cenote-core 0.6.2

Production-grade Python framework for building agentic RAG applications. Multilingual-capable with a roadmap toward Spanish/LATAM-first features.

cenote

Production-grade RAG primitives for Python — Protocol-based, multi-tenant by design, type-strict from day one. Spanish-first since M1.1; the foundation for vertical agent products targeting LATAM regulated industries.

Why cenote

cenote is not a LangChain alternative. LangChain is a kitchen-sink framework with ~100k stars and a full-time team. cenote is the opposite: a small, opinionated set of primitives for teams that hit framework complexity ceilings.

• Production minimalist — clear Protocol interfaces, composition over inheritance, engineering hardenings (batching, rate limiting, transactional upserts) built in.
• Type-strict — mypy --strict clean. py.typed shipped. Your IDE catches wiring errors before runtime.
• Multi-tenant by design — namespace is mandatory on every store and retriever method. Cross-tenant leakage is impossible by construction.
• LATAM-first roadmap — Spanish-aware BM25, ES evaluation datasets, fiscal/regulatory document support land in M1.1+. Multilingual embedders (Voyage, Cohere) already work today.

The name comes from cenotes — natural deep wells in the Yucatán Peninsula used by the Maya as sacred sources of fresh water and knowledge. The metaphor maps to RAG: a deep, structured source of knowledge from which you retrieve context.

When NOT to use cenote

cenote is a focused library, not a universal RAG toolkit. Don't choose it when:

• You need 100+ integrations out-of-the-box. Use LangChain or LlamaIndex — they bundle adapters for nearly every vector DB, LLM, and embedder. cenote ships protocols and a few concrete impls; everything else is your code.
• You want a hosted RAG service. cenote is a library you install. For managed RAG, evaluate Vectara, Pinecone Assistants, or AWS Bedrock Knowledge Bases.
• You need a chatbot UI out-of-the-box. cenote doesn't ship UI. Pair it with gradio, streamlit, or your own web stack.
• Your data is small (<10k chunks) and single-tenant. A 50-line script with numpy.dot and SQLite is enough. cenote's multi-tenancy + production hardenings add value above that scale.
• You can't adopt Python 3.12+. cenote requires modern Python; we don't backport.

Status

Shipped through v0.6.1 (2026-06-02). Reflects actual code state, not roadmap intent.

Module	Shipped	Roadmap
cenote.models	Document, Chunk, EmbeddedChunk, RetrievalResult, Message	—
cenote.errors	CenoteError hierarchy (Configuration, RateLimit, DimensionMismatch, Migration, LLM…)	—
cenote.types	Vector, Namespace, ModelId, ContentHash	—
cenote.chunkers	Chunker Protocol, RecursiveCharacterChunker, MarkdownChunker	Token-aware chunking
cenote.embedders	Embedder Protocol, MockEmbedder, VoyageEmbedder, CohereEmbedder, CachedEmbedder, EmbeddingCache Protocol, InMemoryCache, SqliteCache	RedisCache, streaming embed
cenote.stores	VectorStore Protocol, InMemoryVectorStore, PgVectorStore (HNSW + SET LOCAL transactional)	—
cenote.retrievers	Retriever Protocol, VectorRetriever, BM25Retriever (LRU-cached, picklable), HybridRetriever (RRF fusion)	—
cenote.tokenizers	Tokenizer Protocol, SpanishTokenizer (Snowball stemmer, pickle-safe since v0.4.1)	—
cenote.rerankers	Reranker Protocol, VoyageReranker, CohereReranker	—
cenote.observability	Tracer Protocol, NoopTracer, OTel adapter, Langfuse adapter, TracedVectorStore wrapper	—
cenote.pipeline	IndexingPipeline, IndexingProgress	Resume/retry-failed-batches API
cenote.eval	precision_at_k, recall_at_k, mean_reciprocal_rank, RetrievalBenchmark harness	DeepEval integration, bilingual EN/ES golden dataset
cenote.bench (docs)	MiraclLoader, ranx-backed nDCG/Recall, RRF fusion, BenchRunner, Pyserini-2cr report, cenote bench miracl-es CLI	BEIR sanity check, MTEB-es retrieval slice, real MIRACL-es numbers (Phase F)
cenote.llm	LLMClient Protocol, AnthropicLLM (with prompt-cache awareness), NoopLLM	Tool use, cenote-llm-{openai,bedrock,vertex} as separate packages per ADR-0008
cenote.cli	cenote bench miracl-es (Typer)	Additional subcommands as needs emerge

Quickstart

pip install cenote-core

import asyncio
from cenote.chunkers import RecursiveCharacterChunker
from cenote.embedders import MockEmbedder
from cenote.models import Document
from cenote.retrievers import VectorRetriever
from cenote.stores import InMemoryVectorStore


async def main() -> None:
    chunker = RecursiveCharacterChunker(chunk_size=512, chunk_overlap=64)
    embedder = MockEmbedder(dimensions=128)
    store = InMemoryVectorStore(dimensions=128)
    retriever = VectorRetriever(embedder=embedder, store=store)

    doc = Document(id="d1", content="Cenotes are natural sinkholes in the Yucatán Peninsula.")
    chunks = chunker.chunk(doc)
    embedded = await embedder.embed(chunks)
    await store.upsert(embedded, namespace="quickstart")

    results = await retriever.retrieve("What is a cenote?", namespace="quickstart", limit=3)
    for r in results:
        print(f"[{r.score:.3f}] {r.chunk.content}")


asyncio.run(main())

For real semantic retrieval, swap MockEmbedder for VoyageEmbedder(api_key=..., model="voyage-3") or CohereEmbedder(api_key=..., model="embed-multilingual-v3.0"). For production storage, PgVectorStore.connect(dsn, dimensions=...).

