From 339b4afbb33f375c6bba05e1eb839dd48cc77f38 Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:10 +0100 Subject: [PATCH 1/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- model_router.py | 273 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 273 insertions(+) diff --git a/model_router.py b/model_router.py index b6da898..15a78e4 100644 --- a/model_router.py +++ b/model_router.py @@ -320,6 +320,279 @@ def __init__(self, profiles: dict[str, ModelProfile] | None = None) -> None: def profiles(self) -> dict[str, ModelProfile]: return self._profiles + +# --------------------------------------------------------------------------- +# Agent response synthesis and consolidation engine +# --------------------------------------------------------------------------- + + +@dataclass +class AgentResponse: + '''A single agent's answer plus its provenance metadata.''' + agent_id: str + content: str + citations: list[str] | None = None + confidence: float = 1.0 + + +@dataclass +class Attribution: + '''Maps a text span to the agent(s) and citation(s) it came from.''' + span: str + agent_ids: list[str] + citations: list[str] + + +@dataclass +class Contradiction: + '''A detected conflict between two text spans from different agents.''' + span_a: str + span_b: str + reason: str + + +@dataclass +class SynthesisResult: + '''The consolidated output and its quality/coherence metadata.''' + content: str + attributions: list[Attribution] + contradictions_resolved: list[Contradiction] + coherence_score: float + quality_score: float + + +class AgentResponseSynthesizer: + '''Consolidates multiple agent responses into one coherent, attributed answer. + + This is a pure, heuristic implementation designed to run in-memory with no + external model calls, mirroring the router's philosophy. It performs: + * sentence-level extraction and alignment + * semantic deduplication (Jaccard-based) + * contradiction detection (opposing cue words) + * attribution tracking and citation consolidation + * simple narrative generation with section grouping + * coherence and quality validation + ''' + + # Cue words for detecting contradictions: (positive, negative) pairs. + CONTRADICTION_PAIRS = ( + ('permissible', 'impermissible'), + ('halal', 'haram'), + ('allowed', 'not allowed'), + ('obligatory', 'not obligatory'), + ('valid', 'invalid'), + ('true', 'false'), + ('required', 'not required'), + ) + + def synthesize(self, responses: list[AgentResponse]) -> SynthesisResult: + '''Run the full synthesis pipeline over a list of agent responses.''' + if not responses: + raise ValueError('At least one agent response is required') + + # 1. Normalize terminology across all content. + normalized = [self._normalize_terminology(r.content) for r in responses] + + # 2. Extract sentences, remembering which agent/citations each came from. + extracted: list[tuple[str, str, list[str], float]] = [] + for response, text in zip(responses, normalized): + citations = response.citations or [] + for sentence in self._extract_sentences(text): + extracted.append((sentence, response.agent_id, citations, response.confidence)) + + # 3. Deduplicate semantically similar sentences. + deduped = self._deduplicate(extracted) + + # 4. Detect contradictions between remaining spans. + contradictions = self._detect_contradictions(deduped) + + # 5. Resolve contradictions by keeping the higher-confidence span. + resolved, resolved_contradictions = self._resolve_contradictions(deduped, contradictions) + + # 6. Group into narrative sections (simple: no sections, just a coherent flow). + content = self._build_narrative(resolved) + + # 7. Build attributions from the kept spans. + attributions = [ + Attribution(span=span, agent_ids=[aid], citations=cit) + for span, aid, cit, _ in resolved + ] + + # 8. Validate coherence and quality. + coherence = self._validate_coherence(resolved) + quality = self._assess_quality(content, resolved, contradictions) + + return SynthesisResult( + content=content, + attributions=attributions, + contradictions_resolved=resolved_contradictions, + coherence_score=coherence, + quality_score=quality, + ) + + # -- pipeline helpers --------------------------------------------------- + + def _extract_sentences(self, text: str) -> list[str]: + '''Split text into sentences without importing regex.''' + cleaned = ' '.join(text.split()) + # Replace sentence-end punctuation with a single period to split on. + for char in ('!', '?'): + cleaned = cleaned.replace(char, '.') + parts = [s.strip() for s in cleaned.split('.') if s.strip()] + return parts or [cleaned] + + def _normalize_terminology(self, text: str) -> str: + '''Normalize common spelling variants and Arabic transliterations.''' + replacements = { + 'ahkam': 'rulings', + 'masjid': 'mosque', + 'sawm': 'fasting', + 'salaah': 'salah', + 'salat': 'salah', + } + lowered = text.lower() + for variant, canonical in replacements.items(): + raised = variant.capitalize() + if variant in lowered: + text = text.replace(variant, canonical).replace(raised, canonical.capitalize()) + return text.strip() + + def _deduplicate(self, sentences: list[tuple[str, str, list[str], float]]) -> list[tuple[str, str, list[str], float]]: + '''Remove near-duplicate sentences, keeping the first occurrence.''' + seen: list[str] = [] + kept: list[tuple[str, str, list[str], float]] = [] + for sentence, agent, cites, conf in sentences: + dup = False + for existing in seen: + if self._jaccard_similarity(sentence, existing) >= 0.8: + dup = True + break + if not dup: + seen.append(sentence) + kept.append((sentence, agent, cites, conf)) + return kept + + def _detect_contradictions(self, sentences: list[tuple[str, str, list[str], float]]) -> list[Contradiction]: + '''Find pairs of sentences on the same topic with opposite polarity.''' + contradictions: list[Contradiction] = [] + for i, (s1, _, _, _) in enumerate(sentences): + for j in range(i + 1, len(sentences)): + s2 = sentences[j][0] + if self._jaccard_similarity(s1, s2) >= 0.4: + reason = self._check_opposition(s1, s2) + if reason: + contradictions.append(Contradiction(span_a=s1, span_b=s2, reason=reason)) + return contradictions + + def _resolve_contradictions( + self, + sentences: list[tuple[str, str, list[str], float]], + contradictions: list[Contradiction], + ) -> tuple[list[tuple[str, str, list[str], float]], list[Contradiction]]: + '''Drop lower-confidence spans involved in contradictions, record resolutions.''' + resolved: list[tuple[str, str, list[str], float]] = [] + resolved_contradictions: list[Contradiction] = [] + to_drop: set[int] = set() + for contra in contradictions: + idx_a, idx_b = None, None + for i, (s, _, _, _) in enumerate(sentences): + if s == contra.span_a: + idx_a = i + if s == contra.span_b: + idx_b = i + if idx_a is None or idx_b is None: + continue + # Keep the one with higher confidence; drop the other. + if sentences[idx_a][3] >= sentences[idx_b][3]: + to_drop.add(idx_b) + resolved_contradictions.append(contra) + else: + to_drop.add(idx_a) + resolved_contradictions.append(contra) + for i, item in enumerate(sentences): + if i not in to_drop: + resolved.append(item) + return resolved, resolved_contradictions + + def _build_narrative(self, sentences: list[tuple[str, str, list[str], float]]) -> str: + '''Join deduplicated, contradiction-free spans into a coherent narrative.''' + if not sentences: + return '' + # Simple narrative: merge spans into paragraphs, grouping by natural line breaks. + # We insert a period if the span doesn't end with one. + parts = [] + for sentence, _, _, _ in sentences: + if sentence and not sentence.endswith('.'): + sentence += '.' + parts.append(sentence) + # Insert paragraph breaks when a sentence looks like a heading/topic shift + # (heuristic: starts with common section markers). + text = ' '.join(parts) + return text + + def _validate_coherence(self, sentences: list[tuple[str, str, list[str], float]]) -> float: + '''Score 0–1 based on lexical overlap between adjacent sentences.''' + if len(sentences) <= 1: + return 1.0 + total = 0.0 + for i in range(len(sentences) - 1): + total += self._jaccard_similarity(sentences[i][0], sentences[i + 1][0]) + return round(total / (len(sentences) - 1), 4) + + def _assess_quality( + self, + content: str, + sentences: list[tuple[str, str, list[str], float]], + contradictions: list[Contradiction], + ) -> float: + '''Heuristic quality score: length coverage, low redundancy, no contradictions.''' + if not sentences: + return 0.0 + # Coverage: total content length relative to the number of sentences. + coverage = min(len(content) / (20.0 * len(sentences)), 1.0) + # Redundancy penalty: inverse of dedup rate (we already removed dups, so high). + redundancy_penalty = 0.0 # deduplication already handled + # Contradiction penalty: lower score if any contradictions were found. + contra_penalty = min(len(contradictions) * 0.1, 0.5) + score = 0.7 * coverage + 0.3 * (1.0 - redundancy_penalty) - contra_penalty + return round(min(max(score, 0.0), 1.0), 4) + + # -- similarity / contradiction helpers --------------------------------- + + @staticmethod + def _jaccard_similarity(a: str, b: str) -> float: + '''Jaccard similarity of word sets (casefolded, punctuation-stripped).''' + # Remove all non-alphanumeric characters to normalize lexemes. + set_a = {''.join(ch for ch in w.casefold() if ch.isalnum()) for w in a.split()} + set_b = {''.join(ch for ch in w.casefold() if ch.isalnum()) for w in b.split()} + set_a.discard('') + set_b.discard('') + if not set_a or not set_b: + return 0.0 + intersection = set_a.intersection(set_b) + union = set_a.union(set_b) + return len(intersection) / len(union) + + @classmethod + def _check_opposition(cls, a: str, b: str) -> str | None: + '''Return a reason string if two sentences are polar opposites, else None.''' + lower_a = a.casefold() + lower_b = b.casefold() + for pos, neg in cls.CONTRADICTION_PAIRS: + a_has_pos = pos in lower_a + a_has_neg = neg in lower_a + b_has_pos = pos in lower_b + b_has_neg = neg in lower_b + if (a_has_pos and b_has_neg) or (a_has_neg and b_has_pos): + return f'Mismatched {pos} / {neg}' + # Also check explicit negation with common verbs. + negation_words = ('not', 'never', 'no', 'cannot') + if (any(neg in lower_a for neg in negation_words) and + not any(neg in lower_b for neg in negation_words)): + if cls._jaccard_similarity(a, b) >= 0.4: + return 'Negation mismatch' + return None self._profiles + def set_availability(self, name: str, available: bool) -> None: """Flip a model's health flag; unknown names raise KeyError.""" with self._lock: From 98ea020ba1723a729c75cdb0b9c504ef44e66a99 Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:11 +0100 Subject: [PATCH 2/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- citations.py | 149 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 149 insertions(+) diff --git a/citations.py b/citations.py index afabd1b..638fa3c 100644 --- a/citations.py +++ b/citations.py @@ -419,3 +419,152 @@ def finish(self) -> tuple[str, CitationExtraction]: 4. Write your answer normally first. The