#Docker APP

[pulipuli]

看看網頁版全文 ⇨ 如何將PDF轉換成SVG向量圖？ / How to Convert a PDF Into Vector Graphic Images like SVG? https://blog.pulipuli.info/2024/02/pdfsvg-how-to-convert-a-pdf-into-vector-graphic-images-like-svg.html 如果要將PDF轉換成SVG、EMF等向量圖以及PNG圖片，可以使用我寫的Colab「docker-app-PDF-to-Crop-SVG.ipynb」來轉換。 ---- # 向量圖與點陣圖 / Vector Graphics and Bitmaps。 圖片來源：https://business.oregonstate.edu/student-experience/resources/DAMlab/vector-and-bitmap-image-guide。 向量圖和點陣圖是兩種不同的圖像格式，它們在圖像表示和使用方面有著明顯的差異。 點陣圖較為知名，手機拍照的相片都是以點陣圖組成；向量圖則是在專業排版、海報設計、應用程式開發上比較常用。 以下讓我們看看這兩者的區別。 ## 向量圖 / Vector Image。 圖片來源：https://business.oregonstate.edu/student-experience/resources/DAMlab/vector-and-bitmap-image-guide。 向量圖的特色如下： - 基於數學公式： 向量圖是基於數學公式和幾何形狀的，它使用向量（例如直線、曲線、多邊形等）來描述圖像，而不是像素。 - 無限放大： 由於使用數學公式描述，向量圖可無限放大而不失真，因此非常適合印刷品質圖像。 - 文件大小較小： 由於不包含像素信息，向量圖通常文件大小較小，方便存儲和傳輸。 - 編輯容易： 向量圖易於編輯和修改，可以輕鬆調整形狀、顏色和大小。 - 格式： 常見的向量圖格式包括SVG（Scalable Vector Graphics）和EMF（Enhanced Metafile）。 ## 點陣圖 / Raster Image。 圖片來源：https://business.oregonstate.edu/student-experience/resources/DAMlab/vector-and-bitmap-image-guide。 點陣圖的特性如下： - 基於像素： 點陣圖是由像素（小圖片元）組成的，每個像素都有自己的顏色信息，圖像由一個像素矩陣組成。 - 固定解析度： 點陣圖具有固定的解析度，因此在放大時會出現鋸齒狀和失真，特別是在低解析度圖像上。 - 文件大小較大： 點陣圖文件大小通常較大，尤其在高解析度圖像的情況下。 ---- 繼續閱讀 ⇨ 如何將PDF轉換成SVG向量圖？ / How to Convert a PDF Into Vector Graphic Images like SVG? https://blog.pulipuli.info/2024/02/pdfsvg-how-to-convert-a-pdf-into-vector-graphic-images-like-svg.html

[agiledock]

Harnessing Containerization in Web Development: A Path to Scalability

Explore the transformative impact of containerization in web development. This article delves into the benefits of containerization, microservices architecture, and how Docker for web apps facilitates scalable and efficient applications in today’s cloud-native environment.

[jcmarchi]

📝 Guest Post: Local Agentic RAG with LangGraph and Llama 3*

New Post has been published on https://thedigitalinsider.com/guest-post-local-agentic-rag-with-langgraph-and-llama-3/

📝 Guest Post: Local Agentic RAG with LangGraph and Llama 3*

In this guest post, Stephen Batifol from Zilliz discusses how to build agents capable of tool-calling using LangGraph with Llama 3 and Milvus. Let’s dive in.

LLM agents use planning, memory, and tools to accomplish tasks. Here, we show how to build agents capable of tool-calling using LangGraph with Llama 3 and Milvus.

Agents can empower Llama 3 with important new capabilities. In particular, we will show how to give Llama 3 the ability to perform a web search, call custom user-defined functions

Tool-calling agents with LangGraph use two nodes: an LLM node decides which tool to invoke based on the user input. It outputs the tool name and tool arguments based on the input. The tool name and arguments are passed to a tool node, which calls the tool with the specified arguments and returns the result to the LLM.

Milvus Lite allows you to use Milvus locally without using Docker or Kubernetes. It will store the vectors you generate from the different websites we will navigate to. 

