LexiMerge API The Hyper-Decoupled Style Dictionary Engine: Replacing Hardcoded Arrays with Constrained SLM Infrastructure
🛑 Honest Disclaimer & Liability Shield / 誠實聲明與結構化免責條款### ⚠️ A Tech Joke Grounded in Optimistic Engineering Viability### ⚠️ 一個基於樂觀工程可行性的技術幽默方案> "I have absolutely no clue how coding, backend optimization, or low-latency infrastructure works. Most of this documentation, including the sub-2GB SLM deployment, grammar-constrained decoding, and localized caching pipeline, was deeply authored by Gemini with Google Search AI Mode. I merely provided the initial concept, acted as the editorial voice, and modified the text several times. Use at your own peril.">> 「本人對撰寫程式碼、後端優化及低延遲基礎設施建置一竅不通，純屬技術門外漢。本文件內所有涉及 sub-2GB 小型語言模型（SLM）部署、語法樹限制解碼、本端快取管線等技術架構，皆由 Gemini + Google 搜尋 AI 模式功能深度撰寫（Deeply Authored by Gemini）。本人僅負責提供最初始的構想，並進行了多次內容修改與編校。本文件僅供參考，請自行承擔所有使用風險。」
---### 🛡️ Apache License 2.0 & "AS IS" Legal Defense ### 🛡️ Apache 2.0 授權條款與「按現狀提供」法律免責界線 This repository and the conceptual architecture described herein are licensed under the Apache License 2.0.
本專案及其所述之架構概念全面採用 Apache License 2.0 開源授權條款。
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at apache.org Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
THE CRITICAL LEGAL SHIELD (THE "AS IS" CLAUSE): THE WORK (INCLUDING ARCHITECTURAL CONCEPTS, INFRASTRUCTURE BLUEPRINTS, AND PSEUDOCODE) IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE WORK AND ASSUME ANY RISKS ASSOCIATED WITH YOUR EXERCISE OF PERMISSIONS UNDER THIS LICENSE.
法律免責條款（「按現狀提供」）： 本成果（包括技術概念、架構藍圖及虛擬代碼）均基於**「現狀（AS IS）」形式提供，不附帶任何明示或暗示的保證或條件**，包括但不限於對所有權、非侵權、可商用性或特定用途適用性的保證。您須獨自承擔因使用、修改或重新分發本成果所產生的所有風險。
Let’s be clear: using a constrained small language model as a function interface to entirely replace hardcoded lookup arrays might sound like an industry joke to traditional developers. However, this is NOT a project built on illusions or vaporware. From an engineering standpoint, this architecture is completely robust, highly realizable, and optimized for extreme decoupling. We are serious about the data compliance and structural metrics—we just acknowledge the tactical laziness behind it.
我們必須明確指出：動用受限的小型語言模型來充當函式接口、全面取代硬編碼的靜態尋找陣列（Array），在傳統開發者眼裡可能聽起來像個產業笑話。然而，本專案絕非建立在虛幻或無法實現的空想之上。從硬體解耦與工程實作的角度來看，這套架構是高度可行且穩固的。我們對資料結構、合規指標與傳輸效率的規劃極其嚴肅——我們只是不隱瞞這背後的戰術性懶惰。
If you believe in this framework, feel free to Fork it immediately. The odds of me personally writing the production C++ or Python code to host these edge clusters are next to zero. The roadmap is set; you are welcome to build it yourself.
如果您認同這個框架，歡迎直接 Fork 本專案。因為本人親自去編寫生產級 C++ 或 Python 程式碼來架設這些邊緣叢集的機率趨近於零。技術路徑已然清晰，各位工程師請自便。
CRITICAL NOTICE: I will NOT actively monitor this repository, review Pull Requests, or triage bugs. All typical repository notifications have been disabled to ensure zero distraction from daily workflows.
關鍵公告： 本人「絕對不會」主動關注此專案的後續進展、不會審查 Pull Requests、亦不會處理 Bug。所有日常通知皆已關閉，以確保不干擾個人的日常工作流。
However, if this architecture is successfully deployed into a high-concurrency production stack and successfully deprecates static array dictionaries—please tag me (@<Your-GitHub-Username>) in a GitHub Issue. The server platform might route an email notification to me, and I might check it. However, it's also highly possible that we were busy with other projects and didn't notice at all. Good luck!
