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from __future__ import annotations
import re
from collections import defaultdict
from dataclasses import dataclass
from typing import Sequence
from app.observability.operation import observe_operation, observe_stage
from app.observability.config import get_observability_config
from app.observability.sanitize import sanitize_attribute_value
from app.rag.evidence_store import EvidenceStore, LexicalCandidate
from app.rag.models import (
ConsumerType,
EvidenceCitation,
EvidenceRequest,
EvidenceType,
RetrievedEvidence,
RetrievalDiagnostics,
)
from app.rag.vector_indexes import PaperEvidenceIndex, VectorCandidate
from app.rag.project_memory import ProjectMemoryStore
from app.rag.reranker import EvidenceReranker
from app.rag.pipeline_version import CURRENT_PRODUCT_PIPELINE_VERSION
_RRF_K = 60
_STRUCTURAL_RELATIONS = {"caption_of", "describes", "parent", "continuation", "same_table"}
_SYNTHESIS_CONSUMERS = {
ConsumerType.WIKI,
ConsumerType.DRAFT,
ConsumerType.REPORT,
ConsumerType.PROJECT_GRAPH,
ConsumerType.PAPER_GRAPH,
}
_VISUAL_TYPES = {
EvidenceType.FIGURE,
EvidenceType.PLOT,
EvidenceType.DIAGRAM,
EvidenceType.TABLE,
EvidenceType.FORMULA,
EvidenceType.CAPTION,
}
_REFERENCE_SECTION_RE = re.compile(r"^(references|bibliography|works cited)\b", re.IGNORECASE)
@dataclass(frozen=True)
class SourceRetrievalResult:
evidence: list[RetrievedEvidence]
citations: list[EvidenceCitation]
diagnostics: RetrievalDiagnostics
class PaperEvidenceService:
"""The only product-facing source-evidence retrieval boundary."""
def __init__(
self,
store: EvidenceStore | None = None,
index: PaperEvidenceIndex | None = None,
reranker: EvidenceReranker | None = None,
) -> None:
self.store = store or EvidenceStore()
self.index = index or PaperEvidenceIndex()
self.reranker = reranker or EvidenceReranker()
def retrieve(self, request: EvidenceRequest) -> SourceRetrievalResult:
consumer = request.consumer.value
diagnostics = RetrievalDiagnostics()
with observe_operation(
"rag.retrieve",
subsystem="retrieval",
consumer=consumer,
attributes={
"query_chars": len(request.query),
"document_filter_count": len(request.document_ids),
"anchor_request_count": len(request.anchor_evidence_ids) + len(request.selection_anchors),
"token_budget": request.token_budget,
"pipeline.version": CURRENT_PRODUCT_PIPELINE_VERSION,
},
) as op:
obs_config = get_observability_config()
if obs_config.capture_content:
op.event(
"local_diagnostic_query",
{"query_preview": sanitize_attribute_value(request.query[:240])},
)
with observe_stage(op, "anchor_resolution", subsystem="retrieval", consumer=consumer):
anchor_ids = self._resolve_anchors(request)
diagnostics.anchor_count = len(anchor_ids)
lexical: list[LexicalCandidate] = []
try:
with observe_stage(op, "lexical_search", subsystem="retrieval", consumer=consumer):
lexical = self.store.lexical_search(
request.project_id,
request.query,
limit=40,
document_ids=request.document_ids or None,
)
except Exception as exc:
# SQLite/FTS is one independent retrieval channel. Preserve
# dense/anchor recovery while making the handled degradation
# visible on the parent request.
op.record_exception(
exc,
escaped=False,
stage="lexical_search",
category="lexical_search_fallback",
)
diagnostics.terminal_state = "degraded"
op.mark_terminal("degraded")
diagnostics.lexical_candidate_count = len(lexical)
dense: list[VectorCandidate] = []
try:
