fix: ruff lint errors from merge

Author: laxmanclo

tests/unit/test_factory.py

@@ -3,7 +3,13 @@
 import pytest
 
 from vektori.models.anthropic import AnthropicLLM
-from vektori.models.factory import CHAT_REGISTRY, LLM_REGISTRY, create_chat_model, create_embedder, create_llm
+from vektori.models.factory import (
+    CHAT_REGISTRY,
+    LLM_REGISTRY,
+    create_chat_model,
+    create_embedder,
+    create_llm,
+)
 from vektori.models.nvidia import DEFAULT_EMBEDDING_MODEL, NvidiaEmbedder, NvidiaLLM
 from vektori.models.ollama import OllamaEmbedder, OllamaLLM
 from vektori.models.openai import OpenAIEmbedder, OpenAILLM

tests/unit/test_tools.py

@@ -11,7 +11,6 @@
 from vektori.models.base import ChatCompletionResult
 from vektori.tools.memory import MEMORY_TOOLS, handle_tool_call
 
-
 # — Tool schema tests —
 
 def test_memory_tools_has_three_tools():
@@ -70,8 +69,7 @@ async def test_handle_get_profile_empty():
 
 @pytest.mark.asyncio
 async def test_handle_get_profile_with_patches():
-    from datetime import datetime, timezone
-    from vektori.memory.profile import InMemoryProfileStore, ProfilePatch
+    from vektori.memory.profile import ProfilePatch
 
     store = InMemoryProfileStore()
     await store.save(ProfilePatch(

tests/unit/test_window_persistence.py

@@ -10,7 +10,6 @@
 from vektori.memory.window import MessageWindow, SQLiteWindowStore, WindowState
 from vektori.models.base import ChatCompletionResult
 
-
 # — SQLiteWindowStore tests —
 
 @pytest.mark.asyncio

uv.lock

@@ -3,15 +3,20 @@
 from __future__ import annotations
 
 import asyncio
+import re
 from dataclasses import dataclass, field
 from pathlib import Path
-import re
 from typing import Any
 from uuid import uuid4
 
 from vektori.client import Vektori
 from vektori.context import AgentContextLoader
-from vektori.memory.profile import InMemoryProfileStore, ProfilePatch, ProfileStore, SQLiteProfileStore
+from vektori.memory.profile import (
+    InMemoryProfileStore,
+    ProfilePatch,
+    ProfileStore,
+    SQLiteProfileStore,
+)
 from vektori.memory.window import MessageWindow, SQLiteWindowStore
 from vektori.models.base import ChatModelProvider
 from vektori.prompts import build_prompt_result

vektori/agent.py

@@ -220,16 +220,16 @@ async def add_document(
         Maps source:source_id -> deterministic session_id so re-ingestion upserts, not duplicates.
         """
         session_id = f"{source}:{source_id}"
-        
+
         doc_metadata = metadata or {}
         doc_metadata.update({
             "source": source,
             "source_id": source_id,
             "type": "document",
         })
-        
+
         messages = [{"role": "user", "content": content}]
-        
+
         return await self.add(
             messages=messages,
             session_id=session_id,

vektori/client.py

@@ -3,7 +3,7 @@
 import json
 import os
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 
 class AuthStore:
@@ -20,13 +20,13 @@ def __init__(self, base_dir: str | None = None) -> None:
     def _get_path(self, user_id: str, platform: str) -> Path:
         return self.base_dir / user_id / f"{platform}.json"
 
-    def get_token(self, user_id: str, platform: str) -> Optional[Dict[str, Any]]:
+    def get_token(self, user_id: str, platform: str) -> dict[str, Any] | None:
         """Retrieve the authentication details for a specific user and platform."""
         path = self._get_path(user_id, platform)
         if not path.exists():
             return None
         try:
-            with open(path, "r", encoding="utf-8") as f:
+            with open(path, encoding="utf-8") as f:
                 return json.load(f)
         except json.JSONDecodeError:
             return None
@@ -36,9 +36,9 @@ def set_token(
         user_id: str,
         platform: str,
         access_token: str,
-        refresh_token: Optional[str] = None,
-        expiry: Optional[str] = None,
-        scopes: Optional[List[str]] = None,
+        refresh_token: str | None = None,
+        expiry: str | None = None,
+        scopes: list[str] | None = None,
     ) -> None:
         """Save the authentication details securely."""
         path = self._get_path(user_id, platform)

