Update README and backend functionality for improved news application
- Enhanced README.md with a clearer project overview, features, technologies used, and installation instructions. - Updated vector dimension in config.py from 4096 to 1024 for Cohere embeddings. - Modified main.py to serve HTML responses for the home page, news fetching, and recommendations. - Improved error handling and ensured articles have links in the responses. - Cleaned up news_fetcher.py by removing unnecessary print statements. - Updated recommender.py to refine insights generation and summary extraction. - Added Jinja2 for templating and improved the project structure for better organization. - Included API documentation for better understanding of endpoints and usage.
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# DS Task AI News API Documentation
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## Overview
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The DS Task AI News API is a FastAPI-based application that provides endpoints for fetching, processing, and recommending news articles. The API uses AI-powered analysis to generate insights and recommendations based on news articles from various RSS feeds.
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## Base URL
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```
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http://localhost:8000
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```
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## Endpoints
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### 1. Home Page
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**Endpoint:** `/`
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**Method:** `GET`
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**Description:** Returns the home page with links to other routes.
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**Response:** HTML page with navigation links to other endpoints.
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**Example:**
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```
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GET /
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```
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### 2. Fetch News
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**Endpoint:** `/fetch-news`
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**Method:** `GET`
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**Description:** Fetches news from RSS feeds, processes them, and stores them in the vector database. Returns a page displaying the latest news articles.
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**Response:** HTML page displaying the latest news articles.
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**Example:**
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```
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GET /fetch-news
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```
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### 3. Recommend News
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**Endpoint:** `/recommend-news`
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**Method:** `GET`
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**Description:** Gets news recommendations based on an article ID or search query. Returns a page displaying recommended articles and AI-generated insights.
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**Query Parameters:**
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- `article_id` (optional): ID of an article to base recommendations on.
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- `query` (optional): Search query to base recommendations on.
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**Response:** HTML page displaying recommended articles and AI-generated insights.
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**Example:**
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```
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GET /recommend-news?query=artificial%20intelligence
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```
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### 4. Get Article
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**Endpoint:** `/article/{article_id}`
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**Method:** `GET`
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**Description:** Gets a specific article and its summary.
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**Path Parameters:**
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- `article_id`: ID of the article to retrieve.
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**Response:** JSON object containing the article and its summary.
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**Example Response:**
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```json
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{
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"article": {
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"title": "Example Article Title",
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"content": "Example article content...",
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"link": "https://example.com/article",
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"published": "2023-01-01T12:00:00",
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"source": "Example News",
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"categories": ["Technology", "AI"],
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"id": "article123"
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},
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"summary": "This is a summary of the article..."
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}
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```
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**Example:**
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```
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GET /article/article123
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```
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## Data Models
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### Article
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```json
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{
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"title": "string",
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"content": "string",
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"link": "string",
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"published": "string",
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"source": "string",
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"categories": ["string"],
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"id": "string"
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}
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```
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### Insights
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```json
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{
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"themes": ["string"],
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"insights": ["string"],
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"implications": ["string"],
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"related_areas": ["string"]
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}
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```
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## Error Handling
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The API uses standard HTTP status codes to indicate the success or failure of requests:
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- `200 OK`: The request was successful.
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- `400 Bad Request`: The request was invalid or cannot be served.
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- `404 Not Found`: The requested resource was not found.
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- `500 Internal Server Error`: An error occurred on the server.
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Error responses include a JSON object with a `detail` field containing a description of the error:
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```json
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{
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"detail": "Error message"
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}
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```
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## Authentication
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The API does not currently require authentication.
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## Rate Limiting
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The API does not currently implement rate limiting.
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## Dependencies
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The API relies on the following external services:
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- **Groq API**: For generating article summaries and insights.
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- **Pinecone Vector Database**: For storing and retrieving article embeddings.
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## Configuration
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The API can be configured by modifying the following environment variables:
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- `GROQ_API_KEY`: API key for the Groq service.
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- `PINECONE_API_KEY`: API key for the Pinecone vector database.
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- `PINECONE_ENVIRONMENT`: Environment for the Pinecone vector database.
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- `PINECONE_INDEX`: Index name for the Pinecone vector database.
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## Development
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To run the API locally:
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1. Install the required dependencies:
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```
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pip install -r requirements.txt
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```
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2. Set the required environment variables.
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3. Run the API:
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```
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python backend/main.py
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```
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The API will be available at `http://localhost:8000`.
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.
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