然而，萬一這套架構真的在某個高併發的生產環境中落地，並成功淘汰了傳統的靜態字典——歡迎您在 GitHub Issue 中標記（@）我。屆時我也許會收到系統的信件通知。不過，同樣極其可能的是，我們當時正忙於其他專案，壓根就沒有注意到。祝各位好運！
🧩 The Sovereign Pain Point / 專案核心痛點：被硬編碼囚禁的字串### 🚫 The Brute-Force Illusion of Array Splicing### 🚫 陣列拼接的暴力美學幻象
Traditional software development relies on static, predetermined data collections to generate dynamic names (e.g., in video games, user IDs, or procedural item generation). Developers typically write algorithms that brutally concatenate strings from separate buckets: [Adjective Array] + [Noun Array]. While this approach occupies nearly zero memory and runs in microseconds, it scales into a maintenance nightmare. To prevent linguistically absurd or contradictory combinations (e.g., "Liquid Iron Sword" or "Sacred Skeletal Wand"), engineers are forced to write an infinite web of conditional if-else statement exclusions. The codebase expands into an unmaintainable "spaghetti tower," trying to hardcode human logic into a rigid system that possesses zero awareness of linguistic context.
傳統的軟體開發高度依賴靜態且預先定義好的資料集合，來生成動態名稱（例如：電子遊戲中的玩家隨機 ID、或是程序化生成的道具道具名稱）。開發者通常會編寫算法，極其粗暴地將不同陣列中的字串進行拼接：[形容詞陣列] + [名詞陣列]。這種做法雖然幾乎不佔用記憶體，且能在微秒級內執行完畢，但隨之而來的是維護成本的災難性膨脹。為了解決語意上荒謬或互相矛盾的組合（例如：「液體的鐵劍」或「神聖的骷髏法杖」），工程師被迫編寫無窮無盡的 if-else 條件排除邏輯。隨著字彙量的增加，程式碼迅速退化成難以維護的「義大利麵山」，試圖用死板的硬編碼去模擬人類的語言邏輯。
---### 📉 The Boundary Collapse of Style Fusion### 📉 跨界風格融合的湧現崩塌
The fatal flaw of the traditional array matrix becomes exposed when a project demands a sudden crossover or multi-theme context fusion (e.g., generating an item that is exactly Theme A: Post-Apocalyptic + Theme B: Dark Magic). If your static database does not possess pre-labeled words specifically tagged for both "Wasteland" and "Necromancy," the traditional system is completely paralyzed—it cannot synthesize meaning from vacuum. The system's generative potential is strictly throttled by the manual data-labeling capacity of your development team. To expand the dictionary's thematic boundaries, you must pay an exponential cost in human hours to manually tag, adjust weights, and recalibrate probabilities for thousands of static strings.
當專案需要引入全新的跨界風格、或是多種主題的交集融合時（例如：生成一個完美融合 主題 A：末日廢土 與 主題 B：暗黑魔法 的特殊名詞），傳統標籤矩陣的致命硬傷便會暴露無遺。如果您的靜態資料庫裡沒有預先由人工打上同時具備「廢土」與「死靈」雙標籤的詞彙，傳統系統會當場癱瘓——它完全不具備憑空進行語意提煉與融合的能力。系統的生成潛力被研發團隊的人工標籤產能給攔腰斬斷。為了擴展字典的風格邊界，您必須投入呈指數級增長的人力成本，去手動為成千上萬個靜態字串重新打標籤、調整權重並校正出現機率。
This infrastructure entirely abandons the primitive concept of static string storage. Instead, it treats vocabulary generation as a dynamic, deterministic mathematical function. The system operates through three tightly coupled vertical layers:
本基礎設施徹底拋棄了傳統儲存靜態字串的原始概念，將詞彙生成視為一種動態且具備確定性的數學函式。整體系統由三個緊密耦合的垂直分層協同運作： ---### 📡 Layer 1: Bound Semantic Vector Space (The 1.5B Parameter SLM)### 📡 第一層：受限語意向量空間（1.5B 參數專用小型語言模型） At the core of the engine sits a specialized Small Language Model (SLM) capped at 1.5B parameters (optimized using 4-bit/8-bit quantization to fit entirely within 1.5GB of VRAM/RAM).
