tesserakit-rag 0.4.0

RAG job pack for Tessera: compile a corpus + queries into a validated retrieval eval dataset.

tesserakit-rag

Compile a document corpus plus a set of queries into a validated retrieval eval dataset.

tessera-rag reads a directory holding a corpus/ of documents and a queries file, builds a canonical RagCase dataset (each query with its gold retrieval target documents and optional expected answer), verifies every document reference, and emits a dataset plus reports.

Scope (v0.1)

This pack builds and validates the dataset. It does not run retrieval (no embeddings, no vector store, no scoring). Like the api pack (no HTTP execution) and evals (no LLM calls), execution is a runtime concern deferred to a later version. v0.1 is the offline "is this retrieval eval set well-formed and internally consistent" pass.

Input shape

my_rag_eval/
  corpus/
    refunds.md
    billing/disputes.md
  queries.jsonl        (or queries.yaml)

Document ids are the corpus-relative path without suffix: refunds, billing/disputes.

Each query (one JSON object per line in queries.jsonl, or a YAML list):

{"id": "q1", "query": "Can I get a refund after 45 days?", "expected_answer": "No, the window is 30 days.", "relevant_docs": ["refunds"], "tags": ["billing"]}

relevant_docs is the gold set the retriever should surface. expected_answer is optional; queries without one are flagged for human review.

Compile a RAG eval pack

tessera rag compile --input examples/rag/ --output ./out/rag_pack

Artifacts written:

dataset.jsonl            canonical RagCase rows (query, expected, relevant doc ids)
corpus_index.jsonl       RagDocument rows (id, title, counts, sha256)
validation_report.md     reference + hygiene findings
coverage_report.md       answer/target coverage, orphan docs, avg targets per query
retrieval_targets.md     per-query gold document set (with titles)

Validation rules

• parse_error — a queries line/file could not be parsed
• empty_corpus — no documents found under corpus/
• empty_document, duplicate_doc_id
• missing_query_text, duplicate_query
• dangling_doc_reference — a query references a document not in the corpus
• query_without_relevant_docs — no retrieval target to score against
• query_without_expected_answer — needs human review
• orphan_document — a corpus document no query references