Introduction to Agentic RAG

Language models can’t take actions themselves—they just output text. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs to pass them. After executing actions, the results can be transmitted back into the LLM to determine whether more actions are needed or if it is okay to finish.

They can be used to perform actions such as Searching the web, browsing your emails, correcting RAG to add self-reflection or self-grading on retrieved documents, and many more.

Setting things up

LangGraph – An extension of Langchain aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.

Ollama & Llama 3 – With Ollama you can run open-source large language models locally, such as Llama 3. This allows you to work with these models on your own terms, without the need for constant internet connectivity or reliance on external servers. 

Milvus Lite – Local version of Milvus that can run on your laptop, Jupyter Notebook or Google Colab. Use this vector database we use to store and retrieve your data efficiently.

Using LangGraph and Milvus

We use LangGraph to build a custom local Llama 3-powered RAG agent that uses different approaches:

We implement each approach as a control flow in LangGraph:

Routing (Adaptive RAG) –  Allows the agent to intelligently route user queries to the most suitable retrieval method based on the question itself. The LLM node analyzes the query, and based on keywords or question structure, it can route it to specific retrieval nodes.

Example 1: Questions requiring factual answers might be routed to a document retrieval node searching a pre-indexed knowledge base (powered by Milvus).

Example 2: Open-ended, creative prompts might be directed to the LLM for generation tasks.

Fallback (Corrective RAG) – Ensures the agent has a backup plan if its initial retrieval methods fail to provide relevant results. Suppose the initial retrieval nodes (e.g., document retrieval from the knowledge base) don’t return satisfactory answers (based on relevance score or confidence thresholds). In that case, the agent falls back to a web search node.

The web search node can utilize external search APIs.

Self-correction (Self-RAG) – Enables the agent to identify and fix its own errors or misleading outputs. The LLM node generates an answer, and then it’s routed to another node for evaluation. This evaluation node can use various techniques:

Reflection: The agent can check its answer against the original query to see if it addresses all aspects.

Confidence Score Analysis: The LLM can assign a confidence score to its answer. If the score is below a certain threshold, the answer is routed back to the LLM for revision.

General ideas for Agents

Reflection – The self-correction mechanism is a form of reflection where the LangGraph agent reflects on its retrieval and generations. It loops information back for evaluation and allows the agent to exhibit a form of rudimentary reflection, improving its output quality over time.

Planning – The control flow laid out in the graph is a form of planning, the agent doesn’t just react to the query; it lays out a step-by-step process to retrieve or generate the best answer.

Tool use – The LangGraph agent’s control flow incorporates specific nodes for various tools. These can include retrieval nodes for the knowledge base (e.g., Milvus), demonstrating its ability to tap into a vast pool of information, and web search nodes for external information.

Examples of Agents

To showcase the capabilities of our LLM agents, let’s look into two key components: the Hallucination Grader and the Answer Grader. While the full code is available at the bottom of this post, these snippets will provide a better understanding of how these agents work within the LangChain framework.

Hallucination Grader

The Hallucination Grader tries to fix a common challenge with LLMs: hallucinations, where the model generates answers that sound plausible but lack factual grounding. This agent acts as a fact-checker, assessing if the LLM’s answer aligns with a provided set of documents retrieved from Milvus.

```

### Hallucination Grader 

# LLM

llm = ChatOllama(model=local_llm, format="json", temperature=0)

# Prompt

prompt = PromptTemplate(

    template="""You are a grader assessing whether 

    an answer is grounded in / supported by a set of facts. Give a binary score 'yes' or 'no' score to indicate 

    whether the answer is grounded in / supported by a set of facts. Provide the binary score as a JSON with a 

    single key 'score' and no preamble or explanation.

    Here are the facts:

    documents 

    Here is the answer: 

    generation

    """,

    input_variables=["generation", "documents"],

)

hallucination_grader = prompt | llm | JsonOutputParser()

hallucination_grader.invoke("documents": docs, "generation": generation)

