Odysseus v1.0

routes/memory_routes.py

@@ -0,0 +1,517 @@
+# routes/memory_routes.py
+from fastapi import APIRouter, Form, HTTPException, Request, UploadFile, File
+from typing import Dict, Any, Optional, List
+import json
+import os
+import re
+import tempfile
+import time
+from datetime import datetime
+import logging
+
+# Leading list-marker like "1.", "12)", or "3:" plus surrounding whitespace.
+# Strips one prefix per call so import-from-LLM-output doesn't leave the
+# numbering inside the saved memory text. Bullet markers (-, *, •) are
+# also peeled here for the same reason.
+_LIST_PREFIX_RE = re.compile(r"^\s*(?:\d{1,3}[.):]\s+|[-*•]\s+)")
+
+
+def _strip_list_prefix(text: str) -> str:
+    if not text:
+        return text
+    return _LIST_PREFIX_RE.sub("", text, count=1).strip()
+
+from services.memory import MemoryManager
+from core.session_manager import SessionManager
+from src.request_models import MemoryAddRequest
+from core.database import SessionLocal
+from src.llm_core import llm_call_async
+from services.memory.memory_extractor import audit_memories
+from src.auth_helpers import get_current_user
+
+logger = logging.getLogger(__name__)
+
+def setup_memory_routes(memory_manager: MemoryManager, session_manager: SessionManager, memory_vector=None):
+    """Set up memory-related routes."""
+    router = APIRouter(prefix="/api/memory", tags=["memory"])
+
+    def _owner(request: Request) -> Optional[str]:
+        return get_current_user(request)
+
+    def _verify_memory_owner(memory: dict, user: Optional[str]):
+        """Raise 404 if user doesn't own this memory.
+
+        SECURITY: strict ownership — previously `mem_owner and mem_owner != user`
+        allowed any user to read/edit/delete memories with an empty/null owner
+        field, which leaked legacy data across the multi-user deploy.
+        """
+        if user is None:
+            return  # Auth disabled
+        if memory.get("owner") != user:
+            raise HTTPException(404, "Memory not found")
+
+    @router.post("/debug")
+    def debug_memory_relevance(request: Request, query: str = Form(...)):
+        """Debug which memories would be triggered for a query"""
+        user = _owner(request)
+        memories = memory_manager.load(owner=user)
+        relevant = memory_manager.get_relevant_memories(query, memories, threshold=0.05)
+
+        return {
+            "query": query,
+            "total_memories": len(memories),
+            "relevant_count": len(relevant),
+            "relevant_memories": [{"text": m["text"], "category": m.get("category", "unknown")}
+                                 for m in relevant]
+        }
+
+    @router.post("/add", response_model=Dict[str, Any])
+    async def api_add_memory(
+        request: Request,
+        memory_data: Optional[MemoryAddRequest] = None
+    ):
+        """Add a new memory entry with optional category, source, and session reference."""
+        from src.auth_helpers import require_privilege
+        require_privilege(request, "can_manage_memory")
+        if memory_data is None:
+            form = await request.form()
+            memory_data = MemoryAddRequest(
+                text=form.get("text"),
+                category=form.get("category", "fact"),
+                source=form.get("source", "user"),
+                session_id=form.get("session_id")
+            )
+
+        user = _owner(request)
+        text = (memory_data.text or "").strip()
+        if not text:
+            raise HTTPException(400, "empty memory")
+        user_mem = memory_manager.load(owner=user)
+        if memory_manager.find_duplicates(text, user_mem):
+            return {"ok": True, "count": len(user_mem), "message": "Memory already exists"}
+
+        new_entry = memory_manager.add_entry(text, memory_data.source, memory_data.category, owner=user)
+        if memory_data.session_id:
+            new_entry["session_id"] = memory_data.session_id
+        all_mem = memory_manager.load_all()
+        all_mem.append(new_entry)
+        memory_manager.save(all_mem)
+        # Sync vector index
+        if memory_vector and memory_vector.healthy:
+            memory_vector.add(new_entry["id"], text)
+        try:
+            from src.event_bus import fire_event
+            fire_event("memory_added", user)
+        except Exception:
+            logger.debug("memory_added event dispatch failed", exc_info=True)
+        return {"ok": True, "count": len([m for m in all_mem if m.get("owner") == user])}
+
+    @router.get("")
+    def api_get_memory(request: Request):
+        """Return all memory entries with their metadata."""