vektori/connectors/auth.py

@@ -2,8 +2,9 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Callable, Protocol
+from collections.abc import Callable
 from datetime import datetime
+from typing import TYPE_CHECKING, Any, Protocol
 
 if TYPE_CHECKING:
     from vektori.client import Vektori

vektori/connectors/base.py

@@ -57,36 +57,36 @@ async def ingest(
 
         try:
             repo = g.get_repo(self.repo_name)
-            
+
             kwargs: dict[str, Any] = {"state": "all"}
             if since:
                 kwargs["since"] = since
-                
+
             issues = repo.get_issues(**kwargs)
 
             for issue in issues:
                 # We want to skip raw pull requests if we just want issues
                 # (PyGithub returns PRs as issues too, but we can detect them)
-                
+
                 source_id = f"{self.repo_name}/issues/{issue.number}"
-                
+
                 # Combine issue body and comments into a readable chronological "document"
                 content_parts = [
                     f"Title: {issue.title}",
                     f"State: {issue.state}",
                     f"Author: {issue.user.login}",
                     f"Body:\n{issue.body or 'No description provided.'}",
                 ]
-                
+
                 comments = issue.get_comments()
                 for comment in comments:
                     content_parts.append(
                         f"\n--- Comment by {comment.user.login} at {comment.created_at} ---\n{comment.body}"
                     )
-                
+
                 document_content = "\n".join(content_parts)
                 document_time = issue.updated_at.replace(tzinfo=timezone.utc)
-                
+
                 # Insert into Vektori
                 await vektori.add_document(
                     content=document_content,
@@ -103,7 +103,7 @@ async def ingest(
                         "is_pr": issue.pull_request is not None,
                     }
                 )
-                
+
                 count += 1
                 logger.debug(f"Ingested GitHub issue: {source_id}")
 

vektori/connectors/github.py

@@ -1,7 +1,6 @@
 """Hermes Agent memory provider integration for Vektori."""
 
-from typing import Dict, List, Any
-import asyncio
+from typing import Any
 
 # Assuming a mock BaseMemoryProvider that Hermes Agent uses
 try:
@@ -12,44 +11,45 @@ class BaseMemoryProvider:
 
 from vektori import Vektori
 
+
 class VektoriHermesMemory(BaseMemoryProvider):
     """
     Vektori's episodic graph memory as a Hermes Memory Provider.
     Provides semantic recall that feeds into Hermes' self-improvement loop.
     """
-    
+
     def __init__(self, vektori_instance: Vektori, user_id: str):
         self.memory = vektori_instance
         self.user_id = user_id
-        
-    async def add_memory(self, text: str, metadata: Dict[str, Any] = None) -> str:
+
+    async def add_memory(self, text: str, metadata: dict[str, Any] = None) -> str:
         """Store a new fact/episode from the Hermes agent into Vektori."""
         result = await self.memory.add(
             messages=[{"role": "user", "content": text}],
             user_id=self.user_id,
             metadata=metadata
         )
         return result.get('status', 'success')
-        
-    async def search_memory(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+
+    async def search_memory(self, query: str, limit: int = 5) -> list[dict[str, Any]]:
         """Retrieve L1 (Facts + Episodes) depth context for Hermes prompt injection."""
         results = await self.memory.search(
-            query=query, 
-            user_id=self.user_id, 
+            query=query,
+            user_id=self.user_id,
             depth="l1",
             top_k=limit
         )
-        
+
         # Hermes usually expects a flat list of memory dicts
         memories = []
         for f in results.get("facts", []):
             memories.append({"type": "fact", "content": f["text"], "score": f.get("score")})
         for ep in results.get("episodes", []):
             memories.append({"type": "episode", "content": ep["text"], "score": 1.0})
-            
+
         return memories
 
     async def clear(self) -> None:
         """Clear memory for the current user."""
-        # Vektori users would need an explicit delete endpoint, 
+        # Vektori users would need an explicit delete endpoint,
         pass