Fix pgvector integration: VECTOR(512) for voyage-3-lite, register_vector on connect

- voyage-3-lite returns 512 dims (not 1024) — migrated column + schema
- register_vector now called once at connection time, not per-query
- Removes per-function register_vector calls that caused type cast conflicts

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

EmailsImport/GAL.py

@@ -0,0 +1,86 @@
+import win32com.client
+import pandas as pd
+from pathlib import Path
+
+OUT_XLSX = Path(r"C:\Temp\GAL_export.xlsx")
+
+def safe_get(obj, attr):
+    try:
+        return getattr(obj, attr)
+    except Exception:
+        return None
+
+outlook = win32com.client.Dispatch("Outlook.Application")
+ns = outlook.GetNamespace("MAPI")
+
+gal = ns.GetGlobalAddressList()
+entries = gal.AddressEntries
+
+rows = []
+
+print(f"Počet položek v GAL: {entries.Count}")
+
+for i in range(1, entries.Count + 1):  # Outlook COM je 1-based
+    try:
+        entry = entries.Item(i)
+
+        name = safe_get(entry, "Name")
+        address = safe_get(entry, "Address")
+        entry_type = safe_get(entry, "AddressEntryUserType")
+
+        smtp = None
+        job_title = None
+        department = None
+        company = None
+        office = None
+        phone = None
+        mobile = None
+
+        # Exchange user
+        try:
+            exch_user = entry.GetExchangeUser()
+        except Exception:
+            exch_user = None
+
+        if exch_user:
+            smtp = safe_get(exch_user, "PrimarySmtpAddress")
+            job_title = safe_get(exch_user, "JobTitle")
+            department = safe_get(exch_user, "Department")
+            company = safe_get(exch_user, "CompanyName")
+            office = safe_get(exch_user, "OfficeLocation")
+            phone = safe_get(exch_user, "BusinessTelephoneNumber")
+            mobile = safe_get(exch_user, "MobileTelephoneNumber")
+
+        # Distribution list
+        try:
+            exch_dl = entry.GetExchangeDistributionList()
+        except Exception:
+            exch_dl = None
+
+        if exch_dl and not smtp:
+            smtp = safe_get(exch_dl, "PrimarySmtpAddress")
+
+        rows.append({
+            "name": name,
+            "smtp": smtp,
+            "address": address,
+            "entry_type": entry_type,
+            "job_title": job_title,
+            "department": department,
+            "company": company,
+            "office": office,
+            "phone": phone,
+            "mobile": mobile,
+        })
+
+    except Exception as e:
+        rows.append({
+            "name": None,
+            "smtp": None,
+            "error": str(e),
+        })
+
+df = pd.DataFrame(rows)
+df.to_excel(OUT_XLSX, index=False)
+
+print(f"Hotovo: {OUT_XLSX}")

Knowledgebase/schema.sql

@@ -2,6 +2,7 @@
 -- PostgreSQL, bez pgvector (embeddingy jako double precision[])
 -- pg_trgm pro fuzzy matching (volitelné)
 
+CREATE EXTENSION IF NOT EXISTS vector;
 CREATE EXTENSION IF NOT EXISTS pg_trgm;
 
 -- ─── Conversation sessions ────────────────────────────────────────────────────
@@ -56,7 +57,7 @@ CREATE TABLE IF NOT EXISTS kb_memories (
     -- search
     tags        TEXT[]          DEFAULT '{}',
     importance  FLOAT           DEFAULT 0.5,   -- 0..1
-    embedding   double precision[],  -- Voyage AI voyage-3-lite (1024-dim), Python-side similarity
+    embedding   vector(512),         -- Voyage AI voyage-3-lite (512-dim)
     fts         TSVECTOR,
 
     -- lifecycle
@@ -94,4 +95,5 @@ CREATE INDEX IF NOT EXISTS kb_memories_project_idx   ON kb_memories(project);
 CREATE INDEX IF NOT EXISTS kb_memories_importance_idx ON kb_memories(importance DESC);
 CREATE INDEX IF NOT EXISTS kb_memories_created_idx   ON kb_memories(created_at DESC);
 CREATE INDEX IF NOT EXISTS kb_memories_session_idx   ON kb_memories(session_id);
--- Note: embedding je double precision[] — similarity se počítá Python-side po FTS pre-filtru
+-- Vector index (aktivovat po ~1k řádcích pro lepší recall)
+-- CREATE INDEX kb_memories_vec_idx ON kb_memories USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

Knowledgebase/server.py

@@ -34,7 +34,8 @@ PG_PASSWORD = os.getenv("PG_PASSWORD", "Vlado7309208104++")
 PG_DB       = os.getenv("PG_DB",       "knowledgebase")
 
 VOYAGE_API_KEY = os.getenv("VOYAGE_API_KEY", "")
-EMBED_MODEL    = "voyage-3-lite"   # 1024-dim, fast & cheap
+EMBED_MODEL    = "voyage-3-lite"   # 512-dim, fast & cheap
+EMBED_DIM      = 512
 
 # ─── Logging ─────────────────────────────────────────────────────────────────
 
@@ -60,6 +61,12 @@ def get_conn() -> psycopg.Connection:
         row_factory=dict_row,
         autocommit=False,
     )
+    # register_vector jednou na connection — umožní psycopg správně serializovat np.array jako vector
+    try:
+        from pgvector.psycopg import register_vector
+        register_vector(_conn)
+    except Exception as e:
+        log(f"pgvector register warning: {e}")
     log(f"Connected to {PG_DB}@{PG_HOST}")
     return _conn
 
@@ -156,9 +163,7 @@ def store_memory(
     try:
         with conn.transaction():
             if embedding:
-                from pgvector.psycopg import register_vector
                 import numpy as np
-                register_vector(conn)
                 row = conn.execute(
                     """
                     INSERT INTO kb_memories
@@ -312,9 +317,7 @@ def _insert_memory_in_tx(conn, data: dict):
     """Helper: insert memory within an existing transaction."""
     embedding = data.get("embedding")
     if embedding:
-        from pgvector.psycopg import register_vector
         import numpy as np
-        register_vector(conn)
         conn.execute(
             """
             INSERT INTO kb_memories
@@ -420,9 +423,7 @@ def search(
         query_emb = get_embedding(query)
         if query_emb:
             try:
-                from pgvector.psycopg import register_vector
                 import numpy as np
-                register_vector(conn)
 
                 vec_conditions = ["deleted = FALSE", "embedding IS NOT NULL"]
                 vec_params2: list[Any] = []
@@ -700,9 +701,7 @@ def update_memory(
         params.append(content)
         new_emb = get_embedding(f"{title or ''} {content}")
         if new_emb:
-            from pgvector.psycopg import register_vector
             import numpy as np
-            register_vector(conn)
             updates.append("embedding = %s")
             params.append(np.array(new_emb))
     if title is not None: