Бот работает

src/rag_agent/index/postgres.py

@@ -5,6 +5,7 @@ from datetime import datetime, timezone
 from typing import Iterable, Sequence
 
 import psycopg
+from pgvector import Vector
 from pgvector.psycopg import register_vector
 
 from rag_agent.ingest.chunker import TextChunk
@@ -113,16 +114,20 @@ def ensure_schema(conn: psycopg.Connection, embeddings_dim: int) -> None:
         except psycopg.ProgrammingError:
             conn.rollback()
             pass
-        try:
-            cur.execute(
-                """
+        cur.execute(
+            """
+            DO $$
+            BEGIN
+              IF NOT EXISTS (
+                SELECT 1 FROM pg_constraint
+                WHERE conrelid = 'chunks'::regclass AND conname = 'chunks_change_type_check'
+              ) THEN
                 ALTER TABLE chunks ADD CONSTRAINT chunks_change_type_check
                 CHECK (change_type IN ('added', 'modified', 'unchanged'));
-                """
-            )
-        except psycopg.ProgrammingError:
-            conn.rollback()
-            pass  # constraint may already exist
+              END IF;
+            END $$;
+            """
+        )
         cur.execute(
             """
             CREATE INDEX IF NOT EXISTS idx_documents_story_id
@@ -343,6 +348,7 @@ def fetch_similar(
     top_k: int,
     story_id: int | None = None,
 ) -> list[tuple[str, str, float]]:
+    vec = Vector(query_embedding)
     with conn.cursor() as cur:
         if story_id is not None:
             cur.execute(
@@ -354,7 +360,7 @@ def fetch_similar(
                 ORDER BY c.embedding <=> %s
                 LIMIT %s;
                 """,
-                (query_embedding, story_id, query_embedding, top_k),
+                (vec, story_id, vec, top_k),
             )
         else:
             cur.execute(
@@ -365,7 +371,7 @@ def fetch_similar(
                 ORDER BY c.embedding <=> %s
                 LIMIT %s;
                 """,
-                (query_embedding, query_embedding, top_k),
+                (vec, vec, top_k),
             )
         rows = cur.fetchall()
     return [(row[0], row[1], row[2]) for row in rows]