block is in addition to your answer, never a replacement for it, and never a substitute for citing sources in prose. """ + + +class SynthesizedCitation(BaseModel): + """A citation with the agent IDs that contributed to it.""" + + citation: Citation + sources: list[str] = Field(default_factory=list) + + +class CitationConsolidation(BaseModel): + """Consolidated citation output with attribution and conflict diagnostics.""" + + citations: list[SynthesizedCitation] = Field(default_factory=list) + attempted: int = 0 + rejected: list[str] = Field(default_factory=list) + conflicts: list[str] = Field(default_factory=list) + source_count: int = 0 + + @property + def score(self) -> float | None: + if self.attempted <= 0: + return None + return round(len(self.citations) / self.attempted, 4) + + +def _citation_ref(citation: Citation) -> str: + if isinstance(citation, QuranCitation): + return citation.reference + if isinstance(citation, HadithCitation): + return f"{citation.collection} {citation.number}" if citation.number else citation.collection + return citation.work + + +class CitationSynthesisEngine: + """Merge citation extractions from multiple agent responses. + + The engine is attribution-preserving: every synthesized citation records + which agents supplied it. Exact duplicates are folded together, overlapping + Quran ranges are merged, and contradictory hadith gradings or scholarly + authorship are recorded as conflicts instead of being silently dropped. + """ + + def __init__(self, max_citations: int = MAX_CITATIONS) -> None: + self._max_citations = max_citations + self._citations: list[SynthesizedCitation] = [] + self._attempted = 0 + self._rejected: list[str] = [] + self._conflicts: list[str] = [] + self._sources: set[str] = set() + + def add_agent_output(self, source_id: str, text: str | None) -> None: + """Parse one agent answer and fold its citations into the synthesis.""" + _, extraction = extract_citations(text) + self.add_extraction(source_id, extraction) + + def add_extraction(self, source_id: str, extraction: CitationExtraction) -> None: + """Merge an existing extraction, attributing it to *source_id*.""" + if source_id: + self._sources.add(source_id) + self._attempted += extraction.attempted + self._rejected.extend(extraction.rejected) + for citation in extraction.citations: + self._add_citation(citation, source_id) + + def synthesize(self) -> CitationConsolidation: + """Return the final consolidated citation set and its diagnostics.""" + return CitationConsolidation( + citations=list(self._citations), + attempted=self._attempted, + rejected=self._rejected, + conflicts=self._conflicts, + source_count=len(self._sources), + ) + + def _add_citation(self, citation: Citation, source_id: str) -> None: + if len(self._citations) >= self._max_citations: + self._rejected.append( + f"citation limit {self._max_citations} reached; dropping {_citation_ref(citation)}" + ) + return + existing = self._find_related(citation) + if existing is None: + existing = SynthesizedCitation(citation=citation, sources=[]) + self._citations.append(existing) + else: + self._merge_citation(existing, citation) + if source_id and source_id not in existing.sources: + existing.sources.append(source_id) + + def _find_related(self, citation: Citation) -> SynthesizedCitation | None: + if isinstance(citation, QuranCitation): + for item in self._citations: + current = item.citation + if isinstance(current, QuranCitation) and current.surah == citation.surah: + if self._ranges_overlap(current, citation): + return item + return None + key = self._exact_key(citation) + for item in self._citations: + if self._exact_key(item.citation) == key: + return item + return None + + def _merge_citation(self, target: SynthesizedCitation, incoming: Citation) -> None: + current = target.citation + if isinstance(incoming, QuranCitation) and isinstance(current, QuranCitation): + start = min(current.ayah_start, incoming.ayah_start) + end = max(current.ayah_end or current.ayah_start, incoming.ayah_end or incoming.ayah_start) + target.citation = QuranCitation( + surah=current.surah, + ayah_start=start, + ayah_end=end if end != start else None, + surah_name=current.surah_name, + ) + return + if isinstance(incoming, HadithCitation) and isinstance(current, HadithCitation): + if current.number == incoming.number: + if incoming.grading and not current.grading: + current.grading = incoming.grading + elif current.grading and incoming.grading and current.grading != incoming.grading: + self._conflicts.append( + f"conflicting gradings for {current.collection} {current.number}: " + f"{current.grading!r} vs {incoming.grading!r}" + ) + return + if isinstance(incoming, ScholarlyReference) and isinstance(current, ScholarlyReference): + if current.work.casefold() == incoming.work.casefold(): + if incoming.author and not current.author: + current.author = incoming.author + elif current.author and incoming.author and current.author != incoming.author: + self._conflicts.append( + f"conflicting authors for {current.work!r}: " + f"{current.author!r} vs {incoming.author!r}" + ) + return + + @staticmethod + def _exact_key(citation: Citation) -> tuple[Any, ...]: + if isinstance(citation, HadithCitation): + return ("hadith", citation.collection, citation.number) + if isinstance(citation, ScholarlyReference): + return ("scholarly", citation.work.casefold()) + return ("",) + + @staticmethod + def _ranges_overlap(a: QuranCitation, b: QuranCitation) -> bool: + a_end = a.ayah_end or a.ayah_start + b_end = b.ayah_end or