+        user = _owner(request)
+        return {"memory": memory_manager.load(owner=user)}
+
+    @router.post("/search")
+    def search_memories(request: Request, query: str = Form(...), session_id: str = Form(None), category: str = Form(None)):
+        """Search across all memories with optional filters."""
+        user = _owner(request)
+        memories = memory_manager.load(owner=user)
+
+        if session_id:
+            memories = [m for m in memories if m.get("session_id") == session_id]
+
+        if category:
+            memories = [m for m in memories if category in m.get("categories", [m.get("category", "")])]
+
+        relevant = memory_manager.get_relevant_memories(query, memories, threshold=0.05, max_items=20)
+
+        return {"memories": relevant, "total": len(relevant), "query": query}
+
+    @router.get("/timeline")
+    def memory_timeline(request: Request):
+        """Get memories in chronological order with source session information."""
+        user = _owner(request)
+        memories = memory_manager.load(owner=user)
+        sorted_memories = sorted(memories, key=lambda x: x.get("timestamp", 0), reverse=True)
+
+        results = []
+        for memory in sorted_memories:
+            if "timestamp" in memory:
+                try:
+                    dt = datetime.fromtimestamp(memory["timestamp"])
+                    memory["timestamp_str"] = dt.strftime("%Y-%m-%d %H:%M:%S")
+                except (ValueError, OSError, OverflowError):
+                    memory["timestamp_str"] = "Unknown"
+            else:
+                memory["timestamp_str"] = "Unknown"
+
+            session_id = memory.get("session_id")
+            if session_id and session_id in session_manager.sessions:
+                session = session_manager.get_session(session_id)
+                memory["session_name"] = session.name if session else f"Session {session_id[:6]}"
+            else:
+                memory["session_name"] = "Unknown"
+
+            results.append(memory)
+
+        return {"timeline": results, "total": len(results)}
+
+    @router.get("/by-session/{session_id}")
+    def get_memory_by_session(request: Request, session_id: str):
+        """Get all memories associated with a specific session."""
+        try:
+            session_manager.get_session(session_id)
+        except KeyError:
+            raise HTTPException(404, f"Session {session_id} not found")
+
+        user = _owner(request)
+        memories = memory_manager.load(owner=user)
+        session_memories = [m for m in memories if m.get("session_id") == session_id]
+
+        session_memories.sort(key=lambda x: x.get("timestamp", 0), reverse=True)
+
+        try:
+            session = session_manager.get_session(session_id)
+            session_name = session.name if session else f"Session {session_id[:6]}"
+        except KeyError:
+            session_name = f"Session {session_id[:6]}"
+
+        for memory in session_memories:
+            memory["session_name"] = session_name
+
+        return {
+            "session_id": session_id,
+            "session_name": session_name,
+            "memory_count": len(session_memories),
+            "memories": session_memories
+        }
+
+    @router.post("/extract")
+    async def extract_memory(request: Request, session: str = Form(...)) -> Dict[str, List[str]]:
+        """Analyze a session's chat history and return memory suggestions."""
+        if not get_current_user(request):
+            raise HTTPException(401, "Not authenticated")
+        try:
+            sess = session_manager.get_session(session)
+        except KeyError:
+            raise HTTPException(404, "Session not found")
+
+        system_msg = {
+            "role": "system",
+            "content": (
+                "You are a helpful assistant. Analyze the entire conversation history provided and extract any "
+                "useful factual statements, contacts, addresses, phone numbers, or other information that the user "
+                "might want to remember for future interactions. Return each piece of information as a JSON object "
+                "with a 'text' field. For example: [{'text': 'Alice lives at 123 Main St'}, {'text': 'Bob works at Acme Corp'}]. "
+                "Only include information that is specific and likely to be useful later."
