\n\n\nuser = User(username='johndoe')  # (1)!\nprint(user)\n#> name='johndoe'\nprint(user.model_dump(by_alias=True))  # (2)!\n#>\n#> {'name': 'johndoe'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic AliasPath for Nested Field Aliases\nDESCRIPTION: Demonstrates using AliasPath to specify a path to a field, including array indices, for validation. Shows how to access nested data using aliases.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/alias.md#_snippet_0\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, AliasPath\n\n\nclass User(BaseModel):\n    first_name: str = Field(validation_alias=AliasPath('names', 0))\n    last_name: str = Field(validation_alias=AliasPath('names', 1))\n\nuser = User.model_validate({'names': ['John', 'Doe']})  # (1)!\nprint(user)\n#> first_name='John' last_name='Doe'\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Invalid `create_model` field definitions\nDESCRIPTION: This error occurs when invalid field definitions are provided to the `create_model()` function. It typically arises from incorrect tuple structures for field arguments.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_25\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import PydanticUserError, create_model\n\ntry:\n    create_model('FooModel', foo=(str, 'default value', 'more'))\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'create-model-field-definitions'\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Customization with typing.Annotated\nDESCRIPTION: Shows how to specify JSON schema modifications via the Field constructor using typing.Annotated. This approach allows for cleaner integration of field metadata and constraints directly within the type hint.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_7\n\nLANGUAGE: python\nCODE:\n```\nimport json\nfrom typing import Annotated\nfrom uuid import uuid4\n\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    id: Annotated[str, Field(default_factory=lambda: uuid4().hex)]\n    name: Annotated[str, Field(max_length=256)] = Field(\n        'Bar', title='CustomName'\n    )\n\n\nprint(json.dumps(Foo.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"id\": {\n      \"title\": \"Id\",\n      \"type\": \"string\"\n    },\n    \"name\": {\n      \"default\": \"Bar\",\n      \"maxLength\": 256,\n      \"title\": \"CustomName\",\n      \"type\": \"string\"\n    }\n  },\n  \"title\": \"Foo\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Field Inclusion/Exclusion with __all__\nDESCRIPTION: Corrects the behavior of the `__all__` key when used with index keys in advanced field inclusion/exclusion logic for sequences. This ensures more predictable handling of complex field selection in models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_237\n\nLANGUAGE: python\nCODE:\n```\n# Internal Pydantic logic fix related to field inclusion/exclusion.\n# Affects models with complex field configurations, especially sequences.\n```\n\n----------------------------------------\n\nTITLE: Pydantic Numeric Constraints\nDESCRIPTION: Illustrates the use of numeric constraints like `gt`, `lt`, `ge`, `le`, `multiple_of`, and `allow_inf_nan` in Pydantic fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_17\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Foo(BaseModel):\n    positive: int = Field(gt=0)\n    non_negative: int = Field(ge=0)\n    negative: int = Field(lt=0)\n    non_positive: int = Field(le=0)\n    even: int = Field(multiple_of=2)\n    love_for_pydantic: float = Field(allow_inf_nan=True)\n\n\nfoo = Foo(\n    positive=1,\n    non_negative=0,\n    negative=-1,\n    non_positive=0,\n    even=2,\n    love_for_pydantic=float('inf'),\n)\nprint(foo)\n\"\"\"\npositive=1 non_negative=0 negative=-1 non_positive=0 even=2 love_for_pydantic=inf\n\"\"\"\n\n```\n\n----------------------------------------\n\nTITLE: Demonstrate `frozen_field` Pydantic Error in Python\nDESCRIPTION: This error is raised when attempting to assign a new value to a field marked with `frozen=True` or to delete such a field. It enforces immutability for specific model fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/validation_errors.md#_snippet_36\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, ValidationError\n\n\nclass Model(BaseModel):\n    x: str = Field('test', frozen=True)\n\n\nmodel = Model()\n\ntry:\n    model.x = 'test1'\nexcept ValidationError as exc:\n    print(repr(exc.errors()[0]['type']))\n    #> 'frozen_field'\n\ntry:\n    del model.x\nexcept ValidationError as exc:\n    print(repr(exc.errors()[0]['type']))\n    #> 'frozen_field'\n```\n\n----------------------------------------\n\nTITLE: Pydantic V2 Field Requirements and Nullability\nDESCRIPTION: Illustrates Pydantic V2's updated logic for field requirements and nullability, which now more closely matches Python dataclasses. It clarifies how `Optional` and `Any` annotations behave and how default values impact whether a field is required.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/migration.md#_snippet_24\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Optional\n\nfrom pydantic import BaseModel, ValidationError\n\n\nclass Foo(BaseModel):\n    f1: str  # required, cannot be None\n    f2: Optional[str]  # required, can be None - same as str | None\n    f3: Optional[str] = None  # not required, can be None\n    f4: str = 'Foobar'  # not required, but cannot be None\n\n\ntry:\n    # Example demonstrating validation errors for f1 being None\n    Foo(f1=None, f2=None, f4='b')\nexcept ValidationError as e:\n    print(e)\n    # Output will show validation error for f1\n    # 1 validation error for Foo\n    # f1\n    #   Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]\n```\n\n----------------------------------------\n\nTITLE: Decorator on Missing Field: FieldValidator\nDESCRIPTION: Shows the 'decorator-missing-field' error that occurs when a `field_validator` is applied to a field name ('b') that does not exist on the Pydantic model.