everalgo-rank 0.3.1

EverAlgo rank: 4 business-facing rankers (episodic / profile / case / skill) over a shared fusion / weight / rerank toolkit.

everalgo-rank

Memory ranking for EverAlgo — four business-facing ranker classes (EpisodicRanker / CaseRanker / SkillRanker / profile rank) composed over a shared toolkit (fusion / weight / rerank). Pure in-memory rank, no storage I/O.

See the umbrella project: EverAlgo monorepo and the architecture document at docs/concepts/architecture.md.

Install

pip install everalgo-rank

What this distribution provides

Symbol	Role
EpisodicRanker	Class facade — episodic memory ranking; LLM bound at construction
CaseRanker	Class facade — agent case ranking; LLM bound at construction
SkillRanker	Class facade — agent skill ranking; LLM bound at construction
profile.rank	Module-level sync function — profile ranking; cosine sort + dedup, no LLM
RankConfig	Frozen pydantic config: fusion_mode, rrf_k, alpha, etc.
FusionMode	Literal type: "rrf" / "lr" / "vector_anchored"
DEFAULT_RANK_CONFIG	RankConfig() with defaults (fusion_mode="rrf", rrf_k=60)
arank / rank	Top-level async / sync dispatch — routes to the registered ranker by RankInput.memory_type
arerank / rerank	Low-level LLM rerank step (not top-level: import from everalgo.rank.rerank)

Quick start

import asyncio
import json

from everalgo.llm.types import ChatResponse
from everalgo.rank import EpisodicRanker, DEFAULT_RANK_CONFIG
from everalgo.testing.fake_llm import FakeLLMClient
from everalgo.types import Candidate, RankInput

_RERANK_JSON = json.dumps({"ranked": [{"id": "ep_a", "score": 0.95}]})

async def main() -> None:
    fake = FakeLLMClient(responses=[ChatResponse(content=_RERANK_JSON, model="fake")])
    ranker = EpisodicRanker(llm=fake)

    rank_input = RankInput(
        query="Python async retry patterns",
        memory_type="episodic",
        dense_candidates=[
            Candidate(id="ep_a", score=0.9, metadata={"episode": "Alice asked about async retries."}),
            Candidate(id="ep_b", score=0.5, metadata={"episode": "Team discussed lunch."}),
        ],
        top_k=1,
    )

    output = await ranker.arank(rank_input, config=DEFAULT_RANK_CONFIG, enable_rerank=True)
    for item in output.items:
        print(f"{item.id}  score={item.score:.2f}")

asyncio.run(main())

LLM rerank step

arerank is the standalone LLM reranking primitive used internally by the ranker facades and exposed for direct use:

from everalgo.rank.rerank import arerank
from everalgo.rank.prompts.en.episodic import EPISODIC_RERANK_PROMPT_EN
from everalgo.types import Candidate

reranked = await arerank(
    candidates,
    prompt=EPISODIC_RERANK_PROMPT_EN,
    top_k=5,
    llm=client,
)

API surface

Symbol	Module	Signature summary
EpisodicRanker	everalgo.rank	__init__(*, llm) → arank(rank_input, *, config, prompt, enable_rerank, ...) → RankOutput
CaseRanker	everalgo.rank	Same shape as EpisodicRanker
SkillRanker	everalgo.rank	Same shape as EpisodicRanker
profile.rank	everalgo.rank.profile	(rank_input, *, threshold=0.0) → RankOutput — sync, no LLM
RankConfig	everalgo.rank	fusion_mode: FusionMode = "rrf", rrf_k: int = 60, alpha: float = 1.0, expand_limit: int = 3
arank	everalgo.rank	Top-level async dispatch by rank_input.memory_type
arerank	everalgo.rank.rerank	(items, *, prompt, top_k, llm) → list[Candidate]

Cross-links

• everalgo-core — RankInput, RankOutput, Candidate, ScoredItem, LLMClient
• everalgo-user-memory — produces Episode / AtomicFact / Profile that the caller's recall layer packages into Candidate for ranking
• everalgo-agent-memory — produces AgentCase / AgentSkill candidates