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+# TechFlow Customer Support Agent 🤖
+
+A robust, RAG-based AI agent designed to assist users by answering questions from local PDF documentation and seamlessly creating GitHub support tickets when answers are unavailable.
+
+## hugging face link
+[niddijoris/RAG_Customer_Support](https://huggingface.co/spaces/niddijoris/RAG_Customer_Support)
+## 🌟 Features
+
+*   **RAG (Retrieval-Augmented Generation)**: Answers user queries using a local knowledge base created from PDF documents.
+*   **Smart Search & Synthesis**:
+    *   **Typo Tolerance**: Handles misspellings (e.g., "solutiun") and infers user intent.
+    *   **Relevance Filtering**: Automatically discards search results with a similarity score below **0.7** to prevent hallucinated answers from irrelevant text.
+    *   **Comprehensive Summarization**: Synthesizes answers from multiple document chunks.
+*   **Strict Citations**: Every answer includes mandatory source citations with filenames and page numbers (e.g., `**Source:** manual.pdf (Page 12)`).
+*   **Automated Ticket System**:
+    *   **Condition-Based**: Offers to create a ticket only when the agent explicitly "could not find the answer".
+    *   **GitHub Integration**: Directly creates issues in a specified GitHub repository.
+    *   **Project Organization**: Automatically applies labels and prefixes titles based on the configured `PROJECT_FOLDER`.
+*   **Auto-Ingestion**: Automatically checks and updates the vector database from the `data/` directory when the application starts.
+
+## 🛠️ Prerequisites
+
+*   Python 3.9+
+*   OpenAI API Key
+*   GitHub Personal Access Token (with `repo` scope)
+
+## 🚀 Installation & Setup
+
+1.  **Clone the Repository**
+    ```bash
+    git clone <your-repo-url>
+    cd <project-directory>
+    ```
+
+2.  **Install Dependencies**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Configure Environment**
+    Create a `.env` file in the root directory (copy from `.env.example`):
+    ```bash
+    cp .env.example .env
+    ```
+    
+    Update `.env` with your credentials:
+    ```env
+    OPENAI_API_KEY=sk-...
+    GITHUB_TOKEN=ghp_...
+    REPO_NAME=your_username/your_repo_name
+    PROJECT_FOLDER=if_ur_project_has_folder_name_put_it_here  # Used for ticket labels/titles
+    ```
+
+4.  **Add Knowledge Base**
+    Place your PDF documents into the `data/` folder.
+    *   The system will automatically ingest them when you run the app.
+
+## 💻 Usage
+
+Start the Streamlit application:
+
+```bash
+streamlit run app.py
+```
+
+### How it works:
+1.  **Ask a Question**: Type your query in the chat.
+2.  **Get an Answer**: The agent searches the PDFs. 
+    *   If a high-confidence answer is found (Score > 0.7), it answers with citations.
+    *   If relevant info is NOT found, it replies "I could not find the answer".
+3.  **Raise a Ticket**: If the answer was not found, a form appears allowing you to create a GitHub issue immediately.
+    
+
+*   `app.py`: Main Streamlit application entry point.
+*   `agent.py`: Core logic for the AI agent, RAG search, and TicketSystem.
+*   `ingest.py`: Script handles loading PDFs, chunking, and FAISS vector storage.
+*   `data/`: Directory for source PDF files.
+*   `vector_db/`: Directory where the FAISS index is saved locally.
+<img width="1157" height="818" alt="Image" src="https://github.com/user-attachments/assets/7b69e60d-33f6-4640-b82f-2b6e37978b8e" />
+
+<img width="1189" height="798" alt="Image" src="https://github.com/user-attachments/assets/e6466d6b-cd67-41c4-98e9-1afe7e33f6e4" />
+<img width="1189" height="798" alt="Image" src="https://github.com/user-attachments/assets/8ceb9ce8-731d-448c-9dc2-93cf5a6922af" />
+
+<img width="1338" height="90" alt="Image" src="https://github.com/user-attachments/assets/6d9b3139-c28f-436f-8196-6f5dcc119496" />