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_5\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Any\n\nfrom pydantic import BaseModel, PydanticUserError, field_validator\n\ntry:\n\n    class Model(BaseModel):\n        a: str\n\n        @field_validator('b')\n        def check_b(cls, v: Any):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'decorator-missing-field'\n```\n\n----------------------------------------\n\nTITLE: Programmatic Field Title Generation in Pydantic\nDESCRIPTION: Demonstrates using the `field_title_generator` parameter in Pydantic's Field function to programmatically set a field's title based on its name and information. This is useful for consistent naming conventions across models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/json_schema.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nimport json\n\nfrom pydantic import BaseModel, Field\nfrom pydantic.fields import FieldInfo\n\n\ndef make_title(field_name: str, field_info: FieldInfo) -> str:\n    return field_name.upper()\n\n\nclass Person(BaseModel):\n    name: str = Field(field_title_generator=make_title)\n    age: int = Field(field_title_generator=make_title)\n\n\nprint(json.dumps(Person.model_json_schema(), indent=2))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"properties\": {\n    \"name\": {\n      \"title\": \"NAME\",\n      \"type\": \"string\"\n    },\n    \"age\": {\n      \"title\": \"AGE\",\n      \"type\": \"integer\"\n    }\n  },\n  \"required\": [\n    \"name\",\n    \"age\"\n  ],\n  \"title\": \"Person\",\n  \"type\": \"object\"\n}\n```\n\n----------------------------------------\n\nTITLE: Pydantic Field Ordering and Validation\nDESCRIPTION: Explains that Pydantic preserves field order in JSON Schema, validation errors, and serialization. The example shows how fields are ordered in `model_dump()` and how validation errors report locations based on this order.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/models.md#_snippet_52\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, ValidationError\n\n\nclass Model(BaseModel):\n    a: int\n    b: int = 2\n    c: int = 1\n    d: int = 0\n    e: float\n\n\nprint(Model.model_fields.keys())\n#> dict_keys(['a', 'b', 'c', 'd', 'e'])\nm = Model(e=2, a=1)\nprint(m.model_dump())\n#> {'a': 1, 'b': 2, 'c': 1, 'd': 0, 'e': 2.0}\ntry:\n    Model(a='x', b='x', c='x', d='x', e='x')\nexcept ValidationError as err:\n    error_locations = [e['loc'] for e in err.errors()]\n\nprint(error_locations)\n#> [('a',), ('b',), ('c',), ('d',), ('e',)]\n```\n\n----------------------------------------\n\nTITLE: Marking Fields as Deprecated\nDESCRIPTION: Shows how to use the `deprecated` parameter with a string message to mark Pydantic fields as deprecated. This generates a runtime deprecation warning upon access and sets the `deprecated` keyword in the JSON schema.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_30\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, Field(deprecated='This is deprecated')]\n\n\nprint(Model.model_json_schema()['properties']['deprecated_field'])\n#> {'deprecated': True, 'title': 'Deprecated Field', 'type': 'integer'}\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Default Values with Field (Python)\nDESCRIPTION: Illustrates using `pydantic.Field` to provide default values for model attributes. It highlights the requirement for `default` to be a keyword argument for type checkers like Pyright to correctly infer optional fields, and explains a limitation related to runtime vs. type-checker behavior.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/integrations/visual_studio_code.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field\n\n\nclass Knight(BaseModel):\n    title: str = Field(default='Sir Lancelot')  # this is okay\n    age: int = Field(\n        23\n    )  # this works fine at runtime but will case an error for pyright\n\n\nlance = Knight()  # error: Argument missing for parameter \"age\"\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Invalid validator fields\nDESCRIPTION: This error occurs when a validator is applied to fields that are not specified as separate string arguments. Fields should be passed individually or as a list of strings to the validator decorator.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_28\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError, field_validator\n\ntry:\n\n    class Model(BaseModel):\n        a: str\n        b: str\n\n        @field_validator(['a', 'b'])\n        def check_fields(cls, v):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'validator-invalid-fields'\n```\n\n----------------------------------------\n\nTITLE: Pydantic Dataclass Fields with `doc` Attribute\nDESCRIPTION: Provides support for the `doc` attribute on dataclass fields within Pydantic models. This allows docstrings to be associated directly with individual fields, improving code clarity and enabling better introspection.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_3\n\nLANGUAGE: python\nCODE:\n```\nfrom dataclasses import dataclass, field\nfrom pydantic import BaseModel\n\n@dataclass\nclass UserInfo(BaseModel):\n    name: str = field(metadata={\"doc\": \"The user's full name.\"})\n    age: int = field(metadata={\"doc\": \"The user's age in years.\"})\n\n# The 'doc' metadata can be accessed via model introspection or schema generation.\n# Example access:\n# print(UserInfo.model_fields['name'].json_schema_extra.get('doc'))\n\n```\n\n----------------------------------------\n\nTITLE: Validate JSON with Invalid/Omitted Partial Field\nDESCRIPTION: Illustrates validating a JSON string where a field ('c') is incomplete and fails validation (MinLen(5)). The 'experimental_allow_partial=True' flag allows processing to continue, omitting the invalid field.