#Langflow

[pulipuli]

看看網頁版全文 ⇨ 雜談：怎麽讓AI能根據我的雲端硬碟回答問題 / TALK: How Can I Enable AI to Answer Questions Based on My Cloud Storage? https://blog.pulipuli.info/2025/01/talk-how-can-i-enable-ai-to-answer-questions-based-on-my-cloud-storage.html Nextcloud的AI應用程式不能處理中文，所以我自己用Langflow整合到Nextcloud，讓大型語言模型能夠根據我在雲端硬碟裡面的內容來回答問題。 這篇就講一下大致上的做法。 ---- # Nextcloud的llm2應用程式 /。 (圖片來自：https://www.youtube.com/watch?v=6_BPOZzvzZQ&t=138s )。 https://docs.nextcloud.com/server/latest/admin_manual/ai/app_assistant.html#installation。 Nextcloud在好幾年前就嘗試將LLM (大型語言模型)接入到Nextcloud。 有了LLM的輔助，我們可以在Nextcloud裡面作翻譯、寫作等功能。 Nextcloud的AI應用可以用OpenAI GPT-3.5的API，也可以在本地架設Llama 3.1模型。 它能做到機器翻譯、語音轉文字(透過stt_whisper2)、產生文字、摘要、產生標題、抽取主題詞、根據上下文重新撰寫(context write)、重寫、文字轉圖片(使用tex2image_stablediffusion2 )、上下文對談(context chat，此處的context是指Nextcloud裡面的檔案)、上下文助理 (context agent)，功能非常豐富。 不過要架設具有AI功能的Nextcloud Assistant並不容易。 乍看之下，好像是要用nextcloud aio版本，搭配appapi之類的東西才能運作。 需要的元件很多，稍微複雜了一些，真是令人困擾。 https://github.com/nextcloud/context_chat_backend。 研究的過程中，我發現Nextcloud很多AI元件其實背後也是使用langchain。 既然如此，那我何不自己用langchain來處理就好了呢？。 ---- # Langchain的低程式碼版本：Langflow / Low-Code LangChain: Langflow。 https://www.langflow.org/。 最近因為工作的需求，我開始研究Langchain的相關應用。 基於Dify的開發經驗，我也想找一個low code版本的開發方式，也許這可以讓未來接手的人更容易理解LLM的運作過程。 ---- 繼續閱讀 ⇨ 雜談：怎麽讓AI能根據我的雲端硬碟回答問題 / TALK: How Can I Enable AI to Answer Questions Based on My Cloud Storage? https://blog.pulipuli.info/2025/01/talk-how-can-i-enable-ai-to-answer-questions-based-on-my-cloud-storage.html

[codemonkhq]

Discover how to build a RAG-based Blog Writer API using LangChain and Langflow. This guide covers creating a powerful content generation tool that combines retrieval and language models, enhancing accuracy and efficiency in generating high-quality blog posts.

[cyber-sec]

Recent Langflow Vulnerability Exploited by Flodrix Botnet

Source: https://www.securityweek.com/recent-langflow-vulnerability-exploited-by-flodrix-botnet/

More info: https://www.trendmicro.com/en_us/research/25/f/langflow-vulnerability-flodric-botnet.html

[ahex-technologies]

AI Model Development: How to Build Intelligent Models for Business Success

AI model development is revolutionizing how companies tackle challenges, make informed decisions, and delight customers. If lengthy reports, slow processes, or missed insights are holding you back, you’re in the right place. You’ll learn practical steps to leverage AI models, plus why Ahex Technologies should be your go-to partner.

Read the original article for a more in-depth guide: AI Model Development by Ahex

What Is AI Model Development?

AI model development is the practice of designing, training, and deploying algorithms that learn from your data to automate tasks, make predictions, or uncover hidden insights. By turning raw data into actionable intelligence, you empower your team to focus on strategy , while machines handle the heavy lifting.

The AI Model Development Process

Define the Problem Clarify the business goal: Do you need sales forecasts, customer-churn predictions, or automated text analysis?

Gather & Prepare Data Collect, clean, and structure data from internal systems or public sources. Quality here drives model performance.

Select & Train the Model Choose an algorithm, simple regression for straightforward tasks or neural nets for complex patterns. Split data into training and testing sets for validation.

Test & Validate Measure accuracy, precision, recall, or other KPIs. Tweak hyperparameters until you achieve reliable results.

Deploy & Monitor Integrate the model into your workflows. Continuously track performance and retrain as data evolves.

AI Model Development in 2025

Custom AI models are no longer optional, they’re essential. Off-the-shelf solutions can’t match bespoke systems trained on your data. In 2025, businesses that leverage tailored AI enjoy faster decision-making, sharper insights, and increased competitiveness.

Why Businesses Need Custom AI Model Development

Precision & Relevance: Models built on your data yield more accurate, context-specific insights.

Data Security: Owning your models means full control over sensitive information — crucial in finance, healthcare, and beyond.

Scalability: As your business grows, your AI grows with you. Update and retrain instead of starting from scratch.

How to Create an AI Model from Scratch

Define the Problem

Gather & Clean Data

Choose an Algorithm (e.g., regression, classification, deep learning)

Train & Validate on split datasets

Deploy & Monitor in production

Break each step into weekly sprints, and you’ll have a minimum viable model in just a few weeks.

