update for 1. 优化 2.中英切换

backend/app/api/routes/compliance.py

@@ -85,10 +85,9 @@ async def analyze_stream(
     Events: stage | source | finding | done | error
     """
     from app.application.compliance.pipeline import (
-        check_clause_compliance,
         extract_text_from_doc_id,
         extract_text_from_file,
-        retrieve_for_clause,
+        run_clauses_parallel,
         split_into_clauses,
         synthesize_conclusion,
     )
@@ -136,22 +135,28 @@ async def analyze_stream(
             await asyncio.sleep(0)
             clauses: list[str] = await asyncio.to_thread(split_into_clauses, para_text, client)
 
-            # ── Stage 3: retrieve + gap check per clause ──────────────────
+            # ── Stage 3: retrieve + gap check (parallel across all clauses) ────────────
             findings: list[dict] = []
 
-            for i, clause in enumerate(clauses):
-                yield _sse({
-                    "type": "stage",
-                    "stage": "analyzing",
-                    "label": f"Analyzing clause {i + 1}/{len(clauses)}…",
-                })
-                await asyncio.sleep(0)
+            yield _sse({
+                "type": "stage",
+                "stage": "analyzing",
+                "label": f"Analyzing {len(clauses)} clauses in parallel…",
+            })
+            await asyncio.sleep(0)
 
-                chunks = await asyncio.to_thread(
-                    retrieve_for_clause, clause, retrieval_service, 5, domains or None
-                )
+            clause_results = await run_clauses_parallel(
+                clauses, retrieval_service, client,
+                top_k=5,
+                domains=domains or None,
+            )
 
-                # Emit source events
+            for res in clause_results:
+                i = res["index"]
+                chunks = res["chunks"]
+                finding = res["finding"]
+
+                # Emit source events for this clause
                 for chunk in chunks[:3]:
                     yield _sse({
                         "type": "source",
@@ -161,12 +166,11 @@ async def analyze_stream(
                         "status": "retrieved",
                         "full_content": (getattr(chunk, "text", "") or "")[:300],
                     })
-                await asyncio.sleep(0)
 
-                finding = await asyncio.to_thread(check_clause_compliance, clause, chunks, client)
                 if finding:
                     findings.append(finding)
                     yield _sse({"type": "finding", **finding})
+
                 await asyncio.sleep(0)
 
             # ── Stage 4: synthesize conclusion ────────────────────────────
@@ -178,6 +182,45 @@ async def analyze_stream(
             )
             yield _sse({"type": "done", **conclusion_data})
 
