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pipeline/memory.py

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+"""
+ChromaDB-backed session memory for Lumi.
+
+Each session is stored as a summary document with patient facts in its metadata.
+Session summaries only — no raw conversation logs (privacy).
+
+At conversation start, get_context() injects the last N session summaries + all
+known facts into the system prompt, giving Lumi full continuity without the
+patient needing to repeat themselves.
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+
+import chromadb
+
+_client: chromadb.Client | None = None
+_collection: chromadb.Collection | None = None
+
+
+def _get_collection() -> chromadb.Collection:
+    global _client, _collection
+    if _collection is None:
+        _client = chromadb.Client()
+        _collection = _client.get_or_create_collection("patient_memories")
+    return _collection
+
+
+# ---------------------------------------------------------------------------
+# Write
+# ---------------------------------------------------------------------------
+
+def save_session(
+    patient_id: str,
+    facts: list[str],
+    emotional_state: str,
+    confusion_level: str,
+    summary: str,
+) -> None:
+    col = _get_collection()
+    doc_id = f"{patient_id}_{datetime.now().timestamp()}"
+    col.add(
+        documents=[summary],
+        metadatas=[{
+            "patient_id":      patient_id,
+            "timestamp":       datetime.now().isoformat(),
+            "facts":           json.dumps(facts),
+            "emotional_state": emotional_state,
+            "confusion_level": confusion_level,
+        }],
+        ids=[doc_id],
+    )
+
+
+# ---------------------------------------------------------------------------
+# Read
+# ---------------------------------------------------------------------------
+
+def get_context(patient_id: str, n_sessions: int = 3) -> dict:
+    """
+    Returns a dict with:
+      - summaries : list of the last N session summary strings
+      - facts     : deduplicated list of all known facts for this patient
+    """
+    col = _get_collection()
+    results = col.query(
+        query_texts=["recent sessions"],
+        where={"patient_id": patient_id},
+        n_results=n_sessions,
+    )
+
+    summaries: list[str] = []
+    facts: list[str] = []
+
+    if results["documents"]:
+        for doc, meta in zip(results["documents"][0], results["metadatas"][0]):
+            summaries.append(doc)
+            raw_facts = json.loads(meta.get("facts", "[]"))
+            facts.extend(raw_facts)
+
+    seen: set[str] = set()
+    deduped_facts = []
+    for f in facts:
+        if f not in seen:
+            seen.add(f)
+            deduped_facts.append(f)
+
+    return {
+        "summaries": summaries,
+        "facts":     deduped_facts,
+    }
+
+
+# ---------------------------------------------------------------------------
+# System prompt builder
+# ---------------------------------------------------------------------------
+
+SYSTEM_PROMPT_TEMPLATE = """\
+You are Lumi, a warm and patient companion for {patient_name}.
+
+What you know about {patient_name}:
+{facts_block}
+
+Recent sessions:
+{sessions_block}
+
+Always respond in this exact format:
+[avatar_tag] Short opening line.
+<think>Your internal reasoning here. Never sent to user.</think>
+Full warm response.
+
+Be gentle and patient. Never correct temporal confusion — focus on the emotion, not the timeline.
+"""
+
+
+def build_system_prompt(
+    patient_id: str,
+    patient_name: str = "the patient",
+    n_sessions: int = 3,
+) -> str:
+    ctx = get_context(patient_id, n_sessions)
+
+    facts_block = (
+        "\n".join(f"- {f}" for f in ctx["facts"])
+        if ctx["facts"] else "- No personal facts recorded yet."
+    )
+    sessions_block = (
+        "\n\n".join(ctx["summaries"])
+        if ctx["summaries"] else "No previous sessions recorded."
+    )
+
+    return SYSTEM_PROMPT_TEMPLATE.format(
+        patient_name=patient_name,
+        facts_block=facts_block,
+        sessions_block=sessions_block,
+    )