Update README.md

README.md

@@ -1,54 +1,8 @@
-# LLM x Website URLs
-
-Most of the language models like GPT 4 and even Gemini Pro have the issue of not having the latest information considering they were trained with a set of data for pretraining.
-
-In cases ,We may need to access real-time information from websites to get up-to-date information. LLMs do not have this functionality by default. But through Retrieval Augmented Generation(RAG), we can allow them to access the data that we provide. 
-
-Here, we use Langchain's Webloader and get the data from the site and store the data into a vectorstore, from where we can get the necessary answers for our queries. 
-
-## Usage Instructions:
-
-1. **Install Dependencies:**
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-2. **Run the Application:**
-    ```bash
-    python app.py
-    ```
-
-3. **Access the Gradio Interface:**
-   - Open your web browser and navigate to the provided URL.
-
-4. **Input Parameters:**
-   - Query: Enter your question or input.
-   - URL: Provide the website URL for context.
-   - OpenAI Key: Enter your OpenAI key for authentication.
-
-5. **Interact with the Chatbot:**
-   - Receive responses based on your input and website context.
-
-## Code Overview:
-
-- **main.py:** Python script containing the main application code.
-- **Functions:**
-    - `getvecstore(url)`: Retrieves a vector store from the given website URL.
-    - `getcontext(vector_store)`: Obtains a context-aware retriever chain.
-    - `getragchain(retriever_chain)`: Retrieves a conversational RAG (Retrieval-Augmented Generation) chain.
-    - `getresp(user_input, website_url)`: Gets a response from the chatbot based on user input and website URL.
-    - `interact(Query, URL, OpenAI_Key)`: Function to interact with the chatbot through Gradio interface.
-
-## Future Improvements:
-
-For future enhancements, I am considering incorporating a complete local and private workflow using the following technologies:
-
-- **Ollama for Embeddings:** Explore the use of Ollama for embeddings to enhance local processing and privacy.
-- **LLM (Language Model):** The use of local LLM inference through LM Studio and Ollama can be added to make it completely private to our system alone.
-
-## Important Note:
-
-- Keep your OpenAI key secure and avoid sharing it publicly.
-- Implement additional security measures for handling sensitive information.
-
-Feel free to explore and contribute to the codebase for further improvements and customization.
+title: QueryURL
+emoji: 🔗
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 3.8.2
+app_file: app.py
+pinned: false