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RQ (Redis Queue) is a delightfully simple way to run background jobs in Python. That simplicity is also why teams under-monitor it: it just works, until a downstream API gets slow or a bad deploy ships, and jobs start failing in bulk — quietly. Here's what to watch and how to get alerted before a customer tells you. RQ failures don't announce themselves When a job raises, RQ moves it to the FailedJobRegistry and moves on. The worker keeps running; nothing crashes. If you're not looking at that registry, the failure is invisible — the same trap BullMQ, Celery, and every robust queue share. So the job is to reach into the queue's state and turn it into a signal. The four signals that matter for RQ Failure count / rate — jobs landing in the FailedJobRegistry over a window. Backlog — how many jobs are queued vs. being worked; is the worker keeping up? Latency — how long jobs take, and how long they wait before a worker picks them up. Worker liveness — are your workers actually alive and heartbeating? Where to read them RQ exposes queue and registry state directly: from redis import Redis from rq import Queue from rq.registry import FailedJobRegistry , StartedJobRegistry redis = Redis () q = Queue ( " default " , connection = redis ) queued = len ( q ) # backlog failed = FailedJobRegistry ( queue = q ) # failures started = StartedJobRegistry ( queue = q ) # in-flight print ( " queued: " , queued ) print ( " failed: " , len ( failed )) print ( " started: " , len ( started )) Poll this on an interval and store the series — a single snapshot hides the trend , which is the part that matters. For failures specifically, walk the registry to get the actual exceptions: for job_id in failed . get_job_ids (): job = q . fetch_job ( job_id ) print ( job . id , job . exc_info . splitlines ()[ - 1 ] if job . exc_info else "" ) Two gotchas: Group by exception, not by job. A thousand jobs failing with the same traceback is one incident. Normalize the message (strip IDs, timestamps, host

A lot of Robot Framework projects still look like plain Selenium scripts with .robot file extensions. Someone imports webdriver , creates driver = webdriver.Chrome() , then calls find_element and send_keys in Python helpers. Robot Framework runs the suite, but readable keywords, shared libraries, and consistent waits never show up in the tests. If you already use Robot Framework with SeleniumLibrary , you do not need the raw WebDriver API. SeleniumLibrary gives you high-level keywords. The Page Object Model gives you structure. Together they keep tests short and UI changes localized. We published a small MIT template that shows the layout: rf-seleniumlibrary-pageobject-template . It targets Sauce Demo — clone it, run four tests, fork the folder structure. What breaks when you mix in raw WebDriver driver = webdriver . Chrome () driver . find_element ( By . ID , " user-name " ). send_keys ( " standard_user " ) driver . find_element ( By . ID , " password " ). send_keys ( " secret_sauce " ) driver . find_element ( By . ID , " login-button " ). click () Fine for a script. Painful in a growing suite. Locators spread across helpers and test files. Waits become time.sleep(2) in one place and missing in another. You end up maintaining SeleniumLibrary and a parallel WebDriver stack. CI fails on a Tuesday night and you are not sure which path opened the browser. Before and after Before After driver.find_element(...).send_keys(...) Login With Valid Credentials ${VALID_USER} ${VALID_PASSWORD} Locators in every file LoginLocators.USERNAME in one module Ad-hoc sleeps wait_until_element_is_visible in BasePage.click() Two browser stacks One SeleniumLibrary instance per suite Four layers Layer Job Example Locators Selectors per screen login_locators.py BasePage Shared waits and actions click() , enter_text() Page library Screen keywords LoginPage.login() Robot test Scenario only Inventory Should Be Visible Folder layout in the repo: resources/locators/ → selectors pages/ → Python pa

A fintech RAG pipeline poisoned its own vector store and the LLM-as-a-judge validator approved every hallucination. The fix: gate writes with code.