How to Make an AI Model That Delivers Results

Set Clear Objectives: Tie every metric to a business outcome, revenue growth, cost savings, or customer retention.

Invest in Data Quality: The “garbage in, garbage out” rule is real. High-quality data yields high-quality insights.

Choose Explainable Models: Transparency builds trust with stakeholders and meets regulatory requirements.

Stress-Test in Real Scenarios: Validate your model against edge cases to catch blind spots.

Maintain & Retrain: Commit to ongoing model governance to adapt to new trends and data.

Top Tools & Frameworks to Build AI Models That Work

PyTorch: Flexible dynamic graphs for rapid prototyping.

Keras (TensorFlow): User-friendly API with strong community support.

LangChain: Orchestrates large language models for complex applications.

Vertex AI: Google’s end-to-end platform with AutoML.

Amazon SageMaker: AWS-managed service covering development to deployment.

Langflow & AutoGen: Low-code solutions to accelerate AI workflows.

Breaking Down AI Model Development Challenges

Data Quality & Availability: Address gaps early to avoid costly rework.

Transparency (“Black Box” Issues): Use interpretable models or explainability tools.

High Costs & Skills Gaps: Leverage a specialized partner to access expertise and control budgets.

Integration & Scaling: Plan for seamless API-based deployment into your existing systems.

Security & Compliance: Ensure strict protocols to protect sensitive data.

Typical AI Model Timelines

For simple pilots, expect 1–2 months; complex enterprise AI can take 4+ months.

Cost Factors for AI Development

Why Ahex Technologies Is the Best Choice for Mobile App Development

(Focusing on expertise, without diving into technical app details)

Holistic AI Expertise: Our AI solutions integrate seamlessly with mobile platforms you already use.

Client-First Approach: We tailor every model to your unique workflow and customer journey.

End-to-End Support: From concept to deployment and beyond, we ensure your AI and mobile efforts succeed in lockstep.

Proven Track Record: Dozens of businesses trust us to deliver secure, scalable, and compliant AI solutions.

How Ahex Technologies Can Help You Build Smarter AI Models

At Ahex Technologies, our AI Development Services cover everything from proof-of-concept to full production rollouts. We:

Diagnose your challenges through strategic workshops

Design custom AI roadmaps aligned to your goals

Develop robust, explainable models

Deploy & Manage your AI in the cloud or on-premises

Monitor & Optimize continuously for peak performance

Learn more about our approach: AI Development Services Ready to get started? Contact us

Final Thoughts: Choosing the Right AI Partner

Selecting a partner who understands both the technology and your business is critical. Look for:

Proven domain expertise

Transparent communication

Robust security practices

Commitment to ongoing optimization

With the right partner, like Ahex Technologies, you’ll transform data into a competitive advantage.

FAQs on AI Model Development

1. What is AI model development? Designing, training, and deploying algorithms that learn from data to automate tasks and make predictions.

2. What are the 4 types of AI models? Reactive machines, limited memory, theory of mind, and self-aware AI — ranging from simple to advanced cognitive abilities.

3. What is the AI development life cycle? Problem definition → Data prep → Model building → Testing → Deployment → Monitoring.

4. How much does AI model development cost? Typically $10,000–$500,000+, depending on project complexity, data needs, and integration requirements.

Ready to turn your data into growth? Explore AI Model Development by Ahex Our AI Development Services Let’s talk!

[ericvanderburg]

Critical flaw in AI agent dev tool Langflow under active exploitation

http://securitytc.com/TLR7Cb

[kazifatagar]

DataStax Enhances GitHub Copilot Extension to Streamline GenAI App Development

DataStax has expanded its GitHub Copilot extension to integrate with its AI Platform-as-a-Service (AI PaaS) solution, aiming to streamline the development of generative AI applications for developers. The enhanced Astra DB extension allows developers to manage databases (vector and serverless) and create Langflow AI flows directly from GitHub Copilot in VS Code using natural language commands.…

[3acesnews]

AssemblyAI Partners with Langflow to Enhance Generative AI Capabilities

[criadorderiquezas]

Langflow 1.0 - Super Novidades na Nova Versão! e Competição Valendo Prêmios!

[ai-news]

A Quick Way to Prototype RAG Applications Based on LangChainContinue reading on Towards Data Science » #AI #ML #Automation

[codemonkhq]

Learn to build a Twitter sentiment analysis tool using Langflow and Llama 2, with no coding required. This guide shows you how to classify tweets in real-time through a simple API, helping businesses track brand sentiment and customer feedback effectively.

[hackernewsrobot]

DataStax has acquired Langflow to accelerate generative AI development

https://www.datastax.com/blog/datastax-acquires-langflow-to-accelerate-generative-ai-app-development

[revotalk]

DataStax made a name for itself by commercializing the open source Apache Cassandra NoSQL database, but these days, the company’s focus is squarely on using its database chops to build a “one-stop GenAI stack.” One of the first building blocks for this was to bring vector search capabilities to its hosted Astra DB service last […] © 2024 TechCrunch. All rights reserved. For personal use only.

[ericvanderburg]

CVE-2025-3248 – Unauthenticated Remote Code Execution in Langflow via Insecure Python exec Usage

http://i.securitythinkingcap.com/TLQk9W