This is a simple answer engine test project leveraging PydanticAI for educational purposes.

An answer engine is a tool designed to give you direct, detailed answers to your questions. By searching the web and synthesizing information into clear, up-to-date responses. Unlike traditional search engines, which make you sift through a list of links, this system delivers the insights you’re looking for in a single, easy-to-read response.

An example of a great answer engine is PerplexityAI.

Pydantic AI agent answers questions using the search tool provided to it to search the web using Tavily and infer the answer by LLM models.

1. Rename .env.example file to .env in the root directory and set the environment variables.

2. Run the FastAPI application:

   uvicorn app.api:main --reload

3. You can now access the API at http://127.0.0.1:8000/search.

Sample API collections are available in the docs directory. you can view and work with them using Bruno.

curl -X 'POST' \
 'http://127.0.0.1:8000/search' \
 -H 'accept: application/json' \
 -H 'Content-Type: application/json' \
 -d '{
 "query": "How's the weather like today in Tehran?"
 }'

{
 "answer": "The weather in Tehran today is partly cloudy with a temperature of -2.7°C. The wind is coming from the north-northwest at a speed of 14.0 kph, and the humidity is at 58%. The conditions make it feel like -7.5°C. There is no precipitation, and the visibility is about 10 kilometers."
}

This project is licensed under the MIT License.