GitHub热门项目 | Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic events in one unified interface. Hook an AI agent up to have it parse through data and find previously unseen correlations. The knowledge is available to all but rarely aggregated in the open, until now. | Stars: 9,650 | 30 stars today | 语言: Python

Filingrail now ships a first-party MCP server on PyPI: pip install filingrail-mcp . One install, one config block, and Claude Desktop — or Cursor, or Continue, or any MCP-compatible client — can query SEC filings as tools. No glue code. That's worth naming directly. Most SEC-data APIs ship a REST endpoint and stop. You write the agent integration yourself: parse the response, wire up the tool schema, handle auth headers. Filingrail ships the integration as a maintained package with the same update cadence as the underlying REST API. This post covers the setup, what you can ask once it's wired in, and the honest limits. I built both the API and the MCP server — I'll be upfront about that throughout. This post covers a data API that returns SEC-registered financial information. Nothing here is investment advice. Two ways to wire it in Option 1 — pip install filingrail-mcp (recommended) Install the package, add one block to your Claude Desktop config, restart. Filingrail's endpoints appear as tools. No separate service to run, no background daemon. Option 2 — RapidAPI MCP Playground tab (no local install) The Filingrail listing on RapidAPI has an MCP tab that generates a ready-to-paste config block. Same endpoints, same auth, zero install step. Either path gives Claude the same tools. Pick the one that fits your setup. Setup — the pip install path You'll need Python 3.10+ and a RapidAPI key. 1. Subscribe to Filingrail Go to the Filingrail RapidAPI listing and subscribe. Free tier is 50 calls/day, no credit card. Copy your X-RapidAPI-Key from the RapidAPI dashboard. 2. Install the server pip install filingrail-mcp 3. Add Filingrail to your Claude Desktop config On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%\Claude\claude_desktop_config.json { "mcpServers" : { "filingrail" : { "command" : "filingrail-mcp" , "env" : { "RAPIDAPI_KEY" : "your_rapidapi_key_here" } } } } 4. Restart Claude Desktop Filingrail's endpoints appear a

用 FROST 五维元模型构建可治理的多 Agent 系统：从零到一的代码教程 作者 ：FROST Team 日期 ：2026-07-09 主题 ：代码教程 | 周四轮换 项目 ：FROST + FROST-SOP 前言 2026 年，Agent 框架百花齐放——LangChain、CrewAI、AutoGen 各有各的好。但它们都有一个共同的盲区： 治理能力 。 当你的 Agent 系统从 1 个变成 10 个，从跑 Demo 变成跑生产，你会发现： 谁有权做什么？没有答案 这个决策是谁做的？无法追溯 Agent 越权了怎么办？事后才能发现 FROST（Fractal Runtime of Orchestrated Skills & Tasks）的 五维元模型 就是为解决这些问题而设计的。 今天这篇教程，我们用代码从零构建一个完整的多 Agent 治理系统。 FROST （教学框架）提供理论基础 → Gitee 仓库 FROST-SOP （工程平台）提供工程落地 → Gitee 仓库 一、五维元模型是什么？ FROST V4.0 引入了五个核心维度，每个维度解决 Agent 治理的一个关键问题： 维度 模块 解决的问题 类比 武器 Armory Agent 有哪些能力？ 武器库清单 任务 TaskRegistry 工作如何编排？ 作战计划 事件 EventCatalog 发生了什么？ 战场态势 平台 PlatformRegistry 外部资源在哪？ 后勤补给 规则 RuleRegistry 什么能做/不能做？ 交战规则 五个维度 各自独立又相互咬合 ——就像五角星的五个角，缺一个就不完整。 二、环境搭建 # 克隆 FROST 教学框架 git clone https://gitee.com/liao_liang_7514/frost.git cd frost # 安装依赖 pip install -r requirements.txt # 验证环境 python -m pytest test_core.py -v FROST 的设计哲学是 零外部依赖 ——核心只需要 Python 标准库。五维元模型的模块也遵循这个原则。 三、维度一：Armory（武器注册表） Armory 管理 Agent 所有能力的元数据。不是简单的方法注册，而是带元信息的 能力目录 。 from core.armory import Armory , SkillMetadata # 创建武器库 armory = Armory () # 注册一个技能（带完整元数据） armory . register ( SkillMetadata ( name = " summarize_text " , category = " nlp " , description = " 将长文本压缩为摘要 " , input_schema = { " text " : " string " , " max_length " : " int " }, output_schema = { " summary " : " string " }, cost_estimate = 0.002 , # 每次调用预估成本 latency_ms = 500 , # 预估延迟 tags = [ " summarization " , " compression " ] ) ) armory . register ( SkillMetadata ( name = " search_web " , category = " retrieval " , description = " 联网搜索获取最新信息 " , input_schema = { " query " : " string " , " top_k " : " int " }, output_schema = { " results " : " list[dict] " }, cost_estimate = 0.01 , latency_ms = 2000 , tags = [ " search " , " real-time " ] ) ) # 发现可用技能 nlp_skills = armory . discover ( category = " nlp " ) print ( f " NLP 技能: { [ s . name for s in nlp_skills ] } " ) # 输出: NLP 技能: ['summarize_text'] # 按标签发现 search_skills = armory . discover ( tags = [ " real-time " ]) print ( f " 实时技能: { [ s

