Use Black to format Khoj server code and tests

pyproject.toml

@@ -64,7 +64,8 @@ khoj = "khoj.main:run"
 
 [project.optional-dependencies]
 test = [
-  "pytest == 7.1.2",
+  "pytest >= 7.1.2",
+  "black >= 23.1.0",
 ]
 dev = ["khoj-assistant[test]"]
 
@@ -88,3 +89,6 @@ exclude = [
     "src/khoj/interface/desktop/file_browser.py",
     "src/khoj/interface/desktop/system_tray.py",
 ]
+
+[tool.black]
+line-length = 120

src/khoj/configure.py

@@ -26,10 +26,12 @@ logger = logging.getLogger(__name__)
 def configure_server(args, required=False):
     if args.config is None:
         if required:
-            logger.error(f'Exiting as Khoj is not configured.\nConfigure it via GUI or by editing {state.config_file}.')
+            logger.error(f"Exiting as Khoj is not configured.\nConfigure it via GUI or by editing {state.config_file}.")
             sys.exit(1)
         else:
-            logger.warn(f'Khoj is not configured.\nConfigure it via khoj GUI, plugins or by editing {state.config_file}.')
+            logger.warn(
+                f"Khoj is not configured.\nConfigure it via khoj GUI, plugins or by editing {state.config_file}."
+            )
             return
     else:
         state.config = args.config
@@ -60,7 +62,8 @@ def configure_search(model: SearchModels, config: FullConfig, regenerate: bool,
             config.content_type.org,
             search_config=config.search_type.asymmetric,
             regenerate=regenerate,
-            filters=[DateFilter(), WordFilter(), FileFilter()])
+            filters=[DateFilter(), WordFilter(), FileFilter()],
+        )
 
     # Initialize Org Music Search
     if (t == SearchType.Music or t == None) and config.content_type.music:
@@ -70,7 +73,8 @@ def configure_search(model: SearchModels, config: FullConfig, regenerate: bool,
             config.content_type.music,
             search_config=config.search_type.asymmetric,
             regenerate=regenerate,
-            filters=[DateFilter(), WordFilter()])
+            filters=[DateFilter(), WordFilter()],
+        )
 
     # Initialize Markdown Search
     if (t == SearchType.Markdown or t == None) and config.content_type.markdown:
@@ -80,7 +84,8 @@ def configure_search(model: SearchModels, config: FullConfig, regenerate: bool,
             config.content_type.markdown,
             search_config=config.search_type.asymmetric,
             regenerate=regenerate,
-            filters=[DateFilter(), WordFilter(), FileFilter()])
+            filters=[DateFilter(), WordFilter(), FileFilter()],
+        )
 
     # Initialize Ledger Search
     if (t == SearchType.Ledger or t == None) and config.content_type.ledger:
@@ -90,15 +95,15 @@ def configure_search(model: SearchModels, config: FullConfig, regenerate: bool,
             config.content_type.ledger,
             search_config=config.search_type.symmetric,
             regenerate=regenerate,
-            filters=[DateFilter(), WordFilter(), FileFilter()])
+            filters=[DateFilter(), WordFilter(), FileFilter()],
+        )
 
     # Initialize Image Search
     if (t == SearchType.Image or t == None) and config.content_type.image:
         # Extract Entries, Generate Image Embeddings
         model.image_search = image_search.setup(
-            config.content_type.image,
+            config.content_type.image, search_config=config.search_type.image, regenerate=regenerate
-            search_config=config.search_type.image,
+        )
-            regenerate=regenerate)
 
     # Invalidate Query Cache
     state.query_cache = LRU()
@@ -125,9 +130,9 @@ def configure_conversation_processor(conversation_processor_config):
 
     if conversation_logfile.is_file():
         # Load Metadata Logs from Conversation Logfile
-        with conversation_logfile.open('r') as f:
+        with conversation_logfile.open("r") as f:
             conversation_processor.meta_log = json.load(f)
-        logger.info('Conversation logs loaded from disk.')
+        logger.info("Conversation logs loaded from disk.")
     else:
         # Initialize Conversation Logs
         conversation_processor.meta_log = {}

src/khoj/interface/desktop/file_browser.py

@@ -8,7 +8,7 @@ from khoj.utils.helpers import is_none_or_empty
 
 
 class FileBrowser(QtWidgets.QWidget):
-    def __init__(self, title, search_type: SearchType=None, default_files:list=[]):
+    def __init__(self, title, search_type: SearchType = None, default_files: list = []):
         QtWidgets.QWidget.__init__(self)
         layout = QtWidgets.QHBoxLayout()
         self.setLayout(layout)
@@ -26,47 +26,50 @@ class FileBrowser(QtWidgets.QWidget):
         self.lineEdit = QtWidgets.QPlainTextEdit(self)
         self.lineEdit.setFixedWidth(330)
         self.setFiles(default_files)
-        self.lineEdit.setFixedHeight(min(7+20*len(self.lineEdit.toPlainText().split('\n')),90))
+        self.lineEdit.setFixedHeight(min(7 + 20 * len(self.lineEdit.toPlainText().split("\n")), 90))
         self.lineEdit.textChanged.connect(self.updateFieldHeight)
         layout.addWidget(self.lineEdit)
 
-        self.button = QtWidgets.QPushButton('Add')
+        self.button = QtWidgets.QPushButton("Add")
         self.button.clicked.connect(self.storeFilesSelectedInFileDialog)
         layout.addWidget(self.button)
         layout.addStretch()
 
     def getFileFilter(self, search_type):
         if search_type == SearchType.Org:
-            return 'Org-Mode Files (*.org)'
+            return "Org-Mode Files (*.org)"
         elif search_type == SearchType.Ledger:
-            return 'Beancount Files (*.bean *.beancount)'
+            return "Beancount Files (*.bean *.beancount)"
         elif search_type == SearchType.Markdown:
-            return 'Markdown Files (*.md *.markdown)'
+            return "Markdown Files (*.md *.markdown)"
         elif search_type == SearchType.Music:
-            return 'Org-Music Files (*.org)'
+            return "Org-Music Files (*.org)"
         elif search_type == SearchType.Image:
-            return 'Images (*.jp[e]g)'
+            return "Images (*.jp[e]g)"
 
     def storeFilesSelectedInFileDialog(self):
         filepaths = self.getPaths()
         if self.search_type == SearchType.Image:
-            filepaths.append(QtWidgets.QFileDialog.getExistingDirectory(self, caption='Choose Folder',
+            filepaths.append(
-                                                    directory=self.dirpath))
+                QtWidgets.QFileDialog.getExistingDirectory(self, caption="Choose Folder", directory=self.dirpath)
+            )
         else:
-            filepaths.extend(QtWidgets.QFileDialog.getOpenFileNames(self, caption='Choose Files',
+            filepaths.extend(
-                                                    directory=self.dirpath,
+                QtWidgets.QFileDialog.getOpenFileNames(
-                                                    filter=self.filter_name)[0])
+                    self, caption="Choose Files", directory=self.dirpath, filter=self.filter_name
+                )[0]
+            )
         self.setFiles(filepaths)
 
-    def setFiles(self, paths:list):
+    def setFiles(self, paths: list):
         self.filepaths = [path for path in paths if not is_none_or_empty(path)]
         self.lineEdit.setPlainText("\n".join(self.filepaths))
 
     def getPaths(self) -> list:
-        if self.lineEdit.toPlainText() == '':
+        if self.lineEdit.toPlainText() == "":
             return []
         else:
-            return self.lineEdit.toPlainText().split('\n')
+            return self.lineEdit.toPlainText().split("\n")
 
     def updateFieldHeight(self):
-        self.lineEdit.setFixedHeight(min(7+20*len(self.lineEdit.toPlainText().split('\n')),90))
+        self.lineEdit.setFixedHeight(min(7 + 20 * len(self.lineEdit.toPlainText().split("\n")), 90))

src/khoj/interface/desktop/labelled_text_field.py

@@ -6,7 +6,7 @@ from khoj.utils.config import ProcessorType
 
 
 class LabelledTextField(QtWidgets.QWidget):
-    def __init__(self, title, processor_type: ProcessorType=None, default_value: str=None):
+    def __init__(self, title, processor_type: ProcessorType = None, default_value: str = None):
         QtWidgets.QWidget.__init__(self)
         layout = QtWidgets.QHBoxLayout()
         self.setLayout(layout)

src/khoj/interface/desktop/main_window.py

@@ -31,9 +31,9 @@ class MainWindow(QtWidgets.QMainWindow):
         self.config_file = config_file
         # Set regenerate flag to regenerate embeddings everytime user clicks configure
         if state.cli_args:
-            state.cli_args += ['--regenerate']
+            state.cli_args += ["--regenerate"]
         else:
-            state.cli_args = ['--regenerate']
+            state.cli_args = ["--regenerate"]
 
         # Load config from existing config, if exists, else load from default config
         if resolve_absolute_path(self.config_file).exists():
@@ -49,8 +49,8 @@ class MainWindow(QtWidgets.QMainWindow):
         self.setFixedWidth(600)
 
         # Set Window Icon
-        icon_path = constants.web_directory / 'assets/icons/favicon-144x144.png'
+        icon_path = constants.web_directory / "assets/icons/favicon-144x144.png"
-        self.setWindowIcon(QtGui.QIcon(f'{icon_path.absolute()}'))
+        self.setWindowIcon(QtGui.QIcon(f"{icon_path.absolute()}"))
 
         # Initialize Configure Window Layout
         self.layout = QtWidgets.QVBoxLayout()
@@ -58,13 +58,13 @@ class MainWindow(QtWidgets.QMainWindow):
         # Add Settings Panels for each Search Type to Configure Window Layout
         self.search_settings_panels = []
         for search_type in SearchType:
-            current_content_config = self.current_config['content-type'].get(search_type, {})
+            current_content_config = self.current_config["content-type"].get(search_type, {})
             self.search_settings_panels += [self.add_settings_panel(current_content_config, search_type)]
 
         # Add Conversation Processor Panel to Configure Screen
         self.processor_settings_panels = []
         conversation_type = ProcessorType.Conversation
-        current_conversation_config = self.current_config['processor'].get(conversation_type, {})
+        current_conversation_config = self.current_config["processor"].get(conversation_type, {})
         self.processor_settings_panels += [self.add_processor_panel(current_conversation_config, conversation_type)]
 
         # Add Action Buttons Panel
@@ -81,11 +81,11 @@ class MainWindow(QtWidgets.QMainWindow):
         "Add Settings Panel for specified Search Type. Toggle Editable Search Types"
         # Get current files from config for given search type
         if search_type == SearchType.Image:
-            current_content_files = current_content_config.get('input-directories', [])
+            current_content_files = current_content_config.get("input-directories", [])
-            file_input_text = f'{search_type.name} Folders'
+            file_input_text = f"{search_type.name} Folders"
         else:
-            current_content_files = current_content_config.get('input-files', [])
+            current_content_files = current_content_config.get("input-files", [])
-            file_input_text = f'{search_type.name} Files'
+            file_input_text = f"{search_type.name} Files"
 
         # Create widgets to display settings for given search type
         search_type_settings = QtWidgets.QWidget()
@@ -109,7 +109,7 @@ class MainWindow(QtWidgets.QMainWindow):
     def add_processor_panel(self, current_conversation_config: dict, processor_type: ProcessorType):
         "Add Conversation Processor Panel"
         # Get current settings from config for given processor type
-        current_openai_api_key = current_conversation_config.get('openai-api-key', None)
+        current_openai_api_key = current_conversation_config.get("openai-api-key", None)
 
         # Create widgets to display settings for given processor type
         processor_type_settings = QtWidgets.QWidget()
@@ -137,20 +137,22 @@ class MainWindow(QtWidgets.QMainWindow):
         action_bar_layout = QtWidgets.QHBoxLayout(action_bar)
 
         self.configure_button = QtWidgets.QPushButton("Configure", clicked=self.configure_app)
-        self.search_button = QtWidgets.QPushButton("Search", clicked=lambda: webbrowser.open(f'http://{state.host}:{state.port}/'))
+        self.search_button = QtWidgets.QPushButton(
+            "Search", clicked=lambda: webbrowser.open(f"http://{state.host}:{state.port}/")
+        )
         self.search_button.setEnabled(not self.first_run)
 
         action_bar_layout.addWidget(self.configure_button)
         action_bar_layout.addWidget(self.search_button)
         self.layout.addWidget(action_bar)
 
-    def get_default_config(self, search_type:SearchType=None, processor_type:ProcessorType=None):
+    def get_default_config(self, search_type: SearchType = None, processor_type: ProcessorType = None):
         "Get default config"
         config = constants.default_config
         if search_type:
-            return config['content-type'][search_type]
+            return config["content-type"][search_type]
         elif processor_type:
-            return config['processor'][processor_type]
+            return config["processor"][processor_type]
         else:
             return config
 
@@ -160,7 +162,9 @@ class MainWindow(QtWidgets.QMainWindow):
         for message_prefix in ErrorType:
             for i in reversed(range(self.layout.count())):
                 current_widget = self.layout.itemAt(i).widget()
-                if isinstance(current_widget, QtWidgets.QLabel) and current_widget.text().startswith(message_prefix.value):
+                if isinstance(current_widget, QtWidgets.QLabel) and current_widget.text().startswith(
+                    message_prefix.value
+                ):
                     self.layout.removeWidget(current_widget)
                     current_widget.deleteLater()
 