```

Answer Grader

Following the Hallucination Grader, another agent steps in. This agent checks another crucial aspect: ensuring the LLM’s answer directly addresses the user’s original question. It utilizes the same LLM but with a different prompt, specifically designed to evaluate the answer’s relevance to the question.

```

def grade_generation_v_documents_and_question(state):

    """

    Determines whether the generation is grounded in the document and answers questions.

    Args:

        state (dict): The current graph state

    Returns:

        str: Decision for next node to call

    """

    print("---CHECK HALLUCINATIONS---")

    question = state["question"]

    documents = state["documents"]

    generation = state["generation"]

    score = hallucination_grader.invoke("documents": documents, "generation": generation)

    grade = score['score']

    # Check hallucination

    if grade == "yes":

        print("---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---")

        # Check question-answering

        print("---GRADE GENERATION vs QUESTION---")

        score = answer_grader.invoke("question": question,"generation": generation)

        grade = score['score']

        if grade == "yes":

            print("---DECISION: GENERATION ADDRESSES QUESTION---")

            return "useful"

        else:

            print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")

            return "not useful"

    else:

        pprint("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")

        return "not supported"

```

You can see in the code above that we are checking the predictions by the LLM that we use as a classifier. 

Compiling the LangGraph graph. 

This will compile all the agents that we defined and will make it possible to use different tools for your RAG system.

```

# Compile

app = workflow.compile()

# Test

from pprint import pprint

inputs = "question": "Who are the Bears expected to draft first in the NFL draft?"

for output in app.stream(inputs):

    for key, value in output.items():

        pprint(f"Finished running: key:")

pprint(value["generation"])

```

Conclusion

In this blog post, we showed how to build a RAG system using agents with LangChain/ LangGraph, Llama 3, and Milvus. These agents make it possible for LLMs to have planning, memory, and different tool use capabilities, which can lead to more robust and informative responses. 

Feel free to check out the code available in the Milvus Bootcamp repository. 

If you enjoyed this blog post, consider giving us a star on Github, and share your experiences with the community by joining our Discord.

This is inspired by the Github Repository from Meta with recipes for using Llama 3

*This post was written by Stephen Batifol and originally published on Zilliz.com here. We thank Zilliz for their insights and ongoing support of TheSequence.

[soniskvlog]

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Thanks & Regards,… Krishna Sinha (Subnetting Guru) Intact Technology & Consultant Contact : +1-8176686697 (USA) & +91-9674277941 (India)

[jvalentino2]

With older systems the more data stored, the slower the systems performed. This is because historically Relational Databases are not infinitely horizontally scalable, leading to data warehousing. This is a fully working example.

[hackeocafe]

Dockge: A New Way To Manage Your Docker Containers

Dockge is a self-hosted Docker stack manager, designed to offer a simple and clean interface for managing multiple Docker compose files. It has been developed by the same individual responsible for creating Uptime Kuma, a popular software for monitoring uptime

Dockge is described as an easy-to-use, and reactive self-hosted manager that is focused on Docker compose.yaml stack orientation. It features an interactive editor for compose.yaml, an interactive web terminal, and a reactive UI where everything is responsive, including real-time progress and terminal output.

It allows for the management of compose.yaml files, including creating, editing, starting, stopping, restarting, and deleting, as well as updating Docker images. The user interface is designed to be easy to use and visually appealing, especially for those who appreciate the UI/UX of Uptime Kuma.

Additionally, Dockge can convert docker run … commands into compose.yaml and maintains a file-based structure, meaning that compose files are stored on the user's drive as usual and can be interacted with using normal docker compose commands​​.

The motivation behind Dockge's development includes dissatisfaction with existing solutions like Portainer, especially regarding stack management. Challenges with Portainer included issues like indefinite loading times when deploying stacks and unclear error messages. The developer initially planned to use Deno or Bun.js for Dockge's development but ultimately decided on Node.js due to lack of support for arm64 in the former technologies​​.

In summary, Dockge is a versatile and user-friendly tool for managing Docker stacks, offering a responsive and interactive environment for Docker compose file management. Its development was driven by a desire to improve upon existing tools in terms of usability and clarity.