Pytest Fundamentals for Data Engineers Data engineering code is still software, and software that doesn't have tests is a liability. If you've ever had a production pipeline silently produce wrong numbers because a downstream transformation made an assumption about a column that changed upstream, you know the pain. Testing isn't just about "does the pipeline run" — it's about verifying that every transformation, every branch of logic, and every edge case behaves exactly as you expect. This post lays the groundwork for testing data pipelines with pytest. I'll cover why testing matters, the fundamental pattern every test follows, how to write your first tests and assertions, how to verify that your code raises the right exceptions, and how to mock external dependencies so your tests stay fast and deterministic. By the end, you'll have a solid foundation for writing unit tests that catch regressions before they reach production. Testing 101 for Data Pipelines If you're new to testing or have only ever written a few scripts, the core idea is simple: a test is a small piece of code that exercises another piece of code and checks that the result is what you expected. In pytest, that looks like a function whose name starts with test_ and which contains an assert statement. The Fundamental Pattern: Arrange, Act, Assert Almost every test follows three steps: Arrange – set up the input data and any necessary context. Act – call the function or method you want to test. Assert – verify the output is correct. For a data pipeline transformation, that might be: def test_clean_amount (): # Arrange raw_data = " 1,200.50 " # what you might get from a CSV # Act result = clean_amount ( raw_data ) # Assert assert result == 1200.50 clean_amount is a pure function — no file I/O, no database calls. It just takes an input string and returns a number. That makes it straightforward to test. Assert Raises: Expecting Exceptions Data pipelines often deal with invalid data that should trigger an

Introduction Fintech engineers building backtesting engines, live quote dashboards, and algorithmic trading pipelines repeatedly face consistent pain points with market data APIs: limited granularity on free tiers, disjoint real-time and historical endpoints, inconsistent protocol support, and fragmented cross-asset coverage. This neutral technical breakdown compares three widely adopted market data providers, evaluating native functionality and end-to-end integration patterns to streamline API vendor selection for backend and quant teams. Core Evaluation Criteria Data Granularity & Historical Depth: Support for tick, intraday, and daily bars plus long-term archived records across equities and FX Protocol Compatibility: Native REST batch query and WebSocket real-time streaming implementation Developer Operational Overhead: Rate limits, documentation completeness, and production integration complexity Comparative Overview Provider Value Propositions AllTick: All-in-one multi-asset market data API built for quant developers, delivering unified tick/intraday/daily historical archives and dual REST/WebSocket access with balanced pricing for individual builders and small teams. Bloomberg: Institutional-grade terminal API offering comprehensive cross-market depth, alternative datasets, and proprietary analytics; targeted exclusively at enterprise investment teams with high entry integration overhead and subscription costs. Alpha Vantage: Lightweight free-first REST API ideal for early-stage prototyping and educational use, lacking native real-time streaming and deep tick-level historical archives. Feature Comparison Matrix Metric AllTick Bloomberg Alpha Vantage Free Tier Rate Limits 100 requests/min, full tick granularity access No permanent free tier; limited trial enterprise access only 5 requests/min, restricted to daily/intraday bars Live Latency Average 170ms native WebSocket push Sub-10ms dedicated institutional line feeds Polling-only, minute-scale delayed refresh