@@ -180,18 +184,24 @@ class MainWindow(QtWidgets.QMainWindow):
                     continue
                 if isinstance(child, SearchCheckBox):
                     # Search Type Disabled
-                    if not child.isChecked() and child.search_type in self.new_config['content-type']:
+                    if not child.isChecked() and child.search_type in self.new_config["content-type"]:
-                        del self.new_config['content-type'][child.search_type]
+                        del self.new_config["content-type"][child.search_type]
                     # Search Type (re)-Enabled
                     if child.isChecked():
-                        current_search_config = self.current_config['content-type'].get(child.search_type, {})
+                        current_search_config = self.current_config["content-type"].get(child.search_type, {})
-                        default_search_config = self.get_default_config(search_type = child.search_type)
+                        default_search_config = self.get_default_config(search_type=child.search_type)
-                        self.new_config['content-type'][child.search_type.value] = merge_dicts(current_search_config, default_search_config)
+                        self.new_config["content-type"][child.search_type.value] = merge_dicts(
-                elif isinstance(child, FileBrowser) and child.search_type in self.new_config['content-type']:
+                            current_search_config, default_search_config
+                        )
+                elif isinstance(child, FileBrowser) and child.search_type in self.new_config["content-type"]:
                     if child.search_type.value == SearchType.Image:
-                        self.new_config['content-type'][child.search_type.value]['input-directories'] = child.getPaths() if child.getPaths() != [] else None
+                        self.new_config["content-type"][child.search_type.value]["input-directories"] = (
+                            child.getPaths() if child.getPaths() != [] else None
+                        )
                     else:
-                        self.new_config['content-type'][child.search_type.value]['input-files'] = child.getPaths() if child.getPaths() != [] else None
+                        self.new_config["content-type"][child.search_type.value]["input-files"] = (
+                            child.getPaths() if child.getPaths() != [] else None
+                        )
 
     def update_processor_settings(self):
         "Update config with conversation settings from UI"
@@ -201,16 +211,20 @@ class MainWindow(QtWidgets.QMainWindow):
                     continue
                 if isinstance(child, ProcessorCheckBox):
                     # Processor Type Disabled
-                    if not child.isChecked() and child.processor_type in self.new_config['processor']:
+                    if not child.isChecked() and child.processor_type in self.new_config["processor"]:
-                        del self.new_config['processor'][child.processor_type]
+                        del self.new_config["processor"][child.processor_type]
                     # Processor Type (re)-Enabled
                     if child.isChecked():
-                        current_processor_config = self.current_config['processor'].get(child.processor_type, {})
+                        current_processor_config = self.current_config["processor"].get(child.processor_type, {})
-                        default_processor_config = self.get_default_config(processor_type = child.processor_type)
+                        default_processor_config = self.get_default_config(processor_type=child.processor_type)
-                        self.new_config['processor'][child.processor_type.value] = merge_dicts(current_processor_config, default_processor_config)
+                        self.new_config["processor"][child.processor_type.value] = merge_dicts(
-                elif isinstance(child, LabelledTextField) and child.processor_type in self.new_config['processor']:
+                            current_processor_config, default_processor_config
+                        )
+                elif isinstance(child, LabelledTextField) and child.processor_type in self.new_config["processor"]:
                     if child.processor_type == ProcessorType.Conversation:
-                        self.new_config['processor'][child.processor_type.value]['openai-api-key'] = child.input_field.toPlainText() if child.input_field.toPlainText() != '' else None
+                        self.new_config["processor"][child.processor_type.value]["openai-api-key"] = (
+                            child.input_field.toPlainText() if child.input_field.toPlainText() != "" else None
+                        )
 
     def save_settings_to_file(self) -> bool:
         "Save validated settings to file"
@@ -278,7 +292,7 @@ class MainWindow(QtWidgets.QMainWindow):
         self.show()
         self.setWindowState(Qt.WindowState.WindowActive)
         self.activateWindow()  # For Bringing to Top on Windows
-        self.raise_()          # For Bringing to Top from Minimized State on OSX
+        self.raise_()  # For Bringing to Top from Minimized State on OSX
 
 
 class SettingsLoader(QObject):
@@ -312,6 +326,7 @@ class ProcessorCheckBox(QtWidgets.QCheckBox):
         self.processor_type = processor_type
         super(ProcessorCheckBox, self).__init__(text, parent=parent)
 
+
 class ErrorType(Enum):
     "Error Types"
     ConfigLoadingError = "Config Loading Error"

src/khoj/interface/desktop/system_tray.py

@@ -17,17 +17,17 @@ def create_system_tray(gui: QtWidgets.QApplication, main_window: MainWindow):
     """
 
     # Create the system tray with icon
-    icon_path = constants.web_directory / 'assets/icons/favicon-144x144.png'
+    icon_path = constants.web_directory / "assets/icons/favicon-144x144.png"
-    icon = QtGui.QIcon(f'{icon_path.absolute()}')
+    icon = QtGui.QIcon(f"{icon_path.absolute()}")
     tray = QtWidgets.QSystemTrayIcon(icon)
     tray.setVisible(True)
 
     # Create the menu and menu actions
     menu = QtWidgets.QMenu()
     menu_actions = [
-        ('Search', lambda: webbrowser.open(f'http://{state.host}:{state.port}/')),
+        ("Search", lambda: webbrowser.open(f"http://{state.host}:{state.port}/")),
-        ('Configure', main_window.show_on_top),
+        ("Configure", main_window.show_on_top),
-        ('Quit', gui.quit),
+        ("Quit", gui.quit),
     ]
 
     # Add the menu actions to the menu

src/khoj/main.py

@@ -8,8 +8,8 @@ import warnings
 from platform import system
 
 # Ignore non-actionable warnings
-warnings.filterwarnings("ignore", message=r'snapshot_download.py has been made private', category=FutureWarning)
+warnings.filterwarnings("ignore", message=r"snapshot_download.py has been made private", category=FutureWarning)
-warnings.filterwarnings("ignore", message=r'legacy way to download files from the HF hub,', category=FutureWarning)
+warnings.filterwarnings("ignore", message=r"legacy way to download files from the HF hub,", category=FutureWarning)
 
 # External Packages
 import uvicorn
@@ -43,11 +43,12 @@ rich_handler = RichHandler(rich_tracebacks=True)
 rich_handler.setFormatter(fmt=logging.Formatter(fmt="%(message)s", datefmt="[%X]"))
 logging.basicConfig(handlers=[rich_handler])
 
-logger = logging.getLogger('khoj')
+logger = logging.getLogger("khoj")
+
 
 def run():
     # Turn Tokenizers Parallelism Off. App does not support it.
-    os.environ["TOKENIZERS_PARALLELISM"] = 'false'
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
     # Load config from CLI
     state.cli_args = sys.argv[1:]
@@ -66,7 +67,7 @@ def run():
         logger.setLevel(logging.DEBUG)
 
     # Set Log File
-    fh = logging.FileHandler(state.config_file.parent / 'khoj.log')
+    fh = logging.FileHandler(state.config_file.parent / "khoj.log")
     fh.setLevel(logging.DEBUG)
     logger.addHandler(fh)
 
@@ -87,7 +88,7 @@ def run():
         # On Linux (Gnome) the System tray is not supported.
         # Since only the Main Window is available
         # Quitting it should quit the application
-        if system() in ['Windows', 'Darwin']:
+        if system() in ["Windows", "Darwin"]:
             gui.setQuitOnLastWindowClosed(False)
             tray = create_system_tray(gui, main_window)
             tray.show()
@@ -97,7 +98,7 @@ def run():
         server = ServerThread(app, args.host, args.port, args.socket)
 
         # Show Main Window on First Run Experience or if on Linux
-        if args.config is None or system() not in ['Windows', 'Darwin']:
+        if args.config is None or system() not in ["Windows", "Darwin"]:
             main_window.show()
 
         # Setup Signal Handlers
@@ -112,9 +113,10 @@ def run():
         gui.aboutToQuit.connect(server.terminate)
 
         # Close Splash Screen if still open
-        if system() != 'Darwin':
+        if system() != "Darwin":
             try:
                 import pyi_splash
+
                 # Update the text on the splash screen
                 pyi_splash.update_text("Khoj setup complete")
                 # Close Splash Screen
@@ -167,5 +169,5 @@ class ServerThread(QThread):
         start_server(self.app, self.host, self.port, self.socket)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     run()

src/khoj/processor/conversation/gpt.py

@@ -19,31 +19,27 @@ def summarize(text, summary_type, model, user_query=None, api_key=None, temperat
 
     # Setup Prompt based on Summary Type
     if summary_type == "chat":
-        prompt = f'''
+        prompt = f"""
 You are an AI. Summarize the conversation below from your perspective:
 
 {text}
 
-Summarize the conversation from the AI's first-person perspective:'''
+Summarize the conversation from the AI's first-person perspective:"""
     elif summary_type == "notes":
-        prompt = f'''
+        prompt = f"""
 Summarize the below notes about {user_query}:
 
 {text}
 
-Summarize the notes in second person perspective:'''
+Summarize the notes in second person perspective:"""
 
     # Get Response from GPT
     response = openai.Completion.create(
-        prompt=prompt,
+        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, frequency_penalty=0.2, stop='"""'
-        model=model,
+    )
-        temperature=temperature,
-        max_tokens=max_tokens,
-        frequency_penalty=0.2,
-        stop="\"\"\"")
 
     # Extract, Clean Message from GPT's Response
-    story = response['choices'][0]['text']
+    story = response["choices"][0]["text"]
     return str(story).replace("\n\n", "")
 
 
@@ -53,7 +49,7 @@ def extract_search_type(text, model, api_key=None, temperature=0.5, max_tokens=1
     """
     # Initialize Variables
     openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
-    understand_primer = '''
+    understand_primer = """
 Objective: Extract search type from user query and return information as JSON
 
 Allowed search types are listed below:
@@ -73,7 +69,7 @@ A:{ "search-type": "notes" }
 Q: When did I buy Groceries last?
 A:{ "search-type": "ledger" }
 Q:When did I go surfing last?
-A:{ "search-type": "notes" }'''
+A:{ "search-type": "notes" }"""
 
     # Setup Prompt with Understand Primer
     prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
@@ -82,15 +78,11 @@ A:{ "search-type": "notes" }'''
 
     # Get Response from GPT
     response = openai.Completion.create(
-        prompt=prompt,
+        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, frequency_penalty=0.2, stop=["\n"]
-        model=model,
+    )
-        temperature=temperature,
-        max_tokens=max_tokens,
-        frequency_penalty=0.2,
-        stop=["\n"])
 
     # Extract, Clean Message from GPT's Response
-    story = str(response['choices'][0]['text'])
+    story = str(response["choices"][0]["text"])
     return json.loads(story.strip(empty_escape_sequences))
 
 
@@ -100,7 +92,7 @@ def understand(text, model, api_key=None, temperature=0.5, max_tokens=100, verbo
     """
     # Initialize Variables
     openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
-    understand_primer = '''
+    understand_primer = """
 Objective: Extract intent and trigger emotion information as JSON from each chat message
 
 Potential intent types and valid argument values are listed below:
@@ -142,7 +134,7 @@ A: { "intent": {"type": "remember", "memory-type": "notes", "query": "recommend
 Q: When did I go surfing last?
 A: { "intent": {"type": "remember", "memory-type": "notes", "query": "When did I go surfing last"}, "trigger-emotion": "calm" }
 Q: Can you dance for me?
-A: { "intent": {"type": "generate", "activity": "chat", "query": "Can you dance for me?"}, "trigger-emotion": "sad" }'''
+A: { "intent": {"type": "generate", "activity": "chat", "query": "Can you dance for me?"}, "trigger-emotion": "sad" }"""
 
     # Setup Prompt with Understand Primer
     prompt = message_to_prompt(text, understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
@@ -151,15 +143,11 @@ A: { "intent": {"type": "generate", "activity": "chat", "query": "Can you dance
 
     # Get Response from GPT
     response = openai.Completion.create(
-        prompt=prompt,
+        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, frequency_penalty=0.2, stop=["\n"]
-        model=model,
+    )
-        temperature=temperature,
-        max_tokens=max_tokens,
-        frequency_penalty=0.2,
-        stop=["\n"])
 
     # Extract, Clean Message from GPT's Response
-    story = str(response['choices'][0]['text'])
+    story = str(response["choices"][0]["text"])
     return json.loads(story.strip(empty_escape_sequences))
 
 
@@ -171,15 +159,15 @@ def converse(text, model, conversation_history=None, api_key=None, temperature=0
     max_words = 500
     openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
 
-    conversation_primer = f'''
+    conversation_primer = f"""
 The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and a very friendly companion.
 
 Human: Hello, who are you?
-AI: Hi, I am an AI conversational companion created by OpenAI. How can I help you today?'''
+AI: Hi, I am an AI conversational companion created by OpenAI. How can I help you today?"""
 
