Think Different

  Chatbot Application with Local LLM, RAG, LangChain, and Guardrail I've developed a chatbot application designed for informative and engaging conversationAs you already aware that Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs). By incorporating data from various sources such as relational databases, unstructured document repositories, internet data streams, and media news feeds, RAG can significantly improve the value of generative AI systems. Developers must consider a variety of factors when building a RAG pipeline: from LLM response benchmarking to selecting the right chunk size. In tapplication demopost, I demonstrate how to build a RAG pipeline uslocal LLM which can be converted to ing NVIDIA AI Endpoints for LangChain. FirI have you crdeate a vector storeconnecting with one of the ...

  Photo by Aleksandr Neplokhov at pexel Explore Let’s first clarify the difference between workflow and CI/CD and discuss what GitHub Actions do. Workflow : A workflow is a series of automated steps that define how code changes are built, tested, and deployed. Workflows can include various tasks such as compiling code, running tests, and deploying applications. Workflows are defined in a YAML file (usually named .github/workflows/workflow.yml ) within your repository. They are triggered by specific events (e.g., push to a branch, pull request, etc.). Workflows are not limited to CI/CD; they can automate any process in your development workflow CI/CD (Continuous Integration/Continuous Deployment) : CI/CD refers to the practice of automating the process of integrating code changes, testing them, and deploying them to production. Continuous Integration (CI) focuses on automatically building and testing code changes whenever they are pushed to a repository. Continuous Deployment (...

Step 1: Container Image Build 1. In your source code repository (e.g., Git), include a Dockerfile that specifies how to build your application into a container image. 2. Configure your CI/CD tool (e.g., AWS CodeBuild, Jenkins) to build the Docker image using the Dockerfile. This can be done by executing a `docker build` command within your CI/CD script. 3. Ensure that the Docker image is built with the necessary dependencies and configurations. Step 2: Container Registry 4. Choose a container registry service to store your Docker images. Common choices include:    - AWS Elastic Container Registry (ECR) if you're using AWS.    - Docker Hub for public images.    - Other cloud providers' container registries (e.g., Google Container Registry, Azure Container Registry). Step 3: Pushing Images 5. After building the Docker image, tag it with a version or unique identifier. 6. Use the `docker push` command to push the image to the selected container registry. Step ...

  If you want to delete large files from your Git history without using Git LFS, you can use the `git filter-branch` command along with the `--tree-filter` option to remove the files from your Git history. This process will rewrite the repository's history and remove the specified files. Here's how you can do it: 1. Backup Your Repository :    Before proceeding, make sure to create a backup of your repository to avoid data loss in case something goes wrong. 2. Identify Large Files :    Identify the large files that you want to remove from the Git history, such as `data/hail-2015.csv`. 3. Run the `git filter-branch` Command :    Use the `git filter-branch` command with the `--tree-filter` option to remove the large files from your Git history. Replace `data/hail-2015.csv` with the actual file path you want to remove.    ```bash    git filter-branch --force --index-filter \    "git rm --cached --ignore-unmatch data/hail-2015...

  Being leading diversified team for years. Besides Software Architect for more than 13 years for several different type of projects. I use to review several pull requests each day. Sometimes it is tremendously time consuming. Especially when you have to tackle the not clear commit messages, SCRUM bug reports or work log are not clearly defined the issues or features. Here is an example of an ML code review with a pull request from GitHub: Title: Add a new model to the librar Author: John Doe Reviewer: Jane Doe Description: This pull request adds a new model to the library. The model is a simple linear regression model that can be used to predict house prices. Changes: * Added the new model to the library. * Added unit tests for the new model. * Updated the documentation to include the new model. Review: I have reviewed the pull request and I have found no major issues. The code is well-written and the unit tests are comprehensive. I recommend that the pull reques...