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| title: LanggraphAgenticAI | |
| emoji: 🐨 | |
| colorFrom: blue | |
| colorTo: red | |
| sdk: streamlit | |
| sdk_version: 1.42.0 | |
| app_file: app.py | |
| pinned: false | |
| # Project Setup | |
| ## Steps | |
| ### 1. Open a New Folder | |
| ### 2. Create a New Virtual Environment | |
| ### 3. Install Requirements | |
| ```sh | |
| pip install -r requirements.txt | |
| ``` | |
| ### 4. Define Folder Structure & Commit Code to GitHub | |
| #### a. Create a New Repository | |
| ```sh | |
| git init | |
| ``` | |
| #### b. Add `.gitignore` and Include `venv/` | |
| #### c. Commit One File to Establish Connection with GitHub | |
| #### d. Verify Initial Commit in GitHub | |
| ### 5. Create `src` Folder and Make it a Package | |
| ```sh | |
| mkdir src | |
| ``` | |
| Add `__init__.py` to make it a package. | |
| ### 6. Define Project Structure | |
| Inside `src/`, create a folder `langgraphagenticai/` and add `__init__.py`. Inside it, create: | |
| - `LLMs/` - `__init__.py` | |
| - `Nodes/` - `__init__.py` | |
| - `Graph/` - `__init__.py` | |
| - `State/` - `__init__.py` | |
| - `Tools/` - `__init__.py` | |
| - `Vectorstore/` - `__init__.py` | |
| - `ui/` - `__init__.py` | |
| - Lastly, add `main.py` | |
| ### 7. Create `app.py` Outside `src/` | |
| This is the main execution entry point. | |
| ## Start Writing Code | |
| ### 8. LLM Setup | |
| #### a. Inside `LLMs/`, create `qroqllm.py` to load LLM via Streamlit UI. | |
| ### 9. UI Setup | |
| #### a. Inside `ui/`, create: | |
| - `loadui.py` | |
| - `display_result.py` | |
| #### b. Create a Config File | |
| Inside `ui/`, create `uiconfigfile.ini` (plain text key-value pairs). | |
| #### c. Create a Config Parser | |
| Inside `ui/streamlit_ui/`, create `uiconfigfile.py` to read from `uiconfigfile.ini`. | |
| #### d. Implement `loadui.py` | |
| ```python | |
| from src.langgraphagenticai.ui.uiconfigfile import Config | |
| ``` | |
| - Load sidebar options from the config file | |
| - Store session state | |
| - Load UI components (left & right sections) | |
| ### 10. Running the UI | |
| Run the UI to check if it loads correctly: | |
| ```sh | |
| streamlit run app.py | |
| ``` | |
| ### 11. Implement `main.py` | |
| #### a. Execution starts here based on user choices. | |
| #### b. Load the LLM in `qroqllm.py`. | |
| #### c. Implement `graph_builder.py` inside `Graph/`. | |
| - Import `State` inside `graph_builder.py` | |
| #### d. Implement `Nodes` | |
| - Start with `BasicChatbotNode`. | |
| ```python | |
| from src.langgraphagenticai.nodes.basic_chatbot_node import BasicChatbotNode | |
| ``` | |
| - Define edges, start, and end functions. | |
| #### e. Connect Graph in `main.py` | |
| ```python | |
| from src.langgraphagenticai.graph.graph_builder import GraphBuilder | |
| ``` | |
| - Invoke the graph based on use case. | |
| - Compile and stream results. | |
| #### f. Update Display Results | |
| Modify `display_result.py` to update UI results and call it in `main.py`. | |
| ```python | |
| from src.langgraphagenticai.ui.streamlitui.display_result import DisplayResultStreamlit | |
| ``` | |
| ### 12. Run the Application | |
| ```sh | |
| streamlit run app.py | |
| ``` | |
| # 🚀 CI/CD Setup: GitHub to Hugging Face | |
| ## 🔹 1. Set Up CI/CD Between GitHub and Hugging Face | |
| - **Create** a new Space on Hugging Face. | |
| - A **unique URL** will be generated for your Space. | |
| - Open **`main.yml`** and update the **last line**: | |
| - Replace it with your Hugging Face Space **unique URL** (it will include `$HF_TOKEN`). | |
| - **Update the README**: | |
| - The **first section** of the README should contain the **Streamlit configuration**. | |
| ## 🔹 2. Deploy on Streamlit | |
| - **Go to the Streamlit website** and add your GitHub repo and branch details. | |
| - Deployment example: | |
| 👉 [LangGraph Streamlit App](https://langgraphe2e-4cg4bxcc3ysrhwrmregluy.streamlit.app/) | |
| --- | |
| Your CI/CD pipeline should now be set up for seamless deployment! 🚀✨ | |
| --- | |
| # Chatbot with Tool Integration | |
| ## 📌 Steps to Implement | |
| ### 🔹 1. Add a Search Tool | |
| - Navigate to the `TOOLS` folder. | |
| - **Create** a new file: `search_tool.py`. | |
| - **Add Tavily** as a tool inside this file. | |
| ### 🔹 2. Create a Chatbot with Tool Node | |
| - Navigate to the `NODES` folder. | |
| - **Create** a new file: `Chatbot_with_tool_node.py`. | |
| - Implement chatbot logic with tool integration inside this file. | |
| ### 🔹 3. Modify Graph Builder | |
| - Open `graph_builder.py`. | |
| - **Edit** the file to include tool functionality. | |
| ### 🔹 4. Update the UI | |
| - Modify the following UI components: | |
| - `Load_streamlit_ui.py` | |
| - `uiconfig.ini` | |
| - `display_results.py` | |
| ### 🔹 5. Run the Application | |
| To start the chatbot, run the following command: | |
| streamlit run app.py | |
| 🚀 Now your chatbot with tool integration should be live! | |
| ## Ouput Screenshots | |
| Streamlit UI interface showcasing chatbot utilizing tools | |
|  | |