     # Setup Prompt with Primer or Conversation History
     prompt = message_to_prompt(text, conversation_history or conversation_primer)
-    prompt = ' '.join(prompt.split()[:max_words])
+    prompt = " ".join(prompt.split()[:max_words])
 
     # Get Response from GPT
     response = openai.Completion.create(
@@ -188,14 +176,17 @@ AI: Hi, I am an AI conversational companion created by OpenAI. How can I help yo
         temperature=temperature,
         max_tokens=max_tokens,
         presence_penalty=0.6,
-        stop=["\n", "Human:", "AI:"])
+        stop=["\n", "Human:", "AI:"],
+    )
 
     # Extract, Clean Message from GPT's Response
-    story = str(response['choices'][0]['text'])
+    story = str(response["choices"][0]["text"])
     return story.strip(empty_escape_sequences)
 
 
-def message_to_prompt(user_message, conversation_history="", gpt_message=None, start_sequence="\nAI:", restart_sequence="\nHuman:"):
+def message_to_prompt(
+    user_message, conversation_history="", gpt_message=None, start_sequence="\nAI:", restart_sequence="\nHuman:"
+):
     """Create prompt for GPT from messages and conversation history"""
     gpt_message = f" {gpt_message}" if gpt_message else ""
 
@@ -205,12 +196,8 @@ def message_to_prompt(user_message, conversation_history="", gpt_message=None, s
 def message_to_log(user_message, gpt_message, user_message_metadata={}, conversation_log=[]):
     """Create json logs from messages, metadata for conversation log"""
     default_user_message_metadata = {
-        "intent": {
+        "intent": {"type": "remember", "memory-type": "notes", "query": user_message},
-            "type": "remember",
+        "trigger-emotion": "calm",
-            "memory-type": "notes",
-            "query": user_message
-        },
-        "trigger-emotion": "calm"
     }
     current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
@@ -229,5 +216,4 @@ def message_to_log(user_message, gpt_message, user_message_metadata={}, conversa
 
 def extract_summaries(metadata):
     """Extract summaries from metadata"""
-    return ''.join(
+    return "".join([f'\n{session["summary"]}' for session in metadata])
-        [f'\n{session["summary"]}' for session in metadata])

src/khoj/processor/ledger/beancount_to_jsonl.py

@@ -19,7 +19,11 @@ class BeancountToJsonl(TextToJsonl):
     # Define Functions
     def process(self, previous_entries=None):
         # Extract required fields from config
-        beancount_files, beancount_file_filter, output_file = self.config.input_files, self.config.input_filter,self.config.compressed_jsonl
+        beancount_files, beancount_file_filter, output_file = (
+            self.config.input_files,
+            self.config.input_filter,
+            self.config.compressed_jsonl,
+        )
 
         # Input Validation
         if is_none_or_empty(beancount_files) and is_none_or_empty(beancount_file_filter):
@@ -31,7 +35,9 @@ class BeancountToJsonl(TextToJsonl):
 
         # Extract Entries from specified Beancount files
         with timer("Parse transactions from Beancount files into dictionaries", logger):
-            current_entries = BeancountToJsonl.convert_transactions_to_maps(*BeancountToJsonl.extract_beancount_transactions(beancount_files))
+            current_entries = BeancountToJsonl.convert_transactions_to_maps(
+                *BeancountToJsonl.extract_beancount_transactions(beancount_files)
+            )
 
         # Split entries by max tokens supported by model
         with timer("Split entries by max token size supported by model", logger):
@@ -42,7 +48,9 @@ class BeancountToJsonl(TextToJsonl):
             if not previous_entries:
                 entries_with_ids = list(enumerate(current_entries))
             else:
-                entries_with_ids = self.mark_entries_for_update(current_entries, previous_entries, key='compiled', logger=logger)
+                entries_with_ids = self.mark_entries_for_update(
+                    current_entries, previous_entries, key="compiled", logger=logger
+                )
 
         with timer("Write transactions to JSONL file", logger):
             # Process Each Entry from All Notes Files
@@ -62,9 +70,7 @@ class BeancountToJsonl(TextToJsonl):
         "Get Beancount files to process"
         absolute_beancount_files, filtered_beancount_files = set(), set()
         if beancount_files:
-            absolute_beancount_files = {get_absolute_path(beancount_file)
+            absolute_beancount_files = {get_absolute_path(beancount_file) for beancount_file in beancount_files}
-                                for beancount_file
-                                in beancount_files}
         if beancount_file_filters:
             filtered_beancount_files = {
                 filtered_file
@@ -76,14 +82,13 @@ class BeancountToJsonl(TextToJsonl):
 
         files_with_non_beancount_extensions = {
             beancount_file
-            for beancount_file
+            for beancount_file in all_beancount_files
-            in all_beancount_files
             if not beancount_file.endswith(".bean") and not beancount_file.endswith(".beancount")
         }
         if any(files_with_non_beancount_extensions):
             print(f"[Warning] There maybe non beancount files in the input set: {files_with_non_beancount_extensions}")
 
-        logger.info(f'Processing files: {all_beancount_files}')
+        logger.info(f"Processing files: {all_beancount_files}")
 
         return all_beancount_files
 
@@ -92,19 +97,20 @@ class BeancountToJsonl(TextToJsonl):
         "Extract entries from specified Beancount files"
 
         # Initialize Regex for extracting Beancount Entries
-        transaction_regex = r'^\n?\d{4}-\d{2}-\d{2} [\*|\!] '
+        transaction_regex = r"^\n?\d{4}-\d{2}-\d{2} [\*|\!] "
-        empty_newline = f'^[\n\r\t\ ]*$'
+        empty_newline = f"^[\n\r\t\ ]*$"
 
         entries = []
         transaction_to_file_map = []
         for beancount_file in beancount_files:
             with open(beancount_file) as f:
                 ledger_content = f.read()
-                transactions_per_file = [entry.strip(empty_escape_sequences)
+                transactions_per_file = [
-                for entry
+                    entry.strip(empty_escape_sequences)
-                in re.split(empty_newline, ledger_content, flags=re.MULTILINE)
+                    for entry in re.split(empty_newline, ledger_content, flags=re.MULTILINE)
-                if re.match(transaction_regex, entry)]
+                    if re.match(transaction_regex, entry)
-                transaction_to_file_map += zip(transactions_per_file, [beancount_file]*len(transactions_per_file))
+                ]
+                transaction_to_file_map += zip(transactions_per_file, [beancount_file] * len(transactions_per_file))
                 entries.extend(transactions_per_file)
         return entries, dict(transaction_to_file_map)
 
@@ -113,7 +119,9 @@ class BeancountToJsonl(TextToJsonl):
         "Convert each parsed Beancount transaction into a Entry"
         entries = []
         for parsed_entry in parsed_entries:
-            entries.append(Entry(compiled=parsed_entry, raw=parsed_entry, file=f'{transaction_to_file_map[parsed_entry]}'))
+            entries.append(
+                Entry(compiled=parsed_entry, raw=parsed_entry, file=f"{transaction_to_file_map[parsed_entry]}")
+            )
 
         logger.info(f"Converted {len(parsed_entries)} transactions to dictionaries")
 
@@ -122,4 +130,4 @@ class BeancountToJsonl(TextToJsonl):
     @staticmethod
     def convert_transaction_maps_to_jsonl(entries: List[Entry]) -> str:
         "Convert each Beancount transaction entry to JSON and collate as JSONL"
-        return ''.join([f'{entry.to_json()}\n' for entry in entries])
+        return "".join([f"{entry.to_json()}\n" for entry in entries])

src/khoj/processor/markdown/markdown_to_jsonl.py

@@ -20,7 +20,11 @@ class MarkdownToJsonl(TextToJsonl):
     # Define Functions
     def process(self, previous_entries=None):
         # Extract required fields from config
-        markdown_files, markdown_file_filter, output_file = self.config.input_files, self.config.input_filter, self.config.compressed_jsonl
+        markdown_files, markdown_file_filter, output_file = (
+            self.config.input_files,
+            self.config.input_filter,
+            self.config.compressed_jsonl,
+        )
 
         # Input Validation
         if is_none_or_empty(markdown_files) and is_none_or_empty(markdown_file_filter):
@@ -32,7 +36,9 @@ class MarkdownToJsonl(TextToJsonl):
 
         # Extract Entries from specified Markdown files
         with timer("Parse entries from Markdown files into dictionaries", logger):
-            current_entries = MarkdownToJsonl.convert_markdown_entries_to_maps(*MarkdownToJsonl.extract_markdown_entries(markdown_files))
+            current_entries = MarkdownToJsonl.convert_markdown_entries_to_maps(
+                *MarkdownToJsonl.extract_markdown_entries(markdown_files)
+            )
 
         # Split entries by max tokens supported by model
         with timer("Split entries by max token size supported by model", logger):
@@ -43,7 +49,9 @@ class MarkdownToJsonl(TextToJsonl):
             if not previous_entries:
                 entries_with_ids = list(enumerate(current_entries))
             else:
-                entries_with_ids = self.mark_entries_for_update(current_entries, previous_entries, key='compiled', logger=logger)
+                entries_with_ids = self.mark_entries_for_update(
+                    current_entries, previous_entries, key="compiled", logger=logger
+                )
 
         with timer("Write markdown entries to JSONL file", logger):
             # Process Each Entry from All Notes Files
@@ -75,15 +83,16 @@ class MarkdownToJsonl(TextToJsonl):
 
         files_with_non_markdown_extensions = {
             md_file
-            for md_file
+            for md_file in all_markdown_files
-            in all_markdown_files
+            if not md_file.endswith(".md") and not md_file.endswith(".markdown")
-            if not md_file.endswith(".md") and not md_file.endswith('.markdown')
         }
 
         if any(files_with_non_markdown_extensions):
-            logger.warn(f"[Warning] There maybe non markdown-mode files in the input set: {files_with_non_markdown_extensions}")
+            logger.warn(
+                f"[Warning] There maybe non markdown-mode files in the input set: {files_with_non_markdown_extensions}"
+            )
 
-        logger.info(f'Processing files: {all_markdown_files}')
+        logger.info(f"Processing files: {all_markdown_files}")
 
         return all_markdown_files
 
@@ -92,20 +101,20 @@ class MarkdownToJsonl(TextToJsonl):
         "Extract entries by heading from specified Markdown files"
 
         # Regex to extract Markdown Entries by Heading
-        markdown_heading_regex = r'^#'
+        markdown_heading_regex = r"^#"
 
         entries = []
         entry_to_file_map = []
         for markdown_file in markdown_files:
-            with open(markdown_file, 'r', encoding='utf8') as f:
+            with open(markdown_file, "r", encoding="utf8") as f:
                 markdown_content = f.read()
                 markdown_entries_per_file = []
                 for entry in re.split(markdown_heading_regex, markdown_content, flags=re.MULTILINE):
-                    prefix = '#' if entry.startswith('#') else '# '
+                    prefix = "#" if entry.startswith("#") else "# "
-                    if entry.strip(empty_escape_sequences) != '':
+                    if entry.strip(empty_escape_sequences) != "":
-                        markdown_entries_per_file.append(f'{prefix}{entry.strip(empty_escape_sequences)}')
+                        markdown_entries_per_file.append(f"{prefix}{entry.strip(empty_escape_sequences)}")
 
-                entry_to_file_map += zip(markdown_entries_per_file, [markdown_file]*len(markdown_entries_per_file))
+                entry_to_file_map += zip(markdown_entries_per_file, [markdown_file] * len(markdown_entries_per_file))
                 entries.extend(markdown_entries_per_file)
 
         return entries, dict(entry_to_file_map)
@@ -115,7 +124,7 @@ class MarkdownToJsonl(TextToJsonl):
         "Convert each Markdown entries into a dictionary"
         entries = []
         for parsed_entry in parsed_entries:
-            entries.append(Entry(compiled=parsed_entry, raw=parsed_entry, file=f'{entry_to_file_map[parsed_entry]}'))
+            entries.append(Entry(compiled=parsed_entry, raw=parsed_entry, file=f"{entry_to_file_map[parsed_entry]}"))
 
         logger.info(f"Converted {len(parsed_entries)} markdown entries to dictionaries")
 
@@ -124,4 +133,4 @@ class MarkdownToJsonl(TextToJsonl):
     @staticmethod
     def convert_markdown_maps_to_jsonl(entries: List[Entry]):
         "Convert each Markdown entry to JSON and collate as JSONL"
-        return ''.join([f'{entry.to_json()}\n' for entry in entries])
+        return "".join([f"{entry.to_json()}\n" for entry in entries])

src/khoj/processor/org_mode/org_to_jsonl.py

@@ -18,9 +18,13 @@ logger = logging.getLogger(__name__)
 
 class OrgToJsonl(TextToJsonl):
     # Define Functions
-    def process(self, previous_entries: List[Entry]=None):
+    def process(self, previous_entries: List[Entry] = None):
         # Extract required fields from config
-        org_files, org_file_filter, output_file = self.config.input_files, self.config.input_filter, self.config.compressed_jsonl
+        org_files, org_file_filter, output_file = (
+            self.config.input_files,
+            self.config.input_filter,
+            self.config.compressed_jsonl,
+        )
         index_heading_entries = self.config.index_heading_entries
 
         # Input Validation
@@ -46,7 +50,9 @@ class OrgToJsonl(TextToJsonl):
         if not previous_entries:
             entries_with_ids = list(enumerate(current_entries))
         else:
-            entries_with_ids = self.mark_entries_for_update(current_entries, previous_entries, key='compiled', logger=logger)
+            entries_with_ids = self.mark_entries_for_update(
+                current_entries, previous_entries, key="compiled", logger=logger
+            )
 
         # Process Each Entry from All Notes Files
         with timer("Write org entries to JSONL file", logger):
@@ -66,11 +72,7 @@ class OrgToJsonl(TextToJsonl):
         "Get Org files to process"
         absolute_org_files, filtered_org_files = set(), set()
         if org_files:
-            absolute_org_files = {
+            absolute_org_files = {get_absolute_path(org_file) for org_file in org_files}
-                get_absolute_path(org_file)
-                for org_file
-                in org_files
-            }
         if org_file_filters:
             filtered_org_files = {
                 filtered_file
@@ -84,7 +86,7 @@ class OrgToJsonl(TextToJsonl):
         if any(files_with_non_org_extensions):
             logger.warn(f"There maybe non org-mode files in the input set: {files_with_non_org_extensions}")
 
-        logger.info(f'Processing files: {all_org_files}')
+        logger.info(f"Processing files: {all_org_files}")
 
         return all_org_files
 
@@ -95,13 +97,15 @@ class OrgToJsonl(TextToJsonl):
         entry_to_file_map = []
         for org_file in org_files:
             org_file_entries = orgnode.makelist(str(org_file))
-            entry_to_file_map += zip(org_file_entries, [org_file]*len(org_file_entries))
+            entry_to_file_map += zip(org_file_entries, [org_file] * len(org_file_entries))
             entries.extend(org_file_entries)
 
         return entries, dict(entry_to_file_map)
 
     @staticmethod
-    def convert_org_nodes_to_entries(parsed_entries: List[orgnode.Orgnode], entry_to_file_map, index_heading_entries=False) -> List[Entry]:
+    def convert_org_nodes_to_entries(
+        parsed_entries: List[orgnode.Orgnode], entry_to_file_map, index_heading_entries=False
+    ) -> List[Entry]:
         "Convert Org-Mode nodes into list of Entry objects"
         entries: List[Entry] = []
         for parsed_entry in parsed_entries:
@@ -109,13 +113,13 @@ class OrgToJsonl(TextToJsonl):
                 # Ignore title notes i.e notes with just headings and empty body
                 continue
 
-            compiled = f'{parsed_entry.heading}.'
+            compiled = f"{parsed_entry.heading}."
             if state.verbose > 2:
                 logger.debug(f"Title: {parsed_entry.heading}")
 
             if parsed_entry.tags:
                 tags_str = " ".join(parsed_entry.tags)
-                compiled += f'\t {tags_str}.'
+                compiled += f"\t {tags_str}."
                 if state.verbose > 2:
                     logger.debug(f"Tags: {tags_str}")
 