+            # Auto-save analysis to database
+            try:
+                from app.shared.bootstrap import get_compliance_repository
+                from app.domain.compliance.ports import AnalysisRecord, FindingRecord
+                from datetime import datetime
+
+                repo = get_compliance_repository()
+                finding_records = [
+                    FindingRecord(
+                        id="",
+                        analysis_id="",
+                        seq=i,
+                        title=f.get("title", ""),
+                        description=f.get("desc", ""),
+                        status=f.get("status", "ok"),
+                        clause_ref=f.get("clause_ref"),
+                    )
+                    for i, f in enumerate(findings)
+                ]
+                record = AnalysisRecord(
+                    id="",
+                    created_at=datetime.utcnow(),
+                    created_by=current_user.username if hasattr(current_user, "username") else None,
+                    doc_name=file_name or (title or "Pasted text"),
+                    standard_name=title or "",
+                    risk_score=conclusion_data.get("risk_score", 0),
+                    conclusion=conclusion_data.get("conclusion", ""),
+                    actions=conclusion_data.get("actions", []),
+                    para_text=conclusion_data.get("para_text", ""),
+                    highlight_terms=conclusion_data.get("highlight_terms", []),
+                    findings=finding_records,
+                )
+                analysis_id = await asyncio.to_thread(repo.save_analysis, record)
+                yield _sse({"type": "saved", "analysis_id": analysis_id})
+            except NotImplementedError:
+                pass  # No postgres backend configured — skip saving
+            except Exception as exc:
+                logger.warning("Failed to auto-save compliance analysis: {}", exc)
+
         except Exception as exc:
             logger.exception("analyze-stream pipeline error")
             yield _sse({"type": "error", "text": str(exc)})
@@ -225,3 +268,226 @@ async def compliance_chat(segment_id: int, request: ComplianceChatRequest):
         media_type="text/event-stream",
         headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"},
     )
+
+
+@router.get("/history")
+async def list_history(
+    limit: int = 20,
+    offset: int = 0,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return paginated list of saved compliance analyses (newest first)."""
+    from app.shared.bootstrap import get_compliance_repository
+    try:
+        repo = get_compliance_repository()
+        records = await asyncio.to_thread(repo.list_analyses, limit, offset)
+        return [
+            {
+                "id": r.id,
+                "created_at": r.created_at.isoformat(),
+                "created_by": r.created_by,
+                "doc_name": r.doc_name,
+                "standard_name": r.standard_name,
+                "risk_score": r.risk_score,
+                "finding_count": len(r.findings),
+            }
+            for r in records
+        ]
+    except NotImplementedError:
+        return []
+
+
+@router.get("/history/{analysis_id}")
+async def get_history_item(
+    analysis_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return full analysis record including findings."""
+    from app.shared.bootstrap import get_compliance_repository
+    from fastapi import HTTPException
+    repo = get_compliance_repository()
+    record = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not record:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+    return {
+        "id": record.id,
+        "created_at": record.created_at.isoformat(),
+        "created_by": record.created_by,
+        "doc_name": record.doc_name,
+        "standard_name": record.standard_name,
+        "risk_score": record.risk_score,
+        "conclusion": record.conclusion,
+        "actions": record.actions,
+        "para_text": record.para_text,
+        "highlight_terms": record.highlight_terms,
+        "findings": [
+            {
+                "id": f.id,
+                "seq": f.seq,
+                "title": f.title,
+                "description": f.description,
+                "status": f.status,
+                "clause_ref": f.clause_ref,
+            }
+            for f in record.findings
+        ],
+    }
+
+
+@router.delete("/history/{analysis_id}", status_code=204)
+async def delete_history_item(
+    analysis_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Delete a saved analysis (cascade removes findings and chat messages)."""
+    from app.shared.bootstrap import get_compliance_repository
+    repo = get_compliance_repository()
+    await asyncio.to_thread(repo.delete_analysis, analysis_id)
+
+
+@router.get("/history/{analysis_id}/download")
+async def download_history_docx(
+    analysis_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return a DOCX compliance report for the given analysis."""
+    from app.shared.bootstrap import get_compliance_repository
+    from app.infrastructure.compliance.docx_export import generate_docx
+    from fastapi import HTTPException
+    from fastapi.responses import Response
+
+    repo = get_compliance_repository()
+    record = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not record:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+
+    docx_bytes = await asyncio.to_thread(generate_docx, record)
+    safe_name = (record.doc_name or "report").replace(" ", "_")[:50]
+    filename = f"compliance_{safe_name}_{record.created_at.strftime('%Y%m%d')}.docx"
+    return Response(
+        content=docx_bytes,
+        media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
+@router.get("/analyses/{analysis_id}/findings/{finding_id}/chat")
+async def get_finding_chat_history(
+    analysis_id: str,
+    finding_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Return persisted chat messages for a finding thread, oldest first."""
+    from app.shared.bootstrap import get_compliance_repository
+    try:
+        repo = get_compliance_repository()
+        messages = await asyncio.to_thread(repo.get_messages, finding_id)
+        return messages
+    except NotImplementedError:
+        return []
+
+
+@router.post("/analyses/{analysis_id}/findings/{finding_id}/suggestions")
+async def get_finding_suggestions(
+    analysis_id: str,
+    finding_id: str,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Generate 3 LLM-powered follow-up question suggestions for a finding."""
+    from app.application.compliance.pipeline import generate_suggestions
+    from app.shared.bootstrap import get_compliance_repository
+    from app.services.llm.llm_factory import get_llm_client
+    from fastapi import HTTPException
+
+    repo = get_compliance_repository()
+    analysis = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not analysis:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+
+    finding = next((f for f in analysis.findings if f.id == finding_id), None)
+    if not finding:
+        raise HTTPException(status_code=404, detail="Finding not found")
+
+    client = get_llm_client(provider=settings.llm_provider, model=settings.llm_model)
+    questions = await asyncio.to_thread(generate_suggestions, finding, analysis, client)
+    return {"questions": questions}
+
+
+@router.post("/analyses/{analysis_id}/findings/{finding_id}/chat")
+async def finding_chat(
+    analysis_id: str,
+    finding_id: str,
+    request: ComplianceChatRequest,
+    current_user: UserClaims = Depends(get_current_user),
+):
+    """Stream a grounded chat response for a specific finding.
+
+    Loads the finding and analysis from DB to build grounded context.
+    Persists both user message and assistant response to finding_chat_messages.
+    """
+    from app.application.compliance.pipeline import build_finding_context
+    from app.shared.bootstrap import get_compliance_repository
+    from fastapi import HTTPException
+
+    repo = get_compliance_repository()
+    analysis = await asyncio.to_thread(repo.get_analysis, analysis_id)
+    if not analysis:
+        raise HTTPException(status_code=404, detail="Analysis not found")
+    finding = next((f for f in analysis.findings if f.id == finding_id), None)
+    if not finding:
+        raise HTTPException(status_code=404, detail="Finding not found")
+
+    # Persist user message
+    await asyncio.to_thread(
+        repo.save_message, analysis_id, finding_id, "user", request.query
+    )
+
+    # Build message history (last 10 messages = 5 turns)
+    history = await asyncio.to_thread(repo.get_messages, finding_id)
+    history_messages = [
+        {"role": m["role"], "content": m["content"]}
+        for m in history[-10:]
+    ]
+
+    # Build grounded system context
+    system_context = build_finding_context(finding, analysis)
+    full_query = f"[Compliance Finding Context]\n{system_context}\n\nUser question: {request.query}"
+
+    assistant_buffer: list[str] = []
+
+    async def generate() -> AsyncGenerator[str, None]:
+        try:
+            _, event_stream = get_agent_conversation_service().stream_chat(
+                query=full_query,
+                top_k=5,
+                prompt_template="compliance_qa",
+            )
+            for event in event_stream:
+                event_type = event.get("event", "")
+                if event_type == "content":
+                    text = event.get("data", "")
+                    if text:
+                        assistant_buffer.append(text)
+                        yield _sse({"type": "chunk", "text": text})
+                elif event_type == "done":
+                    yield _sse({"type": "done"})
+                await asyncio.sleep(0)
+        except Exception as exc:
+            logger.exception("finding_chat stream error")
+            yield _sse({"type": "error", "text": str(exc)})
+        finally:
+            # Persist assistant response after stream completes
+            full_response = "".join(assistant_buffer)
+            if full_response:
+                try:
+                    await asyncio.to_thread(
+                        repo.save_message, analysis_id, finding_id, "assistant", full_response
+                    )
+                except Exception as exc:
+                    logger.warning("Failed to persist assistant message: {}", exc)
+
+    return StreamingResponse(
+        generate(),
+        media_type="text/event-stream",
+        headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"},
+    )

backend/app/application/compliance/pipeline.py

@@ -5,6 +5,7 @@ All functions are synchronous — call them via asyncio.to_thread() in async SSE
 
 from __future__ import annotations
 
+import asyncio
 import json
 import os
 import re
@@ -12,10 +13,20 @@ import tempfile
 from typing import TYPE_CHECKING
 
 from loguru import logger
+from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential
+
+# Shared retry policy for LLM calls: 3 attempts, exponential back-off 1–4 s.
+_llm_retry = retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=1, max=4),
+    retry=retry_if_exception_type((ValueError, TimeoutError, ConnectionError)),
+    reraise=True,
+)
 
 if TYPE_CHECKING:
     from app.application.knowledge import KnowledgeRetrievalService
     from app.domain.retrieval import RetrievedChunk
+    from app.domain.compliance.ports import AnalysisRecord, FindingRecord
     from app.services.llm.base_client import BaseLLMClient
 
 
@@ -109,17 +120,67 @@ def retrieve_for_clause(
     return retrieval_service.retrieve(query=clause, top_k=top_k, filters=domains)
 
 
+def process_single_clause(
+    clause: str,
+    index: int,
+    retrieval_service: "KnowledgeRetrievalService",
+    client: "BaseLLMClient",
+    top_k: int = 5,
+    domains: str | None = None,
+) -> dict:
+    """Process one clause: retrieve relevant regulations then check compliance.
+
+    Returns a dict with keys: index, chunks, finding (may be None on LLM failure).
+    Designed to run inside asyncio.to_thread() for parallel execution.
+    """
+    chunks = retrieve_for_clause(clause, retrieval_service, top_k, domains)
+    finding = check_clause_compliance(clause, chunks, client)
+    return {"index": index, "chunks": chunks, "finding": finding}
+
+
+async def run_clauses_parallel(
+    clauses: list[str],
+    retrieval_service: "KnowledgeRetrievalService",
+    client: "BaseLLMClient",
+    top_k: int = 5,
+    domains: str | None = None,
+) -> list[dict]:
+    """Run all clauses through retrieve+gap-check in parallel.
+
+    Results are returned in the original clause order even though processing
+    is concurrent. Exceptions in individual clauses are caught and returned as
+    dicts with finding=None so the stream continues for remaining clauses.
+
+    Both retrieval_service and client must be thread-safe — they are shared
+    across all asyncio.to_thread() calls without locking.
+    """
+    tasks = [
+        asyncio.to_thread(
+            process_single_clause,
+            clause, i, retrieval_service, client, top_k, domains,
+        )
+        for i, clause in enumerate(clauses)
+    ]
+    raw = await asyncio.gather(*tasks, return_exceptions=True)
+    results = []
+    for i, r in enumerate(raw):
+        if isinstance(r, Exception):
+            logger.warning("Clause {} processing failed: {}", i, r)
+            results.append({"index": i, "chunks": [], "finding": None})
+        else:
+            results.append(r)
+    return results
+
+
 def check_clause_compliance(
     clause: str,
     chunks: list["RetrievedChunk"],
     client: "BaseLLMClient",
 ) -> dict | None:
-    if not chunks:
-        return None
     reg_context = "\n".join(
         f"[{i+1}] {c.doc_title} {c.section_title or ''}: {c.text[:300]}"
         for i, c in enumerate(chunks[:5])
-    )
+    ) if chunks else "(no regulatory context retrieved)"
     prompt = (
         "You are a compliance expert. Judge whether the following business clause "
         "complies with the retrieved regulations.\n\n"
@@ -135,9 +196,19 @@ def check_clause_compliance(
         "status: ok=compliant, warn=gap exists, risk=critical/missing\n"
         "Return ONLY the JSON object."
     )
-    response = client.chat([{"role": "user", "content": prompt}], max_tokens=500)
-    if not response.is_success:
+
+    def _do_check():
+        resp = client.chat([{"role": "user", "content": prompt}], max_tokens=500)
+        if not resp.is_success:
+            raise ValueError("LLM returned non-success for gap check")
+        return resp
+
+    try:
+        response = _llm_retry(_do_check)()
+    except Exception as exc:
+        logger.warning("check_clause_compliance LLM call failed after retries: {}", exc)
         return None
+
     try:
         result = _extract_json(response.content)
         if isinstance(result, dict) and "status" in result:
@@ -182,12 +253,11 @@ def synthesize_conclusion(
         '    {"label": "Priority", "value": "High/Medium/Low", "risk": true}\n'
         '  ],\n'
         '  "risk_score": 0-100 (integer, higher=riskier),\n'
-        '  "highlight_terms": ["Key terms to highlight, max 10 terms"],\n'
+        '  "highlight_terms": ["term1", "term2"],  // up to 10 key technical/legal terms actually present in the text\n'
         '  "para_text": "Original text or summary (max 600 chars)"\n'
         "}\n"
         "Return ONLY the JSON object."
     )
-    response = client.chat([{"role": "user", "content": prompt}], max_tokens=1200)
     fallback = {
         "conclusion": "Compliance analysis complete. Review findings and create remediation plan.",
         "actions": [
@@ -198,8 +268,19 @@ def synthesize_conclusion(
         "highlight_terms": [],
         "para_text": para_text[:800],
     }
-    if not response.is_success:
+
+    def _do_synthesize():
+        resp = client.chat([{"role": "user", "content": prompt}], max_tokens=1200)
+        if not resp.is_success:
+            raise ValueError("LLM returned non-success for synthesis")
+        return resp
+
+    try:
+        response = _llm_retry(_do_synthesize)()
+    except Exception as exc:
+        logger.warning("synthesize_conclusion LLM call failed after retries: {}", exc)
         return fallback
+
     try:
         result = _extract_json(response.content)
         if isinstance(result, dict):
@@ -212,4 +293,78 @@ def synthesize_conclusion(
             }
     except (ValueError, TypeError) as exc:
         logger.warning("Conclusion synthesis JSON parse failed: {}", exc)
-    return fallback
+    return fallback
+
+
+_SUGGESTION_FOCUS = {
+    "risk": "Focus on remediation steps, required certifications, and timeline to resolve.",
+    "warn": "Focus on identifying the specific compliance gap and how to close it.",
+    "ok": "Focus on maintaining compliance evidence and monitoring future changes.",
+}
+
+_SUGGESTION_FALLBACK = {
+    "risk": [
+        "What specific certifications or documents are required to remediate this finding?",
+        "What is the typical remediation timeline for this type of non-compliance?",
+        "Which regulation clause defines the exact requirement?",
+    ],
+    "warn": [
+        "What is the exact gap between the current state and the requirement?",
+        "What evidence would demonstrate partial compliance?",
+        "Which regulation clause applies to this warning?",
+    ],
+    "ok": [
+        "What documentation should be maintained to evidence this compliance?",
+        "How should this area be monitored as regulations evolve?",
+        "Are there related clauses that may affect this compliant area?",
+    ],
+}
+
+
+def build_finding_context(finding: "FindingRecord", analysis: "AnalysisRecord") -> str:
+    """Build a grounded system context string for a finding chat thread.
+
+    Combines finding details with analysis metadata so the LLM has full
+    context without relying on the frontend to pass segment_context.
+    """
+    return (
+        f"Document: {analysis.doc_name}\n"
+        f"Standard: {analysis.standard_name}\n"
+        f"Finding [{finding.seq + 1}]: {finding.title}\n"
+        f"Status: {finding.status}\n"
+        f"Clause reference: {finding.clause_ref or 'N/A'}\n"
+        f"Description: {finding.description}\n"
+        f"Overall conclusion: {analysis.conclusion}\n"
+    )
+
+
+def generate_suggestions(
+    finding: "FindingRecord",
+    analysis: "AnalysisRecord",
+    client: "BaseLLMClient",
+) -> list[str]:
+    """Generate 3 context-aware follow-up questions for a finding chat thread.
+
+    Returns exactly 3 question strings. Falls back to static templates on error.
+    """
+    fallback = _SUGGESTION_FALLBACK.get(finding.status, _SUGGESTION_FALLBACK["warn"])
+    context = build_finding_context(finding, analysis)
+    focus = _SUGGESTION_FOCUS.get(finding.status, _SUGGESTION_FOCUS["warn"])
+    prompt = (
+        f"{context}\n\n"
+        f"Task: {focus}\n"
+        "Generate exactly 3 concise follow-up questions a compliance analyst would ask.\n"
+        'Return JSON: {"questions": ["question 1", "question 2", "question 3"]}\n'
+        "Return ONLY the JSON object."
+    )
+    response = client.chat([{"role": "user", "content": prompt}], max_tokens=300)
+    if not response.is_success:
+        return fallback
+    try:
+        result = _extract_json(response.content)
+        questions = result.get("questions", [])
+        if isinstance(questions, list) and len(questions) >= 3:
+            return [str(q) for q in questions[:3]]
+    except (ValueError, TypeError) as exc:
+        logger.warning("generate_suggestions JSON parse failed: {}", exc)
+    return fallback