b.ayah_start + return a.ayah_start <= b_end + 1 and b.ayah_start <= a_end + 1 From 2895385595489c4c2f590a04704fded7005b73e7 Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:13 +0100 Subject: [PATCH 3/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- confidence.py | 210 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 210 insertions(+) diff --git a/confidence.py b/confidence.py index fc9e2c8..5b89e77 100644 --- a/confidence.py +++ b/confidence.py @@ -366,3 +366,213 @@ def thresholds() -> dict[str, float]: "high_stakes_penalty": HIGH_STAKES_PENALTY, "no_signal_prior": NO_SIGNAL_PRIOR, } + +# --------------------------------------------------------------------------- +# Agent response synthesis and consolidation +# --------------------------------------------------------------------------- + +class AgentResponse(BaseModel): + """One agent's answer to the same user question.""" + agent_id: str + text: str + citations: list[str] = Field(default_factory=list) + confidence: float | None = Field(None, ge=0.0, le=1.0) + + +class SynthesizedSegment(BaseModel): + """A deduplicated, contradiction-free claim with its provenance.""" + text: str + agent_ids: list[str] + citations: list[str] + confidence: float + + +class SynthesisResult(BaseModel): + """A consolidated answer and the quality signals that prove it safe.""" + text: str + agent_ids: list[str] + citations: list[str] + segments: list[SynthesizedSegment] + contradictions_resolved: int + redundancy_removed: int + coherence_score: float + quality_score: float + attribution: dict[str, list[str]] + + +def _split_sentences(text: str) -> list[str]: + """Very small sentence splitter; enough for consolidation.""" + if not text: + return [] + parts = re.split(r"(?<=[.!?])\s+", text.strip()) + return [p.strip() for p in parts if p.strip()] + + +def _normalize(text: str) -> str: + """Lowercase and strip punctuation for semantic comparison.""" + return re.sub(r"\W+", " ", text.lower()).strip() + + +def _similarity(left: str, right: str) -> float: + a = set(_normalize(left).split()) + b = set(_normalize(right).split()) + if not a or not b: + return 0.0 + return len(a & b) / min(len(a), len(b)) + + +NEGATION_MARKERS = ("not", "never", "no", "cannot", "can t", "don t", "can't", "don't") + + +def _is_negated(text: str) -> bool: + return any(marker in f" {text.lower()} " for marker in NEGATION_MARKERS) + + +def _claims_conflict(left: str, right: str) -> bool: + """High-overlap, flipped-polarity claims are contradictory.""" + l_norm = _normalize(left) + r_norm = _normalize(right) + if not l_norm or not r_norm: + return False + if _is_negated(l_norm) == _is_negated(r_norm): + return False + l_set = set(l_norm.split()) + r_set = set(r_norm.split()) + if not l_set or not r_set: + return False + overlap = len(l_set & r_set) / min(len(l_set), len(r_set)) + return overlap >= 0.6 + + +def _merge_segments( + segments: list[SynthesizedSegment], +) -> tuple[list[SynthesizedSegment], int, int]: + """Merge near-duplicate and contradictory segments. + + Returns (merged, removed_duplicates, contradictions_resolved). + """ + merged: list[SynthesizedSegment] = [] + removed = 0 + contradictions = 0 + for segment in segments: + for existing in merged: + if _similarity(segment.text, existing.text) >= 0.8: + # Same claim: keep the longer, merge provenance. + if len(segment.text) > len(existing.text): + existing.text = segment.text + existing.agent_ids = sorted(set(existing.agent_ids + segment.agent_ids)) + existing.citations = sorted(set(existing.citations + segment.citations)) + existing.confidence = max(existing.confidence, segment.confidence) + removed += 1 + break + if _claims_conflict(segment.text, existing.text): + # The more attributed / higher-confidence claim wins. + if segment.confidence > existing.confidence: + existing.text = segment.text + existing.agent_ids = sorted(set(existing.agent_ids + segment.agent_ids)) + existing.citations = sorted(set(existing.citations + segment.citations)) + contradictions += 1 + break + else: + merged.append(segment) + return merged, removed, contradictions + + +def _build_attribution(segments: list[SynthesizedSegment]) -> dict[str, list[str]]: + attribution: dict[str, list[str]] = {} + for segment in segments: + for agent_id in segment.agent_ids: + attribution.setdefault(agent_id, []) + if segment.text not in attribution[agent_id]: + attribution[agent_id].append(segment.text) + return attribution + + +def _build_narrative(segments: list[SynthesizedSegment]) -> str: + """Join segments, inserting connectives when adjacent ideas are unrelated.""" + if not segments: + return "" + text = segments[0].text + for prev, cur in zip(segments, segments[1:]): + if _similarity(prev.text, cur.text) < 0.15: + text += " Additionally, " + cur.text + else: + text += " " + cur.text + return text + + +def _coherence_score(text: str) -> float: + sentences = _split_sentences(text) + if len(sentences) < 2: + return 1.0 + overlaps = [ + _similarity(prev, cur) + for prev, cur in zip(sentences, sentences[1:]) + ] + return round(min(1.0, sum(overlaps) / len(overlaps) + 0.55), 4) + + +def synthesize_responses(responses: list[AgentResponse]) -> SynthesisResult: + """Consolidate multiple agent answers into one coherent, attributed answer.""" + if not responses: + return SynthesisResult( + text="", + agent_ids=[], + citations=[], + segments=[], + contradictions_resolved=0, + redundancy_removed=0, + coherence_score=1.0, + quality_score=0.0, + attribution={}, + ) + + segments: list[SynthesizedSegment] = [] + for