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/experimental.md#_snippet_6\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom annotated_types import MinLen\nfrom typing_extensions import NotRequired, TypedDict\n\nfrom pydantic import TypeAdapter\n\n\nclass Foobar(TypedDict):\n    a: int\n    b: NotRequired[float]\n    c: NotRequired[Annotated[str, MinLen(5)]]\n\n\nta = TypeAdapter(list[Foobar])\n\nv = ta.validate_json(\n    '[{\"a\": 1, \"b\": 1.0, \"c\": \"abcd\"', experimental_allow_partial=True\n)\nprint(v)\n#> [{'a': 1, 'b': 1.0}]\n```\n\n----------------------------------------\n\nTITLE: Annotated Pattern for Field Serialization\nDESCRIPTION: Demonstrates using Annotated with PlainSerializer to apply custom serialization logic to fields. This pattern allows for reusable serializers defined directly within type annotations.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_9\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\n\nfrom pydantic import BaseModel, Field, PlainSerializer\n\nDoubleNumber = Annotated[int, PlainSerializer(lambda v: v * 2)]\n\n\nclass Model1(BaseModel):\n    my_number: DoubleNumber\n\n\nclass Model2(BaseModel):\n    other_number: Annotated[DoubleNumber, Field(description='My other number')]\n\n\nclass Model3(BaseModel):\n    list_of_even_numbers: list[DoubleNumber]  # (1)!\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Support typing.Annotated for Field hints\nDESCRIPTION: Demonstrates how to use `typing.Annotated` to specify Pydantic `Field` configurations directly within type hints. Other annotations are ignored but preserved.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_169\n\nLANGUAGE: python\nCODE:\n```\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field\n\nclass MyModel(BaseModel):\n    my_field: Annotated[int, Field(gt=0, description=\"A positive integer\")]\n\n# get_type_hints(MyModel, include_extras=True) would show the Field details.\n```\n\n----------------------------------------\n\nTITLE: Deprecate Field with @warnings.deprecated Decorator\nDESCRIPTION: Demonstrates marking a Pydantic field as deprecated using the `deprecated` decorator from `typing_extensions` or `warnings`. This method is compatible with Python 3.9 and above, with a specific version for Python 3.13+ using the built-in `warnings.deprecated`.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_31\n\nLANGUAGE: Python\nCODE:\n```\nfrom typing import Annotated\n\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, deprecated('This is deprecated')]\n\n    # Or explicitly using `Field`:\n    alt_form: Annotated[int, Field(deprecated=deprecated('This is deprecated'))]\n\n```\n\nLANGUAGE: Python\nCODE:\n```\nfrom typing import Annotated\nfrom warnings import deprecated\n\nfrom pydantic import BaseModel, Field\n\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, deprecated('This is deprecated')]\n\n    # Or explicitly using `Field`:\n    alt_form: Annotated[int, Field(deprecated=deprecated('This is deprecated'))]\n\n```\n\n----------------------------------------\n\nTITLE: Pydantic `computed_field` API\nDESCRIPTION: The `computed_field` decorator from Pydantic is used to include `property` or `cached_property` attributes when serializing a model or dataclass. It also ensures the property is considered in the JSON Schema. While `computed_field` can implicitly convert a method to a property, explicitly using `@property` is preferred for type checking.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_38\n\nLANGUAGE: APIDOC\nCODE:\n```\ncomputed_field\n\nDecorator to include properties in Pydantic models.\n\nUse Cases:\n- Include computed properties in model serialization.\n- Include computed properties in JSON Schema generation.\n- Useful for fields computed from other fields or expensive computations (with caching).\n\nNotes:\n- Pydantic does not perform additional logic (validation, cache invalidation) on the wrapped property.\n- Can be used with `@property` or `@cached_property`.\n- Explicitly using `@property` is recommended for type checking.\n\nExample:\n```python\nfrom pydantic import BaseModel, computed_field\n\nclass MyModel(BaseModel):\n    a: int\n    b: int\n\n    @computed_field\n    @property\n    def sum_ab(self) -> int:\n        return self.a + self.b\n```\n\nRelated:\n- `pydantic.fields.computed_field`\n- `property`\n- `functools.cached_property`\n```\n\n----------------------------------------\n\nTITLE: Pydantic: Validator with no fields specified\nDESCRIPTION: This error is raised when a validator method is defined without specifying any fields it should apply to, either directly or via keyword arguments. Validators must be associated with fields.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_26\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError, field_validator\n\ntry:\n\n    class Model(BaseModel):\n        a: str\n\n        @field_validator\n        def checker(cls, v):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'validator-no-fields'\n```\n\n----------------------------------------\n\nTITLE: Pydantic computed_field with Property for Volume Calculation\nDESCRIPTION: Demonstrates using the computed_field decorator with a property to calculate the volume of a Box model. Includes JSON schema generation and model dumping.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_34\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property  # (1)!\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nprint(Box.model_json_schema(mode='serialization'))\n```\n\nLANGUAGE: json\nCODE:\n```\n{\n    'properties': {\n        'width': {'title': 'Width', 'type': 'number'},\n        'height': {'title': 'Height', 'type': 'number'},\n        'depth': {'title': 'Depth', 'type': 'number'},\n        'volume': {'readOnly': True, 'title': 'Volume', 'type': 'number'},\n    },\n    'required': ['width', 'height', 'depth', 'volume'],\n    'title': 'Box',\n    'type': 'object',\n}\n```\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n\nb = Box(width=1, height=2, depth=3)\nprint(b.model_dump())\n```\n\n----------------------------------------\n\nTITLE: Model Field Overridden: Python Example\nDESCRIPTION: This error is raised when a field defined on a base class was overridden by a non-annotated attribute. The example demonstrates catching this specific PydanticUserError.