+            ),
+        }
+        messages = [system_msg] + sess.get_context_messages()
+
+        try:
+            suggestion_text = await llm_call_async(
+                sess.endpoint_url,
+                sess.model,
+                messages,
+                temperature=0.2,
+                max_tokens=500,
+                headers=sess.headers,
+            )
+            try:
+                suggestions = json.loads(suggestion_text)
+                if isinstance(suggestions, list):
+                    suggestions = [s if isinstance(s, str) else s.get("text", "") for s in suggestions]
+                else:
+                    suggestions = []
+            except json.JSONDecodeError:
+                suggestions = [line.strip() for line in suggestion_text.splitlines() if line.strip()]
+
+            return {"suggestions": [s for s in suggestions if s]}
+        except Exception as e:
+            logger.error(f"LLM memory extraction failed (session {session}): {e}")
+            fallback = memory_manager.extract_memory_from_chat(sess.history, session)
+            return {"suggestions": [item["text"] for item in fallback]}
+
+    @router.post("/audit")
+    async def api_audit_memories(request: Request, session: str = Form(None)):
+        """Deduplicate and consolidate memories via LLM.
+
+        Uses the default model from settings, or falls back to a session's model.
+        Returns before and after memory counts.
+        """
+        from routes.model_routes import _load_settings, _normalize_base, build_chat_url
+        from core.database import ModelEndpoint
+        import json as _json
+
+        endpoint_url = model = None
+        headers = {}
+
+        # Try default model from settings first
+        settings = _load_settings()
+        ep_id = settings.get("default_endpoint_id", "")
+        default_model = settings.get("default_model", "")
+        if ep_id:
+            db = SessionLocal()
+            try:
+                ep = db.query(ModelEndpoint).filter(
+                    ModelEndpoint.id == ep_id, ModelEndpoint.is_enabled == True
+                ).first()
+                if ep:
+                    base = _normalize_base(ep.base_url)
+                    endpoint_url = build_chat_url(base)
+                    model = default_model
+                    if not model and ep.models:
+                        try:
+                            models = _json.loads(ep.models) if isinstance(ep.models, str) else ep.models
+                            if models:
+                                model = models[0]
+                        except Exception:
+                            pass
+                    if ep.api_key:
+                        headers = {"Authorization": f"Bearer {ep.api_key}"}
+            finally:
+                db.close()
+
+        # Fall back to session model if no default configured
+        if not endpoint_url and session:
+            try:
+                sess = session_manager.get_session(session)
+                endpoint_url = sess.endpoint_url
+                model = sess.model
+                headers = sess.headers
+            except KeyError:
+                pass
+
+        if not endpoint_url or not model:
+            raise HTTPException(400, "No default model configured — set one in Settings")
+
+        user = _owner(request)
+        result = await audit_memories(
+            memory_manager,
+            memory_vector,
+            endpoint_url,
+            model,
+            headers,
+            owner=user,
+        )
+
+        if "error" in result and "before" not in result:
+            raise HTTPException(502, f"Audit failed: {result['error']}")
+
+        return {
+            "ok": "error" not in result,
+            "before": result.get("before", 0),
+            "after": result.get("after", 0),
+            "removed": result.get("before", 0) - result.get("after", 0),
+            # True when the audit skipped the LLM because nothing changed
+            # since the last tidy. Frontend already says "Already clean"
+            # for removed==0, so this is here for future use / debugging.
+            "already_tidy": bool(result.get("already_tidy")),
+        }
+
+    @router.post("/import")
+    async def import_memories_from_file(
+        request: Request,
+        session: str = Form(...),
+        file: UploadFile = File(...)