# This parent stage deliberately contains both the existing
# embedding and Chroma child spans; do not add a duplicate
# query-embedding operation here.
with observe_stage(op, "dense_search", subsystem="retrieval", consumer=consumer):
dense = self.index.search(
request.project_id,
request.query,
limit=40,
document_ids=request.document_ids or None,
evidence_types=request.modalities or None,
consumer=consumer,
)
except Exception as exc: # lexical/anchor retrieval remains usable
if not lexical and not anchor_ids:
raise
diagnostics.warnings.append(f"dense_search_fallback:{type(exc).__name__}")
op.record_exception(
exc,
escaped=False,
stage="dense_search",
category="dense_search_fallback",
)
diagnostics.terminal_state = "degraded"
op.mark_terminal("degraded")
diagnostics.dense_candidate_count = len(dense)
with observe_stage(op, "rank_fusion", subsystem="retrieval", consumer=consumer):
fused_ids, scores, reasons = _fuse(anchor_ids, lexical, dense)
diagnostics.duplicates_removed = max(
0,
len(anchor_ids) + len(lexical) + len(dense) - len(fused_ids),
)
units = self.store.get_units(request.project_id, evidence_ids=fused_ids)
unit_map = {unit.evidence_id: unit for unit in units}
if request.modalities:
unit_map = {
evidence_id: unit
for evidence_id, unit in unit_map.items()
if unit.element_type in request.modalities or evidence_id in anchor_ids
}
with observe_stage(op, "structural_expansion", subsystem="retrieval", consumer=consumer):
expanded_ids = self._expand_structure(request.project_id, list(unit_map), limit=16)
missing_ids = [evidence_id for evidence_id in expanded_ids if evidence_id not in unit_map]
for unit in self.store.get_units(request.project_id, evidence_ids=missing_ids):
if request.document_ids and unit.document_id not in request.document_ids:
continue
unit_map[unit.evidence_id] = unit
scores[unit.evidence_id] = max(scores.get(unit.evidence_id, 0.0), 0.015)
reasons[unit.evidence_id].append("structural_neighbor")
ranked = self._rank(request, unit_map, scores, reasons, anchor_ids, lexical, dense)
with observe_stage(
op, "reranking", subsystem="retrieval", consumer=consumer
) as rerank_stage:
decision = self.reranker.decide(request, ranked)
diagnostics.rerank_reason = decision.reason
if decision.use:
try:
ranked = self.reranker.rerank(request.query, ranked)
diagnostics.rerank_used = True
except Exception as exc: # local model failure must preserve fused retrieval
rerank_stage.mark_error(type(exc).__name__)
diagnostics.warnings.append(f"rerank_fallback:{type(exc).__name__}")
diagnostics.rerank_reason = "model_failure_fallback"
op.record_exception(
exc,
escaped=False,
stage="reranking",
category="rerank_fallback",
)
diagnostics.terminal_state = "degraded"
op.mark_terminal("degraded")
with observe_stage(op, "diversity_selection", subsystem="retrieval", consumer=consumer):
selected, truncated = _select_diverse(ranked, request)
diagnostics.context_truncated = truncated
diagnostics.fused_candidate_count = len(ranked)
with observe_stage(op, "context_packing", subsystem="retrieval", consumer=consumer):
diagnostics.documents_represented = len({item.evidence.document_id for item in selected})
diagnostics.sections_represented = len(
{(item.evidence.document_id, tuple(item.evidence.section_path)) for item in selected}
)
diagnostics.source_context_chars = sum(len(item.evidence.index_text) for item in selected)
if not selected and diagnostics.terminal_state == "success":
diagnostics.terminal_state = "success_empty"
op.mark_terminal("success_empty")
op.add_count("query_chars", len(request.query))
op.add_count("anchor_count", diagnostics.anchor_count)
op.add_count("lexical_candidate_count", diagnostics.lexical_candidate_count)
op.add_count("dense_candidate_count", diagnostics.dense_candidate_count)
op.add_count("fused_candidate_count", diagnostics.fused_candidate_count)
op.add_count("returned_evidence_count", len(selected))
op.add_count("duplicates_removed", diagnostics.duplicates_removed)
op.add_count("documents_represented", diagnostics.documents_represented)
op.add_count("sections_represented", diagnostics.sections_represented)
op.add_count("context_token_budget", request.token_budget)
op.add_count("context_truncated", int(diagnostics.context_truncated))
op.add_count("source_context_chars", diagnostics.source_context_chars)
op.add_count("empty_result", int(not selected))
op.set("rerank_used", diagnostics.rerank_used)
op.set("rerank_reason", diagnostics.rerank_reason)
for evidence_type in EvidenceType:
count = sum(1 for item in selected if item.evidence.element_type is evidence_type)
if count:
op.add_count(f"returned_type.{evidence_type.value}", count)
with observe_stage(op, "citation_materialization", subsystem="retrieval", consumer=consumer):
citations = self._citations(request.project_id, selected)
diagnostics.stage_ms = op.stage_durations_ms
return SourceRetrievalResult(evidence=selected, citations=citations, diagnostics=diagnostics)
def ground_graph_nodes(
self,
project_id: str,
requests: Sequence[tuple[str, str, Sequence[str]]],
) -> tuple[dict[str, list[str]], dict[str, list[str]]]:
"""Ground a graph in one evidence read and one memory read.
