59e89772de
Remove legacy storage implementations and deprecated examples: - Delete FAISS, JSON, Memgraph, Milvus, MongoDB, Nano Vector DB, Neo4j, NetworkX, Qdrant, Redis storage backends - Remove Kubernetes deployment manifests and installation scripts - Delete unofficial examples for deprecated backends and offline deployment docs Streamline core infrastructure: - Consolidate storage layer to PostgreSQL-only implementation - Add full-text search caching with FTS cache module - Implement metrics collection and monitoring pipeline - Add explain and metrics API routes Modernize frontend and tooling: - Switch web UI to Bun with bun.lock, remove npm and pnpm lockfiles - Update Dockerfile for PostgreSQL-only deployment - Add Makefile for common development tasks - Update environment and configuration examples Enhance evaluation and testing capabilities: - Add prompt optimization with DSPy and auto-tuning - Implement ground truth regeneration and variant testing - Add prompt debugging and response comparison utilities - Expand test coverage with new integration scenarios Simplify dependencies and configuration: - Remove offline-specific requirement files - Update pyproject.toml with streamlined dependencies - Add Python version pinning with .python-version - Create project guidelines in CLAUDE.md and AGENTS.md
24 lines
534 B
Python
24 lines
534 B
Python
"""
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LightRAG caching infrastructure.
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This module provides caching implementations for various LightRAG components:
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- fts_cache: Full-text search result caching
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"""
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from .fts_cache import (
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FTS_CACHE_ENABLED,
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FTS_CACHE_MAX_SIZE,
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FTS_CACHE_TTL,
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get_cached_fts_results,
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invalidate_fts_cache_for_workspace,
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store_fts_results,
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)
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__all__ = [
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'FTS_CACHE_ENABLED',
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'FTS_CACHE_MAX_SIZE',
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'FTS_CACHE_TTL',
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'get_cached_fts_results',
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'invalidate_fts_cache_for_workspace',
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'store_fts_results',
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]
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