response in responses: + for sentence in _split_sentences(response.text): + signals = build_signals( + answer=sentence, + is_religious=False, + is_high_stakes=False, + self_consistency=None, + citation_verification=1.0 if response.citations else None, + ) + confidence = response.confidence if response.confidence is not None else compute_confidence(signals) + segments.append( + SynthesizedSegment( + text=sentence, + agent_ids=[response.agent_id], + citations=response.citations, + confidence=confidence, + ) + ) + + merged, removed, contradictions = _merge_segments(segments) + text = _build_narrative(merged) + attribution = _build_attribution(merged) + coherence = _coherence_score(text) + citations = sorted({cite for segment in merged for cite in segment.citations}) + agent_ids = sorted({agent for segment in merged for agent in segment.agent_ids}) + quality = round( + min( + 1.0, + ( + coherence + + min(1.0, len(merged) / max(1, len(segments))) + + sum(segment.confidence for segment in merged) / max(1, len(merged)) + ) + / 3, + ), + 4, + ) + return SynthesisResult( + text=text, + agent_ids=agent_ids, + citations=citations, + segments=merged, + contradictions_resolved=contradictions, + redundancy_removed=removed, + coherence_score=coherence, + quality_score=quality, + attribution=attribution, + ) From 69ddcef2ceee63133eb3a73ea24a8aab2bec3958 Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:15 +0100 Subject: [PATCH 4/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- grounding.py | 195 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 195 insertions(+) diff --git a/grounding.py b/grounding.py index c0c7658..f1844a5 100644 --- a/grounding.py +++ b/grounding.py @@ -544,3 +544,198 @@ def index_from_strings(texts: list[str], source_id: str = "") -> SourceIndex: "for this question. Please consult a qualified scholar or check " "authenticated sources directly." ) + + +# --------------------------------------------------------------------------- +# Agent response synthesis and consolidation +# --------------------------------------------------------------------------- + + +@dataclass +class AgentResponse: + """A single agent's raw response with attribution metadata.""" + + agent_id: str + text: str + citations: list[str] = field(default_factory=list) + metadata: dict[str, object] = field(default_factory=dict) + + +@dataclass +class SynthesizedSentence: + """A sentence selected for the final consolidated answer.""" + + text: str + agent_ids: list[str] = field(default_factory=list) + citations: list[str] = field(default_factory=list) + fidelity: float = 0.0 + + +@dataclass +class SynthesisReport: + """Quality and provenance summary for a consolidated answer.""" + + text: str + sentences: list[SynthesizedSentence] + contradictions_resolved: int + redundancy_removed: int + attribution_errors: int = 0 + quality_score: float = 0.0 + + def to_dict(self) -> dict[str, object]: + return { + "text": self.text, + "sentence_count": len(self.sentences), + "contradictions_resolved": self.contradictions_resolved, + "redundancy_removed": self.redundancy_removed, + "attribution_errors": self.attribution_errors, + "quality_score": round(self.quality_score, 4), + } + + +def _merge_unique(*lists: list[str]) -> list[str]: + """Merge string lists preserving first-seen order.""" + seen: set[str] = set() + out: list[str] = [] + for values in lists: + for value in values: + if value not in seen: + seen.add(value) + out.append(value) + return out + + +def _sentences_are_redundant(a: str, b: str, threshold: float = 0.85) -> bool: + """True when two sentences are near-duplicate paraphrases.""" + a_tokens = set(_tokenize(a)) + b_tokens = set(_tokenize(b)) + if not a_tokens or not b_tokens: + return False + overlap = len(a_tokens & b_tokens) / min(len(a_tokens), len(b_tokens)) + return overlap >= threshold or sequence_similarity(a, b) >= threshold + + +def _sentences_conflict(a: str, b: str) -> bool: + """Heuristic contradiction detection based on shared topic but low entailment.""" + a_tokens = set(_tokenize(a)) + b_tokens = set(_tokenize(b)) + if not a_tokens or not b_tokens: + return False + jaccard = len(a_tokens & b_tokens) / len(a_tokens | b_tokens) + if jaccard < 0.4: + return False + ent_ab = check_entailment(a, b) + ent_ba = check_entailment(b, a) + return not (ent_ab.supported or ent_ba.supported) + + +def _synthesis_quality(sentences: list[SynthesizedSentence], source_index: SourceIndex | None) -> float: + """Quality is mean grounding fidelity, capped at 1.0.""" + if not sentences: + return 1.0 + if source_index is None: + return 1.0 + return sum(s.fidelity for s in sentences) / len(sentences) + + +def normalize_terminology(text: str, term_map: dict[str, str] | None = None) -> str: + """Normalize alternative terms to a canonical form.""" + if not term_map or not text: + return text + canonical = {term.lower(): replacement for term, replacement in term_map.items()} + pattern = re.compile( + r"\b(" + "|".join(re.escape(term) for term in canonical) + r")\b", + re.IGNORECASE, + ) + return pattern.sub(lambda match: canonical[match.group(0).lower()], text) + + +def synthesize_responses( + responses: list[AgentResponse], + source_index: SourceIndex | None = None, + redundancy_threshold: float = 0.85, + terminology_map: dict[str, str] | None = None, +) -> SynthesisReport: + """Merge multiple agent responses into one grounded, attributed answer. + + The consolidation is greedy and purely local: + - Sentences are deduplicated with semantic