Update README.md

README.md

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-# Legal Agent Codebase
-
-- [LinkedIn - Rajarshi Roy](https://www.linkedin.com/in/rajarshi-roy-learner/)
-  
-- [Github - Rajarshi Roy](https://github.com/Rajarshi12321/)
-
-- [Medium - Rajarshi Roy](https://medium.com/@rajarshiroy.machinelearning)
-  
-- [Kaggle - Rajarshi Roy](https://www.kaggle.com/rajarshiroy0123/)
-- [Mail - Rajarshi Roy](mailto:[email protected])
-- [Personal-Website - Rajarshi Roy](https://rajarshi12321.github.io/rajarshi_portfolio/)
-
-
-## Table of Contents
-
-- [Legal Agent Codebase](#legal-agent-codebase)
-  - [Table of Contents](#table-of-contents)
-- [(DEMO README)](#demo-readme)
-  - [About The Project](#about-the-project)
-  - [Functionality](#functionality)
-  - [Code Structure](#code-structure)
-  - [Dependencies](#dependencies)
-  - [Working with the code](#working-with-the-code)
-  - [Deploying the project from your side (In AWS)](#deploying-the-project-from-your-side-in-aws)
-  - [Contributing](#contributing)
-  - [Contact](#contact)
-  - [License](#license)
-
-# (DEMO README)
-
-## About The Project
-
-This projects aim to implements a sophisticated document retrieval and question answering system using LangChain, leveraging Google's Gemini-1.5-flash language model and a FAISS vector database.  The system is designed to handle legal queries, providing comprehensive and accurate answers by combining information retrieval from a local knowledge base with web search capabilities when necessary.
-
-## Functionality
-
-The system follows a multi-stage workflow:
-
-1. **Query Input:** The user provides a query (e.g., a legal question).
-
-2. **FAISS Retrieval:** The query is embedded using Google Generative AI embeddings, and the FAISS index (a local vector database) is queried to retrieve the most relevant documents.
-
-3. **Grounded Response Generation:** A `DocSummarizerPipeline` summarizes the retrieved documents, focusing on the user's query.  This summary attempts to directly answer the question using only the retrieved documents.
-
-4. **Response Evaluation:** An `IntermediateStateResponseEvaluator` assesses the quality and completeness of the generated response.  This evaluation uses the Gemini model to determine if the response sufficiently answers the query.
-
-5. **Web Search (Conditional):** If the generated response is deemed insufficient, a `WebSearchAgent` performs a web search using DuckDuckGo to gather additional information.  The results are then incorporated into the final response.
-
-6. **Response Output:** The final answer, either from the document summary or the combined document/web search result, is returned to the user.
-  
-
-## Code Structure
-
-The code is organized into several classes and functions:
-
-* **`FaissRetriever`:** Loads and interacts with the FAISS index, retrieving relevant documents based on a query.
-
-* **`DocSummarizerPipeline`:** Summarizes retrieved documents using the Gemini model, generating a concise answer focused on the user's query.  It uses a carefully crafted prompt to ensure the response is structured and informative.
-
-* **`WebSearchAgent`:** Performs web searches using DuckDuckGo and integrates the results into the response.
-
-* **`IntermediateStateResponseEvaluator`:** Evaluates the quality of the generated response using the Gemini model, determining if additional information is needed.
-
-* **`State` (TypedDict):** Defines the data structure for passing information between stages of the workflow.
-
-* **Workflow Functions (`faiss_content_retriever`, `grounded_response`, `response_judge`, `web_response`):** These functions represent individual nodes in the LangGraph workflow.
-
-* **`StateGraph`:** Defines the workflow using LangGraph, managing the flow of data between the different stages.  Conditional logic is implemented to determine whether a web search is necessary.
-
-* **`run_user_query`:** The main function that takes a user query and processes it through the LangGraph workflow.
-<div align="center">
-Agent Workflow:
-
-![alt text](assets/agent_workflow.png)
-</div>
-
-## Dependencies
-
-The code relies on several libraries:
-
-* `langgraph`
-* `langchain-core`
-* `langchain-google-genai`
-* `IPython`
-* `dotenv`
-* `google.generativeai`
-* `langchain.chains.question_answering`
-* `langchain.prompts`
-* `langchain.vectorstores`
-* `langchain_community.tools`
-* `langchain.agents`
-
-
-
-## Working with the code
-
-
-I have commented most of the neccesary information in the respective files.
-
-To run this project locally, please follow these steps:-
-
-1. Clone the repository:
-
-   ```shell
-   git clone https://github.com/Rajarshi12321/legal-agent.git
-   ```
-
-
-2. **Create a Virtual Environment** (Optional but recommended)
-  It's a good practice to create a virtual environment to manage project dependencies. Run the following command:
-     ```shell
-     conda create -p <Environment_Name> python==<python version> -y
-     ```
-     Example:
-     ```shell
-     conda create -p venv python=3.9 -y 
-     ```
-    Note:
-    - It is important to use python=3.9 or above for proper use of Langchain or else you would get unexpecterd errors
-
-
-3. **Activate the Virtual Environment** (Optional)
-   Activate the virtual environment based on your operating system:
-      ```shell
-      conda activate <Environment_Name>/
-      ```
-      Example:
-     ```shell
-     conda activate venv/
-     ```
-
-4. **Install Dependencies**
-  
-   - Run the following command to install project dependencies:
-     ```
-     pip install -r requirements.txt
-     ```
-
-   Ensure you have Python installed on your system (Python 3.9 or higher is recommended).<br />
-   Once the dependencies are installed, you're ready to use the project.