本引擎的核心是一個特別經過領域裁剪、參數盤整在 15 億（1.5B）左右的小型語言模型（SLM），在工程實作上透過 4-bit 或 8-bit 量化技術，將整體模型體積與運行記憶體死死壓在 1.5GB 左右。
- Linguistic Latent Space: Unlike bloated, general-purpose LLMs that waste parameters on advanced calculus or regional legal codes, this model’s high-dimensional embedding space is aggressively aligned toward semantic relationships, morphology, and thematic constraints. * 語意潛在空間對齊：不同於動輒千億參數、把算力浪費在微積分或各國法律條文的通用大模型，此 1.5B 小模型的向量嵌入空間（Embedding Space）被激進地校正並收斂在「詞性關聯」、「詞綴結構」與「特定風格矩陣」中。
- The Vector Intersection Illusion: When a JSON input requests a blend of
[Style A: Cyberpunk]and[Style B: Dark Fantasy], the model does not "think." It executes a low-latency forward pass to calculate the statistical intersection between the two style vectors. It effortlessly discovers that the phrase "Neon" naturally gravitates toward Cyberpunk, while "Wraith" aligns with Dark Fantasy, seamlessly outputting "Neon Wraith" without relying on any hardcoded linkage rules. - 向量交集運算：當接收到包含
[風格 A：賽博朋克]與[風格 B：暗黑魔法]的 JSON 參數時，模型在底層不進行任何邏輯推理。它純粹是在高維空間中進行低延遲的前向傳播（Forward Pass），計算兩個風格向量的最鄰近交集。模型會發現「霓虹」靠近賽博朋克，而「虛空」靠近暗黑魔法，從而流暢地輸出「霓虹虛空面具」，完全不需要任何人工撰寫的排他性規則（Exclusion Rules）。 ---### 🌲 Layer 2: Firmware-Level Grammar-Constrained Decoding (JSON Mode)### 🌲 第二層：韌體級語法樹限制解碼技術（強硬 JSON 模式） The notorious flaw of small language models is their structural instability—they easily hallucinate extra conversational text or break JSON brackets under heavy load. To elevate this into a production-grade code interface, we implement an absolute firmware-level restriction layer (via integration with sub-millisecond engines likellguidanceorllama.cpp).
小型語言模型在商業落地時的致命傷，在於其結構不穩定性——在高頻發送時極易吐出多餘的聊天廢話，或導致 JSON 括號破損。為了將其改造為工業級的代碼接口，我們在推論引擎層面引入了硬性語法樹限制解碼技術（如整合 llguidance 或 llama.cpp）。
- Logit Masking Execution: During the token-generation phase, the decoding engine evaluates the exact state of the required JSON Schema in real-time. If the model attempts to generate a character that violates valid JSON syntax (e.g., placing a letter where a closing quote
"or a comma,is mathematically required), the token's probability is instantly zeroed out (Logit Masking) before it ever hits the compute registers. - 輸出機率遮蔽：在模型預測下一個字（Token）的微秒間，限制引擎會依據預設的 JSON Schema 進行即時狀態機比對。如果小模型因為隨機性試圖吐出違反 JSON 語法的字元（例如在需要逗號
,或右引號"的地方吐出英文字母），該字元的出現機率會在進入暫存器前被強行歸零（Logit Masking）。* 100% Deterministic Schema Compliance: This guarantees that the API output is never a corrupted string. It either returns a perfectly validated JSON object matching the contract, or it fails-closed immediately. It interfaces with external object-oriented codebases as smoothly as a native system utility.* 100% 結構化物件保證：這確保了此 API 接口絕對不會回傳格式崩潰的破碎字串。它要麼回傳 100% 符合欄位合約的完美 JSON 物件，要麼直接拒絕放行。對外層的物件導向程式碼而言，它的調用體感就像是一個確定性的系統原生底層函式。 ---### 🔄 Layer 3: Automated Adaptive Feedback Pipeline (The Training Loop)### 🔄 第三層：自適應語意距離校正數據管線（訓練內循環） To prevent the high-dimensional space of an ultra-small model from distorting or drifting over long periods, the infrastructure incorporates an automated synthetic data distillation loop.