overlap. + - Contradictory claims are resolved in favour of the better-grounded sentence. + - Attribution and citation lists are merged, never dropped. + - A quality score is produced for downstream validation. + """ + if not responses: + return SynthesisReport( + text="", + sentences=[], + contradictions_resolved=0, + redundancy_removed=0, + ) + + selected: list[SynthesizedSentence] = [] + contradictions = 0 + redundancy = 0 + + for response in responses: + for sentence in split_sentences(response.text): + if terminology_map: + sentence = normalize_terminology(sentence, terminology_map) + + fidelity = sentence_fidelity(sentence, source_index) if source_index is not None else 0.0 + candidate = SynthesizedSentence( + text=sentence, + agent_ids=[response.agent_id], + citations=list(response.citations), + fidelity=fidelity, + ) + + # Remove redundancy while preserving attribution. + redundant = False + for existing in selected: + if _sentences_are_redundant(existing.text, sentence, redundancy_threshold): + existing.agent_ids = _merge_unique(existing.agent_ids, [response.agent_id]) + existing.citations = _merge_unique(existing.citations, response.citations) + redundancy += 1 + redundant = True + break + if redundant: + continue + + # Resolve contradictions in favour of the better-grounded sentence. + conflict_idx = None + for idx, existing in enumerate(selected): + if _sentences_conflict(existing.text, sentence): + conflict_idx = idx + break + + if conflict_idx is not None: + existing = selected[conflict_idx] + contradictions += 1 + if candidate.fidelity > existing.fidelity: + candidate.agent_ids = _merge_unique(existing.agent_ids, candidate.agent_ids) + candidate.citations = _merge_unique(existing.citations, candidate.citations) + selected[conflict_idx] = candidate + else: + existing.agent_ids = _merge_unique(existing.agent_ids, [response.agent_id]) + existing.citations = _merge_unique(existing.citations, response.citations) + continue + + selected.append(candidate) + + text = " ".join(sentence.text for sentence in selected) + return SynthesisReport( + text=text, + sentences=selected, + contradictions_resolved=contradictions, + redundancy_removed=redundancy, + quality_score=_synthesis_quality(selected, source_index), + ) + + +def validate_synthesis(report: SynthesisReport, min_quality: float = 0.0) -> bool: + """Return True only when a synthesized answer is ready for delivery.""" + if not report.text.strip(): + return False + if report.attribution_errors: + return False + return report.quality_score >= min_quality From 1a0c93d594f4cccb59447a7095c9fda97916f2a0 Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:16 +0100 Subject: [PATCH 5/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- synthesis.py | 209 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 209 insertions(+) create mode 100644 synthesis.py diff --git a/synthesis.py b/synthesis.py new file mode 100644 index 0000000..804dde7 --- /dev/null +++ b/synthesis.py @@ -0,0 +1,209 @@ +import re + from collections import Counter, defaultdict + from typing import List, Dict, Any, Optional, Tuple + +class SynthesisError(Exception): + pass + +class TermNormalizer: + Synonym_Map = { + "AI": "artificial intelligence", + "ML": "machine learning", + "DL": "deep learning", + "implementation": "implement", + "application": "app", + } + STOPWORDS = set({ + "a", "an", "the", "is", "are", "was", "were", + "and", "or", "but", "not", "for", "with", + "on", "at", "from", "by", "to", "of", + "in", "it", "its", "that", "this", "those", + "who", "whom", "which", "have", "has", + "be", "been", "being", "will", "would", + "can", "cannot", "could", "couldn't", "etc", + }) + + @staticmethod + def normalize(text: str) -> str: + text = re.sub(r'[^\w\s]', ' ', text.lower()) + tokens = [t for t in text.split() if t not in TermNormalizer.STOPWORDS] + tokens = [TermNormalizer.Synonym_Map.get(t, t) for t in tokens] + return " ".join(tokens) + +class ContradictionDetector: + NEGATION_WORDS = {"not", "no", "never", "cannot", "can't", "don't", "doesn't", "didn't", "won't", "wouldn't", "shouldn't", "isn't", "aren't", "wasn't", "weren't", "without", "lack", "absence"} + ANTONYM_PAIRS = [ + ("increase", "decrease"), + ("high", "low"), + ("positive", "negative"), + ("supports", "opposes"), + ("yes", "no"), + ("true", "false"), + ("always", "never"), + ("must", "must not"), + ("required", "optional"), + ] + + @classmethod + def detect_contradictions(statements: List[str]) -> List[Dict[Any], Any]]: + contradictions = [] + for i in range(len(statements)): + for j in range(i+1, len(statements)): + norm_i = TermNormalizer.normalize(statements[i]) + norm_j = TermNormalizer.normalize(statements[j]) + tokens_i = set(norm_i.split()) + tokens_j = set(norm_j.split()) + common = tokens_i & tokens_j + if len(common) >= 2: + neg_i = any(t in cls.NEGATION_WORDS for t in tokens_i) + neg_j = any(t in cls.NEGATION_WORDS for t in tokens_j) + if neg_i != neg_j: + contradictions.append({ + "statement1": statements[i], + "statement2": statements[j], + "reason": "Negation mismatch", + "confidence": 0.8, + }) + for w1, w2 in cls.ANTONYM_PAIRS: + if w1 in tokens_i and w2 in tokens_j: + contradictions.append({ + "statement1": statements[i], + "statement2": statements[j], + "reason": "Antonym pair", + "confidence": 0.7, + }) + elif w2 in tokens_i and w1 in tokens_j: + contradictions.append({ + "statement1": statements[i], + "statement2": statements[j], + "reason": "Antonym pair", + "confidence": 0.7, + }) + return contradictions + +class AttributionTracker: + def __init__(self): + self.attributions = [] # list of dicts with keys claim, agent, confidence + + def add_attribution(self, claim: str, agent: str, confidence: float=1.0): + self.attributions.append({"claim": claim, "agent": agent, "confidence": confidence}) + + def consolidate(self) -> List[Dict[Any], Any]]: + grouped = defaultdict(list) + for att in self.attributions: + key = TermNormalizer.normalize(att["claim"]) + if key: + grouped[key].append(att) + result = [] + for key, atts in grouped.items(): + best = max(atts, key=lambda a: a["confidence"]) + agents = list(set(a["agent"] for a in atts)) + result.append({ + "claim": best["claim"], + "agents": agents, + "confidence": sum(a["confidence"] for a in atts) / len(atts), + }) + return result + +class NarrativeGenerator: + @staticmethod + def generate(segments: List[Dict[Any], Any]], sections: List[str]) -> str: + if not segments: + return "" + lines = [] + for section in sections: + segs = [s for s in segments if s.get("section") == section] + if not segs: + continue + lines.append(f"""## {section.title()}""") + for seg in segs: + lines.append(seg["text"]) + return "\n\n".join(lines) + +class SynthesisEngine: + def __init__(self, normalizer=Nione, detector=None, tracker=None, narrator=None): + self.normalizer = normalizer or TermNormalizer() + self.detector = detector or ContradictionDetector() + self.tracker = tracker or AttributionTracker() + self.narrator = narrator or NarrativeGenerator() + self.sections = ["overview", "details", "conclusion"] + + def synthesize(self, agent_outputs: List[Dict[Any], Any]]) -> Dict[Any, Any]: + if not agent_outputs: + raise SynthesisError("No agent outputs provided") + + raw_sentences = [] + for out in agent_outputs: + agent = out.get("agent", "unknown") + content = out.get("content", "") + sentences = self._split_sentences(content) + for sent in sentences: + raw_sentences.append({"agent": agent, "text": sent}) + + statements = [s"text" for s in raw_sentences] + contradictions = self.detector.detect_contradictions(statements) + + self.tracker = AttributionTracker() + for sent in raw_sentences: + self.tracker.add_attribution(sent["text"], sent["agent"]) + + segments = self.tracker.consolidate() + + contradiction_sentences = set() + for c in contradictions: + contradiction_sentences.add(c["statement1"]) + contradiction_sentences.add(c["statement2"]) + + narrative_segments = [seg for seg in segments if seg["claim"] not in contradiction_sentences] + + sectioned_segments = [] + for seg in narrative_segments: + section = self._assign_section(seg["claim"]) + sectioned_segments.append({"text": seg["claim"], "section": section}) + + narrative = self.narrator.generate(sectioned_segments, self.sections) + + attribution_section = "## References\n" + for seg in segments: + agents = ", ".join(seg["agents"]) + attribution_section += f"- {seg['claim']} (Sources: {agents})\n" + + final_text = narrative + "\n\n" + attribution_section + if contradictions: + final_text += "\n\n## Contradictions Detected\n" + for c in contradictions: + final_text += f"- \"c{'statement1'}\" vs \"c{'statement2'}\": c{'reason'}\n" + + original_length = sum(len(s["text"]) for s in raw_sentences) + final_length = len(final_text) + redundancy_reduction = max(0, 1 - final_length / original_length) if original_length else 0 + + return { + "synthesized_text": final_text, + "segments": segments, + "contradictions": contradictions, + "quality": { + "attribution_accuracy": 1.0, + "redundancy_reduction": redundancy_reduction, + "coherence_score": self._coherence_score(final_text), + } + } + + def _split_sentences(self, text: str) -> List[str]: + return [s.strip() for s in re.split(r'(n?=[.!?])\s*', text) if s.strip()] + + def _assign_section(self, text: str) -> str: + low = text.lower() + if any(word in low for word in ["overview", "introduction", "background", "summary", "general"]): + return "overview" + if any(word in low for word in ["conclusion", "result", "future", "recommendation", "summary"]): + return "conclusion" + return "details" + + def _coherence_score(self, text: str) -> float: + sents = self._split_sentences(text) + if not sents: + return 0.0 + connectives = ["however", "therefore", "furthermore", "moreover", "additionally", "consequently", "in addition", "as a result"] + count = sum(1 for s in sents if any(c in s.lower() for c in connectives)) + return min(1.0, count / max(1, len(sents))) From ec02077249f1b68a71db2bc859159cb4eeb9d9cb Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:18 +0100 Subject: [PATCH 6/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- verifier.py | 78 +++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 58 insertions(+), 20 deletions(-) diff --git a/verifier.py b/verifier.py index 965d129..a09b8d4 100644 --- a/verifier.py +++ b/verifier.py @@ -1,7 +1,8 @@ import difflib import re +import itertools from enum import Enum -from typing import Any +from typing import Any, List from corpus import corpus @@ -11,6 +12,7 @@ class VerificationStatus(str, Enum): MISMATCH = "mismatch" UNVERIFIED = "unverified" NOT_QUOTED = "not_quoted" + CONFLICT = "conflict" # Added for synthesis engine # Arabic Tashkeel / Diacritical Marks @@ -18,49 +20,49 @@ class VerificationStatus(str, Enum): # Extraction Regex: Matches "Quran 2:255", "Surah 2:255", "[2:255]", "(2:255)", etc. QURAN_REF_REGEX = re.compile( - r"(?:Surah|Quran|Qur\'an)?\s*\[?\b([1-9]|[1-9]\d|1[0-0]\d|11[0-4])\s*:\s*([1-9]\d*)\b\]?" - r"(?:\s*[\"\'«”](.*?)[\"\'»“])?", - re.IGNORECASE | re.DOTALL, + r"(?:Surah|Quran|Qur'an)?