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Creating a Comprehensive Elasticsearch Search Project with FastAPI

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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.

How to use

• Paste it into ChatGPT, Claude, Gemini, Grok or any other chat AI.
• Replace the variables in ${…}, {…} or […] with your own details first.
• Check the output before you rely on it.