What Is Document Retrieval?

What Is Document Retrieval?

Document retrieval in LLM systems is the RAG process of finding the most relevant source documents or chunks before a model generates an answer. It shows up as the retrieval step in a production trace, a top-k context set in an eval dataset, or a knowledge-base query in fi.kb.KnowledgeBase. FutureAGI evaluates document retrieval with ContextRelevance, ContextRecall, and ChunkAttribution so engineers can tell whether the right evidence was found, ranked, and used.

Why Document Retrieval Matters in Production LLM and Agent Systems

Bad document retrieval looks like a good answer until someone checks the source. If the retriever misses the one policy clause, engineering note, or invoice record that matters, the generator may still produce a fluent answer from adjacent evidence. The failure mode is not only hallucination; it is wrong grounding: a model faithfully summarizes context that should never have been selected.

Developers feel it when retrieval regressions appear after an embedding migration, chunking change, or metadata filter rollout. SREs see retrieval p99 latency climb because top-k was increased to hide recall misses. Compliance teams cannot prove that regulated answers came from approved documents. Product teams see thumbs-down clusters around “the bot answered the wrong policy” instead of clean 500s.

The production symptoms are measurable: low context relevance for specific cohorts, high citation mismatch, repeated query rewrites, answer refusals for questions the corpus can answer, and a gap between user success rate and model-level groundedness. In 2026-era agentic systems, the damage multiplies. An agent may retrieve a stale return policy, choose a refund tool, update a CRM record, then send a customer email. A single bad retrieval step becomes an incorrect action chain, not just a bad paragraph.

How FutureAGI Handles Document Retrieval

FutureAGI’s approach is to keep document retrieval attached to the corpus object, the trace, and the eval record. In a RAG workflow, a team creates or updates the corpus through sdk:KnowledgeBase (fi.kb.KnowledgeBase), which is the FutureAGI SDK surface for creating, updating, deleting, and managing uploaded knowledge-base files. The retrieval run then records the query, top-k setting, document ids, retrieval scores, index version, and generated answer for evaluation.

A real example: a support agent answers, “Can an EU customer export invoices for a 2026 audit?” The knowledge base contains invoice API docs, billing UI help pages, and the EU retention policy. The first retriever returns API docs at ranks 1-3 and the retention policy at rank 12. ContextRelevance flags the top chunks as adjacent but incomplete, ContextRecall fails against the reference answer, and ContextPrecision shows the relevant policy is ranked too low. ChunkAttribution then confirms the final answer cited the wrong chunk, while Groundedness explains why the answer still looked supported.

Compared with a standalone Ragas notebook, this keeps the failed retrieval linked to the KnowledgeBase version and production trace instead of a detached spreadsheet row. The engineer adds a jurisdiction metadata filter, tunes the reranker, reruns the regression dataset, and ships only when EU-audit queries clear the threshold without pushing retrieval p99 above budget.

How to Measure or Detect Document Retrieval Quality

Measure document retrieval before measuring the generated answer:

• fi.evals.ContextRelevance: returns a relevance score and reason for whether retrieved context can answer the query.
• fi.evals.ContextRecall: checks whether the reference answer’s required information appears in retrieved context.
• fi.evals.ContextPrecision: scores whether relevant chunks are ranked above irrelevant chunks.
• fi.evals.ChunkAttribution: verifies whether the final answer used or cited retrieved evidence.
• Trace and dashboard signals: knowledge_base_id, top-k, document ids, retrieval scores, index version, retrieval p99 latency, eval-fail-rate-by-cohort, and thumbs-down rate.

from fi.evals import ContextRelevance

result = ContextRelevance().evaluate(
    input="Can an EU customer export invoices for audit?",
    context="EU invoices can be exported from Billing > Exports for seven years."
)
print(result.score, result.reason)

Pair retrieval metrics with Groundedness. A grounded answer can still be wrong if the retrieved documents were wrong, stale, or incomplete.

Common Mistakes

• Optimizing only final-answer groundedness. A model can stay grounded while faithfully using irrelevant retrieved context; measure retrieval before generation.
• Increasing top-k as the first fix. Larger context windows raise token cost and can add distractors; compare recall, precision, and p99 latency together.
• Mixing document versions inside one index. Stale and current policy chunks can both rank well; version the corpus and re-evaluate after every upload.
• Treating semantic retrieval as exact lookup. Dense retrieval handles paraphrase, but IDs, SKUs, clause numbers, and account names often need filters or hybrid search.
• Skipping negative queries. Test questions the corpus should not answer, or the retriever will learn to return plausible but unsafe context for out-of-scope requests.