feat: scaffold Brain of Reese — FastAPI RAG chat over Postgres 17 + pgvector

Foundation (phase 01, verified):
- FastAPI app: /api/health, /api/suggestions, /api/chat (placeholder),
  static frontend served locally (no CDN)
- Postgres 17 + pgvector via db/Containerfile + compose.yaml
  (podman compose up -d db), Alembic initial migration (documents,
  chunks with vector(768), query_log)
- LLM client targeting https://aipi.reeseapps.com/v1 (turbo/embed);
  scripts/llm_probe.py verified models + 768-dim embeddings live
- Conditional debugpy: imported only when DEBUGPY=1 (attach on demand,
  :5678); logging config for clean single-line logs
- Frontend shell: mobile-first chat + Sources pages, tokens, a11y baselines
- Tests: 24 unit+integration (99% coverage on app/), ruff + pyright clean,
  Playwright smoke E2E (3 tests) against a deterministic mock LLM
- Planning: .agent/PLAN.md (architecture + LOCKED decisions), AGENTS.md,
  6 user stories, 7 phase files (one story / one phase / one Playwright
  suite each)

alembic/versions/0001_initial_schema.py

@@ -0,0 +1,86 @@
+"""initial schema: documents, chunks (pgvector), query_log
+
+Revision ID: 0001
+Revises:
+Create Date: 2026-08-21
+
+"""
+from __future__ import annotations
+
+import sqlalchemy as sa
+from pgvector.sqlalchemy import Vector
+from sqlalchemy.dialects import postgresql
+
+from alembic import op
+
+EMBEDDING_DIM = 768  # keep in sync with app/models.py (PLAN anchor A6)
+
+revision = "0001"
+down_revision = None
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.execute("CREATE EXTENSION IF NOT EXISTS vector")
+
+    op.create_table(
+        "documents",
+        sa.Column("id", postgresql.UUID(as_uuid=True), primary_key=True),
+        sa.Column("source", sa.String(120), nullable=False),
+        sa.Column("path", sa.String(1000), nullable=False),
+        sa.Column("full_path", sa.String(2000), nullable=False),
+        sa.Column("title", sa.String(500), nullable=False),
+        sa.Column("content", sa.Text(), nullable=False),
+        sa.Column("content_hash", sa.String(64), nullable=False),
+        sa.Column(
+            "indexed_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.func.now(),
+            nullable=False,
+        ),
+        sa.UniqueConstraint("source", "path", name="uq_documents_source_path"),
+    )
+    op.create_index("ix_documents_source", "documents", ["source"])
+    op.create_index("ix_documents_path", "documents", ["path"])
+    op.create_index("ix_documents_content_hash", "documents", ["content_hash"])
+
+    op.create_table(
+        "chunks",
+        sa.Column("id", postgresql.UUID(as_uuid=True), primary_key=True),
+        sa.Column(
+            "document_id",
+            postgresql.UUID(as_uuid=True),
+            sa.ForeignKey("documents.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("position", sa.Integer(), nullable=False),
+        sa.Column("content", sa.Text(), nullable=False),
+        sa.Column("embedding", Vector(EMBEDDING_DIM), nullable=True),
+    )
+    op.create_index("ix_chunks_document_id", "chunks", ["document_id"])
+
+    op.create_table(
+        "query_log",
+        sa.Column("id", postgresql.UUID(as_uuid=True), primary_key=True),
+        sa.Column("question", sa.Text(), nullable=False),
+        sa.Column("top_score", sa.Float(), nullable=False, server_default="0"),
+        sa.Column("chunk_hits", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("deflected", sa.Boolean(), nullable=False, server_default=sa.false()),
+        sa.Column("sources", sa.Text(), nullable=False, server_default=""),
+        sa.Column("latency_ms", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.func.now(),
+            nullable=False,
+        ),
+    )
+    op.create_index("ix_query_log_created_at", "query_log", ["created_at"])
+
+
+def downgrade() -> None:
+    op.drop_table("query_log")
+    op.drop_table("chunks")
+    op.drop_table("documents")
+    op.execute("DROP EXTENSION IF EXISTS vector")