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Functional Resources

Resources are the high-level functional blocks of the Aether SDK. They consume adapters and protocols to perform complex tasks.

IngestionResource

Orchestrates the data entry pipeline. - Responsibilities: Chunking, Embedding, Vector Upsert, KG Extraction.

ingestion = IngestionResource(
    project_id="my-project",
    vector_store=qdrant_adapter,
    embedding_adapter=openai_embedder,
    extraction_resource=extractor_resource, # Optional
    graph_store=neo4j_adapter               # Optional
)
await ingestion.ingest_document(my_doc)

RetrievalResource

Manages the context search logic. - Hybrid Search: Combines vector similarity with graph traversal (when available).

retriever = RetrievalResource(
    project_id="my-project",
    vector_store=qdrant_adapter,
    embedding_adapter=openai_embedder
)
context = await retriever.retrieve_hybrid("What are the project goals?")

BrainResource

The primary interface for intelligence. It connects Retrieval to Distillation.

brain = BrainResource(
    retrieval_resource=retriever,
    distillation_resource=distiller
)
answer = await brain.generate_answer("Who is the lead engineer?")

ParsingResource

Handles file conversion. - Supports PDF, HTML, Markdown, and more via docling or parsing adapters.

Extraction & Distillation

  • ExtractionResource: Uses LLMs to find entities and relations in raw text.
  • DistillationResource: Uses LLMs to summarize retrieved context into a coherent answer.