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_14\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError\n\n\nclass Foo(BaseModel):\n    a: float\n\n\ntry:\n\n    class Bar(Foo):\n        x: float = 12.3\n        a = 123.0\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'model-field-overridden'\n```\n\n----------------------------------------\n\nTITLE: Pydantic `Field` Default Value Mutation\nDESCRIPTION: Prevents accidental mutation of default values associated with `Field` instances. This ensures that default values remain unchanged across multiple uses or validations.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_246\n\nLANGUAGE: python\nCODE:\n```\nAvoid mutating `Field` default value\nRelated Issue: [#1412](https://github.com/pydantic/pydantic/pull/1412)\n```\n\n----------------------------------------\n\nTITLE: Pydantic computed_field with Deprecated Property\nDESCRIPTION: Shows how to mark a computed field as deprecated using the @deprecated decorator from typing_extensions.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_35\n\nLANGUAGE: python\nCODE:\n```\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n----------------------------------------\n\nTITLE: Multiple Field Serializers in Pydantic\nDESCRIPTION: This error is raised when multiple `model_serializer` functions are defined for a single field. It prevents ambiguity in serialization logic.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_41\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, PydanticUserError, field_serializer\n\ntry:\n\n    class MyModel(BaseModel):\n        x: int\n        y: int\n\n        @field_serializer('x', 'y')\n        def serializer1(v):\n            return f'{v:,}'\n\n        @field_serializer('x')\n        def serializer2(v):\n            return v\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'multiple-field-serializers'\n```\n\n----------------------------------------\n\nTITLE: Schema Generation for Optional Fields\nDESCRIPTION: Fixed schema generation logic to correctly handle fields annotated with `Optional`, ensuring accurate schema representation.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_275\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel\nfrom typing import Optional\n\nclass ModelWithOptional(BaseModel):\n    name: Optional[str] = None\n    age: int\n```\n\n----------------------------------------\n\nTITLE: Mark Pydantic `computed_field` as Deprecated\nDESCRIPTION: This example demonstrates how to mark a `computed_field` as deprecated using the `@deprecated` decorator from `typing_extensions`. This allows developers to signal that a computed property is no longer recommended for use, providing clear guidance for API evolution.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_41\n\nLANGUAGE: python\nCODE:\n```\nfrom typing_extensions import deprecated\n\nfrom pydantic import BaseModel, computed_field\n\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n----------------------------------------\n\nTITLE: Pydantic Forward Refs and Optional Fields\nDESCRIPTION: Fixes behavior with forward references and optional fields in nested Pydantic models.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_210\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel\nfrom typing import Optional, TYPE_CHECKING\n\nif TYPE_CHECKING:\n    # Forward reference to a model defined later or in another module\n    class ParentModel(BaseModel):\n        child: Optional['ChildModel']\nelse:\n    # Actual definition for runtime\n    class ChildModel(BaseModel):\n        value: int\n\n    class ParentModel(BaseModel):\n        child: Optional[ChildModel]\n\n# This ensures that optional fields with forward references are handled correctly.\n```\n\n----------------------------------------\n\nTITLE: Model Field Missing Annotation: Python Example\nDESCRIPTION: This error occurs when a field within a Pydantic model lacks a type annotation. The example shows how to catch this error and demonstrates resolving it using ClassVar or ignored_types.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/errors/usage_errors.md#_snippet_15\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, Field, PydanticUserError\n\ntry:\n\n    class Model(BaseModel):\n        a = Field('foobar')\n        b = None\n\nexcept PydanticUserError as exc_info:\n    assert exc_info.code == 'model-field-missing-annotation'\n```\n\n----------------------------------------\n\nTITLE: Default Factory with Data Argument\nDESCRIPTION: Illustrates using `default_factory` with a lambda that accepts the validated data dictionary as an argument to derive a default value.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_8\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, EmailStr, Field\n\n\nclass User(BaseModel):\n    email: EmailStr\n    username: str = Field(default_factory=lambda data: data['email'])\n\n\nuser = User(email='user@example.com')\nprint(user.username)\n#> user@example.com\n```\n\n----------------------------------------\n\nTITLE: JSON Schema for String Constraints\nDESCRIPTION: Illustrates the JSON schema generated by Pydantic for string constraints, showing how 'min_length', 'max_length', and 'pattern' are translated to 'minLength', 'maxLength', and 'pattern' respectively.