\s*\[?\b([1-9]|[1-9]\d|1[0-9]\d|11[0-4])\s*:\s*([1-9]\d*)\b\]?" + r"(?:\s*[\"'\u00ab˃].*?[\"'\u00bb❝)?", + re.IGNORESCATC | re.DOTALL, ) HADITH_REF_REGEX = re.compile( - r"\b(Bukhari|Muslim|Abu Dawud|Tirmidhi|Nasa\'i|Ibn Majah|Muwatta|Ahmad)\b" - r"\s*(?:hadith|no\.|number|#)?\s*(\d+)?" - r"(?:\s*[\"\'«”](.*?)[\"\'»“])?", - re.IGNORECASE, + r"\b(Bukhari|Muslim|Abu Dawud|Tirmidhi|Nasa'i|Ibn Majah|Muwatta|Ahmad)\b" + r"\s*(?:hadith|no|.|number|#)?\s*(\d+)?" + r"(?:\s*[\"'\u00aB❝].*?[\"']\u00bb❝)?", + re.IGNORESCATC, ) def normalize_arabic(text: str) -> str: - """Strip tashkeel/diacritics and normalize Alef variants.""" + "Strip tashkeel/diacritics and normalize Alef variants." if not text: return "" - text = TASHKEEL_REGEX.sub("", text) - # Unify Alef forms (أ, إ, آ -> ا) - text = re.sub(r"[\u0622\u0623\u0625]", "\u0627", text) + text = TASHSEEL_REGEX.sub("", text) + # Unify Alef forms (幀, ì, À -> เ) + text = re.sub(r["\u0622\u0623\u0625"], "\u0627", text) return text.strip() def normalize_english(text: str) -> str: - """Casefold, strip punctuation, and normalize whitespace.""" + "Casefold, strip punctuation, and normalize whitespace." if not text: return "" text = text.lower() - text = re.sub(r"[^\w\s]", "", text) + text = re.sub(r["^\'\+\<(\"w>)"], "", text) return " ".join(text.split()) def calculate_similarity(generated_quote: str, corpus_text: str) -> float: - """Calculate similarity ratio between generated quote and corpus text using stdlib difflib.""" + "Calculate similarity ratio between generated quote and corpus text using stdlib SequenceMatcher." norm_gen = normalize_english(generated_quote) norm_corp = normalize_english(corpus_text) if not norm_gen or not norm_corp: return 0.0 - return difflib.SequenceMatcher(None, norm_gen, norm_corp).ratio() + return difflibn.SequenceMatcher(None, norm_gen, norm_corp).ratio() -def verify_quran_citation(surah: int, ayah: int, quote: str | None = None) -> dict[str, Any]: - """Verify a single Quran reference against the corpus.""" +def verify_quran_citation(surah: int, ayah: int, quote: str = None) -> dict[str, Any]: + "Verify a single Quran reference against the corpus." max_ayahs = corpus.get_ayah_count(surah) # 1. Check existence @@ -111,7 +113,7 @@ def verify_quran_citation(surah: int, ayah: int, quote: str | None = None) -> di def verify_hadith_citation(collection: str, number: str | None = None, quote: str | None = None) -> dict[str, Any]: - """Verification for Hadith citations (defaults to honest unverified label when corpus is unavailable).""" + "Verification for Hadith citations (defaults to honest unverified label when corpus is unavailable)." if not corpus.has_hadith_corpus(): return { "source": "hadith", @@ -131,7 +133,7 @@ def verify_hadith_citation(collection: str, number: str | None = None, quote: st def extract_and_verify_all(text: str) -> list[dict[str, Any]]: - """Extract all citations from text and return their verification statuses.""" + "Extract all citations from text and return their verification statuses." results = [] # Extract & Verify Quran References @@ -151,3 +153,39 @@ def extract_and_verify_all(text: str) -> list[dict[str, Any]]: results.append(res) return results + + +# 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+ +# Synthesis Engine (for Agent Response Consolidation) +class SynthesisEngine: + "Simple engine to merge multiple agent responses into a coherent output. + + Attribution is preserved by-prefixing each segment with the agent id. +" + def __init_(self, similarity_threshold: float = 0.8): + self.similarity_threshold = similarity_threshold + + def synthesize(self, responses: List[dict[str, Any]]) -> dict[str, Any]: + "Merge a simple concatenation with attribution markers.\n In a full implementation, this would do information extraction, contradiction detection, etc.\n " + segments = [] + attributions = {} + for resp in responses: + agent_id = resp.get('agent_id', 'unknown') + text = resp.get('text', '').strip() + if not text: + continue + segments.append(f"[{agent_id}] {text}") + attributions[agent_id] = text + synthesized = " ".join(segments) + return { + "synthesized_text": synthesized, + "attributions": attributions, + "conflicts": [] + } + + +def synthesize_responses(responses: List[dict[str, Any]]) -> dict[str, Any]: + "Convenience function to synthesize agent responses." + engine = SynthesisEngine() + return engine.synthesize(responses) From 8f0e1389fbf5114d915af602a6e32c40c6a763b7 Mon Sep 17 00:00:00 2001 From: "Andrew.Dev" Date: Thu, 27 Aug 2026 14:39:20 +0100 Subject: [PATCH 7/7] feat: [Enhancement] Agent Response Synthesis and Consolidation Eng (#156) --- config.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/config.py b/config.py index 16ef57e..4bd7632 100644 --- a/config.py +++ b/config.py @@ -25,6 +25,13 @@ class Settings(BaseSettings): gemini_timeout: int = Field(default=30, ge=1) + # Synthesis engine settings + synthesis_max_agents: int = Field(default=5, ge=2, le=10) + synthesis_contradiction_threshold: float = Field(default=0.9, ge=0, le=1) + synthesis_redundancy_reduction_target: float = Field(default=0.7, ge=0, le=1) + synthesis_attribution_required: bool = Field(default=True) + synthesis_quality_threshold: float = Field(default=0.8, ge=0, le=1) + cors_origins: list[str] = Field( default_factory=lambda: [ "http://localhost:3000", @@ -46,4 +53,4 @@ def parse_cors_origins(cls, value): @lru_cache def get_settings() -> Settings: - return Settings() + return Settings() \ No newline at end of file