backend/app/domain/compliance/ports.py

@@ -0,0 +1,66 @@
+"""Domain ports for compliance history persistence."""
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Optional
+
+
+@dataclass
+class FindingRecord:
+    """Single finding row linked to an analysis."""
+    id: str
+    analysis_id: str
+    seq: int
+    title: str
+    description: str
+    status: str          # "ok" | "warn" | "risk"
+    clause_ref: Optional[str] = None
+
+
+@dataclass
+class AnalysisRecord:
+    """Full compliance analysis record with nested findings."""
+    id: str              # UUID string; empty string means not yet persisted
+    created_at: datetime
+    created_by: Optional[str]
+    doc_name: str
+    standard_name: str
+    risk_score: int
+    conclusion: str
+    actions: list        # list[dict] — serialised action items
+    para_text: str
+    highlight_terms: list  # list[str]
+    findings: list[FindingRecord] = field(default_factory=list)
+
+
+class ComplianceRepository(ABC):
+    """Port for persisting and retrieving compliance analysis records."""
+
+    @abstractmethod
+    def save_analysis(self, record: AnalysisRecord) -> str:
+        """Persist a new analysis record and return the assigned UUID string."""
+
+    @abstractmethod
+    def list_analyses(self, limit: int = 50, offset: int = 0) -> list[AnalysisRecord]:
+        """Return analyses ordered by created_at DESC, without nested findings."""
+
+    @abstractmethod
+    def get_analysis(self, analysis_id: str) -> Optional[AnalysisRecord]:
+        """Return a single analysis with all nested findings, or None."""
+
+    @abstractmethod
+    def delete_analysis(self, analysis_id: str) -> None:
+        """Delete an analysis and all related findings and chat messages (cascade)."""
+
+    @abstractmethod
+    def save_message(self, analysis_id: str, finding_id: str, role: str, content: str) -> str:
+        """Persist a chat message and return its UUID string."""
+
+    @abstractmethod
+    def get_messages(self, finding_id: str) -> list[dict]:
+        """Return chat messages for a finding ordered by created_at ASC.
+
+        Each dict has keys: id, role, content, created_at (ISO string).
+        """

