boladeE
bc485b44b8
- 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
Overview
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.
Base URL
http://localhost:8000
Endpoints
1. Home Page
Endpoint: /
Method: GET
Description: Returns the home page with links to other routes.
Response: HTML page with navigation links to other endpoints.
Example:
GET /
2. Fetch News
Endpoint: /fetch-news
Method: GET
Description: Fetches news from RSS feeds, processes them, and stores them in the vector database. Returns a page displaying the latest news articles.
Response: HTML page displaying the latest news articles.
Example:
GET /fetch-news
3. Recommend News
Endpoint: /recommend-news
Method: GET
Description: Gets news recommendations based on an article ID or search query. Returns a page displaying recommended articles and AI-generated insights.
Query Parameters:
article_id(optional): ID of an article to base recommendations on.query(optional): Search query to base recommendations on.
Response: HTML page displaying recommended articles and AI-generated insights.
Example:
GET /recommend-news?query=artificial%20intelligence
4. Get Article
Endpoint: /article/{article_id}
Method: GET
Description: Gets a specific article and its summary.
Path Parameters:
article_id: ID of the article to retrieve.
Response: JSON object containing the article and its summary.
Example Response:
{
"article": {
"title": "Example Article Title",
"content": "Example article content...",
"link": "https://example.com/article",
"published": "2023-01-01T12:00:00",
"source": "Example News",
"categories": ["Technology", "AI"],
"id": "article123"
},
"summary": "This is a summary of the article..."
}
Example:
GET /article/article123
Data Models
Article
{
"title": "string",
"content": "string",
"link": "string",
"published": "string",
"source": "string",
"categories": ["string"],
"id": "string"
}
Insights
{
"themes": ["string"],
"insights": ["string"],
"implications": ["string"],
"related_areas": ["string"]
}
Error Handling
The API uses standard HTTP status codes to indicate the success or failure of requests:
200 OK: The request was successful.400 Bad Request: The request was invalid or cannot be served.404 Not Found: The requested resource was not found.500 Internal Server Error: An error occurred on the server.
Error responses include a JSON object with a detail field containing a description of the error:
{
"detail": "Error message"
}
Authentication
The API does not currently require authentication.
Rate Limiting
The API does not currently implement rate limiting.
Dependencies
The API relies on the following external services:
- Groq API: For generating article summaries and insights.
- Pinecone Vector Database: For storing and retrieving article embeddings.
Configuration
The API can be configured by modifying the following environment variables:
GROQ_API_KEY: API key for the Groq service.PINECONE_API_KEY: API key for the Pinecone vector database.PINECONE_ENVIRONMENT: Environment for the Pinecone vector database.PINECONE_INDEX: Index name for the Pinecone vector database.
Development
To run the API locally:
-
Install the required dependencies:
pip install -r requirements.txt -
Set the required environment variables.
-
Run the API:
python backend/main.py
The API will be available at http://localhost:8000.
License
This project is licensed under the MIT License - see the LICENSE file for details.