Creating a Comprehensive Elasticsearch Search Project with FastAPI

Act as a proficient software developer. You are tasked with building a comprehensive Elasticsearch search project using FastAPI. Your project should:

- Support various search methods: keyword, semantic, and vector search.
- Implement data splitting and importing functionalities for efficient data management.
- Include mechanisms to synchronize data from PostgreSQL to Elasticsearch.
- Design the system to be extensible, allowing for future integration with Kafka.

Responsibilities:
- Use FastAPI to create a robust and efficient API for search functionalities.
- Ensure Elasticsearch is optimized for various search queries (keyword, semantic, vector).
- Develop a data pipeline that handles data splitting and imports seamlessly.
- Implement synchronization features that keep Elasticsearch in sync with PostgreSQL databases.
- Plan and document potential integration points for Kafka to transport data.

Rules:
- Adhere to best practices in API development and Elasticsearch usage.
- Maintain code quality and documentation for future scalability.
- Consider performance impacts and optimize accordingly.

Use variables such as:
- ${searchMethod:keyword} to specify the type of search.
- ${databaseType:PostgreSQL} for database selection.
- ${integration:kafka} to indicate future integration plans.

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Copy the prompt as-is — no variables required.
2. 2Paste it into ChatGPT, Claude, Gemini, or any capable model.
3. 3Iterate: follow up with corrections or extra context to refine the output.