Graph construction can contain 25 nodes. It must not issue 25 FTS
queries plus selective embedding calls on the request path.
"""
with observe_operation(
"rag.ground_graph_nodes",
subsystem="retrieval",
consumer="project_graph",
attributes={"node_request_count": len(requests)},
) as op:
units = self.store.get_units(project_id)
memories = ProjectMemoryStore(self.index.db).list_project(project_id)
evidence_result: dict[str, list[str]] = {}
memory_result: dict[str, list[str]] = {}
for node_id, query, document_ids in requests:
allowed = set(document_ids)
ranked_units = sorted(
(
(_term_overlap(query, unit.index_text), unit.ordinal, unit.evidence_id)
for unit in units
if unit.index_text.strip() and (not allowed or unit.document_id in allowed)
),
key=lambda item: (-item[0], item[1]),
)
evidence_result[node_id] = [
evidence_id for score, _, evidence_id in ranked_units if score > 0
][:4]
ranked_memories = sorted(
((_term_overlap(query, item.statement), item.memory_id) for item in memories),
key=lambda item: (-item[0], item[1]),
)
memory_result[node_id] = [
memory_id for score, memory_id in ranked_memories if score > 0
][:2]
op.add_count("source_evidence_count", sum(map(len, evidence_result.values())))
op.add_count("project_memory_count", sum(map(len, memory_result.values())))
if not evidence_result:
op.mark_terminal("success_empty")
return evidence_result, memory_result
def project_outlines(self, project_id: str) -> list[dict[str, object]]:
"""Return canonical structural evidence for initial graph generation."""
with observe_operation(
"rag.project_outlines",
subsystem="retrieval",
consumer="project_graph",
) as op:
outlines: list[dict[str, object]] = []
structural_types = {
EvidenceType.TITLE,
EvidenceType.HEADING,
EvidenceType.SECTION_CARD,
EvidenceType.TABLE,
EvidenceType.FIGURE,
EvidenceType.PLOT,
EvidenceType.DIAGRAM,
}
for document in self.store.list_documents(project_id):
units = [
unit
for unit in self.store.get_units(
project_id,
document_ids=[document.document_id],
)
if unit.element_type in structural_types
][:80]
outlines.append({
"filename": document.filename,
"document_id": document.document_id,
"structure": "\n\n".join(
f"[{unit.evidence_id} | page {unit.page_start} | {unit.element_type.value}]\n{unit.index_text}"
for unit in units
)[:16000],
"evidence_ids": [unit.evidence_id for unit in units],
})
op.add_count("documents_represented", len(outlines))
op.add_count(
"source_evidence_count",
sum(len(item["evidence_ids"]) for item in outlines),
)
if not outlines:
op.mark_terminal("success_empty")
return outlines
def project_citation_material(self, project_id: str) -> list[dict[str, object]]:
"""Return front matter and bibliography from canonical evidence only."""