+    ):
+        """Extract memory suggestions from an uploaded file (PDF, TXT, MD, etc.)."""
+        from src.auth_helpers import require_privilege
+        require_privilege(request, "can_manage_memory")
+        try:
+            sess = session_manager.get_session(session)
+        except KeyError:
+            raise HTTPException(404, "Session not found — needed for LLM config")
+
+        # Read file content
+        content = await file.read()
+        filename = file.filename or "upload"
+        _, ext = os.path.splitext(filename.lower())
+
+        allowed = {".txt", ".md", ".pdf", ".csv", ".log", ".json", ".py", ".js", ".html"}
+        if ext not in allowed:
+            raise HTTPException(400, f"Unsupported file type: {ext}")
+
+        # Extract text based on file type
+        if ext == ".pdf":
+            from src.document_processor import _process_pdf
+            with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp:
+                tmp.write(content)
+                tmp_path = tmp.name
+            try:
+                text = _process_pdf(tmp_path)
+            finally:
+                os.unlink(tmp_path)
+        else:
+            try:
+                text = content.decode("utf-8")
+            except UnicodeDecodeError:
+                from charset_normalizer import detect
+                encoding = (detect(content) or {}).get("encoding") or "utf-8"
+                text = content.decode(encoding, errors="replace")
+
+        if not text.strip():
+            return {"suggestions": [], "message": "No readable content found"}
+
+        # Fast path: a .json upload that already looks like a memories export
+        # (list of {text, category, ...} dicts, or list of strings) round-trips
+        # directly without spending an LLM call to re-extract its own output.
+        # Without this, re-importing a memories.json from another account
+        # ran the file through the extractor, which often re-emitted the
+        # entries as a numbered list (and the numbering leaked into the
+        # `text` field).
+        if ext == ".json":
+            try:
+                parsed = json.loads(text)
+            except json.JSONDecodeError:
+                parsed = None
+            if isinstance(parsed, list) and parsed:
+                direct = []
+                for item in parsed:
+                    if isinstance(item, dict) and item.get("text"):
+                        direct.append({
+                            "text": _strip_list_prefix(str(item["text"])),
+                            "category": item.get("category") or "fact",
+                        })
+                    elif isinstance(item, str) and item.strip():
+                        direct.append({
+                            "text": _strip_list_prefix(item.strip()),
+                            "category": "fact",
+                        })
+                if direct:
+                    return {"suggestions": direct, "filename": filename}
+
+        # Truncate very long documents
+        if len(text) > 15000:
+            text = text[:15000] + "\n[Truncated]"
+
+        # Send to LLM for memory extraction
+        import_prompt = (
+            "You are a memory extraction assistant. The user uploaded a document. "
+            "Analyze the text below and extract specific, useful facts — things like "
+            "names, preferences, jobs, locations, relationships, opinions, projects, "
+            "goals, contacts, or any other personal details worth remembering.\n\n"
+            "Rules:\n"
+            "- Each fact should be a short, self-contained statement\n"
+            "- Do NOT extract generic knowledge\n"
+            "- Focus on personal, memorable information\n"
+            "- If there are no useful facts, return an empty array\n\n"
+            "Return a JSON array of objects with 'text' and 'category' fields.\n"
+            "Categories: 'identity', 'preference', 'fact', 'contact', 'project', 'goal'\n\n"
+            "Return ONLY valid JSON, no markdown fences."