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_snippet_21\n\nLANGUAGE: json\nCODE:\n```\n{\n  \"title\": \"Foo\",\n  \"type\": \"object\",\n  \"properties\": {\n    \"short\": {\n      \"title\": \"Short\",\n      \"type\": \"string\",\n      \"minLength\": 3\n    },\n    \"long\": {\n      \"title\": \"Long\",\n      \"type\": \"string\",\n      \"maxLength\": 10\n    },\n    \"regex\": {\n      \"title\": \"Regex\",\n      \"type\": \"string\",\n      \"pattern\": \"^\\\\d*$\"\n    }\n  },\n  \"required\": [\n    \"short\",\n    \"long\",\n    \"regex\"\n  ]\n}\n```\n\n----------------------------------------\n\nTITLE: Support typing.Counter as model field\nDESCRIPTION: Adds support for using `typing.Counter` as a valid type for model fields. This allows Pydantic models to directly incorporate `collections.Counter` objects.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/HISTORY.md#_snippet_120\n\nLANGUAGE: python\nCODE:\n```\nAdd ability to use `typing.Counter` as a model field type, [#2060](https://github.com/pydantic/pydantic/pull/2060) by @uriyyo\n```\n\n----------------------------------------\n\nTITLE: SerializeAsAny Annotation Example\nDESCRIPTION: Demonstrates using the SerializeAsAny annotation to achieve duck typing serialization behavior for a Pydantic model field. This allows a field to be serialized as if its type hint was Any.\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/serialization.md#_snippet_17\n\nLANGUAGE: python\nCODE:\n```\nfrom pydantic import BaseModel, SerializeAsAny\n\n\nclass User(BaseModel):\n    name: str\n\n\nclass UserLogin(User):\n    password: str\n\n\nclass OuterModel(BaseModel):\n    as_any: SerializeAsAny[User]\n    as_user: User\n\n\nuser = UserLogin(name='pydantic', password='password')\n\nprint(OuterModel(as_any=user, as_user=user).model_dump())\n```\n\n========================\nQUESTIONS AND ANSWERS\n========================\nTOPIC: Pydantic Field Customization\nQ: What is the difference between applying `Field()` to a type versus a union type in Pydantic with `Annotated`?\nA: When `Field()` is applied to a specific type within an `Annotated` union (e.g., `Annotated[int, Field(deprecated=True)] | None`), the metadata might not affect the field as intended. Applying `Field()` to the top-level union type (e.g., `Annotated[int | None, Field(deprecated=True)]`) ensures the metadata is applied to the field itself.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_6\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: What is the purpose of the `Field()` function in Pydantic?\nA: The `Field()` function in Pydantic is used to customize model fields by specifying default values, JSON Schema metadata, constraints, and other attributes. It functions similarly to the `field()` function in Python's standard library for dataclasses.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_0\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: Can Pydantic's `Field()` function be used to make a field immutable?\nA: Yes, the `Field()` function in Pydantic can be used with the `frozen=True` argument to make a field immutable, meaning its value cannot be changed after initialization.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_8\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: How can Pydantic fields be customized?\nA: Pydantic fields can be customized using the `Field()` function to set default values, JSON Schema metadata, and constraints. The `Annotated` typing construct can also be used to attach metadata like `Field()` or `WithJsonSchema` to model fields.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_1\n\n----------------------------------------\n\nTOPIC: Pydantic Dataclasses\nQ: Can both Pydantic's `Field()` and stdlib's `field()` be used with Pydantic dataclasses?\nA: Yes, you can use both Pydantic's `Field()` and the standard library's `field()` functions when defining fields in Pydantic dataclasses. This allows for customization and validation using Pydantic's features.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/dataclasses.md#_qa_6\n\n----------------------------------------\n\nTOPIC: \nQ: What is the purpose of the `pydantic.fields` module?\nA: The `pydantic.fields` module in Pydantic provides core components for defining and managing fields within data models. It includes classes like `Field` and `FieldInfo` for detailed field configuration and metadata.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/api/fields.md#_qa_0\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: What is the role of `typing.Annotated` when customizing Pydantic fields?\nA: The `Annotated` typing construct in Pydantic allows attaching metadata, such as `Field()` or `WithJsonSchema`, to a model field's annotation. This provides a way to add validation logic and type constraints without altering the field's base type from a static type checker's perspective.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_2\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: What are the advantages of using the `Annotated` pattern for Pydantic fields?\nA: The `Annotated` pattern avoids confusion about whether a field is required or has a default value, unlike the `f: <type> = Field(...)` form. It also allows for an arbitrary amount of metadata elements to be provided for a field, enabling the use of different Pydantic utilities like `WithJsonSchema`.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_3\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: Can Pydantic fields be made immutable?\nA: Yes, Pydantic fields can be made immutable by setting the `frozen=True` argument within the `Field()` function.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_7\n\n----------------------------------------\n\nTOPIC: Pydantic Field Customization\nQ: When should the standard assignment form be preferred over the `Annotated` pattern in Pydantic?\nA: The standard assignment form (`f: <type> = Field(...)`) should be used when arguments like `default`, `default_factory`, or `alias` are provided to `Field()`. This is because static type checkers do not understand the `Annotated` pattern for synthesizing `__init__` methods.\n\n\nSOURCE: https://github.com/pydantic/pydantic/blob/main/docs/concepts/fields.md#_qa_4"}]}