with observe_operation(
"rag.project_citation_material",
subsystem="retrieval",
consumer="paper_graph",
) as op:
payloads: list[dict[str, object]] = []
for document in self.store.list_documents(project_id):
units = self.store.get_units(
project_id,
document_ids=[document.document_id],
)
front_units = [
unit for unit in units
if unit.page_start == 1
and unit.element_type in {
EvidenceType.TITLE,
EvidenceType.HEADING,
EvidenceType.PARAGRAPH,
}
][:20]
reference_units = [
unit for unit in units
if (
unit.element_type is EvidenceType.REFERENCE
or self._is_reference_section_unit(unit.section_path)
)
and unit.index_text.strip()
]
reference_units.sort(key=lambda unit: unit.ordinal)
references = [
unit.raw_text.strip() or unit.index_text
for unit in reference_units
]
# ``index_text`` includes an internal retrieval envelope
# (Paper/Section/Element labels). Citation identity must use
# the source text, never that implementation detail.
front_text = "\n".join(
unit.raw_text.strip() or unit.index_text
for unit in front_units
)
body = "\n".join(
part for part in (
document.title,
document.abstract,
front_text,
"References\n" + "\n".join(references) if references else "",
) if part
)
payloads.append({
"file_id": document.document_id,
"filename": document.filename,
"text": body,
"document_metadata": {
"Title": document.title,
"Author": "; ".join(document.authors),
},
"front_matter_text": front_text,
"reference_evidence_ids": [
unit.evidence_id for unit in reference_units
],
})
op.add_count("documents_represented", len(payloads))
op.add_count(
"source_evidence_count",
sum(len(item["reference_evidence_ids"]) for item in payloads),
)
if not payloads:
op.mark_terminal("success_empty")
return payloads
@staticmethod
def _is_reference_section_unit(section_path: Sequence[str]) -> bool:
"""Support evidence written before bibliography headings were normalized."""
if not section_path:
return False
heading = re.sub(r"[*_`]+", "", section_path[-1] or "").strip()
return bool(_REFERENCE_SECTION_RE.match(heading))
def _resolve_anchors(self, request: EvidenceRequest) -> list[str]:
ids = list(request.anchor_evidence_ids)
for anchor in request.selection_anchors:
if anchor.evidence_id:
ids.append(anchor.evidence_id)
if anchor.region_id:
ids.extend(
self.store.resolve_region(
request.project_id,
anchor.document_id,
anchor.region_id,
)
)
ids.extend(
self.store.resolve_selection(
request.project_id,
anchor.document_id,
anchor.page_number,
anchor.boxes,
anchor.text,
)
)
existing = self.store.get_units(request.project_id, evidence_ids=list(dict.fromkeys(ids)))
allowed_documents = set(request.document_ids)
return [
unit.evidence_id
for unit in existing
if not allowed_documents or unit.document_id in allowed_documents
]
def _expand_structure(self, project_id: str, evidence_ids: Sequence[str], limit: int) -> list[str]:
relations = self.store.get_relations(evidence_ids)
expanded: list[str] = []
for relation in relations:
if str(relation.relation_type) not in _STRUCTURAL_RELATIONS:
continue
if relation.source_evidence_id in evidence_ids:
expanded.append(relation.target_evidence_id)
if relation.target_evidence_id in evidence_ids:
expanded.append(relation.source_evidence_id)
return list(dict.fromkeys(expanded))[:limit]
def _rank(
self,
request: EvidenceRequest,
units: dict,
fused_scores: dict[str, float],
reasons: dict[str, list[str]],
anchor_ids: Sequence[str],
lexical: Sequence[LexicalCandidate],
dense: Sequence[VectorCandidate],
) -> list[RetrievedEvidence]:
lexical_scores = {candidate.evidence_id: candidate.score for candidate in lexical}
dense_scores = {candidate.item_id: candidate.score for candidate in dense}
anchors = set(anchor_ids)