+        )
+
+        try:
+            raw = await llm_call_async(
+                sess.endpoint_url,
+                sess.model,
+                [
+                    {"role": "system", "content": import_prompt},
+                    {"role": "user", "content": f"Document: {filename}\n\n{text}"},
+                ],
+                temperature=0.2,
+                max_tokens=2000,
+                headers=sess.headers,
+            )
+
+            # Parse JSON
+            raw = raw.strip()
+            if raw.startswith("```"):
+                raw = raw.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
+
+            suggestions = json.loads(raw)
+            if isinstance(suggestions, list):
+                normalized = []
+                for s in suggestions:
+                    if not s:
+                        continue
+                    if isinstance(s, dict):
+                        s = dict(s)
+                        if s.get("text"):
+                            s["text"] = _strip_list_prefix(str(s["text"]))
+                        normalized.append(s)
+                    else:
+                        normalized.append({"text": _strip_list_prefix(str(s)), "category": "fact"})
+                suggestions = normalized
+            else:
+                suggestions = []
+
+            return {"suggestions": suggestions, "filename": filename}
+
+        except json.JSONDecodeError:
+            # Fallback: split by lines, stripping any "1.", "2)" markdown-list
+            # numbering the model added so saved memories don't keep the prefix.
+            lines = [_strip_list_prefix(l.strip()) for l in raw.splitlines() if l.strip() and len(l.strip()) > 5]
+            return {"suggestions": [{"text": l, "category": "fact"} for l in lines[:20]], "filename": filename}
+        except Exception as e:
+            logger.error(f"Memory import extraction failed: {e}")
+            raise HTTPException(502, f"LLM extraction failed: {str(e)}")
+
+    @router.post("/{memory_id}/pin")
+    def pin_memory(request: Request, memory_id: str, pinned: bool = Form(True)):
+        """Pin or unpin a memory. Pinned memories are always included in context."""
+        user = _owner(request)
+        all_mem = memory_manager.load_all()
+        for i, memory in enumerate(all_mem):
+            if memory["id"] == memory_id:
+                _verify_memory_owner(memory, user)
+                all_mem[i]["pinned"] = pinned
+                memory_manager.save(all_mem)
+                return {"ok": True, "pinned": pinned}
+        raise HTTPException(404, f"Memory item {memory_id} not found")
+
+    # Wildcard routes MUST come last — otherwise they swallow /import, /search, etc.
+    @router.get("/{memory_id}")
+    def get_memory_item(request: Request, memory_id: str):
+        """Get a specific memory item by ID."""
+        user = _owner(request)
+        memories = memory_manager.load(owner=user)
+        for memory in memories:
+            if memory["id"] == memory_id:
+                return {"memory": memory}
+
+        raise HTTPException(404, "Memory not found")
+
+    @router.put("/{memory_id}")
+    def update_memory(request: Request, memory_id: str, text: str = Form(...), category: str = Form(None)):
+        """Update an existing memory item with new text and optional category."""
+        user = _owner(request)
+        all_mem = memory_manager.load_all()
+        for i, memory in enumerate(all_mem):
+            if memory["id"] == memory_id:
+                _verify_memory_owner(memory, user)
+                all_mem[i]["text"] = text.strip()
+                if category:
+                    all_mem[i]["category"] = category
+                all_mem[i]["timestamp"] = int(time.time())
+
+                memory_manager.save(all_mem)
+                # Sync vector index (remove old, add updated)
+                if memory_vector and memory_vector.healthy:
+                    memory_vector.remove(memory_id)
+                    memory_vector.add(memory_id, text.strip())
+                return {"ok": True, "message": "Memory updated successfully"}
+
+        raise HTTPException(404, f"Memory item {memory_id} not found")
+
+    @router.delete("/{memory_id}")
+    def delete_memory(request: Request, memory_id: str):
+        """Delete a memory item by its ID."""
+        user = _owner(request)
+        all_mem = memory_manager.load_all()
+
+        # Find and verify ownership before deleting
+        target = next((m for m in all_mem if m["id"] == memory_id), None)
+        if not target:
+            raise HTTPException(404, f"Memory item {memory_id} not found")
+        _verify_memory_owner(target, user)
+
+        all_mem = [m for m in all_mem if m["id"] != memory_id]
+        memory_manager.save(all_mem)
+        # Sync vector index
+        if memory_vector and memory_vector.healthy:
+            memory_vector.remove(memory_id)
+        return {"ok": True, "message": "Memory deleted successfully"}
+
+    return router