為了防止極小模型在高維度空間計算中因為資料稀疏（Data Sparsity）而產生距離失真或風格偏向，本架構內建了一套自動化的合成數據蒸餾與校正管線。
- Teacher-to-Student Distillation: A tier-1 heavy model (e.g., GPT-4o) acts as the offline Teacher, automatically pre-generating hundreds of thousands of high-quality style-matrix combinations along with their precise linguistic linguistic equivalents.
- 大模型合成數據對齊：利用通用大模型作為離線「導師」，在背景自動批量生成數十萬條包含跨界風格交集的
[風格 A + 風格 B = 融合名詞 JSON]高質量對照組數據。* Continuous Edge Correction: These optimized datasets are continuously injected into the 1.5B model via low-rank adaptation (LoRA) or automated incremental training. This iteratively shrinks the "semantic gaps" inside the small model, ensuring that the distance metrics between disparate concepts remain mathematically precise without requiring a bloated parameter footprint.* 向量距離動態校正：這套數據流會透過全自動的管線，定期對邊緣端的小模型進行低秩適應（LoRA）微調。這等同於在不增加實體模型參數體積的前提下，在背景不斷微調校正小模型內部的向量坐標距離，消除小模型對未知或冷門跨界風格的理解偏差。
To completely decouple software engineering from the high API token expenses of general-purpose LLMs, this infrastructure implements a deterministic "pay-per-hit" and tier-based commercial framework. The business strategy splits into two clear tracks tailored to different development budgets and legal boundaries:
為了解決軟體工程高度依賴通用大模型帶來的昂貴 Token 計費焦慮，本基礎設施採用了確定性的「按次計費（Pay-per-hit）」與階梯式訂閱的商業定價框架。整體商業策略劃分為兩條截然不同的軌道，以精準對接不同的開發預算與法律合規邊界： ---### 🟢 Track 1: Foundation Plan (Pure Semantic Fusion Interface)### 🟢 第一軌：基礎免合規方案（純語意融合接口） This tier is designed for independent developers, sandbox testing, or high-performance scenarios where absolute legal compliance checking is handled at another layer. It provides raw semantic vector cross-attention processing.
本方案專為獨立開發者、沙盒測試、或任何已在其他架構層面處理完合規審查的高性能場景設計。本接口僅提供純粹的語意向量交叉注意力（Cross-Attention）運算。
- The Efficiency Physics: The model runs on optimized commodity GPUs (such as an NVIDIA L4). Because the text outputs are strictly restricted by the JSON grammar tree to around 50 tokens per request, a single GPU cluster instance can smoothly process 200 concurrent requests per second.* 極致算力效率：後端模型運行於優化後的消費級高效能顯卡（如單張 NVIDIA L4）。由於輸出字串受到 JSON 語法樹的強硬限制，單次請求通常不超過 50 個 Token，這讓單個伺服器實例每秒能輕鬆吞吐並穩定處理 200 次高併發請求。* The Cost Metric: The raw compute hardware cost scales down to approximately $0.0083 USD per 10,000 requests. Including baseline API gateway infrastructure and bandwidth, the total internal cost floats around $0.05 USD per 10,000 requests. * 運行成本核算：在此高性能狀態下，每 10,000 次請求的純算力硬體成本被壓縮至約 $0.0083 美元。加計基礎 API 閘道器與網路頻寬攤提後，真實總成本約為每萬次 $0.05 美元。* Market Pricing: $1.00 USD per 10,000 requests (Pay-as-you-go). It offers independent studios an elite runtime generator where serving an active user for an entire day costs less than a fraction of a cent, eliminating the engineering overhead of hosting local models.* 市場定價模型：每 10,000 次呼叫固定收取 $1.0 美元（隨收隨付）。這給予中小型工作室極低且預算透明的運行時（Runtime）生成組件，服務單一活躍用戶一整天的成本幾可忽略不計，徹底免除自行架設與運維小模型的代價。 ---### 🔴 Track 2: Premium Compliance Plan (Enterprise Intermediate Middleware)### 🔴 第二軌：高級合規方案（企業級核心中間件） Large-scale commercial enterprises do not buy raw compute; they buy risk mitigation. When an enterprise deploys an open-source model locally, the stochastic nature of token prediction poses massive legal vulnerabilities (e.g., hallucinating trademarked terms from competitors or leaking cross-project black terminology). Track 2 acts as a fully compliant legal firewall.