ranked: list[RetrievedEvidence] = []
for evidence_id, unit in units.items():
structural = 0.0
if evidence_id in anchors:
structural += 1.0
if unit.element_type in {EvidenceType.SECTION_CARD, EvidenceType.LOCAL_WINDOW}:
structural += 0.05 if request.consumer in _SYNTHESIS_CONSUMERS else -0.01
if unit.element_type in _VISUAL_TYPES and request.consumer in {
ConsumerType.VISUALIZATION,
ConsumerType.CHAT,
ConsumerType.WIKI,
}:
structural += 0.04
if unit.quality_flags:
structural -= 0.01 * min(3, len(unit.quality_flags))
ranked.append(
RetrievedEvidence(
evidence=unit,
fused_score=fused_scores.get(evidence_id, 0.0) + structural,
dense_score=dense_scores.get(evidence_id),
lexical_score=lexical_scores.get(evidence_id),
structural_score=structural,
retrieval_reasons=list(dict.fromkeys(reasons.get(evidence_id, []))),
)
)
ranked.sort(key=lambda item: (-item.fused_score, item.evidence.ordinal))
return ranked
def _citations(
self,
project_id: str,
evidence: Sequence[RetrievedEvidence],
) -> list[EvidenceCitation]:
documents = {doc.document_id: doc for doc in self.store.list_documents(project_id)}
return [
EvidenceCitation(
evidence_id=item.evidence.evidence_id,
document_id=item.evidence.document_id,
filename=documents.get(item.evidence.document_id).filename
if item.evidence.document_id in documents
else "",
page_start=item.evidence.page_start,
page_end=item.evidence.page_end,
bbox_norm=item.evidence.bbox_norm,
section_path=item.evidence.section_path,
)
for item in evidence
]
def _fuse(
anchor_ids: Sequence[str],
lexical: Sequence[LexicalCandidate],
dense: Sequence[VectorCandidate],
) -> tuple[list[str], dict[str, float], dict[str, list[str]]]:
scores: dict[str, float] = defaultdict(float)
reasons: dict[str, list[str]] = defaultdict(list)
order: list[str] = []
for anchor_rank, evidence_id in enumerate(anchor_ids):
scores[evidence_id] += max(1.25, 2.0 - anchor_rank * 0.08)
reasons[evidence_id].append("explicit_anchor")
order.append(evidence_id)
for rank, candidate in enumerate(lexical, 1):
scores[candidate.evidence_id] += 1.0 / (_RRF_K + rank)
reasons[candidate.evidence_id].append("lexical")
order.append(candidate.evidence_id)
for rank, candidate in enumerate(dense, 1):
scores[candidate.item_id] += 1.0 / (_RRF_K + rank)
reasons[candidate.item_id].append("dense")
order.append(candidate.item_id)
return list(dict.fromkeys(order)), scores, reasons
def _term_overlap(query: str, text: str) -> int:
terms = {
term
for term in re.findall(r"[a-zA-Z][a-zA-Z0-9_-]{2,}", query.casefold())
if term not in {"about", "from", "paper", "that", "this", "with"}
}
lowered = text.casefold()
return sum(1 for term in terms if term in lowered)
def _select_diverse(
ranked: Sequence[RetrievedEvidence],
request: EvidenceRequest,
) -> tuple[list[RetrievedEvidence], bool]:
char_budget = max(1024, request.token_budget * 4)
per_document = 8 if request.consumer in _SYNTHESIS_CONSUMERS else 5
per_section = 3
selected: list[RetrievedEvidence] = []
document_counts: dict[str, int] = defaultdict(int)
section_counts: dict[tuple[str, tuple[str, ...]], int] = defaultdict(int)
used_chars = 0
truncated = False
for item in ranked:
unit = item.evidence
section_key = (unit.document_id, tuple(unit.section_path))
anchored = "explicit_anchor" in item.retrieval_reasons
if not anchored and (
document_counts[unit.document_id] >= per_document or section_counts[section_key] >= per_section
):
continue
size = max(1, len(unit.index_text))
if selected and used_chars + size > char_budget:
truncated = True
continue
selected.append(item)
document_counts[unit.document_id] += 1
section_counts[section_key] += 1
used_chars += size
return selected, truncated
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