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Retrieving available tables\n  - Full-text search across documents\n  - Vector-based semantic search using embeddings\n- Example usage can be found in `src/examples/lex_db_search_example.py`\n\nExample configuration:\n```python\nfrom lex_db_api.configuration import Configuration\nfrom lex_db_api.api.lex_db_api import LexDbApi\nfrom lex_db_api.api_client import ApiClient\n\napi_host = \"http://0.0.0.0:8000\"  # Configure your LexDB host\napi_client = ApiClient(configuration=Configuration(host=api_host))\napi = LexDbApi(api_client=api_client)\n```\n\n### For Users\nThe LexDB integration enables:\n- Fast full-text search across all stored documents\n- Semantic search using vector embeddings\n- Access to structured document metadata and content\n\nThe database connection is automatically handled by the application, requiring no additional setup from end users.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcentre-for-humanities-computing%2Flex-llm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcentre-for-humanities-computing%2Flex-llm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcentre-for-humanities-computing%2Flex-llm/lists"}