Retrieval-Augmented Generation (RAG)

Sources and context for
traceable answers.

RAG is an internal Apeirum capability: search retrieves approved context before generation to support more reviewable answers.

Ingestion & Parsing

We extract text from PDFs, DOCX, and MD with structure, table, and metadata recognition.

We split content into semantic blocks to preserve context and improve retrieval.

We use high-density embeddings to find the most relevant excerpts for your question.

Selected excerpts enter the prompt to support a clearer and more reviewable answer.

The flow is designed to retrieve the right excerpt, reduce noise, and keep the answer ready for review.

Query Optimization (Multi-query retrieval)

Result re-ranking by semantic relevance

Dynamic context window control

Direct source citation (traceability)

RAG_PIPELINE_TRACE.log

[PROCESS] Ingesting Document: Contrato_Alpha.pdf

[STEP 1] OCR_Engine: Success (32 pages extracted)

[STEP 2] Semantic_Chunking: 142 segments created

[STEP 3] Vector_Sync: Upserting to private_namespace_712

[QUERY] "Qual o prazo de rescisão?"

[SEARCH] Top-k results fetched from VectorDB

[RE-RANK] Higher priority given to Clause 12.1

[INJECT] Prompt augmented with 3 verified sources

[RESPONSE] Generated based on injected sources.

Upload a complex document and see which excerpts Apeirum retrieves to answer.