@@ -130,19 +134,16 @@ class OrgToJsonl(TextToJsonl):
                     logger.debug(f'Scheduled: {parsed_entry.scheduled.strftime("%Y-%m-%d")}')
 
             if parsed_entry.hasBody:
-                compiled += f'\n {parsed_entry.body}'
+                compiled += f"\n {parsed_entry.body}"
                 if state.verbose > 2:
                     logger.debug(f"Body: {parsed_entry.body}")
 
             if compiled:
-                entries += [Entry(
+                entries += [Entry(compiled=compiled, raw=f"{parsed_entry}", file=f"{entry_to_file_map[parsed_entry]}")]
-                    compiled=compiled,
-                    raw=f'{parsed_entry}',
-                    file=f'{entry_to_file_map[parsed_entry]}')]
 
         return entries
 
     @staticmethod
     def convert_org_entries_to_jsonl(entries: Iterable[Entry]) -> str:
         "Convert each Org-Mode entry to JSON and collate as JSONL"
-        return ''.join([f'{entry_dict.to_json()}\n' for entry_dict in entries])
+        return "".join([f"{entry_dict.to_json()}\n" for entry_dict in entries])

src/khoj/processor/org_mode/orgnode.py

@@ -39,182 +39,197 @@ from pathlib import Path
 from os.path import relpath
 from typing import List
 
-indent_regex = re.compile(r'^ *')
+indent_regex = re.compile(r"^ *")
+
 
 def normalize_filename(filename):
-   "Normalize and escape filename for rendering"
+    "Normalize and escape filename for rendering"
-   if not Path(filename).is_absolute():
+    if not Path(filename).is_absolute():
-      # Normalize relative filename to be relative to current directory
+        # Normalize relative filename to be relative to current directory
-      normalized_filename = f'~/{relpath(filename, start=Path.home())}'
+        normalized_filename = f"~/{relpath(filename, start=Path.home())}"
-   else:
+    else:
-      normalized_filename = filename
+        normalized_filename = filename
-   escaped_filename = f'{normalized_filename}'.replace("[","\[").replace("]","\]")
+    escaped_filename = f"{normalized_filename}".replace("[", "\[").replace("]", "\]")
-   return escaped_filename
+    return escaped_filename
+
 
 def makelist(filename):
-   """
+    """
-   Read an org-mode file and return a list of Orgnode objects
+    Read an org-mode file and return a list of Orgnode objects
-   created from this file.
+    created from this file.
-   """
+    """
-   ctr = 0
+    ctr = 0
 
-   f = open(filename, 'r')
+    f = open(filename, "r")
 
-   todos         = { "TODO": "", "WAITING": "", "ACTIVE": "",
+    todos = {
-                     "DONE": "", "CANCELLED": "", "FAILED": ""} # populated from #+SEQ_TODO line
+        "TODO": "",
-   level         = ""
+        "WAITING": "",
-   heading       = ""
+        "ACTIVE": "",
-   bodytext      = ""
+        "DONE": "",
-   tags          = list()      # set of all tags in headline
+        "CANCELLED": "",
-   closed_date   = ''
+        "FAILED": "",
-   sched_date    = ''
+    }  # populated from #+SEQ_TODO line
-   deadline_date = ''
+    level = ""
-   logbook       = list()
+    heading = ""
-   nodelist: List[Orgnode] = list()
+    bodytext = ""
-   property_map  = dict()
+    tags = list()  # set of all tags in headline
-   in_properties_drawer = False
+    closed_date = ""
-   in_logbook_drawer = False
+    sched_date = ""
-   file_title = f'{filename}'
+    deadline_date = ""
+    logbook = list()
+    nodelist: List[Orgnode] = list()
+    property_map = dict()
+    in_properties_drawer = False
+    in_logbook_drawer = False
+    file_title = f"{filename}"
 
-   for line in f:
+    for line in f:
-       ctr += 1
+        ctr += 1
-       heading_search = re.search(r'^(\*+)\s(.*?)\s*$', line)
+        heading_search = re.search(r"^(\*+)\s(.*?)\s*$", line)
-       if heading_search:  # we are processing a heading line
+        if heading_search:  # we are processing a heading line
-          if heading: # if we have are on second heading, append first heading to headings list
+            if heading:  # if we have are on second heading, append first heading to headings list
-             thisNode = Orgnode(level, heading, bodytext, tags)
+                thisNode = Orgnode(level, heading, bodytext, tags)
-             if closed_date:
+                if closed_date:
-                thisNode.closed = closed_date
+                    thisNode.closed = closed_date
-                closed_date = ''
+                    closed_date = ""
-             if sched_date:
+                if sched_date:
-                thisNode.scheduled = sched_date
+                    thisNode.scheduled = sched_date
-                sched_date = ""
+                    sched_date = ""
-             if deadline_date:
+                if deadline_date:
-                thisNode.deadline = deadline_date
+                    thisNode.deadline = deadline_date
-                deadline_date = ''
+                    deadline_date = ""
-             if logbook:
+                if logbook:
-                thisNode.logbook = logbook
+                    thisNode.logbook = logbook
-                logbook = list()
+                    logbook = list()
-             thisNode.properties = property_map
+                thisNode.properties = property_map
-             nodelist.append( thisNode )
+                nodelist.append(thisNode)
-          property_map = {'LINE': f'file:{normalize_filename(filename)}::{ctr}'}
+            property_map = {"LINE": f"file:{normalize_filename(filename)}::{ctr}"}
-          level = heading_search.group(1)
+            level = heading_search.group(1)
-          heading =  heading_search.group(2)
+            heading = heading_search.group(2)
-          bodytext = ""
+            bodytext = ""
-          tags = list()       # set of all tags in headline
+            tags = list()  # set of all tags in headline
-          tag_search = re.search(r'(.*?)\s*:([a-zA-Z0-9].*?):$',heading)
+            tag_search = re.search(r"(.*?)\s*:([a-zA-Z0-9].*?):$", heading)
-          if tag_search:
+            if tag_search:
-              heading = tag_search.group(1)
+                heading = tag_search.group(1)
-              parsedtags = tag_search.group(2)
+                parsedtags = tag_search.group(2)
-              if parsedtags:
+                if parsedtags:
-                 for parsedtag in parsedtags.split(':'):
+                    for parsedtag in parsedtags.split(":"):
-                    if parsedtag != '': tags.append(parsedtag)
+                        if parsedtag != "":
-       else:      # we are processing a non-heading line
+                            tags.append(parsedtag)
-           if line[:10] == '#+SEQ_TODO':
+        else:  # we are processing a non-heading line
-              kwlist = re.findall(r'([A-Z]+)\(', line)
+            if line[:10] == "#+SEQ_TODO":
-              for kw in kwlist: todos[kw] = ""
+                kwlist = re.findall(r"([A-Z]+)\(", line)
+                for kw in kwlist:
+                    todos[kw] = ""
 
-           # Set file title to TITLE property, if it exists
+            # Set file title to TITLE property, if it exists
-           title_search = re.search(r'^#\+TITLE:\s*(.*)$', line)
+            title_search = re.search(r"^#\+TITLE:\s*(.*)$", line)
-           if title_search and title_search.group(1).strip() != '':
+            if title_search and title_search.group(1).strip() != "":
-               title_text = title_search.group(1).strip()
+                title_text = title_search.group(1).strip()
-               if file_title == f'{filename}':
+                if file_title == f"{filename}":
-                  file_title = title_text
+                    file_title = title_text
-               else:
+                else:
-                  file_title += f' {title_text}'
+                    file_title += f" {title_text}"
-               continue
+                continue
 
-           # Ignore Properties Drawers Completely
+            # Ignore Properties Drawers Completely
-           if re.search(':PROPERTIES:', line):
+            if re.search(":PROPERTIES:", line):
-              in_properties_drawer=True
+                in_properties_drawer = True
-              continue
+                continue
-           if in_properties_drawer and re.search(':END:', line):
+            if in_properties_drawer and re.search(":END:", line):
-              in_properties_drawer=False
+                in_properties_drawer = False
-              continue
+                continue
 
-           # Ignore Logbook Drawer Start, End Lines
+            # Ignore Logbook Drawer Start, End Lines
-           if re.search(':LOGBOOK:', line):
+            if re.search(":LOGBOOK:", line):
-              in_logbook_drawer=True
+                in_logbook_drawer = True
-              continue
+                continue
-           if in_logbook_drawer and re.search(':END:', line):
+            if in_logbook_drawer and re.search(":END:", line):
-              in_logbook_drawer=False
+                in_logbook_drawer = False
-              continue
+                continue
 
-           # Extract Clocking Lines
+            # Extract Clocking Lines
-           clocked_re = re.search(r'CLOCK:\s*\[([0-9]{4}-[0-9]{2}-[0-9]{2} [a-zA-Z]{3} [0-9]{2}:[0-9]{2})\]--\[([0-9]{4}-[0-9]{2}-[0-9]{2} [a-zA-Z]{3} [0-9]{2}:[0-9]{2})\]', line)
+            clocked_re = re.search(
-           if clocked_re:
+                r"CLOCK:\s*\[([0-9]{4}-[0-9]{2}-[0-9]{2} [a-zA-Z]{3} [0-9]{2}:[0-9]{2})\]--\[([0-9]{4}-[0-9]{2}-[0-9]{2} [a-zA-Z]{3} [0-9]{2}:[0-9]{2})\]",
-              # convert clock in, clock out strings to datetime objects
+                line,
-              clocked_in = datetime.datetime.strptime(clocked_re.group(1), '%Y-%m-%d %a %H:%M')
+            )
-              clocked_out = datetime.datetime.strptime(clocked_re.group(2), '%Y-%m-%d %a %H:%M')
+            if clocked_re:
-              # add clocked time to the entries logbook list
+                # convert clock in, clock out strings to datetime objects
-              logbook += [(clocked_in, clocked_out)]
+                clocked_in = datetime.datetime.strptime(clocked_re.group(1), "%Y-%m-%d %a %H:%M")
-              line = ""
+                clocked_out = datetime.datetime.strptime(clocked_re.group(2), "%Y-%m-%d %a %H:%M")
+                # add clocked time to the entries logbook list
+                logbook += [(clocked_in, clocked_out)]
+                line = ""
 
-           property_search = re.search(r'^\s*:([a-zA-Z0-9]+):\s*(.*?)\s*$', line)
+            property_search = re.search(r"^\s*:([a-zA-Z0-9]+):\s*(.*?)\s*$", line)
-           if property_search:
+            if property_search:
-              # Set ID property to an id based org-mode link to the entry
+                # Set ID property to an id based org-mode link to the entry
-              if property_search.group(1) == 'ID':
+                if property_search.group(1) == "ID":
-                 property_map['ID'] = f'id:{property_search.group(2)}'
+                    property_map["ID"] = f"id:{property_search.group(2)}"
-              else:
+                else:
-                 property_map[property_search.group(1)] = property_search.group(2)
+                    property_map[property_search.group(1)] = property_search.group(2)
-              continue
+                continue
 
-           cd_re = re.search(r'CLOSED:\s*\[([0-9]{4})-([0-9]{2})-([0-9]{2})', line)
+            cd_re = re.search(r"CLOSED:\s*\[([0-9]{4})-([0-9]{2})-([0-9]{2})", line)
-           if cd_re:
+            if cd_re:
-              closed_date = datetime.date(int(cd_re.group(1)),
+                closed_date = datetime.date(int(cd_re.group(1)), int(cd_re.group(2)), int(cd_re.group(3)))
-                                         int(cd_re.group(2)),
+            sd_re = re.search(r"SCHEDULED:\s*<([0-9]+)\-([0-9]+)\-([0-9]+)", line)
-                                         int(cd_re.group(3)) )
+            if sd_re:
-           sd_re = re.search(r'SCHEDULED:\s*<([0-9]+)\-([0-9]+)\-([0-9]+)', line)
+                sched_date = datetime.date(int(sd_re.group(1)), int(sd_re.group(2)), int(sd_re.group(3)))
-           if sd_re:
+            dd_re = re.search(r"DEADLINE:\s*<(\d+)\-(\d+)\-(\d+)", line)
-              sched_date = datetime.date(int(sd_re.group(1)),
+            if dd_re:
-                                         int(sd_re.group(2)),
+                deadline_date = datetime.date(int(dd_re.group(1)), int(dd_re.group(2)), int(dd_re.group(3)))
-                                         int(sd_re.group(3)) )
-           dd_re = re.search(r'DEADLINE:\s*<(\d+)\-(\d+)\-(\d+)', line)
-           if dd_re:
-              deadline_date = datetime.date(int(dd_re.group(1)),
-                                            int(dd_re.group(2)),
-                                            int(dd_re.group(3)) )
 
-           # Ignore property drawer, scheduled, closed, deadline, logbook entries and # lines from body
+            # Ignore property drawer, scheduled, closed, deadline, logbook entries and # lines from body
-           if not in_properties_drawer and not cd_re and not sd_re and not dd_re and not clocked_re and line[:1] != '#':
+            if (
-               bodytext = bodytext + line
+                not in_properties_drawer
+                and not cd_re
+                and not sd_re
+                and not dd_re
+                and not clocked_re
+                and line[:1] != "#"
+            ):
+                bodytext = bodytext + line
 
-   # write out last node
+    # write out last node
-   thisNode = Orgnode(level, heading or file_title, bodytext, tags)
+    thisNode = Orgnode(level, heading or file_title, bodytext, tags)
-   thisNode.properties = property_map
+    thisNode.properties = property_map
-   if sched_date:
+    if sched_date:
-      thisNode.scheduled = sched_date
+        thisNode.scheduled = sched_date
-   if deadline_date:
+    if deadline_date:
-      thisNode.deadline = deadline_date
+        thisNode.deadline = deadline_date
-   if closed_date:
+    if closed_date:
-      thisNode.closed = closed_date
+        thisNode.closed = closed_date
-   if logbook:
+    if logbook:
-      thisNode.logbook = logbook
+        thisNode.logbook = logbook
-   nodelist.append( thisNode )
+    nodelist.append(thisNode)
 