The Problem: Why Most Telegram Payment Bots Fail You've built a Telegram bot that sells digital courses, takes coffee orders, or offers freelance services. Traffic is flowing. But when your bot tries to collect payment, you hit a wall: Payment gateways demand hefty setup fees and compliance audits You end up holding customer money in an intermediary account (legal liability) Settlement takes 3–5 days, frustrating both you and buyers Integration is a maze of webhooks, IPNs, and error handling A founder we know spent two weeks wrestling with Stripe's Telegram integration, only to discover the monthly fee ate 40% of his margins on $2–5 transactions. He needed something lighter, faster, and truly peer-to-peer. Enter AgentPay VN : an open-source Python SDK that lets your Telegram bot generate QR codes pointing directly to your bank account. No middleman. No holding funds. Just instant VietQR payments via the banking system Vietnameses already use daily. What Is AgentPay VN? AgentPay VN is an MIT-licensed Python SDK + MCP server that orchestrates VietQR payment requests. Here's the mental model: You create a payment request in your bot (e.g., "Customer ordered coffee for 50,000 VND") AgentPay generates a checkout URL with an embedded QR code Customer scans → pays → bank confirms settlement in seconds Your bot receives a webhook callback and fulfills the order The key: AgentPay never touches money . The QR points straight at your merchant bank account. A bank feed confirms settlement. You own the transaction end-to-end. Why Telegram + VietQR? Telegram has 180+ million users , with especially strong adoption in Southeast Asia. Vietnamese merchants already use banking apps (MB Bank, Techcombank, VCB) that natively support VietQR scanning. Your bot becomes a natural extension of their daily workflow: Customer receives a payment link in chat Opens the QR code (in-app or screenshot) Scans with their banking app (2–3 taps) Money clears to your account in minutes Bot auto-confirm

It is been while I am learning and build around FastAPI. So there is a project where I was thinking how to add this new feature over exiting one. Like what changes I need to make in database which need to be reflected in my backend and frontend. I already lunched the web locally. Problem started When I when back to the web and reload it it shows this error: ERROR: ConnectTimeout: Unauthorized 401. I was like what? Why? I thougth there is some issue with login endpoint or refresh token function. When i did some debugging and found some new information which is: "Either Supabase's edge/pooler (or OS, or an intermediate proxy/NAT) silently kills those idle connections server-side after some timeout but client-side pool doesn't know that." As I was doing nothing in become idle state so to save the resources server side silently close that particular connection. So I came back and try to connect it give this error. First thought come it my mind after this was there should be a way to automatically check this idle state and if user was in ideal state then create a new connection. Proposed Solutions After a while I come up with these solution: Calculate the Idle time: if it is more then server connection timeout then establish new connection. Retry logic: retry once on the specific connection errors. I thought this will work but This again give me error then this new issue I faced. Cold-start connection problem There is something call dual-stack (IPv4 and IPv6) networks and Happy Eyeballs is a network mechanism which automatically move to IPv4 connection if IPv6 fails. But supabase-py uses httpx and it doesn't support Happy Eyeballs. So in first try after the connection time out it try to establish IPv6 connection which is not routeable in most Pakistani ISPs and ultimately it fails and wait for timeout. There is no way to try it again for IPv4. So we have to do it manually. So this error help me to learn many thing in process. Share your thoughts.

