readme updates

README.md

@@ -4,22 +4,21 @@ emoji: 🐨
 colorFrom: yellow
 colorTo: purple
 sdk: streamlit
-sdk_version: 1.44.1
+sdk_version: 1.45.1
 app_file: app.py
 pinned: false
 license: mit
 short_description: Let AI agents plan your next vacation!
 ---
 
-# 🏖️ VacAIgent: Streamlit-Integrated AI Crew for Trip Planning
+# 🏖️ VacAIgent: Let AI agents plan your next vacation!
 
-VacAIgent leverages the CrewAI framework to automate and enhance the trip planning experience, integrating a user-friendly Streamlit interface. This project demonstrates how autonomous AI agents can collaborate and execute complex tasks efficiently.
+VacAIgent leverages the CrewAI agentic framework to automate and enhance the trip planning experience, integrating a user-friendly Streamlit interface. This project demonstrates how autonomous AI agents can collaborate and execute complex tasks efficiently. It takes advantage of the inference endpoint called [Intel® AI for Enterprise Inference](https://github.com/opea-project/Enterprise-Inference) with an OpenAI-compatible API key, hosted on cloud provider [Denvr Dataworks](https://www.denvrdata.com/intel).  
 
-_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner). You can find the application hosted on Hugging Face Spaces here:
+_Forked and enhanced from the_ [_crewAI examples repository_](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner). You can find the application hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/Intel/vacaigent):
 
 [![](images/hf_vacaigent.png)](https://huggingface.co/spaces/Intel/vacaigent)
 
-
 **Check out the video below for code walkthrough** 👇
 
 <a href="https://youtu.be/nKG_kbQUDDE">
@@ -28,110 +27,65 @@ _Forked and enhanced from the_ [_crewAI examples repository_](https://github.com
 
 (_Trip example originally developed by [@joaomdmoura](https://x.com/joaomdmoura)_)
 
-## CrewAI Framework
-
-CrewAI simplifies the orchestration of role-playing AI agents. In VacAIgent, these agents collaboratively decide on cities and craft a complete itinerary for your trip based on specified preferences, all accessible via a Streamlit user interface.
-
 
-## Running the Application
 
-To experience the VacAIgent app:
+## Installing and Using the Application
 
 ### Pre-Requisites
-1. Get the API key from **scrapinagent.com** from  [scrapinagent](https://scrapingant.com/)
-2. Get the API from **SERPER API** from [serper]( https://serper.dev/)
+1. Get the API key from **scrapinagent.com** from  [scrapinagent](https://scrapingant.com/) for HTML web-scraping.
+2. Get the API from **SERPER API** from [serper]( https://serper.dev/) for Google Search API.
 3. Bring your OpenAI compatible API key
-4. Bring your model endpoint URL and LLM model ID that you want to use
+4. Bring your model endpoint URL and LLM model ID
 
-### Deploy Trip Planner
+### Installation steps
 
-####  Step 1
-Clone the repository:
+First, clone the repository:
 ```sh
 git clone https://github.com/opea-project/Enterprise-Inference/
 cd examples/vacaigent
 ```
-
-#### Step 2
-
-Insall Dependencies
+Then, install the necessary libraries:
 ```sh
 pip install -r requirements.txt
 ```
-####  Step 3
 Add Streamlit secrets. Create a `.streamlit/secrets.toml` file and update the variables below:
 
 ```sh
-SERPER_API_KEY=""
-SCRAPINGANT_API_KEY=""
-OPENAI_API_KEY=""
+SERPER_API_KEY="serper-api-key"
+SCRAPINGANT_API_KEY="scrapingant_api_key"
+OPENAI_API_KEY="open_api_key"
 MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
 MODEL_BASE_URL="https://api.inference.denvrdata.com/v1/"
-
 ```
-**Note**: You can alternatively add these secrets directly to Hugging Face Spaces Secrets, under the Settings tab, if deploying the Streamlit application directly on Hugging Face.
 
-#### Step 4
+Here we are using the model [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) by default, and the model endpoint is from Denvr Dataworks; but you can bring your own  OpenAI-compatible API key, model ID, and model endpoint.
 
-Run the application
+**Note**: You can alternatively add these secrets directly to Hugging Face Spaces Secrets, under the Settings tab, if deploying the Streamlit application directly on Hugging Face.
 
+### Run the application
+To run the application locally, you should be able to execute this command to pull up a Streamlit interface in your web browser:
 ```sh
 streamlit run app.py
 ```
 
-Your application should be up and running in your web browser.
-
-★ **Disclaimer**: The application uses meta-llama/Llama-3.3-70B-Instruct by default. Ensure you have access to an OpenAI-compatible API and be aware of any associated costs.
-
-## Details & Explanation
-
-- **Components**:
+### Components:
   - [trip_tasks.py](trip_tasks.py): Contains task prompts for the agents.
   - [trip_agents.py](trip_agents.py): Manages the creation of agents.
   - [tools](tools) directory: Houses tool classes used by agents.
   - [app.py](app.py): The heart of the frontend Streamlit app.
 
-## LLM Model
-
-To switch the LLM model being used, you can switch the `MODEL_ID` in the `.streamlit/secrets.toml` file.
-
 ## Using Local Models with Ollama
 
-For enhanced privacy and customization, you can integrate local models like Ollama:
-
-### Setting Up Ollama
-
-- **Installation**: Follow Ollama's guide for installation.
-- **Configuration**: Customize the model as per your requirements.
-
-### Integrating Ollama with CrewAI
-
-Pass the Ollama model to agents in the CrewAI framework:
-
-```python
-from langchain.llms import Ollama
+For enhanced privacy and customization, you could easily substitute cloud-hosted models with locally-hosted models from [Ollama](https://ollama.com/). 
 
-ollama_model = Ollama(model="agent")
-
-class TripAgents:
-    # ... existing methods
+## License
 
-    def local_expert(self):
-        return Agent(
-            role='Local Expert',
-            tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
-            llm=ollama_model,
-            verbose=True
-        )
+VacAIgent is open-sourced under the MIT license.
 
-```
+### Follow Up
 
-## Benefits of Local Models
+Connect to LLMs on Intel® Gaudi® accelerators with just an endpoint and an OpenAI-compatible API key, courtesy of cloud-provider Denvr Dataworks: https://www.denvrdata.com/intel
 
-- **Privacy**: Process sensitive data in-house.
-- **Customization**: Tailor models to fit specific needs.
-- **Performance**: Potentially faster responses with on-premises models.
-
-## License
+Chat with 6K+ fellow developers on the Intel DevHub Discord: https://discord.gg/kfJ3NKEw5t
 
-VacAIgent is open-sourced under the MIT license.
+Connect with me on LinkedIn: https://linkedin.com/in/bconsolvo