Update README.md

README.md

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-# 💸 Finance Assistant - FinLLM RAG
+# 💸 Finance Assistant
 
-A comprehensive financial assistant application that helps with banking compliance, industry classification, and financial calculations using RAG (Retrieval-Augmented Generation) technology powered by FAISS vector search and LLM processing.
+This project is a multi-functional financial assistant built with Streamlit. It leverages large language models and retrieval-augmented generation (RAG) to provide a suite of tools for financial analysis, compliance, and data retrieval.
 
-## 🚀 Features
+## Features
 
-### 1. **Calculation Methodology**
-Automated financial calculations essential for banking operations:
-- **MPBF (Maximum Permissible Bank Finance)**: Calculate the maximum working capital finance a bank can provide based on RBI norms
-- **Drawing Power (DP)**: Calculate borrowing limits based on current assets with applicable margins
+The application is divided into several key functionalities:
 
-### 2. **Circular Compliance Assistant**
-AI-powered RBI compliance analysis using Master Circular on Management of Advances:
-- Analyzes complex banking scenarios for compliance
-- Provides detailed compliance status with certainty levels
-- References specific circular sections and paragraphs
-- Offers actionable recommendations for compliance
+  * **Circular Compliance Assistant**: Analyzes user-provided scenarios for compliance against RBI Master Circulars on Management of Advances. It uses a FAISS vector database to retrieve relevant sections of the circular and a language model to generate a detailed compliance report.
+  * **Industry Classification Assistant**: Suggests appropriate industry classification codes based on user-provided keywords. This feature also utilizes a RAG pipeline to search through an industry classification master document.
+  * **Calculation Methodology**: Provides interactive calculators for key financial metrics:
+      * **Maximum Permissible Bank Finance (MPBF)**
+      * **Drawing Power (DP)**
+  * **Financial Data Assistant**: Answers questions about historical (1980-2015) state-wise financial data for India. It can retrieve specific metrics for a given state and year.
+  * **Model 1 Chat**: A general-purpose chat interface powered by the `gemma2-9b-it` model via the Groq API.
 
-### 3. **Industry Classification Assistant**
-Intelligent industry code classification system:
-- Suggests appropriate industry classification codes based on keywords
-- Helps with accurate business categorization for regulatory purposes
-- Uses vector search to match business descriptions with standard classifications
+## How It Works
 
-## 🛠️ Technology Stack
+The core of the "Circular Compliance" and "Industry Classification" assistants is a Retrieval-Augmented Generation (RAG) pipeline.
 
-- **Frontend**: Streamlit
-- **Vector Search**: FAISS (Facebook AI Similarity Search)
-- **Embeddings**: SentenceTransformers (all-MiniLM-L6-v2)
-- **LLM**: Groq API with Gemma2-9B-IT model
-- **Data Processing**: Pandas, Pickle
+1.  **Indexing**: Source documents (`Master Circular.pdf`, `Industry Classification Master.pdf`) are chunked, and the text chunks are converted into vector embeddings using a `SentenceTransformer` model. These embeddings are stored in a FAISS index for efficient similarity search.
+2.  **Retrieval**: When a user enters a query, the query is embedded, and the FAISS index is searched to find the most relevant document chunks.
+3.  **Generation**: The retrieved chunks are passed as context, along with the user's query, to a large language model (`gemma2-9b-it`). The model then generates a comprehensive and context-aware response.
 
-## 📋 Prerequisites
+The "Financial Data Assistant" works by directly parsing the user's query for state, year, and metric information and looking up the corresponding data from a pre-loaded data file.
 
-- Python 3.8+
-- GROQ_API_KEY environment variable
-- Pre-built FAISS indexes and chunk files:
-  - `industry_index.faiss` & `industry_chunks.pkl`
-  - `circular_index.faiss` & `circular_chunks.pkl`
+## Setup and Installation
 
-## 🎯 Use Cases
+1.  **Clone the Repository**:
 
-- Banking professionals ensuring RBI compliance
-- Financial analysts performing regulatory calculations  
-- Business categorization for regulatory filings
-- Compliance officers analyzing complex scenarios
-- Financial institutions streamlining operations
+    ```bash
+    git clone <your-repository-url>
+    cd <your-repository-directory>
+    ```
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+2.  **Install Dependencies**:
+    Install the necessary Python libraries using the `requirements.txt` file.
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Set Up Assets**:
+    The application requires pre-built FAISS indexes and data files.
+
+      * Create a folder named `assets` in the root directory.
+      * Generate and place the following files into the `assets` folder (You will need a separate script to process the source PDFs and JSON to create these files):
+          * `industry_index.faiss`
+          * `industry_chunks.pkl`
+          * `circular_index.faiss`
+          * `circular_chunks.pkl`
+          * `financial_index.faiss`
+          * `financial_statements.pkl`
+
+4.  **API Key**:
+    Insert your Groq API key directly into the `app.py` file at the following line:
+
+    ```python
+    GROQ_API_KEY = "your-groq-api-key-here"
+    ```
+
+## Usage
+
+1.  **Run the Streamlit App**:
+    Execute the following command in your terminal:
+
+    ```bash
+    streamlit run app.py
+    ```
+
+2.  **Interact with the Application**:
+
+      * Open the URL provided by Streamlit (usually `http://localhost:8501`) in your web browser.
+      * Use the radio buttons at the top of the page to navigate between the different functionalities: "Calculation Methodology", "Circular Compliance", "Industry Classification", "Model 1", and "Model 2".
+      * Follow the on-screen instructions for each tool.
+
+## Dependencies
+
+This project relies on the following major libraries:
+
+  * `streamlit`: For creating the web application interface.
+  * `groq`: The client for accessing the Groq API.
+  * `sentence-transformers`: For generating text embeddings.
+  * `faiss-cpu`: For efficient similarity search in the vector database.
+  * `pandas`: For data manipulation, particularly in the financial data assistant.
+  * `numpy`: For numerical operations.
+  * `torch` & `transformers`: Core dependencies for the sentence transformer models.