-   # using the list of TODO keywords found in the file
+    # using the list of TODO keywords found in the file
-   # process the headings searching for TODO keywords
+    # process the headings searching for TODO keywords
-   for n in nodelist:
+    for n in nodelist:
-       todo_search = re.search(r'([A-Z]+)\s(.*?)$', n.heading)
+        todo_search = re.search(r"([A-Z]+)\s(.*?)$", n.heading)
-       if todo_search:
+        if todo_search:
-           if todo_search.group(1) in todos:
+            if todo_search.group(1) in todos:
-               n.heading = todo_search.group(2)
+                n.heading = todo_search.group(2)
-               n.todo = todo_search.group(1)
+                n.todo = todo_search.group(1)
 
-       # extract, set priority from heading, update heading if necessary
+        # extract, set priority from heading, update heading if necessary
-       priority_search = re.search(r'^\[\#(A|B|C)\] (.*?)$', n.heading)
+        priority_search = re.search(r"^\[\#(A|B|C)\] (.*?)$", n.heading)
-       if priority_search:
+        if priority_search:
-          n.priority = priority_search.group(1)
+            n.priority = priority_search.group(1)
-          n.heading = priority_search.group(2)
+            n.heading = priority_search.group(2)
 
-       # Set SOURCE property to a file+heading based org-mode link to the entry
+        # Set SOURCE property to a file+heading based org-mode link to the entry
-       if n.level == 0:
+        if n.level == 0:
-         n.properties['LINE'] = f'file:{normalize_filename(filename)}::0'
+            n.properties["LINE"] = f"file:{normalize_filename(filename)}::0"
-         n.properties['SOURCE'] = f'[[file:{normalize_filename(filename)}]]'
+            n.properties["SOURCE"] = f"[[file:{normalize_filename(filename)}]]"
-       else:
+        else:
-         escaped_heading = n.heading.replace("[","\\[").replace("]","\\]")
+            escaped_heading = n.heading.replace("[", "\\[").replace("]", "\\]")
-         n.properties['SOURCE'] = f'[[file:{normalize_filename(filename)}::*{escaped_heading}]]'
+            n.properties["SOURCE"] = f"[[file:{normalize_filename(filename)}::*{escaped_heading}]]"
+
+    return nodelist
 
-   return nodelist
 
 ######################
 class Orgnode(object):
@@ -222,6 +237,7 @@ class Orgnode(object):
     Orgnode class represents a headline, tags and text associated
     with the headline.
     """
+
     def __init__(self, level, headline, body, tags):
         """
         Create an Orgnode object given the parameters of level (as the
@@ -232,14 +248,14 @@ class Orgnode(object):
         self._level = len(level)
         self._heading = headline
         self._body = body
-        self._tags = tags          # All tags in the headline
+        self._tags = tags  # All tags in the headline
         self._todo = ""
-        self._priority = ""        # empty of A, B or C
+        self._priority = ""  # empty of A, B or C
-        self._scheduled = ""       # Scheduled date
+        self._scheduled = ""  # Scheduled date
-        self._deadline = ""        # Deadline date
+        self._deadline = ""  # Deadline date
-        self._closed = ""          # Closed date
+        self._closed = ""  # Closed date
         self._properties = dict()
-        self._logbook = list()     # List of clock-in, clock-out tuples representing logbook entries
+        self._logbook = list()  # List of clock-in, clock-out tuples representing logbook entries
 
         # Look for priority in headline and transfer to prty field
 
@@ -270,7 +286,7 @@ class Orgnode(object):
         """
         Returns True if node has non empty body, else False
         """
-        return self._body and re.sub(r'\n|\t|\r| ', '', self._body) != ''
+        return self._body and re.sub(r"\n|\t|\r| ", "", self._body) != ""
 
     @property
     def level(self):
@@ -417,20 +433,20 @@ class Orgnode(object):
         text as used to construct the node.
         """
         # Output heading line
-        n = ''
+        n = ""
         for _ in range(0, self._level):
-           n = n + '*'
+            n = n + "*"
-        n = n + ' '
+        n = n + " "
         if self._todo:
-           n = n + self._todo + ' '
+            n = n + self._todo + " "
         if self._priority:
-           n = n +  '[#' + self._priority + '] '
+            n = n + "[#" + self._priority + "] "
         n = n + self._heading
-        n = "%-60s " % n     # hack - tags will start in column 62
+        n = "%-60s " % n  # hack - tags will start in column 62
-        closecolon = ''
+        closecolon = ""
         for t in self._tags:
-           n = n + ':' + t
+            n = n + ":" + t
-           closecolon = ':'
+            closecolon = ":"
         n = n + closecolon
         n = n + "\n"
 
@@ -439,24 +455,24 @@ class Orgnode(object):
 
         # Output Closed Date, Scheduled Date, Deadline Date
         if self._closed or self._scheduled or self._deadline:
-           n = n + indent
+            n = n + indent
         if self._closed:
-           n = n + f'CLOSED: [{self._closed.strftime("%Y-%m-%d %a")}] '
+            n = n + f'CLOSED: [{self._closed.strftime("%Y-%m-%d %a")}] '
         if self._scheduled:
-           n = n + f'SCHEDULED: <{self._scheduled.strftime("%Y-%m-%d %a")}> '
+            n = n + f'SCHEDULED: <{self._scheduled.strftime("%Y-%m-%d %a")}> '
         if self._deadline:
-           n = n + f'DEADLINE: <{self._deadline.strftime("%Y-%m-%d %a")}> '
+            n = n + f'DEADLINE: <{self._deadline.strftime("%Y-%m-%d %a")}> '
         if self._closed or self._scheduled or self._deadline:
-           n = n + '\n'
+            n = n + "\n"
 
         # Ouput Property Drawer
         n = n + indent + ":PROPERTIES:\n"
         for key, value in self._properties.items():
-           n = n + indent + f":{key}: {value}\n"
+            n = n + indent + f":{key}: {value}\n"
         n = n + indent + ":END:\n"
 
         # Output Body
         if self.hasBody:
-           n = n + self._body
+            n = n + self._body
 
         return n

src/khoj/processor/text_to_jsonl.py

@@ -17,14 +17,17 @@ class TextToJsonl(ABC):
         self.config = config
 
     @abstractmethod
-    def process(self, previous_entries: List[Entry]=None) -> List[Tuple[int, Entry]]: ...
+    def process(self, previous_entries: List[Entry] = None) -> List[Tuple[int, Entry]]:
+        ...
 
     @staticmethod
     def hash_func(key: str) -> Callable:
-        return lambda entry: hashlib.md5(bytes(getattr(entry, key), encoding='utf-8')).hexdigest()
+        return lambda entry: hashlib.md5(bytes(getattr(entry, key), encoding="utf-8")).hexdigest()
 
     @staticmethod
-    def split_entries_by_max_tokens(entries: List[Entry], max_tokens: int=256, max_word_length: int=500) -> List[Entry]:
+    def split_entries_by_max_tokens(
+        entries: List[Entry], max_tokens: int = 256, max_word_length: int = 500
+    ) -> List[Entry]:
         "Split entries if compiled entry length exceeds the max tokens supported by the ML model."
         chunked_entries: List[Entry] = []
         for entry in entries:
@@ -32,13 +35,15 @@ class TextToJsonl(ABC):
             # Drop long words instead of having entry truncated to maintain quality of entry processed by models
             compiled_entry_words = [word for word in compiled_entry_words if len(word) <= max_word_length]
             for chunk_index in range(0, len(compiled_entry_words), max_tokens):
-                compiled_entry_words_chunk = compiled_entry_words[chunk_index:chunk_index + max_tokens]
+                compiled_entry_words_chunk = compiled_entry_words[chunk_index : chunk_index + max_tokens]
-                compiled_entry_chunk = ' '.join(compiled_entry_words_chunk)
+                compiled_entry_chunk = " ".join(compiled_entry_words_chunk)
                 entry_chunk = Entry(compiled=compiled_entry_chunk, raw=entry.raw, file=entry.file)
                 chunked_entries.append(entry_chunk)
         return chunked_entries
 
-    def mark_entries_for_update(self, current_entries: List[Entry], previous_entries: List[Entry], key='compiled', logger=None) -> List[Tuple[int, Entry]]:
+    def mark_entries_for_update(
+        self, current_entries: List[Entry], previous_entries: List[Entry], key="compiled", logger=None
+    ) -> List[Tuple[int, Entry]]:
         # Hash all current and previous entries to identify new entries
         with timer("Hash previous, current entries", logger):
             current_entry_hashes = list(map(TextToJsonl.hash_func(key), current_entries))
@@ -54,10 +59,7 @@ class TextToJsonl(ABC):
             existing_entry_hashes = set(current_entry_hashes) & set(previous_entry_hashes)
 
             # Mark new entries with -1 id to flag for later embeddings generation
-            new_entries = [
+            new_entries = [(-1, hash_to_current_entries[entry_hash]) for entry_hash in new_entry_hashes]
-                (-1, hash_to_current_entries[entry_hash])
-                for entry_hash in new_entry_hashes
-            ]
             # Set id of existing entries to their previous ids to reuse their existing encoded embeddings
             existing_entries = [
                 (previous_entry_hashes.index(entry_hash), hash_to_previous_entries[entry_hash])

src/khoj/routers/api.py

@@ -22,27 +22,30 @@ logger = logging.getLogger(__name__)
 
 
 # Create Routes
-@api.get('/config/data/default')
+@api.get("/config/data/default")
 def get_default_config_data():
     return constants.default_config
 
-@api.get('/config/data', response_model=FullConfig)
+
+@api.get("/config/data", response_model=FullConfig)
 def get_config_data():
     return state.config
 
-@api.post('/config/data')
+
+@api.post("/config/data")
 async def set_config_data(updated_config: FullConfig):
     state.config = updated_config
-    with open(state.config_file, 'w') as outfile:
+    with open(state.config_file, "w") as outfile:
         yaml.dump(yaml.safe_load(state.config.json(by_alias=True)), outfile)
         outfile.close()
     return state.config
 
-@api.get('/search', response_model=List[SearchResponse])
+
+@api.get("/search", response_model=List[SearchResponse])
 def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Optional[bool] = False):
     results: List[SearchResponse] = []
-    if q is None or q == '':
+    if q is None or q == "":
-        logger.info(f'No query param (q) passed in API call to initiate search')
+        logger.info(f"No query param (q) passed in API call to initiate search")
         return results
 
     # initialize variables
@@ -50,9 +53,9 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
     results_count = n
 
     # return cached results, if available
-    query_cache_key = f'{user_query}-{n}-{t}-{r}'
+    query_cache_key = f"{user_query}-{n}-{t}-{r}"
     if query_cache_key in state.query_cache:
-        logger.info(f'Return response from query cache')
+        logger.info(f"Return response from query cache")
         return state.query_cache[query_cache_key]
 
     if (t == SearchType.Org or t == None) and state.model.orgmode_search:
@@ -95,7 +98,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
         # query images
         with timer("Query took", logger):
             hits = image_search.query(user_query, results_count, state.model.image_search)
-            output_directory = constants.web_directory / 'images'
+            output_directory = constants.web_directory / "images"
 
         # collate and return results
         with timer("Collating results took", logger):
@@ -103,8 +106,9 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
                 hits,
                 image_names=state.model.image_search.image_names,
                 output_directory=output_directory,
-                image_files_url='/static/images',
+                image_files_url="/static/images",
-                count=results_count)
+                count=results_count,
+            )
 
     # Cache results
     state.query_cache[query_cache_key] = results
@@ -112,7 +116,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
     return results
 
 
-@api.get('/update')
+@api.get("/update")
 def update(t: Optional[SearchType] = None, force: Optional[bool] = False):
     try:
         state.search_index_lock.acquire()
@@ -132,4 +136,4 @@ def update(t: Optional[SearchType] = None, force: Optional[bool] = False):
     else:
         logger.info("Processor reconfigured via API call")
 
-    return {'status': 'ok', 'message': 'khoj reloaded'}
+    return {"status": "ok", "message": "khoj reloaded"}

src/khoj/routers/api_beta.py

@@ -9,7 +9,14 @@ from fastapi import APIRouter
 
 # Internal Packages
 from khoj.routers.api import search
-from khoj.processor.conversation.gpt import converse, extract_search_type, message_to_log, message_to_prompt, understand, summarize
+from khoj.processor.conversation.gpt import (
+    converse,
+    extract_search_type,
+    message_to_log,
+    message_to_prompt,
+    understand,
+    summarize,
+)
 from khoj.utils.config import SearchType
 from khoj.utils.helpers import get_from_dict, resolve_absolute_path
 from khoj.utils import state
@@ -21,7 +28,7 @@ logger = logging.getLogger(__name__)
 
 
 # Create Routes
-@api_beta.get('/search')
+@api_beta.get("/search")
 def search_beta(q: str, n: Optional[int] = 1):
     # Initialize Variables
     model = state.processor_config.conversation.model
@@ -32,16 +39,16 @@ def search_beta(q: str, n: Optional[int] = 1):
         metadata = extract_search_type(q, model=model, api_key=api_key, verbose=state.verbose)
         search_type = get_from_dict(metadata, "search-type")
     except Exception as e:
-        return {'status': 'error', 'result': [str(e)], 'type': None}
+        return {"status": "error", "result": [str(e)], "type": None}
 
     # Search
     search_results = search(q, n=n, t=SearchType(search_type))
 
     # Return response
-    return {'status': 'ok', 'result': search_results, 'type': search_type}
+    return {"status": "ok", "result": search_results, "type": search_type}
 
 
-@api_beta.get('/summarize')
+@api_beta.get("/summarize")
 def summarize_beta(q: str):
     # Initialize Variables
     model = state.processor_config.conversation.model
@@ -54,23 +61,25 @@ def summarize_beta(q: str):
     # Converse with OpenAI GPT
     result_list = search(q, n=1, r=True)
     collated_result = "\n".join([item.entry for item in result_list])
-    logger.debug(f'Semantically Similar Notes:\n{collated_result}')
+    logger.debug(f"Semantically Similar Notes:\n{collated_result}")
     try:
         gpt_response = summarize(collated_result, summary_type="notes", user_query=q, model=model, api_key=api_key)
-        status = 'ok'
+        status = "ok"
     except Exception as e:
         gpt_response = str(e)
-        status = 'error'
+        status = "error"
 