→ Full quickstart: https://jovandyaz.github.io/cenote/quickstart/

Extending cenote

Every primitive is a typing.Protocol — implement the interface and plug it in. No inheritance required.

from cenote.models import Chunk, EmbeddedChunk
from cenote.types import Vector


class MyEmbedder:
    """Satisfies the Embedder protocol via structural typing."""

    @property
    def model_id(self) -> str:
        return "my-provider:my-model"

    @property
    def dimensions(self) -> int:
        return 768

    async def embed(self, chunks: list[Chunk]) -> list[EmbeddedChunk]:
        ...

    async def embed_query(self, query: str) -> Vector:
        ...

→ Full example: examples/custom_embedder.py → Custom chunker: https://jovandyaz.github.io/cenote/extending/custom-chunker/

Architecture

Three diagrams document the system at different zoom levels:

• Ecosystem — cenote's position in the wider RAG ecosystem
• Internal architecture — 5 layers + future-API stubs
• Runtime flow — indexing path and query path sequence

GitHub renders .drawio files inline natively (since 2024). Click any link above to view.

→ Full architecture page: https://jovandyaz.github.io/cenote/architecture/

Roadmap

• ✅ M1.0 (released as v0.1.0) — Core primitives: chunker, embedders, stores, retrievers, future-API stubs
• ✅ M1.1 (released as v0.2.0) — MarkdownChunker, BM25 + Hybrid retrievers, Spanish-aware tokenizer, concrete rerankers, RetrievalBenchmark
• ✅ M1.2 (released as v0.3.0) — OTel + Langfuse adapters, Traced wrappers, AnthropicLLM with prompt caching, SqliteCache
• ✅ Foundation hardening (v0.4.0) — Sigstore + SBOM + Trusted Publishing, release-please, gitlint, observability wrappers, hardening pass on retrievers (LRU cache + invalidation), HNSW SET LOCAL fix
• ✅ Bug fixes (v0.4.1) — SpanishTokenizer pickle-safe, _http.retrying honors Retry-After header, embedder max_retries raised to 6
• ✅ Retrieval benchmark harness (v0.5.0) — cenote.bench module with MIRACL-es loader, ranx-backed metrics, RRF fusion, Pyserini-2cr report generator, and cenote bench miracl-es CLI (docs, ADR-0009)
• ✅ Maintenance + security (v0.6.0–v0.6.1) — release-please changelog consolidation and tooling cleanup, lxml>=6.1 security pin (CVE PYSEC-2026-87)
• 📋 M1.3+ — Tool use in AnthropicLLM, RedisCache, agent primitives, CFDI domain pack, MIRACL-es Phase F (real numbers)

See M1.1 baselines for the Spanish BM25 + hybrid retrieval scaffold. Full Pyserini-2cr table follows after the v0.5.0 Phase F embedding pass — see docs/benchmarks.md.

See CHANGELOG.md for a granular record of what shipped when.

Downstream products

cenote is the shared core for a portfolio of vertical agents serving LATAM regulated industries. Each downstream product stays in its own repository (per ADR-0008) and consumes cenote-core via PyPI.

Committed (Tier A):

• cfdi-agent — Accounting reconciliation + CFDI 4.0 compliance for Mexican PYMEs (first vertical)
• kyc-agent — KYC/AML for LATAM fintechs over CNBV, UIF, Banxico, DOF, PEP lists
• bank-reco-agent — Bank statement ↔ CFDI reconciliation (composes with cfdi-agent for higher ARPU)
• knowtis-ai — RAG + research agent over the Knowtis notes platform

Validated post-Tier-A (priority order based on internal analysis):

• jurisprudencia-agent — SCJN, Corte Constitucional CO, CSJN AR retrieval with audit-grade grounding
• cofepris-agent — Pharma regulatory intelligence over COFEPRIS, INVIMA, ANVISA, ANMAT
• nomina-agent (validate first) — LFT / IMSS / INFONAVIT copilot over existing payroll stacks

Each vertical validates cenote-core from a different angle: deterministic-correctness (cfdi, kyc, bank-reco), creative synthesis (knowtis-ai, jurisprudencia), regulatory tracking (cofepris).

License

Apache 2.0.

Author

Jovan Díaz — github.com/jovandyaz

Contributions: see CONTRIBUTING.md. Security: see SECURITY.md.