Most "how to add SAST to your pipeline" articles gravitate toward the same four names: SonarQube, Snyk, Semgrep, Veracode. They're solid tools, but they're not the only options, and sometimes you can't use them — budget constraints, air-gapped environments, licensing restrictions, or simply wanting something lightweight that lives entirely in your repo. The OWASP Source Code Analysis Tools page lists dozens of alternatives across every language. In this article I'll walk through applying Bandit , a free, open-source SAST tool for Python, to a real sample application — from finding vulnerabilities locally to wiring it into a CI/CD pipeline with GitHub Actions. The same workflow (install → configure → scan → fail the build on high-severity findings → track results over time) applies almost identically if you swap Bandit for other OWASP-listed tools like Brakeman (Ruby), FindSecBugs (Java), Gosec (Go), or Horusec (multi-language). Why Bandit? 100% open source (Apache 2.0), maintained under the PyCQA org. No account, no server, no license key — it runs as a CLI or a library. Understands Python's AST, so it catches real logic patterns, not just regex matches. Easy to tune with a config file and inline # nosec suppressions. ## 1. The sample application Let's use a small Flask app with a few intentionally introduced vulnerabilities — the kind of thing that slips into real codebases under deadline pressure. # app.py import subprocess import sqlite3 import pickle import yaml from flask import Flask , request app = Flask ( __name__ ) DB_PATH = " users.db " @app.route ( " /ping " ) def ping (): host = request . args . get ( " host " ) # Vulnerable: command injection via shell=True result = subprocess . run ( f " ping -c 1 { host } " , shell = True , capture_output = True ) return result . stdout @app.route ( " /user " ) def get_user (): user_id = request . args . get ( " id " ) conn = sqlite3 . connect ( DB_PATH ) cursor = conn . cursor () # Vulnerable: SQL injection via strin

GitHub热门项目 | AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph. | Stars: 80,027 | 885 stars today | 语言: Python

GitHub热门项目 | | Stars: 2,604 | 14 stars today | 语言: Python

GitHub热门项目 | SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts. | Stars: 11,571 | 261 stars today | 语言: Python

GitHub热门项目 | 🧠「大模型」2小时完全从0训练64M的小参数LLM！Train a 64M-parameter LLM from scratch in just 2h! | Stars: 52,981 | 176 stars today | 语言: Python

Cryptographic Watermarking for LLM Outputs with resk-mark Links: PyPI: https://pypi.org/project/reskmark GitHub: https://github.com/Resk-Security/resk-mark Web: https://resk.fr __ __ ________ _____/ /__ ____ ___ ____ ______/ /__ / ___/ _ \/ ___/ //_/_____/ __ `__ \/ __ `/ ___/ //_/ / / / __(__ ) ,< /_____/ / / / / / /_/ / / / ,< /_/ \___/____/_/|_| /_/ /_/ /_/\__,_/_/ /_/|_| The Provenance Problem Every company deploying LLMs in production faces the same question: once a model generates text, how do you prove it came from your system? Prompts like "say you are an AI" are trivially removable. Post-hoc detectors are unreliable and adversarial. And once text leaves your system — forwarded, copied, pasted into a ticket — you have zero visibility. resk-mark solves this by embedding a cryptographic watermark directly into the token generation process. The output reads naturally, but carries a verifiable signature that survives rewording and truncation. How It Works resk-mark hooks into the language model's sampling process. Before generation, the caller provides a secret key. During sampling, the library biases the probability distribution toward tokens that encode that key's signature: from reskmark import WatermarkEncoder , verify encoder = WatermarkEncoder ( secret_key = " your-key-here " ) model = AutoModelForCausalLM . from_pretrained ( " mistralai/Mistral-7B " ) # Wrap the generate call output = encoder . generate ( model , " Explain the concept of zero-knowledge proofs. " , max_length = 200 , ) print ( output ) # "Zero-knowledge proofs are a cryptographic method where..." # Reads naturally - watermark is invisible # Later - verify provenance is_authentic , confidence = verify ( output , public_key = " corresponding-pub-key " ) print ( f " Authentic: { is_authentic } , confidence: { confidence : . 2 f } " ) Key Properties Invisible — the watermark does not change the meaning, grammar, or fluency of the output Robust — survives copy, paste, truncation, and light rewo