     # Update Conversation History
     state.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
-    state.processor_config.conversation.meta_log['chat'] = message_to_log(q, gpt_response, conversation_log=meta_log.get('chat', []))
+    state.processor_config.conversation.meta_log["chat"] = message_to_log(
+        q, gpt_response, conversation_log=meta_log.get("chat", [])
+    )
 
-    return {'status': status, 'response': gpt_response}
+    return {"status": status, "response": gpt_response}
 
 
-@api_beta.get('/chat')
+@api_beta.get("/chat")
-def chat(q: Optional[str]=None):
+def chat(q: Optional[str] = None):
     # Initialize Variables
     model = state.processor_config.conversation.model
     api_key = state.processor_config.conversation.openai_api_key
@@ -81,10 +90,10 @@ def chat(q: Optional[str]=None):
 
     # If user query is empty, return chat history
     if not q:
-        if meta_log.get('chat'):
+        if meta_log.get("chat"):
-            return {'status': 'ok', 'response': meta_log["chat"]}
+            return {"status": "ok", "response": meta_log["chat"]}
         else:
-            return {'status': 'ok', 'response': []}
+            return {"status": "ok", "response": []}
 
     # Converse with OpenAI GPT
     metadata = understand(q, model=model, api_key=api_key, verbose=state.verbose)
@@ -94,32 +103,39 @@ def chat(q: Optional[str]=None):
         query = get_from_dict(metadata, "intent", "query")
         result_list = search(query, n=1, t=SearchType.Org, r=True)
         collated_result = "\n".join([item.entry for item in result_list])
-        logger.debug(f'Semantically Similar Notes:\n{collated_result}')
+        logger.debug(f"Semantically Similar Notes:\n{collated_result}")
         try:
             gpt_response = summarize(collated_result, summary_type="notes", user_query=q, model=model, api_key=api_key)
-            status = 'ok'
+            status = "ok"
         except Exception as e:
             gpt_response = str(e)
-            status = 'error'
+            status = "error"
     else:
         try:
             gpt_response = converse(q, model, chat_session, api_key=api_key)
-            status = 'ok'
+            status = "ok"
         except Exception as e:
             gpt_response = str(e)
-            status = 'error'
+            status = "error"
 
     # Update Conversation History
     state.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
-    state.processor_config.conversation.meta_log['chat'] = message_to_log(q, gpt_response, metadata, meta_log.get('chat', []))
+    state.processor_config.conversation.meta_log["chat"] = message_to_log(
+        q, gpt_response, metadata, meta_log.get("chat", [])
+    )
 
-    return {'status': status, 'response': gpt_response}
+    return {"status": status, "response": gpt_response}
 
 
 @schedule.repeat(schedule.every(5).minutes)
 def save_chat_session():
     # No need to create empty log file
-    if not (state.processor_config and state.processor_config.conversation and state.processor_config.conversation.meta_log and state.processor_config.conversation.chat_session):
+    if not (
+        state.processor_config
+        and state.processor_config.conversation
+        and state.processor_config.conversation.meta_log
+        and state.processor_config.conversation.chat_session
+    ):
         return
 
     # Summarize Conversation Logs for this Session
@@ -130,19 +146,19 @@ def save_chat_session():
     session = {
         "summary": summarize(chat_session, summary_type="chat", model=model, api_key=openai_api_key),
         "session-start": conversation_log.get("session", [{"session-end": 0}])[-1]["session-end"],
-        "session-end": len(conversation_log["chat"])
+        "session-end": len(conversation_log["chat"]),
-        }
+    }
-    if 'session' in conversation_log:
+    if "session" in conversation_log:
-        conversation_log['session'].append(session)
+        conversation_log["session"].append(session)
     else:
-        conversation_log['session'] = [session]
+        conversation_log["session"] = [session]
-    logger.info('Added new chat session to conversation logs')
+    logger.info("Added new chat session to conversation logs")
 
     # Save Conversation Metadata Logs to Disk
     conversation_logfile = resolve_absolute_path(state.processor_config.conversation.conversation_logfile)
-    conversation_logfile.parent.mkdir(parents=True, exist_ok=True) # create conversation directory if doesn't exist
+    conversation_logfile.parent.mkdir(parents=True, exist_ok=True)  # create conversation directory if doesn't exist
-    with open(conversation_logfile, "w+", encoding='utf-8') as logfile:
+    with open(conversation_logfile, "w+", encoding="utf-8") as logfile:
         json.dump(conversation_log, logfile)
 
     state.processor_config.conversation.chat_session = None
-    logger.info('Saved updated conversation logs to disk.')
+    logger.info("Saved updated conversation logs to disk.")

src/khoj/routers/web_client.py

@@ -18,9 +18,11 @@ templates = Jinja2Templates(directory=constants.web_directory)
 def index():
     return FileResponse(constants.web_directory / "index.html")
 
-@web_client.get('/config', response_class=HTMLResponse)
+
+@web_client.get("/config", response_class=HTMLResponse)
 def config_page(request: Request):
-    return templates.TemplateResponse("config.html", context={'request': request})
+    return templates.TemplateResponse("config.html", context={"request": request})
+
 
 @web_client.get("/chat", response_class=FileResponse)
 def chat_page():

src/khoj/search_filter/base_filter.py

@@ -8,10 +8,13 @@ from khoj.utils.rawconfig import Entry
 
 class BaseFilter(ABC):
     @abstractmethod
-    def load(self, entries: List[Entry], *args, **kwargs): ...
+    def load(self, entries: List[Entry], *args, **kwargs):
+        ...
 
     @abstractmethod
-    def can_filter(self, raw_query:str) -> bool: ...
+    def can_filter(self, raw_query: str) -> bool:
+        ...
 
     @abstractmethod
-    def apply(self, query:str, entries: List[Entry]) -> Tuple[str, Set[int]]: ...
+    def apply(self, query: str, entries: List[Entry]) -> Tuple[str, Set[int]]:
+        ...

src/khoj/search_filter/date_filter.py

@@ -26,21 +26,19 @@ class DateFilter(BaseFilter):
     # - dt:"2 years ago"
     date_regex = r"dt([:><=]{1,2})\"(.*?)\""
 
-
+    def __init__(self, entry_key="raw"):
-    def __init__(self, entry_key='raw'):
         self.entry_key = entry_key
         self.date_to_entry_ids = defaultdict(set)
         self.cache = LRU()
 
-
     def load(self, entries, *args, **kwargs):
         with timer("Created date filter index", logger):
             for id, entry in enumerate(entries):
                 # Extract dates from entry
-                for date_in_entry_string in re.findall(r'\d{4}-\d{2}-\d{2}', getattr(entry, self.entry_key)):
+                for date_in_entry_string in re.findall(r"\d{4}-\d{2}-\d{2}", getattr(entry, self.entry_key)):
                     # Convert date string in entry to unix timestamp
                     try:
-                        date_in_entry = datetime.strptime(date_in_entry_string, '%Y-%m-%d').timestamp()
+                        date_in_entry = datetime.strptime(date_in_entry_string, "%Y-%m-%d").timestamp()
                     except ValueError:
                         continue
                     self.date_to_entry_ids[date_in_entry].add(id)
@@ -49,7 +47,6 @@ class DateFilter(BaseFilter):
         "Check if query contains date filters"
         return self.extract_date_range(raw_query) is not None
 
-
     def apply(self, query, entries):
         "Find entries containing any dates that fall within date range specified in query"
         # extract date range specified in date filter of query
@@ -61,8 +58,8 @@ class DateFilter(BaseFilter):
             return query, set(range(len(entries)))
 
         # remove date range filter from query
-        query = re.sub(rf'\s+{self.date_regex}', ' ', query)
+        query = re.sub(rf"\s+{self.date_regex}", " ", query)
-        query = re.sub(r'\s{2,}', ' ', query).strip()  # remove multiple spaces
+        query = re.sub(r"\s{2,}", " ", query).strip()  # remove multiple spaces
 
         # return results from cache if exists
         cache_key = tuple(query_daterange)
@@ -87,7 +84,6 @@ class DateFilter(BaseFilter):
 
         return query, entries_to_include
 
-
     def extract_date_range(self, query):
         # find date range filter in query
         date_range_matches = re.findall(self.date_regex, query)
@@ -98,7 +94,7 @@ class DateFilter(BaseFilter):
         # extract, parse natural dates ranges from date range filter passed in query
         # e.g today maps to (start_of_day, start_of_tomorrow)
         date_ranges_from_filter = []
-        for (cmp, date_str) in date_range_matches:
+        for cmp, date_str in date_range_matches:
             if self.parse(date_str):
                 dt_start, dt_end = self.parse(date_str)
                 date_ranges_from_filter += [[cmp, (dt_start.timestamp(), dt_end.timestamp())]]
@@ -111,15 +107,15 @@ class DateFilter(BaseFilter):
         effective_date_range = [0, inf]
         date_range_considering_comparator = []
         for cmp, (dtrange_start, dtrange_end) in date_ranges_from_filter:
-            if cmp == '>':
+            if cmp == ">":
                 date_range_considering_comparator += [[dtrange_end, inf]]
-            elif cmp == '>=':
+            elif cmp == ">=":
                 date_range_considering_comparator += [[dtrange_start, inf]]
-            elif cmp == '<':
+            elif cmp == "<":
                 date_range_considering_comparator += [[0, dtrange_start]]
-            elif cmp == '<=':
+            elif cmp == "<=":
                 date_range_considering_comparator += [[0, dtrange_end]]
-            elif cmp == '=' or cmp == ':' or cmp == '==':
+            elif cmp == "=" or cmp == ":" or cmp == "==":
                 date_range_considering_comparator += [[dtrange_start, dtrange_end]]
 
         # Combine above intervals (via AND/intersect)
@@ -129,48 +125,48 @@ class DateFilter(BaseFilter):
         for date_range in date_range_considering_comparator:
             effective_date_range = [
                 max(effective_date_range[0], date_range[0]),
-                min(effective_date_range[1], date_range[1])]
+                min(effective_date_range[1], date_range[1]),
+            ]
 
         if effective_date_range == [0, inf] or effective_date_range[0] > effective_date_range[1]:
             return None
         else:
             return effective_date_range
 
-
     def parse(self, date_str, relative_base=None):
         "Parse date string passed in date filter of query to datetime object"
         # clean date string to handle future date parsing by date parser
-        future_strings = ['later', 'from now', 'from today']
+        future_strings = ["later", "from now", "from today"]
-        prefer_dates_from = {True: 'future', False: 'past'}[any([True for fstr in future_strings if fstr in date_str])]
+        prefer_dates_from = {True: "future", False: "past"}[any([True for fstr in future_strings if fstr in date_str])]
-        clean_date_str = re.sub('|'.join(future_strings), '', date_str)
+        clean_date_str = re.sub("|".join(future_strings), "", date_str)
 
         # parse date passed in query date filter
         parsed_date = dtparse.parse(
             clean_date_str,
-            settings= {
+            settings={
-                'RELATIVE_BASE': relative_base or datetime.now(),
+                "RELATIVE_BASE": relative_base or datetime.now(),
-                'PREFER_DAY_OF_MONTH': 'first',
+                "PREFER_DAY_OF_MONTH": "first",
-                'PREFER_DATES_FROM': prefer_dates_from
+                "PREFER_DATES_FROM": prefer_dates_from,
-            })
+            },
+        )
 
         if parsed_date is None:
             return None
 
         return self.date_to_daterange(parsed_date, date_str)
 
-
     def date_to_daterange(self, parsed_date, date_str):
         "Convert parsed date to date ranges at natural granularity (day, week, month or year)"
 
         start_of_day = parsed_date.replace(hour=0, minute=0, second=0, microsecond=0)
 
-        if 'year' in date_str:
+        if "year" in date_str:
-            return (datetime(parsed_date.year, 1, 1, 0, 0, 0), datetime(parsed_date.year+1, 1, 1, 0, 0, 0))
+            return (datetime(parsed_date.year, 1, 1, 0, 0, 0), datetime(parsed_date.year + 1, 1, 1, 0, 0, 0))
-        if 'month' in date_str:
+        if "month" in date_str:
             start_of_month = datetime(parsed_date.year, parsed_date.month, 1, 0, 0, 0)
             next_month = start_of_month + relativedelta(months=1)
             return (start_of_month, next_month)
-        if 'week' in date_str:
+        if "week" in date_str:
             # if week in date string, dateparser parses it to next week start
             # so today = end of this week
             start_of_week = start_of_day - timedelta(days=7)

src/khoj/search_filter/file_filter.py

@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 class FileFilter(BaseFilter):
     file_filter_regex = r'file:"(.+?)" ?'
 
-    def __init__(self, entry_key='file'):
+    def __init__(self, entry_key="file"):
         self.entry_key = entry_key
         self.file_to_entry_map = defaultdict(set)
         self.cache = LRU()
@@ -40,13 +40,13 @@ class FileFilter(BaseFilter):
             # e.g. "file:notes.org" -> "file:.*notes.org"
             files_to_search = []
             for file in sorted(raw_files_to_search):
-                if '/' not in file and '\\' not in file and '*' not in file:
+                if "/" not in file and "\\" not in file and "*" not in file:
-                    files_to_search += [f'*{file}']
+                    files_to_search += [f"*{file}"]
                 else:
                     files_to_search += [file]
 
         # Return item from cache if exists
-        query = re.sub(self.file_filter_regex, '', query).strip()
+        query = re.sub(self.file_filter_regex, "", query).strip()
         cache_key = tuple(files_to_search)
         if cache_key in self.cache:
             logger.info(f"Return file filter results from cache")
@@ -58,10 +58,15 @@ class FileFilter(BaseFilter):
 
         # Mark entries that contain any blocked_words for exclusion
         with timer("Mark entries satisfying filter", logger):
-            included_entry_indices = set.union(*[self.file_to_entry_map[entry_file]
+            included_entry_indices = set.union(
+                *[
+                    self.file_to_entry_map[entry_file]
                     for entry_file in self.file_to_entry_map.keys()
                     for search_file in files_to_search
-                    if fnmatch.fnmatch(entry_file, search_file)], set())
+                    if fnmatch.fnmatch(entry_file, search_file)
+                ],
+                set(),
+            )
             if not included_entry_indices:
                 return query, {}
 

src/khoj/search_filter/word_filter.py

@@ -17,26 +17,26 @@ class WordFilter(BaseFilter):
     required_regex = r'\+"([a-zA-Z0-9_-]+)" ?'
     blocked_regex = r'\-"([a-zA-Z0-9_-]+)" ?'
 