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{"parentUuid":"4769da80-324a-464a-af7e-719bdfc5bcbb","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","message":{"id":"msg_01DWekQwLfBVS7dHSBBgJzQX","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_01GYsq2pAAyPMNQcjCAtZ865","name":"Write","input":{"file_path":"/home/wei/repo/claude-code/Pydantic_Field_Usage_Guide.md","content":"# Pydantic Field 用法指南\n\n## 概述\n\nPydantic 的 `Field()` 函數用於自定義模型字段，提供默認值、JSON Schema 元數據、約束條件等功能。它類似於 Python 標準庫 dataclasses 中的 `field()` 函數。\n\n## 基本用法\n\n### 1. 基礎字段自定義\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    name: str = Field(frozen=True)  # 不可變字段\n    name_required: str = Field(..., frozen=True)  # 明確標記為必需\n```\n\n### 2. 使用 Annotated 模式\n\n```python\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field, WithJsonSchema\n\nclass Model(BaseModel):\n    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]\n```\n\n## 字段約束\n\n### 數值約束\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Foo(BaseModel):\n    positive: int = Field(gt=0)           # 大於 0\n    non_negative: int = Field(ge=0)       # 大於等於 0\n    negative: int = Field(lt=0)           # 小於 0\n    non_positive: int = Field(le=0)       # 小於等於 0\n    even: int = Field(multiple_of=2)      # 2 的倍數\n    love_for_pydantic: float = Field(allow_inf_nan=True)  # 允許無窮大和 NaN\n```\n\n### 字符串約束\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Foo(BaseModel):\n    short: str = Field(min_length=3)           # 最小長度\n    long: str = Field(max_length=10)           # 最大長度\n    regex: str = Field(pattern=r'^\\d*$')       # 正則表達式\n```\n\n### Decimal 約束\n\n```python\nfrom decimal import Decimal\nfrom pydantic import BaseModel, Field\n\nclass Foo(BaseModel):\n    precise: Decimal = Field(max_digits=5, decimal_places=2)\n```\n\n## 默認值\n\n### 基本默認值\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = 'John Doe'               # 直接賦值\n    age: int = Field(default=20)         # 使用 Field\n```\n\n### 默認值工廠\n\n```python\nfrom pydantic import BaseModel, EmailStr, Field\n\nclass User(BaseModel):\n    email: EmailStr\n    username: str = Field(default_factory=lambda data: data['email'])\n```\n\n## 字段別名\n\n### 驗證和序列化別名\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(alias='username')\n\nuser = User(username='johndoe')\nprint(user.model_dump(by_alias=True))  # {'username': 'johndoe'}\n```\n\n### 僅驗證別名\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(validation_alias='username')\n\nuser = User(username='johndoe')\nprint(user.model_dump(by_alias=True))  # {'name': 'johndoe'}\n```\n\n### 僅序列化別名\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(serialization_alias='username')\n\nuser = User(name='johndoe')\nprint(user.model_dump(by_alias=True))  # {'username': 'johndoe'}\n```\n\n### 嵌套字段別名 (AliasPath)\n\n```python\nfrom pydantic import BaseModel, Field, AliasPath\n\nclass User(BaseModel):\n    first_name: str = Field(validation_alias=AliasPath('names', 0))\n    last_name: str = Field(validation_alias=AliasPath('names', 1))\n\nuser = User.model_validate({'names': ['John', 'Doe']})\n```\n\n## 字段控制\n\n### 字段排除\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str\n    age: int = Field(exclude=True)        # 序列化時排除\n\n# 條件性排除\nclass Transaction(BaseModel):\n    id: int\n    private_id: int = Field(exclude=True)\n    value: int = Field(ge=0, exclude_if=lambda v: v == 0)\n```\n\n### 字段表示控制\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(repr=True)      # 在 __repr__ 中顯示\n    age: int = Field(repr=False)      # 在 __repr__ 中隱藏\n```\n\n### 不可變字段\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(frozen=True)    # 創建後不可修改\n    age: int\n\nuser = User(name='John', age=42)\n# user.name = 'Jane'  # 會引發 ValidationError\n```\n\n## 嚴格模式\n\n### 字段級嚴格驗證\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str\n    age: int = Field(strict=True)     # 此字段使用嚴格驗證\n    n_pets: int = Field(strict=False) # 此字段允許類型轉換\n```\n\n## 判別聯合 (Discriminated Unions)\n\n```python\nfrom typing import Literal, Union\nfrom pydantic import BaseModel, Field\n\nclass Cat(BaseModel):\n    pet_type: Literal['cat']\n    age: int\n\nclass Dog(BaseModel):\n    pet_type: Literal['dog']\n    age: int\n\nclass Model(BaseModel):\n    pet: Union[Cat, Dog] = Field(discriminator='pet_type')\n```\n\n## 字段棄用\n\n### 使用 deprecated 參數\n\n```python\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, Field(deprecated=True)]\n    alt_deprecated: Annotated[int, Field(deprecated='This field is deprecated')]\n```\n\n### 使用 @deprecated 裝飾器\n\n```python\nfrom typing import Annotated\nfrom typing_extensions import deprecated\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, deprecated('This is deprecated')]\n```\n\n## 計算字段 (Computed Fields)\n\n```python\nfrom pydantic import BaseModel, computed_field\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n# 使用\nbox = Box(width=1, height=2, depth=3)\nprint(box.model_dump())  # 包含 volume: 6.0\n```\n\n### 棄用計算字段\n\n```python\nfrom typing_extensions import deprecated\nfrom pydantic import BaseModel, computed_field\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n## JSON Schema 自定義\n\n```python\nimport json\nfrom pydantic import BaseModel, EmailStr, Field, SecretStr\n\nclass User(BaseModel):\n    age: int = Field(description='Age of the user')\n    email: EmailStr = Field(examples=['marcelo@mail.com'])\n    name: str = Field(title='Username')\n    password: SecretStr = Field(\n        json_schema_extra={\n            'title': 'Password',\n            'description': 'Password of the user',\n            'examples': ['123456'],\n        }\n    )\n\nprint(json.dumps(User.model_json_schema(), indent=2))\n```\n\n## 數據類 (Dataclasses) 中的字段\n\n```python\nimport dataclasses\nfrom typing import Optional\nfrom pydantic import Field\nfrom pydantic.dataclasses import dataclass\n\n@dataclass\nclass User:\n    id: int\n    name: str = 'John Doe'\n    friends: list[int] = dataclasses.field(default_factory=lambda: [0])\n    age: Optional[int] = dataclasses.field(\n        default=None,\n        metadata={'title': 'The age of the user', 'description': 'do not lie!'