Shubham Saboo Profile picture
May 21, 2024 10 tweets 3 min read Read on X
Build an AI Search Assistant with GPT-4o in just 15 lines of Python Code (step-by-step instructions):
1. Install the necessary Python Libraries

Run the following command from your terminal. Image
2. Import necessary libraries

• Streamlit for building the web app
• Phidata for building AI agents
• OpenAI for using the LLM
• Duckduckgo for the search functionality Image
3. Set up the Streamlit App

Streamlit lets you create user interface with just python code, for this app we will:
• Add a title to the app using 'st.title()'
• Add a description for the app using 'st.caption()' Image
4. Create and Initialize the AI assistant

• Create a text input to enter their OpenAI API key using 'st.text_input()'
• If the OpenAI API key is provided, create an instance of Assistant with gpt-4 as LLM and DuckDuckGo as the tool. Image
5. Search the Web with your Generative AI assistant

• Create a text input for the user to enter their query using 'st.text_input()'
• If a question is asked, run the assistant to get the answer and display it using 'st.write()' Image
Full Application code for Generative AI Search Assistant👇 Image
Working Application demo using Streamlit

Paste the above code in vscode or pycharm and run the following command: 'streamlit run ai_webagent.py'
Find all the awesome LLM Apps demo with RAG and AI agents in the following Github Repo.

P.S: Don't forget to star the repo to show your support 🌟
github.com/Shubhamsaboo/a…
If you find this useful, RT to share it with your friends.

Don't forget to follow me @Saboo_Shubham_ for more such LLMs tips and tutorials.

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More from @Saboo_Shubham_

Sep 29
Google ADK meets the AG-UI protocol.

You can now easily connect your ADK AI Agents with interactive frontends using AG-UI in minutes.

100% Opensource. Image
You can build generative UI for your agents with ADK as the agent backend and AG-UI as the communication layer between agent and the UI.

Generative UI goes beyond text and let your agent generate and render UI components directly in the chat.
Learn more about connecting beautiful frontends with your ADK AI Agents.

developers.googleblog.com/en/delight-use…
Read 4 tweets
Sep 27
Build a Multi-agent app with MCP using Google ADK without writing a single line of Python Code, in simple YAML.

All of this in ~5 mins. Step-by-step tutorial with opensource code:
1. Install the @Google ADK Python Package and @firecrawl_dev for MCP Image
2. Create an Agent Template with this single command by following the terminal prompts and entering your Gemini API Key.

It will create a my_agent/ folder, containing a root_agent.yaml file and an .env file to store the API keys. Image
Read 10 tweets
Sep 14
China's Alibaba just dropped a Python framework for building multi-agent apps.

AgentScope lets you build AI agents visually with MCP tools, memory, rag, and reasoning capabilities.

Works with any LLM and supports real-time steering.

100% Opensource. Image
100+ free step-by-step tutorials with code covering:

🚀 AI Agents
📀 RAG Systems
🗣️ Voice AI Agents
🌐 MCP AI Agents
🤝 Multi-agent Teams
🎮 Autonomous Game Playing Agents

P.S: Don't forget to subscribe for FREE to access future tutorials.

theunwindai.com
Read 4 tweets
Sep 12
JSON prompting for LLMs, explained with examples:
What is JSON prompting?

JSON prompting is a way of asking an LLM using a clear, structured format (with keys and values) and expecting the response in the same structured style.

Text prompts → inconsistent, messy outputs
JSON prompt → consistent, parseable data Image
The Problem With Text Prompts

Natural language is strong, but in AI it’s loose.

“Summarize this email” or “give key takeaways” leaves room for guesswork.

You wouldn’t tell a junior: “Make it better. Do what feels right.”

Yet we do that with AI all the time. Image
Read 12 tweets
Sep 7
Let's build a local RAG Agent using Google's EmbeddingGemma and Ollama running locally on your machine.

100% Opensource and works without internet.
You can follow along this tutorials with step-by-step code instructions on @unwind_ai_

theunwindai.com/p/build-a-loca…
1. Install the necessary Python Libraries

Run the following command from your terminal.

Then pull the required Ollama models:
• ollama pull embeddinggemma:latest
• ollama pull llama3.2:latest Image
Read 11 tweets
Sep 4
Stop building AI agents that ignore your instructions.

This Python framework guarantees LLM Agents follow your rules in production. Every single time.

100% Opensource. Image
Traditional approach: Write 47-rule system prompts and pray the LLM follows them.

Parlant (@EmcieCo) approach: Define clear guidelines that are contextually matched and enforced.

No more "roll of the dice" conversations. Image
Parlant Version 3 just dropped:

• Guidelines: Rules you set are enforced every single time
• Journeys: Conversations adapt when users go off-script
• Playground: Watch, test, and debug in full context
• Widget: Production-ready chat UI you can drop anywhere
Read 4 tweets

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