-    def __init__(self, entry_key='raw'):
+    def __init__(self, entry_key="raw"):
         self.entry_key = entry_key
         self.word_to_entry_index = defaultdict(set)
         self.cache = LRU()
 
-
     def load(self, entries, *args, **kwargs):
         with timer("Created word filter index", logger):
             self.cache = {}  # Clear cache on filter (re-)load
-            entry_splitter = r',|\.| |\]|\[\(|\)|\{|\}|\<|\>|\t|\n|\:|\;|\?|\!|\(|\)|\&|\^|\$|\@|\%|\+|\=|\/|\\|\||\~|\`|\"|\''
+            entry_splitter = (
+                r",|\.| |\]|\[\(|\)|\{|\}|\<|\>|\t|\n|\:|\;|\?|\!|\(|\)|\&|\^|\$|\@|\%|\+|\=|\/|\\|\||\~|\`|\"|\'"
+            )
             # Create map of words to entries they exist in
             for entry_index, entry in enumerate(entries):
                 for word in re.split(entry_splitter, getattr(entry, self.entry_key).lower()):
-                    if word == '':
+                    if word == "":
                         continue
                     self.word_to_entry_index[word].add(entry_index)
 
         return self.word_to_entry_index
 
-
     def can_filter(self, raw_query):
         "Check if query contains word filters"
         required_words = re.findall(self.required_regex, raw_query)
@@ -44,14 +44,13 @@ class WordFilter(BaseFilter):
 
         return len(required_words) != 0 or len(blocked_words) != 0
 
-
     def apply(self, query, entries):
         "Find entries containing required and not blocked words specified in query"
         # Separate natural query from required, blocked words filters
         with timer("Extract required, blocked filters from query", logger):
             required_words = set([word.lower() for word in re.findall(self.required_regex, query)])
             blocked_words = set([word.lower() for word in re.findall(self.blocked_regex, query)])
-            query = re.sub(self.blocked_regex, '', re.sub(self.required_regex, '', query)).strip()
+            query = re.sub(self.blocked_regex, "", re.sub(self.required_regex, "", query)).strip()
 
         if len(required_words) == 0 and len(blocked_words) == 0:
             return query, set(range(len(entries)))
@@ -70,12 +69,16 @@ class WordFilter(BaseFilter):
         with timer("Mark entries satisfying filter", logger):
             entries_with_all_required_words = set(range(len(entries)))
             if len(required_words) > 0:
-                entries_with_all_required_words = set.intersection(*[self.word_to_entry_index.get(word, set()) for word in required_words])
+                entries_with_all_required_words = set.intersection(
+                    *[self.word_to_entry_index.get(word, set()) for word in required_words]
+                )
 
             # mark entries that contain any blocked_words for exclusion
             entries_with_any_blocked_words = set()
             if len(blocked_words) > 0:
-                entries_with_any_blocked_words = set.union(*[self.word_to_entry_index.get(word, set()) for word in blocked_words])
+                entries_with_any_blocked_words = set.union(
+                    *[self.word_to_entry_index.get(word, set()) for word in blocked_words]
+                )
 
         # get entries satisfying inclusion and exclusion filters
         included_entry_indices = entries_with_all_required_words - entries_with_any_blocked_words

src/khoj/search_type/image_search.py

@@ -35,9 +35,10 @@ def initialize_model(search_config: ImageSearchConfig):
 
     # Load the CLIP model
     encoder = load_model(
-        model_dir  = search_config.model_directory,
+        model_dir=search_config.model_directory,
-        model_name = search_config.encoder,
+        model_name=search_config.encoder,
-        model_type = search_config.encoder_type or SentenceTransformer)
+        model_type=search_config.encoder_type or SentenceTransformer,
+    )
 
     return encoder
 
@@ -46,12 +47,12 @@ def extract_entries(image_directories):
     image_names = []
     for image_directory in image_directories:
         image_directory = resolve_absolute_path(image_directory, strict=True)
-        image_names.extend(list(image_directory.glob('*.jpg')))
+        image_names.extend(list(image_directory.glob("*.jpg")))
-        image_names.extend(list(image_directory.glob('*.jpeg')))
+        image_names.extend(list(image_directory.glob("*.jpeg")))
 
     if logger.level >= logging.INFO:
-        image_directory_names = ', '.join([str(image_directory) for image_directory in image_directories])
+        image_directory_names = ", ".join([str(image_directory) for image_directory in image_directories])
-        logger.info(f'Found {len(image_names)} images in {image_directory_names}')
+        logger.info(f"Found {len(image_names)} images in {image_directory_names}")
     return sorted(image_names)
 
 
@@ -59,7 +60,9 @@ def compute_embeddings(image_names, encoder, embeddings_file, batch_size=50, use
     "Compute (and Save) Embeddings or Load Pre-Computed Embeddings"
 
     image_embeddings = compute_image_embeddings(image_names, encoder, embeddings_file, batch_size, regenerate)
-    image_metadata_embeddings = compute_metadata_embeddings(image_names, encoder, embeddings_file, batch_size, use_xmp_metadata, regenerate)
+    image_metadata_embeddings = compute_metadata_embeddings(
+        image_names, encoder, embeddings_file, batch_size, use_xmp_metadata, regenerate
+    )
 
     return image_embeddings, image_metadata_embeddings
 
@@ -74,15 +77,12 @@ def compute_image_embeddings(image_names, encoder, embeddings_file, batch_size=5
         image_embeddings = []
         for index in trange(0, len(image_names), batch_size):
             images = []
-            for image_name in image_names[index:index+batch_size]:
+            for image_name in image_names[index : index + batch_size]:
                 image = Image.open(image_name)
                 # Resize images to max width of 640px for faster processing
                 image.thumbnail((640, image.height))
                 images += [image]
-            image_embeddings += encoder.encode(
+            image_embeddings += encoder.encode(images, convert_to_tensor=True, batch_size=min(len(images), batch_size))
-                images,
-                convert_to_tensor=True,
-                batch_size=min(len(images), batch_size))
 
         # Create directory for embeddings file, if it doesn't exist
         embeddings_file.parent.mkdir(parents=True, exist_ok=True)
@@ -94,7 +94,9 @@ def compute_image_embeddings(image_names, encoder, embeddings_file, batch_size=5
     return image_embeddings
 
 
-def compute_metadata_embeddings(image_names, encoder, embeddings_file, batch_size=50, use_xmp_metadata=False, regenerate=False, verbose=0):
+def compute_metadata_embeddings(
+    image_names, encoder, embeddings_file, batch_size=50, use_xmp_metadata=False, regenerate=False, verbose=0
+):
     image_metadata_embeddings = None
 
     # Load pre-computed image metadata embedding file if exists
@@ -106,14 +108,17 @@ def compute_metadata_embeddings(image_names, encoder, embeddings_file, batch_siz
     if use_xmp_metadata and image_metadata_embeddings is None:
         image_metadata_embeddings = []
         for index in trange(0, len(image_names), batch_size):
-            image_metadata = [extract_metadata(image_name, verbose) for image_name in image_names[index:index+batch_size]]
+            image_metadata = [
+                extract_metadata(image_name, verbose) for image_name in image_names[index : index + batch_size]
+            ]
             try:
                 image_metadata_embeddings += encoder.encode(
-                    image_metadata,
+                    image_metadata, convert_to_tensor=True, batch_size=min(len(image_metadata), batch_size)
-                    convert_to_tensor=True,
+                )
-                    batch_size=min(len(image_metadata), batch_size))
             except RuntimeError as e:
-                logger.error(f"Error encoding metadata for images starting from\n\tindex: {index},\n\timages: {image_names[index:index+batch_size]}\nException: {e}")
+                logger.error(
+                    f"Error encoding metadata for images starting from\n\tindex: {index},\n\timages: {image_names[index:index+batch_size]}\nException: {e}"
+                )
                 continue
         torch.save(image_metadata_embeddings, f"{embeddings_file}_metadata")
         logger.info(f"Saved computed metadata embeddings to {embeddings_file}_metadata")
@@ -123,8 +128,10 @@ def compute_metadata_embeddings(image_names, encoder, embeddings_file, batch_siz
 
 def extract_metadata(image_name):
     image_xmp_metadata = Image.open(image_name).getxmp()
-    image_description = get_from_dict(image_xmp_metadata, 'xmpmeta', 'RDF', 'Description', 'description', 'Alt', 'li', 'text')
+    image_description = get_from_dict(
-    image_subjects = get_from_dict(image_xmp_metadata, 'xmpmeta', 'RDF', 'Description', 'subject', 'Bag', 'li')
+        image_xmp_metadata, "xmpmeta", "RDF", "Description", "description", "Alt", "li", "text"
+    )
+    image_subjects = get_from_dict(image_xmp_metadata, "xmpmeta", "RDF", "Description", "subject", "Bag", "li")
     image_metadata_subjects = set([subject.split(":")[1] for subject in image_subjects if ":" in subject])
 
     image_processed_metadata = image_description
@@ -141,7 +148,7 @@ def query(raw_query, count, model: ImageSearchModel):
     if raw_query.startswith("file:") and pathlib.Path(raw_query[5:]).is_file():
         query_imagepath = resolve_absolute_path(pathlib.Path(raw_query[5:]), strict=True)
         query = copy.deepcopy(Image.open(query_imagepath))
-        query.thumbnail((640, query.height)) # scale down image for faster processing
+        query.thumbnail((640, query.height))  # scale down image for faster processing
         logger.info(f"Find Images by Image: {query_imagepath}")
     else:
         # Truncate words in query to stay below max_tokens supported by ML model
@@ -155,36 +162,42 @@ def query(raw_query, count, model: ImageSearchModel):
 
     # Compute top_k ranked images based on cosine-similarity b/w query and all image embeddings.
     with timer("Search Time", logger):
-        image_hits = {result['corpus_id']: {'image_score': result['score'], 'score': result['score']}
+        image_hits = {
-                    for result
+            result["corpus_id"]: {"image_score": result["score"], "score": result["score"]}
-                    in util.semantic_search(query_embedding, model.image_embeddings, top_k=count)[0]}
+            for result in util.semantic_search(query_embedding, model.image_embeddings, top_k=count)[0]
+        }
 
     # Compute top_k ranked images based on cosine-similarity b/w query and all image metadata embeddings.
     if model.image_metadata_embeddings:
         with timer("Metadata Search Time", logger):
-            metadata_hits = {result['corpus_id']: result['score']
+            metadata_hits = {
-                            for result
+                result["corpus_id"]: result["score"]
-                            in util.semantic_search(query_embedding, model.image_metadata_embeddings, top_k=count)[0]}
+                for result in util.semantic_search(query_embedding, model.image_metadata_embeddings, top_k=count)[0]
+            }
 
         # Sum metadata, image scores of the highest ranked images
         for corpus_id, score in metadata_hits.items():
             scaling_factor = 0.33
-            if 'corpus_id' in image_hits:
+            if "corpus_id" in image_hits:
-                image_hits[corpus_id].update({
+                image_hits[corpus_id].update(
-                    'metadata_score': score,
+                    {
-                    'score': image_hits[corpus_id].get('score', 0) + scaling_factor*score,
+                        "metadata_score": score,
-                })
+                        "score": image_hits[corpus_id].get("score", 0) + scaling_factor * score,
+                    }
+                )
             else:
-                image_hits[corpus_id] = {'metadata_score': score, 'score': scaling_factor*score}
+                image_hits[corpus_id] = {"metadata_score": score, "score": scaling_factor * score}
 
     # Reformat results in original form from sentence transformer semantic_search()
     hits = [
         {
-            'corpus_id': corpus_id,
+            "corpus_id": corpus_id,
-            'score': scores['score'],
+            "score": scores["score"],
-            'image_score': scores.get('image_score', 0),
+            "image_score": scores.get("image_score", 0),
-            'metadata_score': scores.get('metadata_score', 0),
+            "metadata_score": scores.get("metadata_score", 0),
-        } for corpus_id, scores in image_hits.items()]
+        }
+        for corpus_id, scores in image_hits.items()
+    ]
 
     # Sort the images based on their combined metadata, image scores
     return sorted(hits, key=lambda hit: hit["score"], reverse=True)
@@ -194,7 +207,7 @@ def collate_results(hits, image_names, output_directory, image_files_url, count=
     results: List[SearchResponse] = []
 
     for index, hit in enumerate(hits[:count]):
-        source_path = image_names[hit['corpus_id']]
+        source_path = image_names[hit["corpus_id"]]
 
         target_image_name = f"{index}{source_path.suffix}"
         target_path = resolve_absolute_path(f"{output_directory}/{target_image_name}")
@@ -207,17 +220,18 @@ def collate_results(hits, image_names, output_directory, image_files_url, count=
         shutil.copy(source_path, target_path)
 
         # Add the image metadata to the results
-        results += [SearchResponse.parse_obj(
+        results += [
-            {
+            SearchResponse.parse_obj(
-                "entry": f'{image_files_url}/{target_image_name}',
-                "score": f"{hit['score']:.9f}",
-                "additional":
                 {
-                    "image_score": f"{hit['image_score']:.9f}",
+                    "entry": f"{image_files_url}/{target_image_name}",
-                    "metadata_score": f"{hit['metadata_score']:.9f}",
+                    "score": f"{hit['score']:.9f}",
+                    "additional": {
+                        "image_score": f"{hit['image_score']:.9f}",
+                        "metadata_score": f"{hit['metadata_score']:.9f}",
+                    },
                 }
-            }
+            )
-        )]
+        ]
 
     return results
 
@@ -248,9 +262,7 @@ def setup(config: ImageContentConfig, search_config: ImageSearchConfig, regenera
         embeddings_file,
         batch_size=config.batch_size,
         regenerate=regenerate,
-        use_xmp_metadata=config.use_xmp_metadata)
+        use_xmp_metadata=config.use_xmp_metadata,
+    )
 