backend/app/infrastructure/compliance/docx_export.py

@@ -0,0 +1,101 @@
+"""DOCX report generator for compliance analysis results.
+
+Uses python-docx (already in requirements.txt). Returns raw bytes so the
+caller can stream the response without writing to disk.
+"""
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from io import BytesIO
+
+from docx import Document
+from docx.shared import Pt, RGBColor
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+
+from app.domain.compliance.ports import AnalysisRecord
+
+_STATUS_LABEL = {"ok": "Compliant", "warn": "Warning", "risk": "Non-Compliant"}
+_STATUS_COLOR = {
+    "ok": RGBColor(0x22, 0x8B, 0x22),
+    "warn": RGBColor(0xFF, 0x8C, 0x00),
+    "risk": RGBColor(0xDC, 0x14, 0x3C),
+}
+
+
+def generate_docx(record: AnalysisRecord) -> bytes:
+    """Generate a compliance report DOCX and return its raw bytes.
+
+    Structure:
+    - Cover: document name, standard, date, risk score
+    - Executive summary (conclusion)
+    - Findings table
+    - Recommended actions
+    - Footer note
+    """
+    doc = Document()
+
+    # ── Cover ──────────────────────────────────────────────────────────────────
+    title_para = doc.add_heading("Compliance Analysis Report", level=0)
+    title_para.alignment = WD_ALIGN_PARAGRAPH.CENTER
+
+    doc.add_paragraph("")
+    meta_table = doc.add_table(rows=4, cols=2)
+    meta_table.style = "Table Grid"
+    labels = ["Document", "Standard", "Date", "Risk Score"]
+    values = [
+        record.doc_name,
+        record.standard_name,
+        record.created_at.strftime("%Y-%m-%d %H:%M UTC") if record.created_at else "",
+        f"{record.risk_score} / 100",
+    ]
+    for i, (label, value) in enumerate(zip(labels, values)):
+        meta_table.cell(i, 0).text = label
+        meta_table.cell(i, 1).text = value
+
+    # ── Executive Summary ──────────────────────────────────────────────────────
+    doc.add_heading("Executive Summary", level=1)
+    doc.add_paragraph(record.conclusion)
+
+    # ── Findings ───────────────────────────────────────────────────────────────
+    doc.add_heading("Findings", level=1)
+    if record.findings:
+        table = doc.add_table(rows=1, cols=4)
+        table.style = "Table Grid"
+        hdr = table.rows[0].cells
+        for i, h in enumerate(["#", "Status", "Title", "Description / Clause"]):
+            hdr[i].text = h
+            for run in hdr[i].paragraphs[0].runs:
+                run.bold = True
+
+        for f in record.findings:
+            row = table.add_row().cells
+            row[0].text = str(f.seq + 1)
+            row[1].text = _STATUS_LABEL.get(f.status, f.status)
+            row[2].text = f.title
+            desc = f.description
+            if f.clause_ref:
+                desc += f"\n[{f.clause_ref}]"
+            row[3].text = desc
+    else:
+        doc.add_paragraph("No findings recorded.")
+
+    # ── Recommended Actions ────────────────────────────────────────────────────
+    doc.add_heading("Recommended Actions", level=1)
+    for i, action in enumerate(record.actions, start=1):
+        label = action.get("label", "Action")
+        value = action.get("value", "")
+        doc.add_paragraph(f"{i}. {label}: {value}", style="List Number")
+
+    # ── Footer note ────────────────────────────────────────────────────────────
+    doc.add_paragraph("")
+    footer = doc.add_paragraph(
+        f"Generated by AI Regulation Analysis System — {datetime.now(timezone.utc).strftime('%Y-%m-%d')}"
+    )
+    footer.alignment = WD_ALIGN_PARAGRAPH.CENTER
+    for run in footer.runs:
+        run.font.size = Pt(8)
+        run.font.color.rgb = RGBColor(0x88, 0x88, 0x88)
+
+    buf = BytesIO()
+    doc.save(buf)
+    return buf.getvalue()