},\n    )\n    height: Optional[int] = Field(\n        default=None, title='The height in cm', ge=50, le=300\n    )\n```\n\n## 驗證器與字段\n\n### 字段驗證器\n\n```python\nfrom pydantic import BaseModel, field_validator\n\nclass Model(BaseModel):\n    a: str\n    b: str\n\n    @field_validator('a', 'b')  # 應用於多個字段\n    def check_fields(cls, v):\n        return v.upper()\n```\n\n## 最佳實踐\n\n1. **優先使用 Annotated 模式** - 避免字段是否必需的混淆\n2. **合理使用別名** - 根據需要選擇驗證別名、序列化別名或兩者\n3. **適當設置約束** - 使用字段約束確保數據質量\n4. **明智使用嚴格模式** - 在需要時啟用嚴格驗證\n5. **文檔化字段** - 使用 description 和 title 提供清晰的字段說明\n\n## 常見錯誤\n\n1. **混淆類型和字段元數據** - 確保 Field() 應用到正確的類型層級\n2. **驗證器字段不存在** - 使用 `check_fields=False` 處理繼承場景\n3. **重複字段序列化器** - 避免為同一字段定義多個序列化器\n\n## 參考資料\n\n- [Pydantic 官方文檔 - Fields](https://docs.pydantic.dev/latest/concepts/fields/)\n- [Pydantic 官方文檔 - JSON Schema](https://docs.pydantic.dev/latest/concepts/json_schema/)\n- [Pydantic 官方文檔 - Serialization](https://docs.pydantic.dev/latest/concepts/serialization/)"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":505,"cache_read_input_tokens":71538,"cache_creation":{"ephemeral_5m_input_tokens":505,"ephemeral_1h_input_tokens":0},"output_tokens":36,"service_tier":"standard"}},"requestId":"req_011CSaAYTHqnUGdhnHkD6wDk","type":"assistant","uuid":"fe35d9fd-df93-4945-a358-8b1b3f50569f","timestamp":"2025-08-28T13:13:07.315Z"}
{"parentUuid":"fe35d9fd-df93-4945-a358-8b1b3f50569f","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GYsq2pAAyPMNQcjCAtZ865","type":"tool_result","content":"File created successfully at: /home/wei/repo/claude-code/Pydantic_Field_Usage_Guide.md"}]},"uuid":"c625a9a7-8f67-419c-b144-499b1662b044","timestamp":"2025-08-28T13:13:19.348Z","toolUseResult":{"type":"create","filePath":"/home/wei/repo/claude-code/Pydantic_Field_Usage_Guide.md","content":"# Pydantic Field 用法指南\n\n## 概述\n\nPydantic 的 `Field()` 函數用於自定義模型字段，提供默認值、JSON Schema 元數據、約束條件等功能。它類似於 Python 標準庫 dataclasses 中的 `field()` 函數。\n\n## 基本用法\n\n### 1. 基礎字段自定義\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    name: str = Field(frozen=True)  # 不可變字段\n    name_required: str = Field(..., frozen=True)  # 明確標記為必需\n```\n\n### 2. 使用 Annotated 模式\n\n```python\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field, WithJsonSchema\n\nclass Model(BaseModel):\n    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]\n```\n\n## 字段約束\n\n### 數值約束\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Foo(BaseModel):\n    positive: int = Field(gt=0)           # 大於 0\n    non_negative: int = Field(ge=0)       # 大於等於 0\n    negative: int = Field(lt=0)           # 小於 0\n    non_positive: int = Field(le=0)       # 小於等於 0\n    even: int = Field(multiple_of=2)      # 2 的倍數\n    love_for_pydantic: float = Field(allow_inf_nan=True)  # 允許無窮大和 NaN\n```\n\n### 字符串約束\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Foo(BaseModel):\n    short: str = Field(min_length=3)           # 最小長度\n    long: str = Field(max_length=10)           # 最大長度\n    regex: str = Field(pattern=r'^\\d*$')       # 正則表達式\n```\n\n### Decimal 約束\n\n```python\nfrom decimal import Decimal\nfrom pydantic import BaseModel, Field\n\nclass Foo(BaseModel):\n    precise: Decimal = Field(max_digits=5, decimal_places=2)\n```\n\n## 默認值\n\n### 基本默認值\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = 'John Doe'               # 直接賦值\n    age: int = Field(default=20)         # 使用 Field\n```\n\n### 默認值工廠\n\n```python\nfrom pydantic import BaseModel, EmailStr, Field\n\nclass User(BaseModel):\n    email: EmailStr\n    username: str = Field(default_factory=lambda data: data['email'])\n```\n\n## 字段別名\n\n### 驗證和序列化別名\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(alias='username')\n\nuser = User(username='johndoe')\nprint(user.model_dump(by_alias=True))  # {'username': 'johndoe'}\n```\n\n### 僅驗證別名\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(validation_alias='username')\n\nuser = User(username='johndoe')\nprint(user.model_dump(by_alias=True))  # {'name': 'johndoe'}\n```\n\n### 僅序列化別名\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(serialization_alias='username')\n\nuser = User(name='johndoe')\nprint(user.model_dump(by_alias=True))  # {'username': 'johndoe'}\n```\n\n### 嵌套字段別名 (AliasPath)\n\n```python\nfrom pydantic import BaseModel, Field, AliasPath\n\nclass User(BaseModel):\n    first_name: str = Field(validation_alias=AliasPath('names', 0))\n    last_name: str = Field(validation_alias=AliasPath('names', 1))\n\nuser = User.model_validate({'names': ['John', 'Doe']})\n```\n\n## 字段控制\n\n### 字段排除\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str\n    age: int = Field(exclude=True)        # 序列化時排除\n\n# 條件性排除\nclass Transaction(BaseModel):\n    id: int\n    private_id: int = Field(exclude=True)\n    value: int = Field(ge=0, exclude_if=lambda v: v == 0)\n```\n\n### 字段表示控制\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(repr=True)      # 在 __repr__ 中顯示\n    age: int = Field(repr=False)      # 在 __repr__ 中隱藏\n```\n\n### 不可變字段\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str = Field(frozen=True)    # 創建後不可修改\n    age: int\n\nuser = User(name='John', age=42)\n# user.name = 'Jane'  # 會引發 ValidationError\n```\n\n## 嚴格模式\n\n### 字段級嚴格驗證\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass User(BaseModel):\n    name: str\n    age: int = Field(strict=True)     # 此字段使用嚴格驗證\n    n_pets: int = Field(strict=False) # 此字段允許類型轉換\n```\n\n## 判別聯合 (Discriminated Unions)\n\n```python\nfrom typing import Literal, Union\nfrom pydantic import BaseModel, Field\n\nclass Cat(BaseModel):\n    pet_type: Literal['cat']\n    age: int\n\nclass Dog(BaseModel):\n    pet_type: Literal['dog']\n    age: int\n\nclass Model(BaseModel):\n    pet: Union[Cat, Dog] = Field(discriminator='pet_type')\n```\n\n## 字段棄用\n\n### 使用 deprecated 