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Automation Technical Lead

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LLM Powered Custom Activities built using LangChain Framework. These activities build up the core elements to enable **context-aware** and **memory-backed** interaction with your favorite large language models.

  

  

**Host Python Backend using Docker**

  

The following steps portray Docker container running on a local machine. In a production scenario, it’s best to host the API on a proper remote app server.

1.  Download & Install Docker - [https://www.docker.com/products/docker-desktop/](https://www.docker.com/products/docker-desktop/).
2.  Make sure Docker is running by starting up the application. Verify docker is running by checking docker icon in the right icons panel.
3.  Run the following script in command line --&gt; docker run -p 5000:5000 zell12/qblangchainapi:latest.
4.  Verify if http://localhost:5000 is running in the browser, which means the API is running.

  

**Video Walkthrough**

*   00:10 - LLM Framework Activities Overview
*   00:38 - Host Backend API with Docker
*   02:18 - Add Activity Package
*   04:11 - Generate Embeddings API Key
*   05:16 - Create Vector Store
*   11:08 - Configurations via LLM Framework Template
*   11:53 - Memory Reference
*   13:31 - Execute basic setup

Easily build your own Generative AI powered automation using these custom activities powered by LangChain Framework for context-aware, memory-backed and multi-input chain LLM calls

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LLM Framework - Langchain Activities