citenexus 0.2.1

Evidence-first, multilingual, S3-native RAG for domains where a wrong answer is worse than no answer.

CiteNexus

Multilingual RAG that answers only when the evidence is strong.

Evidence-first, multilingual, S3-native RAG for domains where a wrong answer is worse than no answer (legal, medical, finance/compliance, enterprise search). CiteNexus answers only from retrieved evidence — every claim is grounded in a bbox-cited source passage, and it refuses or states uncertainty when evidence is weak, missing, or conflicting. The guarantee is "no ungrounded claim," not "zero hallucination."

The library bundles no models — embedding, LLM, reranker, and vision are injected endpoints. CiteNexus owns orchestration, storage, retrieval, fusion, grounding, and evaluation.

CiteNexus supports pluggable vector databases. Storage is two protocols — VectorStore (dense) and TextSearch (lexical) — and each backend is a named (vector, text) pair:

Backend	Vector	Text	When
Lance (recommended)	LanceVectorStore	LanceTextSearch (BM25-lite)	Zero infra, S3-native: point at a bucket and go
Postgres	PostgresVectorStore (pgvector)	PostgresTextSearch (native tsvector)	You already run Postgres — pip install 'citenexus[postgres]', set vector_store.backend: "postgres"
Yours	implement VectorStore	implement TextSearch	Qdrant, Weaviate, Elasticsearch, Tantivy, …

The seams are independent: mix LanceDB vectors with an Elasticsearch text_search=, or let one Postgres serve both.

from citenexus import CiteNexus, S3

rag = CiteNexus(
    S3(bucket="my-bucket"),
    embedder=my_embedding_endpoint,
    generator=my_llm_endpoint,
)
rag.ingest("policy.pdf")                         # any supported input type
answer = rag.ask("Can the employee disclose this information?")
print(answer)                                    # grounded answer; .sources are bbox-cited

See the workspace root README for the full polyglot (python/ golang/ js/ rust/) layout.