backend/app/infrastructure/compliance/repository.py

@@ -0,0 +1,280 @@
+# backend/app/infrastructure/compliance/repository.py
+"""PostgreSQL-backed compliance analysis repository.
+
+Follows the same psycopg2 pattern as PostgresDocumentRepository:
+ThreadedConnectionPool + RealDictCursor for reads, _ensure_schema on init.
+"""
+from __future__ import annotations
+
+import json
+from contextlib import contextmanager
+from datetime import datetime
+from typing import Optional
+
+import psycopg2
+import psycopg2.extras
+import psycopg2.pool
+from loguru import logger
+
+from app.domain.compliance.ports import (
+    AnalysisRecord,
+    ComplianceRepository,
+    FindingRecord,
+)
+
+
+class PostgresComplianceRepository(ComplianceRepository):
+    """Stores compliance analyses, findings, and finding chat messages in PostgreSQL."""
+
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        user: str,
+        password: str,
+        dbname: str,
+        minconn: int = 1,
+        maxconn: int = 5,
+    ) -> None:
+        self._pool = psycopg2.pool.ThreadedConnectionPool(
+            minconn=minconn,
+            maxconn=maxconn,
+            host=host,
+            port=port,
+            user=user,
+            password=password,
+            dbname=dbname,
+        )
+        self._ensure_schema()
+
+    @contextmanager
+    def _conn(self):
+        conn = self._pool.getconn()
+        try:
+            yield conn
+        finally:
+            self._pool.putconn(conn)
+
+    def _ensure_schema(self) -> None:
+        """Create tables if they do not exist."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS compliance_analyses (
+                        id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                        created_at      TIMESTAMPTZ NOT NULL DEFAULT now(),
+                        created_by      VARCHAR(255),
+                        doc_name        VARCHAR(500),
+                        standard_name   VARCHAR(500),
+                        risk_score      INTEGER,
+                        conclusion      TEXT,
+                        actions         JSONB,
+                        para_text       TEXT,
+                        highlight_terms JSONB
+                    );
+                """)
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS compliance_findings (
+                        id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                        analysis_id UUID NOT NULL REFERENCES compliance_analyses(id) ON DELETE CASCADE,
+                        seq         INTEGER NOT NULL,
+                        title       VARCHAR(500),
+                        description TEXT,
+                        status      VARCHAR(50),
+                        clause_ref  VARCHAR(200)
+                    );
+                """)
+                cur.execute("""
+                    CREATE TABLE IF NOT EXISTS finding_chat_messages (
+                        id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+                        analysis_id UUID NOT NULL REFERENCES compliance_analyses(id) ON DELETE CASCADE,
+                        finding_id  UUID NOT NULL REFERENCES compliance_findings(id) ON DELETE CASCADE,
+                        role        VARCHAR(20) NOT NULL,
+                        content     TEXT NOT NULL,
+                        created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
+                    );
+                """)
+            conn.commit()
+
+    def save_analysis(self, record: AnalysisRecord) -> str:
+        """Insert analysis + findings; return the new analysis UUID."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    INSERT INTO compliance_analyses
+                        (created_by, doc_name, standard_name, risk_score,
+                         conclusion, actions, para_text, highlight_terms)
+                    VALUES
+                        (%(created_by)s, %(doc_name)s, %(standard_name)s, %(risk_score)s,
+                         %(conclusion)s, %(actions)s, %(para_text)s, %(highlight_terms)s)
+                    RETURNING id
+                    """,
+                    {
+                        "created_by": record.created_by,
+                        "doc_name": record.doc_name,
+                        "standard_name": record.standard_name,
+                        "risk_score": record.risk_score,
+                        "conclusion": record.conclusion,
+                        "actions": json.dumps(record.actions, ensure_ascii=False),
+                        "para_text": record.para_text,
+                        "highlight_terms": json.dumps(record.highlight_terms, ensure_ascii=False),
+                    },
+                )
+                row = cur.fetchone()
+                analysis_id = str(row["id"])
+
+            if record.findings:
+                with conn.cursor() as cur:
+                    for f in record.findings:
+                        cur.execute(
+                            """
+                            INSERT INTO compliance_findings
+                                (analysis_id, seq, title, description, status, clause_ref)
+                            VALUES
+                                (%(analysis_id)s, %(seq)s, %(title)s, %(desc)s, %(status)s, %(clause_ref)s)
+                            """,
+                            {
+                                "analysis_id": analysis_id,
+                                "seq": f.seq,
+                                "title": f.title,
+                                "desc": f.description,
+                                "status": f.status,
+                                "clause_ref": f.clause_ref,
+                            },
+                        )
+            conn.commit()
+        return analysis_id
+
+    def list_analyses(self, limit: int = 50, offset: int = 0) -> list[AnalysisRecord]:
+        """Return analyses without nested findings, ordered newest first."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    SELECT id, created_at, created_by, doc_name, standard_name,
+                           risk_score, conclusion, actions, para_text, highlight_terms
+                    FROM compliance_analyses
+                    ORDER BY created_at DESC
+                    LIMIT %(limit)s OFFSET %(offset)s
+                    """,
+                    {"limit": limit, "offset": offset},
+                )
+                rows = cur.fetchall()
+        return [self._row_to_record(dict(r)) for r in rows]
+
+    def get_analysis(self, analysis_id: str) -> Optional[AnalysisRecord]:
+        """Return analysis with nested findings list."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    "SELECT * FROM compliance_analyses WHERE id = %(id)s",
+                    {"id": analysis_id},
+                )
+                row = cur.fetchone()
+                if not row:
+                    return None
+                record = self._row_to_record(dict(row))
+
+                cur.execute(
+                    """
+                    SELECT id, analysis_id, seq, title, description, status, clause_ref
+                    FROM compliance_findings
+                    WHERE analysis_id = %(id)s
+                    ORDER BY seq
+                    """,
+                    {"id": analysis_id},
+                )
+                findings = [
+                    FindingRecord(
+                        id=str(r["id"]),
+                        analysis_id=str(r["analysis_id"]),
+                        seq=r["seq"],
+                        title=r["title"] or "",
+                        description=r["description"] or "",
+                        status=r["status"] or "ok",
+                        clause_ref=r["clause_ref"],
+                    )
+                    for r in cur.fetchall()
+                ]
+                record.findings = findings
+        return record
+
+    def delete_analysis(self, analysis_id: str) -> None:
+        """Delete analysis; findings and chat messages cascade automatically."""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "DELETE FROM compliance_analyses WHERE id = %(id)s",
+                    {"id": analysis_id},
+                )
+            conn.commit()
+
+    def save_message(self, analysis_id: str, finding_id: str, role: str, content: str) -> str:
+        """Persist a chat message; return its UUID."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    INSERT INTO finding_chat_messages
+                        (analysis_id, finding_id, role, content)
+                    VALUES
+                        (%(analysis_id)s, %(finding_id)s, %(role)s, %(content)s)
+                    RETURNING id
+                    """,
+                    {
+                        "analysis_id": analysis_id,
+                        "finding_id": finding_id,
+                        "role": role,
+                        "content": content,
+                    },
+                )
+                row = cur.fetchone()
+            conn.commit()
+        return str(row["id"])
+
+    def get_messages(self, finding_id: str) -> list[dict]:
+        """Return messages for a finding, oldest first."""
+        with self._conn() as conn:
+            with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                cur.execute(
+                    """
+                    SELECT id, role, content, created_at
+                    FROM finding_chat_messages
+                    WHERE finding_id = %(finding_id)s
+                    ORDER BY created_at ASC
+                    """,
+                    {"finding_id": finding_id},
+                )
+                rows = cur.fetchall()
+        return [
+            {
+                "id": str(r["id"]),
+                "role": r["role"],
+                "content": r["content"],
+                "created_at": r["created_at"].isoformat() if r["created_at"] else "",
+            }
+            for r in rows
+        ]
+
+    def _row_to_record(self, row: dict) -> AnalysisRecord:
+        """Convert a RealDictCursor row to an AnalysisRecord (no findings)."""
+        actions = row.get("actions") or []
+        if isinstance(actions, str):
+            actions = json.loads(actions)
+        highlight_terms = row.get("highlight_terms") or []
+        if isinstance(highlight_terms, str):
+            highlight_terms = json.loads(highlight_terms)
+        return AnalysisRecord(
+            id=str(row["id"]),
+            created_at=row["created_at"] if isinstance(row["created_at"], datetime) else datetime.utcnow(),
+            created_by=row.get("created_by"),
+            doc_name=row.get("doc_name") or "",
+            standard_name=row.get("standard_name") or "",
+            risk_score=int(row.get("risk_score") or 0),
+            conclusion=row.get("conclusion") or "",
+            actions=actions,
+            para_text=row.get("para_text") or "",
+            highlight_terms=highlight_terms,
+            findings=[],
+        )