參數\n\n```python\nfrom typing import Annotated\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, Field(deprecated=True)]\n    alt_deprecated: Annotated[int, Field(deprecated='This field is deprecated')]\n```\n\n### 使用 @deprecated 裝飾器\n\n```python\nfrom typing import Annotated\nfrom typing_extensions import deprecated\nfrom pydantic import BaseModel, Field\n\nclass Model(BaseModel):\n    deprecated_field: Annotated[int, deprecated('This is deprecated')]\n```\n\n## 計算字段 (Computed Fields)\n\n```python\nfrom pydantic import BaseModel, computed_field\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n\n# 使用\nbox = Box(width=1, height=2, depth=3)\nprint(box.model_dump())  # 包含 volume: 6.0\n```\n\n### 棄用計算字段\n\n```python\nfrom typing_extensions import deprecated\nfrom pydantic import BaseModel, computed_field\n\nclass Box(BaseModel):\n    width: float\n    height: float\n    depth: float\n\n    @computed_field\n    @property\n    @deprecated(\"'volume' is deprecated\")\n    def volume(self) -> float:\n        return self.width * self.height * self.depth\n```\n\n## JSON Schema 自定義\n\n```python\nimport json\nfrom pydantic import BaseModel, EmailStr, Field, SecretStr\n\nclass User(BaseModel):\n    age: int = Field(description='Age of the user')\n    email: EmailStr = Field(examples=['marcelo@mail.com'])\n    name: str = Field(title='Username')\n    password: SecretStr = Field(\n        json_schema_extra={\n            'title': 'Password',\n            'description': 'Password of the user',\n            'examples': ['123456'],\n        }\n    )\n\nprint(json.dumps(User.model_json_schema(), indent=2))\n```\n\n## 數據類 (Dataclasses) 中的字段\n\n```python\nimport dataclasses\nfrom typing import Optional\nfrom pydantic import Field\nfrom pydantic.dataclasses import dataclass\n\n@dataclass\nclass User:\n    id: int\n    name: str = 'John Doe'\n    friends: list[int] = dataclasses.field(default_factory=lambda: [0])\n    age: Optional[int] = dataclasses.field(\n        default=None,\n        metadata={'title': 'The age of the user', 'description': 'do not lie!'},\n    )\n    height: Optional[int] = Field(\n        default=None, title='The height in cm', ge=50, le=300\n    )\n```\n\n## 驗證器與字段\n\n### 字段驗證器\n\n```python\nfrom pydantic import BaseModel, field_validator\n\nclass Model(BaseModel):\n    a: str\n    b: str\n\n    @field_validator('a', 'b')  # 應用於多個字段\n    def check_fields(cls, v):\n        return v.upper()\n```\n\n## 最佳實踐\n\n1. **優先使用 Annotated 模式** - 避免字段是否必需的混淆\n2. **合理使用別名** - 根據需要選擇驗證別名、序列化別名或兩者\n3. **適當設置約束** - 使用字段約束確保數據質量\n4. **明智使用嚴格模式** - 在需要時啟用嚴格驗證\n5. **文檔化字段** - 使用 description 和 title 提供清晰的字段說明\n\n## 常見錯誤\n\n1. **混淆類型和字段元數據** - 確保 Field() 應用到正確的類型層級\n2. **驗證器字段不存在** - 使用 `check_fields=False` 處理繼承場景\n3. **重複字段序列化器** - 避免為同一字段定義多個序列化器\n\n## 參考資料\n\n- [Pydantic 官方文檔 - Fields](https://docs.pydantic.dev/latest/concepts/fields/)\n- [Pydantic 官方文檔 - JSON Schema](https://docs.pydantic.dev/latest/concepts/json_schema/)\n- [Pydantic 官方文檔 - Serialization](https://docs.pydantic.dev/latest/concepts/serialization/)","structuredPatch":[]}}
{"parentUuid":"c625a9a7-8f67-419c-b144-499b1662b044","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","message":{"id":"msg_01BNRu91TW2fVAiyy3sVfLR5","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"tool_use","id":"toolu_01NGfeBcwxzS5iFXUdnK1Vuy","name":"TodoWrite","input":{"todos":[{"content":"搜尋 pydantic 庫的 Library ID","status":"completed","activeForm":"搜尋 pydantic 庫的 Library ID"},{"content":"獲取 pydantic Field 用法文檔","status":"completed","activeForm":"獲取 pydantic Field 用法文檔"},{"content":"整理成 markdown 格式並保存","status":"completed","activeForm":"整理成 markdown 格式並保存"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":2927,"cache_read_input_tokens":72043,"cache_creation":{"ephemeral_5m_input_tokens":2927,"ephemeral_1h_input_tokens":0},"output_tokens":34,"service_tier":"standard"}},"requestId":"req_011CSaAdYH7xw6RWxtJJhuvP","type":"assistant","uuid":"ceff3c5f-d9a2-4ae3-97c2-7e56f41a3b0e","timestamp":"2025-08-28T13:13:24.407Z"}
{"parentUuid":"ceff3c5f-d9a2-4ae3-97c2-7e56f41a3b0e","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NGfeBcwxzS5iFXUdnK1Vuy","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"uuid":"9dd8813e-1524-4256-8831-da6edba8acb2","timestamp":"2025-08-28T13:13:24.469Z","toolUseResult":{"oldTodos":[],"newTodos":[{"content":"搜尋 pydantic 庫的 Library ID","status":"completed","activeForm":"搜尋 pydantic 庫的 Library ID"},{"content":"獲取 pydantic Field 用法文檔","status":"completed","activeForm":"獲取 pydantic Field 用法文檔"},{"content":"整理成 markdown 格式並保存","status":"completed","activeForm":"整理成 markdown 格式並保存"}]}}
{"parentUuid":"9dd8813e-1524-4256-8831-da6edba8acb2","isSidechain":false,"userType":"external","cwd":"/home/wei/repo/claude-code","sessionId":"b162b1ae-97bc-475f-9b5f-ffbf55ca5b3f","version":"1.0.95","gitBranch":"main","message":{"id":"msg_01ERGLd4aYX9Pvd23CJcv3us","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[{"type":"text","text":"已完成！我從 Context7 獲取了 Pydantic Field 的詳細文檔，並整理成完整的 Markdown 指南保存為 `Pydantic_Field_Usage_Guide.md`。\n\n這份指南包含：\n\n## 主要內容\n- **基本用法** - Field() 函數和 Annotated 模式\n- **字段約束** - 數值、字符串、Decimal 約束\n- **默認值** - 基本默認值和工廠函數\n- **字段別名** - 驗證、序列化和嵌套別名\n- **字段控制** - 排除、表示控制、不可變字段\n- **嚴格模式** - 字段級嚴格驗證\n- **判別聯合** - 使用 discriminator 的聯合類型\n- **字段棄用** - deprecated 參數和裝飾器\n- **計算字段** - @computed_field 裝飾器\n- **JSON Schema 自定義** - 元數據和文檔\n- **數據類支持** - Pydantic 數據類中的字段\n- **驗證器** - 字段驗證器的使用\n\n## 特色\n- 包含實際程式碼範例\n- 涵蓋最佳實踐建議\n- 列出常見錯誤和解決方案\n- 提供官方文檔參考連結\n\n文件已保存在: `Pydantic_Field_Usage_Guide.md`"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":415,"cache_read_input_tokens":74970,"cache_creation":{"ephemeral_5m_input_tokens":415,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CSaAdvAkg1PShaLUifqd5","type":"assistant","uuid":"96098017-30e9-4922-9d3b-624f16baa11e","timestamp":"2025-08-28T13:13:34.627Z"}
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