大型企業與大廠客戶付費的核心從來不是算力，而是風險精算與控制。當大廠在私有雲自建開源小模型時，AI 預測的隨機性會帶來巨大的法務漏洞（例如隨機吐出競品的註冊商標、或導致不同專案組的黑話交叉污染）。第二軌方案即是為此設計的完整合規防禦中間件。
- The Compliance Toll (+50% Overhead): To enforce absolute compliance at the hardware register level, the engine executes real-time logit masking. It matches every token prediction against active global trademark registries, multi-tenant sandboxes, and safety constraints. This extra processing reduces the hardware throughput from 200 to approximately 130 requests per second.* 合規的硬代價（50% 性能開銷）：為了在晶片暫存器層面強行套用合規鐵律，推論引擎必須在每個 Token 生成的微秒間進行動態 Logit 遮蔽。這需要與全球商標數據庫、多租戶隔離矩陣進行即時比對，這項硬核安全防護會將硬體並行吞吐量從每秒 200 次降至約 130 次。* The Data Engineering Cycle: Keeping the compliance layer updated requires an ongoing automated pipeline where heavy teacher models constantly sanitize data and output low-rank adaptation (LoRA) updates to the edge clusters.* 數據管線維護成本：為了維持版權與合規資料庫的最新狀態，後端管線需要常態性利用大模型（導師）清洗資料，並將增量訓練（LoRA）權重滾動注入到全球邊緣節點，這是持續發生的固定維護成本。* Market Pricing: Subscription starts at $199 USD/month (includes 2,000,000 requests, with an enterprise volume discount of $0.80 USD per 10,000 requests for overages). It delivers an airtight SLA insurance policy guaranteeing 100% syntactical success and dynamic trademark isolation.* 市場定價模型：固定包月 $199 美元起（內含 200 萬次呼叫額度，超出部分給予大宗折扣每萬次 $0.8 美元）。這為企業大廠提供了一份包含 100% 格式防護、商標動態隔離與格式不崩潰的硬核 SLA 保險合規合約。 ---### 📉 Comparative Analysis: The "Traditional Static Dictionary" Trap### 📉 對比分析：傳統靜態標籤字典的維護內耗陷阱 Some engineering teams argue that large enterprises can easily use AI agents to automate the creation of a massive, multi-tagged traditional static dictionary database and run string-splicing code for free. This is a massive engineering trap:
部分大廠的技術團隊可能會認為，企業完全可以利用現有的 AI 代理（Agents）去全自動編寫代碼，強行洗出一個包含數十萬條資料、具備多對多標籤的「傳統靜態字典資料庫」，隨後用傳統代碼去隨機拼接，從而達到硬體成本為零的狀態。然而，這是一個嚴重的工程成本陷阱：
- Massive Upfront R&D Costs ($5,000+ USD): To prepopulate and tag a static matrix that successfully blends 100+ conflicting themes without linguistic collisions requires pulling millions of synthetic dataset records from expensive tier-1 models, leading to significant initial engineering and audit outlays.2. 前期開發內耗（高達 $5,000 美元以上）：為了在傳統代碼中完美融合超過 100 種跨界風格，而不出現語意生硬與字串衝突，團隊前期必須動用高階大模型進行極大規模的數據生成、打標籤、人工清洗與衝突校正。這筆一次性的研發與時間沉沒成本極高。3. The Sisyphus Maintenance Nightmare (Infinite Lifecycle Cost): Language is alive. The moment a new trend, internet meme, or expansion theme arrives, a static hardcoded database cannot adapt—its ability to handle unknown crossovers is zero. The engineering team must restart the data-cleaning cycle, write new exclusion rules to fix broken grammar, and force users to download an app update patch. 