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README.md

@@ -14,7 +14,7 @@ sdk_version: 5.30.0
 
 
 Queries network documentation with natural languague.
-Recommend ports to users
+Recommend interface ports to users based on network fabric documentation.
 
 ## 🚀 Key Features
 
@@ -26,74 +26,27 @@ Recommend ports to users
 - **LLM Integration**: Powered by Google's Gemini Flash model
 - **Customizable Prompts**: Configure system and user prompts through YAML
 
-## 🛠️ Technical Stack
-
-- **Vector Search**: ChromaDB for efficient semantic search
-- **Embeddings**: Nomic AI's text embeddings (nomic-embed-text-v1)
-- **LLM Orchestration**: SmolagentsCodeAgent for tool use and reasoning
-- **UI Framework**: Gradio for the web interface
-- **Model**: Google's Gemini 2.0 Flash via LiteLLM
-
 ## 📋 Requirements
 
 - Python 3.8+
 - Dependencies listed in `requirements.txt`
 
-## 🔧 Installation
-
-1. **Clone the repository**:
-   ```bash
-   git clone https://github.com/yourusername/chat-with-avd-doc.git
-   cd chat-with-avd-doc
-   ```
-
-2. **Create a virtual environment** (recommended):
-   ```bash
-   python -m venv venv
-   source venv/bin/activate  # On Windows: venv\Scripts\activate
-   ```
-
-3. **Install dependencies**:
-   ```bash
-   pip install -r requirements.txt
-   ```
 
-4. **Configure environment variables** (if needed):
-   - For LiteLLM to access Gemini, you may need to set API keys
-   - Create a `.env` file or set them directly in your environment
 
 ## 🔍 Working with the Vector Database
 
-The application uses ChromaDB to store document embeddings. The database is created in the `vector_db` directory.
+The application uses FAISS to store document embeddings. The database is created in the `faiss_index` directory.
 
 - Documents are organized by "fabric" collection
 - Metadata includes source information that can identify device-specific documentation
 - The system distinguishes between global network information and device-specific details
 
-## 📝 Configuration
-
-### Prompt Templates
-
-Edit `prompts.yaml` to customize:
-
-- System instructions for the AI agent
-- Formatting of responses
-- Behavior and capabilities
-
-### Model Configuration
-
-The application uses Gemini Flash by default. To modify:
-
-```python
-# In app.py
-model = LiteLLMModel("gemini/gemini-2.0-flash")  # Change model here
-```
 
 ## 🚀 Usage
 
 1. **Start the application**:
    ```bash
-   python app.py
+   uv run app.py
    ```
 
 2. **Access the UI**:
@@ -103,6 +56,8 @@ model = LiteLLMModel("gemini/gemini-2.0-flash")  # Change model here
    - Ask natural language questions about your network
    - Example: "What is the loopback Pool address used by the fabric?"
    - Example: "How many IP addresses are in use in the management network?"
+   - Example: "I need unused ports to connect 6 servers. provide me a pair of connections per server."
+   - Example: "What is the name server in this network? -> 8.8.8.8"
 
 ## 📂 Project Structure
 
@@ -111,19 +66,10 @@ chat-with-avd-doc/
 ├── app.py              # Main application with Retriever Tool and Gradio UI
 ├── prompts.yaml        # Configuration for AI prompts and behavior
 ├── requirements.txt    # Python dependencies
-└── vector_db/          # ChromaDB database files
-    ├── chroma.sqlite3  # SQLite database for ChromaDB
-    └── [UUID folders]  # Vector data storage
+└── faiss_index/        # FAISS database files
+    ├── faiss.index     # FAISS index file
 ```
 
-## 🧪 How It Works
-
-1. **User Query**: User enters a natural language question through the Gradio UI
-2. **Semantic Encoding**: The query is converted to a vector embedding
-3. **Vector Search**: ChromaDB searches for similar document vectors
-4. **Context Assembly**: Top matches are assembled with source metadata
-5. **LLM Processing**: The Gemini model processes the query with retrieved context
-6. **Response Generation**: The system returns a natural language answer
 
 ## 📄 License
 
@@ -139,6 +85,3 @@ Contributions are welcome! Please feel free to submit a Pull Request.
 4. Push to the branch (`git push origin feature/amazing-feature`)
 5. Open a Pull Request
 
-## Test Questions
-
-1. What is the name server in this network? 8.8.8.8

requirements.txt

@@ -1,12 +1,8 @@
 PyYAML
-chromadb
-sentence-transformers
-smolagents
-gradio
-smolagents[litellm]
-einops
-langchain-community
-langchain
-faiss-cpu
-unstructured
-gradio[mcp]
+langchain-community    # For FAISS, HuggingFaceEmbeddings
+langchain              # Core Langchain
+faiss-cpu              # FAISS vector store
+sentence-transformers  # For HuggingFaceEmbeddings
+openai-agents          # OpenAI Agents SDK
+gradio[mcp]
+unstructured           # Required by loader.py