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 compose the answer context to make review easier and reduce generic output.

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.

See how the API organizes input, retrieval, reviewable answers, and structured output.