4. 西西弗斯式維護噩夢（無上限的生命週期成本）：語言是活的。當市場上每出現一個新的潮流迷因、或遊戲要新增一種資料片風格時，傳統靜態硬編碼系統的跨界泛化能力為零。團隊必須重新啟動數據清洗流程、手動補寫排他規則、並強制用戶下載 App 更新檔。5. The Structural Verdict: A static dictionary uses manual labor to fight the fluid nature of software evolution. By utilizing our paid API, the infrastructure cost becomes highly predictable and completely stable, transforming what would be an unmaintainable "maintenance debt" into a clean, smooth native system function.6. 終局判詞：傳統靜態字典是在用昂貴的人力去對抗軟體更新的本質。採用本專案的付費 API 接口，可以讓高頻率的語意代換成本變得高度可預測且絕對穩定，將高昂的「運維負債」直接轉化為優雅乾淨的原生系統函式。
⚖️ Legal License & "AS IS" Disclaimers / 法律授權條款與終極免責聲明### 📜 Apache License 2.0 (The Formal Text)### 📜 Apache License 2.0 (正式法律條文)
Apache License Version 2.0, January 2004 apache.org TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
- Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. ...
To read the full license text, please visit the official Apache License 2.0 Page.
欲查閱完整的授權條文細節，請直接造訪官方的 Apache License 2.0 頁面。
This project is an exercise in optimistic engineering design. While the infrastructure metrics, token logit-masking, and deterministic API schemas are completely viable, the author offers zero warranties of execution.
本專案是一次基於樂觀實用主義的工程設計推演。雖然文件所述之硬體指標、Token 級機率遮蔽以及確定性 API 設計在軟體架構上完全可行，但作者不提供任何執行層面的保證。
DISCLAIMER OF WARRANTY. THE WORK IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE WORK AND ASSUME ANY RISKS ASSOCIATED WITH YOUR EXERCISE OF PERMISSIONS UNDER THIS LICENSE. LIMITATION OF LIABILITY. IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE, UNLESS REQUIRED BY APPLICABLE LAW (SUCH AS DELIBERATE AND GROSSLY NEGLIGENT ACTS) OR AGREED TO IN WRITING, SHALL ANY CONTRIBUTOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY CHARACTER ARISING AS A RESULT OF THIS LICENSE OR OUT OF THE USE OR INABILITY TO USE THE WORK.
核心法律免責界線（大白話版）：
- 無保證（No Warranty）：本倉庫所含之所有藍圖、架構邏輯及文字皆為「現狀（AS IS）」提供。不保證能在您的特定顯示卡（GPU）上直接編譯，亦不保證在您應對高併發流量時 100% 不發生延遲。
- 免責聲明（Limitation of Liability）：在任何法律理論下（不論是契約、侵權行為如疏忽或其他），除非適用法律另有強制規定，否則本專案的創始人、總編輯或 AI 撰寫端，對於您因使用或無法使用本成果所導致之任何直接、間接、特別、偶發或衍生性損害（包括但不限於伺服器損壞、格式解析錯誤導致的代碼漏洞、或商業線上游戲的斷線損失），皆不承擔任何法律與賠償責任。
以下資訊獨立提供，適合分別複製填入 GitHub 專案的對應設定欄位中：
A highly-decoupled, low-latency style dictionary API engine designed to deprecate hardcoded string arrays using grammar-constrained 1.5B SLM infrastructure with multi-tenant semantic isolation.
(請注意：已嚴格遵循規範，僅使用空白鍵分隔各個標籤)
small-language-models grammar-constrained-decoding structured-outputs slm json-mode decoupling software-architecture compiler-engineering api-design middleware
(適合放在 README 最底部、給未來搜尋引擎看的技術關鍵字)
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