Resource links:

Github: https://github.com/louislam/dockge

FAQ: https://github.com/louislam/dockge#faq

[virtualizationhowto]

Best Self-hosted Apps in 2023

Best Self-hosted Apps in 2023 #homelab #selfhosting #BestSelfHostedApps2023 #ComprehensiveGuideToSelfHosting #TopMediaServersForPersonalUse #SecurePasswordManagersForSelfHost #EssentialToolsForSelfHostedSetup #RaspberryPiCompatibleHostingApps

You can run many great self-hosted apps in your home lab or on your media server with only a small amount of tinkering. Let’s look at the best self-hosted apps in 2023 and a list of apps you should check out. Table of contentsWhy Self-hosting?Plex: The Media Server KingJellyfin: Open Source Media FreedomEmby: A Balanced Media ContenderNextcloud: Your Personal Cloud ServiceHome Assistant:…

[appandsoftware1]

Activepieces:10 Best Reasons to Choose This No-Code Wonder

Activepieces: The no-code Zapier alternative. It helps you do tasks, and make content without any code. It improves your work better. Try it right now! Are you sick of running your business like rocket science? You probably feel bad when you need someone who knows computer code to fix your computers. Hiring someone who has complete knowledge of computer coding for your website or business, is a…

[pulipuli]

看看網頁版全文 ⇨ 如何使用Docker APP？ / How to Use Docker APP? https://blog.pulipuli.info/2024/01/how-to-use-docker-app.html 你可以在線上透過Colab使用Docker APP，也可以在本機端使用Docker APP喔。 ---- # 什麼是Docker APP？ / What is Docker APP。 https://github.com/pulipulichen/docker-app-PDF-to-Crop-SVG。 Docker APP是我以Docker容器虛擬化技術包裝建立的跨平臺工具種類。 Docker APP通常用於檔案格式的轉換或是資料分析上。 如果我在工作時需要對檔案執行一連串的資料處理動作，通常我就會把它做成Docker APP，下次就能一口氣自動地轉換與分析，並直接產生處理後的結果。 舉例來說，「docker-app-PDF-to-Crop-SVG」就是將PDF檔案轉換成SVG、裁邊、再轉換成EMF跟PNG的格式。 有些Docker APP還能直接用來架站，例如「docker-web-apache-solr」就能直接架設Apache Solr全文檢索引擎，並且直接用Cloudflare建立臨時的公開網址。 在我的GitHub裡搜尋「docker-app」即可找到更多Docker APP。 網址如下：。 https://github.com/pulipulichen?tab=repositories&q=docker-app&type=&language=&sort=。 Docker APP並不是單純的腳本檔，而是將所有會用到的環境、軟體都包含到Docker映像檔的工具。 因此Docker APP可以在能夠安裝Docker環境的本機端上執行，包括了Linux、Windows、Mac OS等電腦環境。 此外，Docker APP也能夠在Google提供的Colab平臺上執行。 在Colab執行Docker APP時只需要瀏覽器，不用另外安裝Docker環境，因此即使是手機或平板電腦也可以透過Colab執行Docker APP。 接下來我們就來看看怎麽使用Docker APP吧。 ---- # 在Colab使用Docker APP / Use Docker APP in Colab。 通常我會在Docker APP的README.md裡面註明Colab的網址。 舉例來說，「docker-app-PDF-to-Crop-SVG」的Colab網址如下：。 ---- 繼續閱讀 ⇨ 如何使用Docker APP？ / How to Use Docker APP? https://blog.pulipuli.info/2024/01/how-to-use-docker-app.html

[luminiera-merge]

programming as a field suffers so fucking bad from “now draw the rest of the owl” syndrome. like every time i try to do anything i find out i need to get two different services, one of which requires another service to use, which requires an install and a signup to use, and it just goes on forever. nothing is ever simple with this godforsaken field

[survivetoread]

I'm a big fan of extensive reading apps for language learning, and even collaborated on such an app some 10 years ago. It eventually had to be shut down, sadly enough.

Right now, the biggest one in the market is the paywalled LingQ, which is pretty good, but well, requires money.

There's also the OG programs, LWT (Learning With Texts) and FLTR (Foreign Language Text Reader), which are so cumbersome to set up and use that I'm not going to bother with them.