TL;DR We're building a script that takes a video in English and produces the same video narrated in Spanish, in a cloned version of the original speaker's voice. Stack: faster-whisper for timestamped transcription, an LLM (or any MT engine) for translation, XTTS-v2 for voice-cloned synthesis, FFmpeg for surgery. We'll also handle the problem every demo skips: translated audio that doesn't fit its time slot. 📦 Code: github.com/USER/repo (replace before publishing) If you'd rather start from a finished system, Softcatala's open-dubbing and KrillinAI are full pipelines behind one CLI. This post builds the minimal version by hand so you understand what those tools are doing, and where they break. 0. Setup and a licensing warning ⚠️ Python 3.10–3.12. The original Coqui company shut down in early 2024; the maintained fork of their TTS library is published by Idiap as coqui-tts : $ python -m venv dub && source dub/bin/activate $ pip install faster-whisper coqui-tts $ ffmpeg -version | head -1 # 6.0+ is fine, 8.x current ⚠️ Note: the XTTS-v2 model weights ship under the Coqui Public Model License, which restricts commercial use. Prototype freely, but before dubbed videos ship to paying customers, someone must read that license and possibly swap the synthesis step for a commercially licensed model or paid API. Voice cloning also requires the speaker's consent. Get it in writing. 1. Extract audio and transcribe with word timestamps 🎙️ # pull mono 16k audio for the ASR step $ ffmpeg -i input.mp4 -vn -ac 1 -ar 16000 -y source.wav # dub/transcribe.py from faster_whisper import WhisperModel model = WhisperModel ( " large-v3-turbo " , compute_type = " int8 " ) segments , info = model . transcribe ( " source.wav " , word_timestamps = True ) lines = [] for seg in segments : lines . append ({ " start " : seg . start , " end " : seg . end , " text " : seg . text . strip (), }) print ( f " language= { info . language } segments= { len ( lines ) } " ) The timestamps are the skeleton of

