https://github.com/s-gani/advanced_dms_bot
The user communicates with the AI bot (RASA) through the Android app for queries.
https://github.com/s-gani/advanced_dms_bot
advanced-dms android-application chat chatbot content-search dms document-management-system java pdf python rasa rasa-nlu
Last synced: 5 months ago
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The user communicates with the AI bot (RASA) through the Android app for queries.
- Host: GitHub
- URL: https://github.com/s-gani/advanced_dms_bot
- Owner: S-Gani
- Created: 2024-07-09T11:21:41.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-17T06:22:28.000Z (over 1 year ago)
- Last Synced: 2025-10-23T08:37:34.948Z (9 months ago)
- Topics: advanced-dms, android-application, chat, chatbot, content-search, dms, document-management-system, java, pdf, python, rasa, rasa-nlu
- Language: Java
- Homepage:
- Size: 433 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Bot App which Integration with RASA for Content Search
## Overview
This project integrates an Android client with a RASA-based AI bot running on a server. The bot can respond to user queries and facilitate content searches using the information stored in specific directories.
---
## Steps and Guidelines to Make It Work
### Server Setup
1. **Install Python**:
- Ensure Python 3.7, 3.8, 3.9, or 3.10 is installed. Recommended version: 3.9.13.
- Download Python from the [official site](https://www.python.org/downloads/).
2. **Install RASA Open Source**:
- Run the following command to install RASA using pip:
```bash
pip install rasa
```
- Verify the installation:
```bash
rasa --version
```
3. **Create a New RASA Project**:
- Run the following command to initialize a new RASA project:
```bash
rasa init --no-prompt
```
- Navigate to your RASA project directory.
4. **Train the RASA Model**:
- To train your RASA model, use:
```bash
rasa train
```
5. **Test and Run the RASA Assistant**:
- Run the bot in the terminal using:
```bash
rasa shell
```
- Or, run the server with API support using:
```bash
rasa run --enable-api
```
6. **Training Data**:
- The model can be trained using files located within your project directory such as `nlu.md`, `domain.yml`, `stories.md`, etc.
7. **Install spaCy for NLU**:
- Install the `spaCy` library using pip:
```bash
pip install spacy
```
8. **Install Other Required Packages**:
- Install any additional dependencies using pip as required by your project.
---
### Client Setup (Android App)
1. **Install the Application**:
- Download and install the Android application on the device.
2. **Grant Permissions**:
- Ensure the app has the required permissions to access the device's storage.
3. **Network Security Configuration**:
- The server system must be configured to allow cleartext traffic using the server's IP address. This is crucial for enabling communication between the Android app and the server over HTTP.
---
### Guidelines
- Both the client (Android app) and server (RASA bot) should be able to communicate over the network.
- The bot can provide services regardless of how much it has been trained.
- All files needed for content search must be placed in a folder named `Rasabot` in your device's internal storage.
---
### Working Process
1. **User Interaction**:
- The user communicates with the AI bot (RASA) through the Android app for queries.
2. **Content Search**:
- The bot provides instructions for content search, and the user's request is sent via an HTTP call to the server.
- On the server side, the bot processes the request and sends the appropriate response back through the same communication path.
---
## Tools Used
### Client-Side
- **Android Studio**
- **Java**
- **ITextpdf Library** (for text extraction)
### Server-Side
- **Python**
- **RASA Framework**
- **spaCy Library** (for NLU)
---
## Contact
For further information, feel free to contact.