backend/app/infrastructure/vectorstore/pass_through_reranker.py

@@ -0,0 +1,21 @@
+"""No-op reranker stub.
+
+Returns the original candidate list sliced to top_k.
+Replace with CrossEncoderReranker when a local cross-encoder model is available.
+"""
+from __future__ import annotations
+
+from app.domain.retrieval.models import RetrievedChunk
+from app.domain.retrieval.ports import Reranker
+
+
+class PassThroughReranker(Reranker):
+    """Pass-through reranker that preserves original retrieval order.
+
+    Acts as a placeholder for future cross-encoder reranking (e.g. ms-marco-MiniLM).
+    Wire via bootstrap.get_compliance_reranker() when ready to swap.
+    """
+
+    def rerank(self, query: str, chunks: list[RetrievedChunk], top_k: int) -> list[RetrievedChunk]:
+        """Return the first top_k chunks without reordering."""
+        return chunks[:top_k]

backend/app/shared/bootstrap.py

@@ -40,6 +40,8 @@ from app.infrastructure.vectorstore.cross_encoder_reranker import OpenAICompatib
 from app.infrastructure.vectorstore.dense_retriever import DenseRetriever
 from app.infrastructure.vectorstore.milvus_vector_index import MilvusVectorIndex
 from app.services.llm.llm_factory import LLMFactory
+from app.domain.compliance.ports import ComplianceRepository
+from app.infrastructure.compliance.repository import PostgresComplianceRepository
 # Keep shared wiring centralized so dependency construction remains consistent.
 
 
@@ -311,6 +313,28 @@ def get_event_store() -> BaseEventStore:
     return MockEventStore()
 
 
+@lru_cache
+def get_compliance_repository() -> ComplianceRepository:
+    """Return the compliance analysis repository.
+
+    Requires document_repository_backend=postgres and valid postgres_* settings.
+    Raises NotImplementedError for any other backend value.
+    """
+    if settings.document_repository_backend != "postgres":
+        raise NotImplementedError(
+            f"ComplianceRepository requires document_repository_backend=postgres, "
+            f"got '{settings.document_repository_backend}'. "
+            "Set DOCUMENT_REPOSITORY_BACKEND=postgres in your .env file."
+        )
+    return PostgresComplianceRepository(
+        host=settings.postgres_host,
+        port=settings.postgres_port,
+        user=settings.postgres_user,
+        password=settings.postgres_password,
+        dbname=settings.postgres_db,
+    )
+
+
 @lru_cache
 def get_perception_service() -> PerceptionService:
     return PerceptionService(