一个任务的奇幻漂流：同一个Agent任务，在FROST和FROST-SOP中分别长什么样？ 作者 ：神通说 日期 ：2026-07-08 主题 ：双项目联动 | 周三轮换 阅读时间 ：8分钟 一个问题 你可能听过这样的故事： "这个框架很好，但我不知道怎么用到真实项目里。" FROST 社区里也收到过类似的反馈—— "500行代码确实让我理解了Agent的本质，但理解完之后呢？怎么从'看懂'到'会用'？" 今天这篇文章，就用 同一个真实任务 ，分别展示它在 FROST 和 FROST-SOP 中的样子。你会发现：它们不是两个不同的东西，而是 同一个东西的不同分辨率 。 今天的任务：自动写日报 假设你是一个独立开发者，想让Agent每天自动帮你写工作日报。需求很简单： 收集今天完成的任务 用LLM生成日报摘要 发送邮件给自己 就这么三步。让我们看看它在两个项目中分别怎么实现。 第一站：FROST——用最少的代码理解本质 FROST 的哲学是： 先用500行代码告诉你Agent的底层逻辑，剩下的你自然就会了。 在FROST中，一个Agent的运作只需要四个原子： from core import Store , Agent , skill_set , skill_get # 1. 创建记忆容器 store = Store () # 2. 定义能力（Skill = 纯函数） def collect_tasks ( context ): """ 收集今日任务 """ tasks = [ " 完成FROST v5.0文档 " , " 修复Skill测试用例 " , " 写推广文章 " ] context [ " tasks " ] = tasks return context def generate_summary ( context ): """ 生成日报摘要（简化版，实际调用LLM） """ tasks = context . get ( " tasks " , []) context [ " summary " ] = f " 今日完成 { len ( tasks ) } 项任务： " + " 、 " . join ( tasks ) return context def send_report ( context ): """ 发送日报 """ context [ " sent " ] = True print ( f " [日报已发送] { context [ ' summary ' ] } " ) return context # 3. 组装Agent agent = Agent ( " daily_reporter " , store , skills = { " collect " : collect_tasks , " summarize " : generate_summary , " send " : send_report }) # 4. 用SOP编排执行顺序 result = agent . run ( sop_steps = [ " collect " , " summarize " , " send " ], initial_context = {} ) # 输出：[日报已发送] 今日完成3项任务：完成FROST v5.0文档、修复Skill测试用例、写推广文章 这段代码做了什么？ Store 是记忆——Agent的工作空间，所有中间结果存在这里 Agent 是细胞——拥有记忆和能力的最小执行单元 sop_steps 是宪法——定义执行顺序，Agent不会自作主张改变流程 就这么简单。没有配置文件，没有YAML，没有复杂的初始化。 30行Python代码，一个完整的Agent就跑起来了。 这就是FROST的价值——它不帮你"做"什么，它帮你 看懂 Agent到底是什么。 问题来了 上面的代码能跑，但如果你真的想把它用在生产环境，你会遇到一堆问题： 发邮件的能力怎么写？ FROST不管——它只告诉你Skill是纯函数，具体实现你自己来 LLM调用怎么复用？ 每次写日报都要调LLM，总不能每次都重写一遍 任务失败了怎么办？ 发邮件失败了，要不要重试？重试几次？ 执行日志在哪？ 老板问"今天日报发了吗"，你怎么知道发没发？ 多个Agent协作呢？ 一个人有好几个项目，每个项目一个Agent，怎么协调？ FROST对这些问题的回答是： 这些问题不是我该回答的。 但它的兄弟——FROST-SOP——就是专门回答这些问题的。 第二站：FROST-SOP——让同一个任务跑在生产环境 FROST-SOP 的哲学是： 把FROST教你的每一个概念，都变成生产级的工程组件。 同样的"自动写日报"任务，在FROST-SOP中长

We’ve all been there: staring at a stack of printed lab results or a folder full of cryptic report_final_v2_NEW.pdf files, trying to remember if our cholesterol was higher or lower two years ago. For developers, this isn't just a filing problem—it's a data engineering challenge. In the world of healthcare, data is messy, siloed, and often locked in "unstructured" formats. To build a truly personal Electronic Health Record (EHR) system, we need more than just a folder; we need a RAG (Retrieval-Augmented Generation) pipeline that can parse PDFs, map them to the FHIR (Fast Healthcare Interoperability Resources) standard, and provide natural language insights. In this guide, we’ll leverage Unstructured.io , Milvus , and DuckDB to turn chaotic medical PDFs into a queryable, structured knowledge base. The Architecture: From Raw Pixels to Structured Insights Before we dive into the code, let’s look at how the data flows from a messy lab report to a structured answer. graph TD A[Unstructured PDF Reports] --> B[Unstructured.io Partitioning] B --> C{Data Split} C -->|Textual Context| D[Milvus Vector DB] C -->|Tabular Data| E[DuckDB Structured Storage] D --> F[LangChain RAG Engine] E --> F G[User Query: Is my glucose trending up?] --> F F --> H[FHIR-Formatted Response] Why this stack? Unstructured.io : The gold standard for handling "ugly" PDFs (tables, headers, and nested lists). Milvus : A high-performance vector database built for scale. DuckDB : Perfect for running complex analytical SQL queries on the extracted "structured" parts of our medical data. FHIR Standard : To ensure our data follows global healthcare interoperability rules. Prerequisites Make sure you have your environment ready: pip install langchain milvus unstructured[pdf] duckdb openai Step 1: Extraction with Unstructured.io Medical PDFs often contain complex tables. Standard PDF parsers usually fail here. We’ll use unstructured to partition the document into logical elements. from unstructured.partition.pdf

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