I presently use Vocab Tracker as my daily driver, but I took a spin around GitHub to see what fresh new stuff is being developed. Here's an overview of what I found, as well as VT itself.

(There were a few more, like Aprelendo and TextLingo, which did not have end-user-friendly installations, so I'm not counting them).

Vocab Tracker

++ Available on web ++ 1-5 word-marking hotkeys and instant meanings makes using it a breeze ++ Supports websites

-- Default meaning/translation is not always reliable -- No custom languages -- Ugliest interface by far -- Does not always recognise user-selected phrases -- Virtually unusable on mobile -- Most likely no longer maintained/developed

Lute

++ Supports virtually all languages (custom language support), including Hindi and Sanskrit ++ Per-language, customisable dictionary settings ++ Excellent, customisable hotkey support

-- No instant meaning look-up makes it cumbersome to use, as you have to load an external dictionary for each word -- Docker installation

LinguaCafe

++ Instant meanings thanks to pre-loaded dictionaries ++ Supports ebooks, YouTube, subtitles, and websites ++ Customisable fonts ++ Best interface of the bunch

== Has 7 word learning levels, which may be too many for some

-- Hotkeys are not customisable (yet) and existing ones are a bit cumbersome (0 for known, for eg.) -- No online dictionary look-up other than DeepL, which requires an API key (not an intuitive process) -- No custom languages -- Supports a maximum of 15,000 characters per "chapter", making organising longer texts cumbersome -- Docker installation

Dzelda

++ Supports pdf and epub ++ Available on web

-- Requires confirming meaning for each word to mark that word, making it less efficient to read through -- No custom languages, supports only some Latin-script languages -- No user-customisable dictionaries (has a Google Form to suggest more dictionaries)

[dragon-in-a-fez]

I made it easier to back up your blog with tumblr-utils

Hey friends! I've seen a few posts going around about how to back up your blog in case tumblr disappears. Unfortunately the best backup approach I've seen is not the built-in backup option from tumblr itself, but a python app called tumblr-utils. tumblr-utils is a very, very cool project that deserves a lot of credit, but it can be tough to get working. So I've put together something to make it a bit easier for myself that hopefully might help others as well.

If you've ever used Docker, you know how much of a game-changer it is to have a pre-packaged setup for running code that someone else got working for you, rather than having to cobble together a working environment yourself. Well, I just published a tumblr-utils Docker container! If you can get Docker running on your system - whether Windows, Linux, or Mac - you can tell it to pull this container from dockerhub and run it to get a full backup of your tumblr blog that you can actually open in a web browser and navigate just like the real thing!

This is still going to be more complicated than grabbing a zip file from the tumblr menu, but hopefully it lowers the barrier a little bit by avoiding things like python dependency errors and troubleshooting for your specific operating system.

If you happen to have an Unraid server, I'm planning to submit it to the community apps repository there to make it even easier.

Drop me a message or open an issue on github if you run into problems!

[themonsterunderthebed]

ended up having to go around the whole damn thing and use a different fuction altogether. then had to solve some dumb git error that took another dumb amount of time wiht a dumb workaround. 10:45 pm and hte interview is in 12 hours and 15 minutes maybe now i can actually get to checking the functionality of the goddamn program

trying to brush off code for an interview fukcign tomorrwo and i cannot solve this initial fucking erorr and i'm gonna fucking lose it oh my god

WHY is the html document not fukcing rendering when it has rendered for four years now jesus FUKC. someoen tell me how to use fukcing iframe in r shiny bc i am out of my whole entire mind

[codeonedigest]

AWS APP Runner Tutorial for Amazon Cloud Developers

Full Video Link - https://youtube.com/shorts/_OgnzyiP8TI Hi, a new #video #tutorial on #apprunner #aws #amazon #awsapprunner is published on #codeonedigest #youtube channel. @java @awscloud @AWSCloudIndia @YouTube #youtube @codeonedigest #code

AWS App Runner is a fully managed container application service that lets you build, deploy, and run containerized applications without prior infrastructure or container experience. AWS App Runner also load balances the traffic with encryption, scales to meet your traffic needs, and allows to communicate with other AWS applications in a private VPC. You can use App Runner to build and run API…