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Implements the `ReRanker` contract from\n> `@graphorin/memory/search`.\n>\n> Project Graphorin · v0.15.1 · MIT License · © 2026 Oleksiy Stepurenko ·\n> <https://github.com/o-stepper/graphorin>\n\n---\n\n## Status\n\n- **Published:** v0.15.1 (optional sub-pack)\n- **Default temperature:** `0` (deterministic).\n- **Default batch size:** `5` parallel provider calls.\n- **Default max score:** `10` (operator-tunable; finer scales improve\n  ordering precision at the cost of model variance).\n- **Default scoring prompt:** English; locale-agnostic by design.\n  Operators targeting non-English deployments pass `scoringPrompt:\n  <localised builder>`.\n\n---\n\n## Install\n\n```bash\npnpm add @graphorin/reranker-llm\n```\n\nThe reranker reuses your existing `Provider` instance - no extra\nnetwork credentials beyond what the provider already needs.\n\n---\n\n## Usage\n\n### Drop-in replacement for the built-in RRF reranker\n\n```ts\nimport { createMemory } from '@graphorin/memory';\nimport { createLlmReranker } from '@graphorin/reranker-llm';\n\nconst memory = createMemory({\n  store,\n  embedder,\n  reranker: createLlmReranker({ provider }),\n});\n```\n\n### Tighter batching for rate-limited providers\n\n```ts\nconst reranker = createLlmReranker({\n  provider,\n  batchSize: 2, // 2 concurrent calls per merged batch\n  maxOutputTokens: 4,\n});\n```\n\n### Custom scoring prompt (localisation / domain tuning)\n\n```ts\nimport { createLlmReranker } from '@graphorin/reranker-llm';\n\nconst reranker = createLlmReranker({\n  provider,\n  maxScore: 100,\n  scoringPrompt: ({ query, passage, maxScore }) => ({\n    system:\n      'Você é um avaliador preciso de relevância. Dado uma consulta e uma passagem, ' +\n      `retorne um único inteiro de 0 a ${maxScore} indicando a relevância. ` +\n      'Saída SOMENTE o inteiro; sem explicações.',\n    user: `CONSULTA:\\n${query}\\n\\nPASSAGEM:\\n${passage}\\n\\nINTEIRO (0-${maxScore}):`,\n  }),\n});\n```\n\n### Custom passage extractor\n\n```ts\nconst reranker = createLlmReranker<MyRecord>({\n  provider,\n  passageExtractor: (record) => `${record.title}\\n\\n${record.body}`,\n});\n```\n\n---\n\n## Cost / latency considerations\n\nEvery candidate triggers one provider call. For a memory hybrid-search\nthat retrieves 50 candidates the LLM-as-reranker therefore makes 50\ncalls (parallelised in batches of 5 by default = 10 sequential\nbatches). Pair with:\n\n- A **smaller** judge model (e.g. `gpt-4o-mini`, `claude-3-5-haiku`,\n  Gemini Flash) to keep per-call cost down.\n- A **two-stage** pipeline (vector + FTS5 → RRF top-50 → LLM-judge\n  top-10) so only the most-promising candidates pay the LLM cost.\n- Provider middleware (`withRetry`, `withFallback`, `withCostTracking`)\n  for rate-limit + budget enforcement.\n\n---\n\n## Output signals\n\nEvery result attaches:\n\n| Signal              | Meaning                                                                |\n|---------------------|-------------------------------------------------------------------------|\n| `llm_score`         | Raw integer the model returned (0..maxScore).                          |\n| `llm_score_norm`    | Normalised score in `[0, 1]` (`raw / maxScore`).                       |\n| `cross_encoder`/etc | Pre-existing signals from upstream rankers (passed through unchanged). |\n\n---\n\n## Related decisions\n\n- ADR-024 - Reciprocal Rank Fusion default + pluggable rerankers.\n\n---\n\n## License\n\nMIT © 2026 Oleksiy Stepurenko\n\n---\n\n**Project Graphorin** · v0.15.1 · MIT License · © 2026 Oleksiy Stepurenko · <https://github.com/o-stepper/graphorin>\n","readmeFilename":"README.md"}