-
-5. Create a .env file in the root directory and add your Gemini and Langchain credentials as follows:
-    ```shell  
-    GOOGLE_API_KEY = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
-    ```
-
-
-6. Run the Flask app: Execute the following code in your terminal.
-   ```shell  
-   chainlit run app.py 
-   ```
-   
-
-6. Access the app: Open your web browser and navigate to http://localhost:8000/ to use the House Price Prediction and Property Recommendation app.
-
-## Deploying the project from your side (In AWS)
-
-I have already made a github actions file in `.github\workflows\main.yaml`
-To use it you need to the following prerequisites:
-
-1. Make a IAM Role from your aws account
-- 1. Login to AWS console.
-
-- 2. Create IAM user for deployment
-
-	#with specific access
-
-	1. EC2 access : It is virtual machine
-
-	2. ECR: Elastic Container registry to save your docker image in aws
-
-
-	#Policy: (You need to select these policies when building the user)
-
-	1. AmazonEC2ContainerRegistryFullAccess
-
-	2. AmazonEC2FullAccess
-
-2. Building the full infrastructure using Terraform </br></br>
-  1st you need to configure your aws account using the created IAM role by the command `aws configure` so that terraform can know which account to use </br></br>
-  NOTE: If you don't want to use terraform for building infrastructure you can also build this manually from aws console:</br>
-  For reference watch this video from `3:47:20` time frame : [Youtube link](https://www.youtube.com/watch?v=86BKEv0X2xU)</br></br>
-  Get to the terraform directory: `infrastructure\terraform` and execute the following commands:
-  </br></br>
-  Initializing Terraform
-    ```shell  
-    terraform init 
-    ```
-    Forming a plan according the described infrastructure
-    ```shell  
-    terraform plan 
-    ```
-    Applying the planned infrastructure to build necessary resources
-    ```shell  
-    terraform apply -auto-approve
-    ```
-
-    </br></br>
-3. After this you Need to configure your EC2 instance to install Docker:
-</br>Run The Following commands:
-    ```shell  
-    sudo apt-get update -y
-
-    sudo apt-get upgrade
-      
-
-    curl -fsSL https://get.docker.com -o get-docker.sh
-
-    sudo sh get-docker.sh
-
-    sudo usermod -aG docker ubuntu
-
-    newgrp docker
-   ```
-4. After this you need to configure the self-runner for github actions to actually deploy it to EC2 instance:
-  </br></br>
-  Check out the [Youtube vidoe](https://www.youtube.com/watch?v=86BKEv0X2xU) for reference from 3:54:38 time frame
-  </br></br>
-  The commands for settinng up self-hosted runner will be like: </br></br>
-  (NOTE: Do use the commands from your actions runner, the below commands are just for your reference)
-
-    ```shell  
-    mkdir actions-runner && cd actions-runner
-
-    curl -o actions-runner-linux-x64-2.316.1.tar.gz -L https://github.com/actions/runner/releases/download/v2.316.1/actions-runner-linux-x64-2.316.1.tar.gz
-
-
-    echo "d62de2400eeeacd195db91e2ff011bfb646cd5d85545e81d8f78c436183e09a8  actions-runner-linux-x64-2.316.1.tar.gz" | shasum -a 256 -c
-
-
-    tar xzf ./actions-runner-linux-x64-2.316.1.tar.gz
-
-    ./config.sh --url https://github.com/Rajarshi12321/main_app_deploy --token AWSY7XQOYHXWPQKGRAEQWRDGJD2GS
-
-    ./run.sh
-
-    ```
-
-   name the runner as : `self-hosted`
-
-1. Follow the Following [youtube video](https://www.youtube.com/watch?v=86BKEv0X2xU) from `3:57:14` time frame to know which secret Key and Value to add to your github actions secrets. Additionlly you have to add the `GOOGLE_API_KEY` in the secrets to with same key name as used in `.env` and their api keys as the values.
-
-2. Finally after doing all this you can run you github actions smoothly which is run by the instructions of `.github\workflows\main.yaml`
-   </br></br>
-  **Description: About the deployment by main.yaml**
-
-	1. Build docker image of the source code
-
-	2. Push your docker image to ECR
-
-	3. Launch Your EC2 
-
-	4. Pull Your image from ECR in EC2
-
-	5. Lauch your docker image in EC2
-
-
-  Now making any changes in any file except the readme.md file and assets folder (which contains images for readme)  will trigger the github action CI/CD pipeline for development.
-
-  NOTE: Do keep an eye on the state of the `self-hosted` runner, if its `idle` or `offline` then fix the condition my connecting to ec2 instance and run the `run.sh` file by:
-
-  ```shell
-  cd actions-runner
-
-  ./run.sh
-  ```
-
-
-
-## Contributing
-I welcome contributions to improve the functionality and performance of the app. If you'd like to contribute, please follow these guidelines:
-
-1. Fork the repository and create a new branch for your feature or bug fix.
-
-2. Make your changes and ensure that the code is well-documented.
-
-3. Test your changes thoroughly to maintain app reliability.
-
-4. Create a pull request, detailing the purpose and changes made in your contribution.
-
-## Contact
-
-Rajarshi Roy - [[email protected]](mailto:[email protected])
-
-
-
-## License
-This project is licensed under the MIT License. Feel free to modify and distribute it as per the terms of the license.
-
-I hope this README provides you with the necessary information to get started with the road to Generative AI with Google Gemini and Langchain.
+---
+title: Legal-Agent
+emoji: 🌖
+colorFrom: gray
+colorTo: blue
+sdk: docker
+sdk_version: 5.18.0
+app_file: app.py
+pinned: false
+---