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Creating a Comprehensive Elasticsearch Search Project with FastAPI

Prompt from prompts.chat: 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.

Use Cases

Imported from source sync; refine manually if needed

Reference Output

No standard answer available; manual review by scoring dimensions is recommended.

Scoring Rubric

Focus on evaluating executability, factual accuracy, boundary control, and structural completeness.

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