https://github.com/srbhr/api-powered-rag
API powered RAG using Apideck, LangChain, Ollama, and Gemma
https://github.com/srbhr/api-powered-rag
Last synced: 7 months ago
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API powered RAG using Apideck, LangChain, Ollama, and Gemma
- Host: GitHub
- URL: https://github.com/srbhr/api-powered-rag
- Owner: srbhr
- License: mit
- Created: 2025-06-22T16:43:01.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-06-22T17:29:42.000Z (7 months ago)
- Last Synced: 2025-06-22T18:29:03.069Z (7 months ago)
- Language: Python
- Size: 5.17 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# API Powered RAG using Apideck, LangChain, Ollama, and Gemma 3
An example of API powered RAG using Apideck's File Storage API.
This project is a simple web application built with Streamlit that demonstrates an API-based Retrieval-Augmented Generation (RAG) workflow. The application connects to a user's Box.com account using the Apideck File Storage API, fetches a list of PDF files, and allows the user to select one for summarization. The summary is generated by a local Large Language Model running via Ollama and orchestrated by LangChain.
### Core Technologies
- Backend & API: Apideck File Storage API (Box Connector)
- AI Orchestration: LangChain
- Language Model: Ollama (with Google's Gemma model)
- Web Framework: Streamlit
- Environment Management: uv
### Setup and Installation
1. Prerequisites
- Python 3.11+
- Ollama installed and running.
- The desired model pulled via Ollama (e.g., ollama run gemma3:1b-it-qat).
- An Apideck account with the File Storage API enabled and a Box connection configured.
2. Set Up the Environment
First, clone the repository. Then, set up and activate the Python virtual environment using uv.
```bash
# Create the virtual environment
uv venv
# Activate the environment (macOS/Linux)
source .venv/bin/activate
```
3. Install Dependencies
Install all the required Python packages from the requirements.txt file.
```bash
uv pip install -r requirements.txt
```
4. Configure Credentials
Create a .env file in the root of the project directory and add your Apideck credentials.
```bash
APIDECK_API_KEY="sk_live_..."
APIDECK_APP_ID="YOUR_APP_ID..."
APIDECK_CONSUMER_ID="test-consumer"
```
You can get your API_KEY and APP_ID from your Apideck dashboard.
### How to Run
With your Ollama server running and the environment set up, start the Streamlit application with the following command:
```bash
streamlit run app.py
```
The application will open in your web browser.