-    return ImageSearchModel(all_image_files,
+    return ImageSearchModel(all_image_files, image_embeddings, image_metadata_embeddings, encoder)
-                            image_embeddings,
-                            image_metadata_embeddings,
-                            encoder)

src/khoj/search_type/text_search.py

@@ -38,17 +38,19 @@ def initialize_model(search_config: TextSearchConfig):
 
     # The bi-encoder encodes all entries to use for semantic search
     bi_encoder = load_model(
-        model_dir  = search_config.model_directory,
+        model_dir=search_config.model_directory,
-        model_name = search_config.encoder,
+        model_name=search_config.encoder,
-        model_type = search_config.encoder_type or SentenceTransformer,
+        model_type=search_config.encoder_type or SentenceTransformer,
-        device=f'{state.device}')
+        device=f"{state.device}",
+    )
 
     # The cross-encoder re-ranks the results to improve quality
     cross_encoder = load_model(
-        model_dir  = search_config.model_directory,
+        model_dir=search_config.model_directory,
-        model_name = search_config.cross_encoder,
+        model_name=search_config.cross_encoder,
-        model_type = CrossEncoder,
+        model_type=CrossEncoder,
-        device=f'{state.device}')
+        device=f"{state.device}",
+    )
 
     return bi_encoder, cross_encoder, top_k
 
@@ -58,7 +60,9 @@ def extract_entries(jsonl_file) -> List[Entry]:
     return list(map(Entry.from_dict, load_jsonl(jsonl_file)))
 
 
-def compute_embeddings(entries_with_ids: List[Tuple[int, Entry]], bi_encoder: BaseEncoder, embeddings_file: Path, regenerate=False):
+def compute_embeddings(
+    entries_with_ids: List[Tuple[int, Entry]], bi_encoder: BaseEncoder, embeddings_file: Path, regenerate=False
+):
     "Compute (and Save) Embeddings or Load Pre-Computed Embeddings"
     new_entries = []
     # Load pre-computed embeddings from file if exists and update them if required
@@ -69,17 +73,23 @@ def compute_embeddings(entries_with_ids: List[Tuple[int, Entry]], bi_encoder: Ba
         # Encode any new entries in the corpus and update corpus embeddings
         new_entries = [entry.compiled for id, entry in entries_with_ids if id == -1]
         if new_entries:
-            new_embeddings = bi_encoder.encode(new_entries, convert_to_tensor=True, device=state.device, show_progress_bar=True)
+            new_embeddings = bi_encoder.encode(
+                new_entries, convert_to_tensor=True, device=state.device, show_progress_bar=True
+            )
             existing_entry_ids = [id for id, _ in entries_with_ids if id != -1]
             if existing_entry_ids:
-                existing_embeddings = torch.index_select(corpus_embeddings, 0, torch.tensor(existing_entry_ids, device=state.device))
+                existing_embeddings = torch.index_select(
+                    corpus_embeddings, 0, torch.tensor(existing_entry_ids, device=state.device)
+                )
             else:
                 existing_embeddings = torch.tensor([], device=state.device)
             corpus_embeddings = torch.cat([existing_embeddings, new_embeddings], dim=0)
     # Else compute the corpus embeddings from scratch
     else:
         new_entries = [entry.compiled for _, entry in entries_with_ids]
-        corpus_embeddings = bi_encoder.encode(new_entries, convert_to_tensor=True, device=state.device, show_progress_bar=True)
+        corpus_embeddings = bi_encoder.encode(
+            new_entries, convert_to_tensor=True, device=state.device, show_progress_bar=True
+        )
 
     # Save regenerated or updated embeddings to file
     if new_entries:
@@ -112,7 +122,9 @@ def query(raw_query: str, model: TextSearchModel, rank_results: bool = False) ->
 
     # Find relevant entries for the query
     with timer("Search Time", logger, state.device):
-        hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=model.top_k, score_function=util.dot_score)[0]
+        hits = util.semantic_search(
+            question_embedding, corpus_embeddings, top_k=model.top_k, score_function=util.dot_score
+        )[0]
 
     # Score all retrieved entries using the cross-encoder
     if rank_results:
@@ -128,26 +140,33 @@ def query(raw_query: str, model: TextSearchModel, rank_results: bool = False) ->
 
 
 def collate_results(hits, entries: List[Entry], count=5) -> List[SearchResponse]:
-    return [SearchResponse.parse_obj(
+    return [
-        {
+        SearchResponse.parse_obj(
-            "entry": entries[hit['corpus_id']].raw,
+            {
-            "score": f"{hit['cross-score'] if 'cross-score' in hit else hit['score']:.3f}",
+                "entry": entries[hit["corpus_id"]].raw,
-            "additional": {
+                "score": f"{hit['cross-score'] if 'cross-score' in hit else hit['score']:.3f}",
-                "file": entries[hit['corpus_id']].file,
+                "additional": {"file": entries[hit["corpus_id"]].file, "compiled": entries[hit["corpus_id"]].compiled},
-                "compiled": entries[hit['corpus_id']].compiled
             }
-        })
+        )
-        for hit
+        for hit in hits[0:count]
-        in hits[0:count]]
+    ]
 
 
-def setup(text_to_jsonl: Type[TextToJsonl], config: TextContentConfig, search_config: TextSearchConfig, regenerate: bool, filters: List[BaseFilter] = []) -> TextSearchModel:
+def setup(
+    text_to_jsonl: Type[TextToJsonl],
+    config: TextContentConfig,
+    search_config: TextSearchConfig,
+    regenerate: bool,
+    filters: List[BaseFilter] = [],
+) -> TextSearchModel:
     # Initialize Model
     bi_encoder, cross_encoder, top_k = initialize_model(search_config)
 
     # Map notes in text files to (compressed) JSONL formatted file
     config.compressed_jsonl = resolve_absolute_path(config.compressed_jsonl)
-    previous_entries = extract_entries(config.compressed_jsonl) if config.compressed_jsonl.exists() and not regenerate else None
+    previous_entries = (
+        extract_entries(config.compressed_jsonl) if config.compressed_jsonl.exists() and not regenerate else None
+    )
     entries_with_indices = text_to_jsonl(config).process(previous_entries)
 
     # Extract Updated Entries
@@ -158,7 +177,9 @@ def setup(text_to_jsonl: Type[TextToJsonl], config: TextContentConfig, search_co
 
     # Compute or Load Embeddings
     config.embeddings_file = resolve_absolute_path(config.embeddings_file)
-    corpus_embeddings = compute_embeddings(entries_with_indices, bi_encoder, config.embeddings_file, regenerate=regenerate)
+    corpus_embeddings = compute_embeddings(
+        entries_with_indices, bi_encoder, config.embeddings_file, regenerate=regenerate
+    )
 
     for filter in filters:
         filter.load(entries, regenerate=regenerate)
@@ -166,8 +187,10 @@ def setup(text_to_jsonl: Type[TextToJsonl], config: TextContentConfig, search_co
     return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, filters, top_k)
 
 
-def apply_filters(query: str, entries: List[Entry], corpus_embeddings: torch.Tensor, filters: List[BaseFilter]) -> Tuple[str, List[Entry], torch.Tensor]:
+def apply_filters(
-    '''Filter query, entries and embeddings before semantic search'''
+    query: str, entries: List[Entry], corpus_embeddings: torch.Tensor, filters: List[BaseFilter]
+) -> Tuple[str, List[Entry], torch.Tensor]:
+    """Filter query, entries and embeddings before semantic search"""
 
     with timer("Total Filter Time", logger, state.device):
         included_entry_indices = set(range(len(entries)))
@@ -178,45 +201,50 @@ def apply_filters(query: str, entries: List[Entry], corpus_embeddings: torch.Ten
 
         # Get entries (and associated embeddings) satisfying all filters
         if not included_entry_indices:
-            return '', [], torch.tensor([], device=state.device)
+            return "", [], torch.tensor([], device=state.device)
         else:
             entries = [entries[id] for id in included_entry_indices]
-            corpus_embeddings = torch.index_select(corpus_embeddings, 0, torch.tensor(list(included_entry_indices), device=state.device))
+            corpus_embeddings = torch.index_select(
+                corpus_embeddings, 0, torch.tensor(list(included_entry_indices), device=state.device)
+            )
 
     return query, entries, corpus_embeddings
 
 
 def cross_encoder_score(cross_encoder: CrossEncoder, query: str, entries: List[Entry], hits: List[dict]) -> List[dict]:
-    '''Score all retrieved entries using the cross-encoder'''
+    """Score all retrieved entries using the cross-encoder"""
     with timer("Cross-Encoder Predict Time", logger, state.device):
-        cross_inp = [[query, entries[hit['corpus_id']].compiled] for hit in hits]
+        cross_inp = [[query, entries[hit["corpus_id"]].compiled] for hit in hits]
         cross_scores = cross_encoder.predict(cross_inp)
 
     # Store cross-encoder scores in results dictionary for ranking
     for idx in range(len(cross_scores)):
-        hits[idx]['cross-score'] = cross_scores[idx]
+        hits[idx]["cross-score"] = cross_scores[idx]
 
     return hits
 
 
 def sort_results(rank_results: bool, hits: List[dict]) -> List[dict]:
-    '''Order results by cross-encoder score followed by bi-encoder score'''
+    """Order results by cross-encoder score followed by bi-encoder score"""
     with timer("Rank Time", logger, state.device):
-        hits.sort(key=lambda x: x['score'], reverse=True) # sort by bi-encoder score
+        hits.sort(key=lambda x: x["score"], reverse=True)  # sort by bi-encoder score
         if rank_results:
-            hits.sort(key=lambda x: x['cross-score'], reverse=True) # sort by cross-encoder score
+            hits.sort(key=lambda x: x["cross-score"], reverse=True)  # sort by cross-encoder score
     return hits
 
 
 def deduplicate_results(entries: List[Entry], hits: List[dict]) -> List[dict]:
-    '''Deduplicate entries by raw entry text before showing to users
+    """Deduplicate entries by raw entry text before showing to users
     Compiled entries are split by max tokens supported by ML models.
-    This can result in duplicate hits, entries shown to user.'''
+    This can result in duplicate hits, entries shown to user."""
 
     with timer("Deduplication Time", logger, state.device):
         seen, original_hits_count = set(), len(hits)
-        hits = [hit for hit in hits
+        hits = [
-                if entries[hit['corpus_id']].raw not in seen and not seen.add(entries[hit['corpus_id']].raw)]  # type: ignore[func-returns-value]
+            hit
+            for hit in hits
+            if entries[hit["corpus_id"]].raw not in seen and not seen.add(entries[hit["corpus_id"]].raw)  # type: ignore[func-returns-value]
+        ]
         duplicate_hits = original_hits_count - len(hits)
 
     logger.debug(f"Removed {duplicate_hits} duplicates")

src/khoj/utils/cli.py

@@ -10,21 +10,36 @@ from khoj.utils.yaml import parse_config_from_file
 
 def cli(args=None):
     # Setup Argument Parser for the Commandline Interface
-    parser = argparse.ArgumentParser(description="Start Khoj; A Natural Language Search Engine for your personal Notes, Transactions and Photos")
+    parser = argparse.ArgumentParser(
-    parser.add_argument('--config-file', '-c', default='~/.khoj/khoj.yml', type=pathlib.Path, help="YAML file to configure Khoj")
+        description="Start Khoj; A Natural Language Search Engine for your personal Notes, Transactions and Photos"
-    parser.add_argument('--no-gui', action='store_true', default=False, help="Do not show native desktop GUI. Default: false")
+    )
-    parser.add_argument('--regenerate', action='store_true', default=False, help="Regenerate model embeddings from source files. Default: false")
+    parser.add_argument(
-    parser.add_argument('--verbose', '-v', action='count', default=0, help="Show verbose conversion logs. Default: 0")
+        "--config-file", "-c", default="~/.khoj/khoj.yml", type=pathlib.Path, help="YAML file to configure Khoj"
-    parser.add_argument('--host', type=str, default='127.0.0.1', help="Host address of the server. Default: 127.0.0.1")
+    )
-    parser.add_argument('--port', '-p', type=int, default=8000, help="Port of the server. Default: 8000")
+    parser.add_argument(
-    parser.add_argument('--socket', type=pathlib.Path, help="Path to UNIX socket for server. Use to run server behind reverse proxy. Default: /tmp/uvicorn.sock")
+        "--no-gui", action="store_true", default=False, help="Do not show native desktop GUI. Default: false"
-    parser.add_argument('--version', '-V', action='store_true', help="Print the installed Khoj version and exit")
+    )
+    parser.add_argument(
+        "--regenerate",
+        action="store_true",
+        default=False,
+        help="Regenerate model embeddings from source files. Default: false",
+    )
+    parser.add_argument("--verbose", "-v", action="count", default=0, help="Show verbose conversion logs. Default: 0")
+    parser.add_argument("--host", type=str, default="127.0.0.1", help="Host address of the server. Default: 127.0.0.1")
+    parser.add_argument("--port", "-p", type=int, default=8000, help="Port of the server. Default: 8000")
+    parser.add_argument(
+        "--socket",
+        type=pathlib.Path,
+        help="Path to UNIX socket for server. Use to run server behind reverse proxy. Default: /tmp/uvicorn.sock",
+    )
+    parser.add_argument("--version", "-V", action="store_true", help="Print the installed Khoj version and exit")
 
     args = parser.parse_args(args)
 
     if args.version:
         # Show version of khoj installed and exit
-        print(version('khoj-assistant'))
+        print(version("khoj-assistant"))
         exit(0)
 
     # Normalize config_file path to absolute path

src/khoj/utils/config.py

@@ -28,8 +28,16 @@ class ProcessorType(str, Enum):
     Conversation = "conversation"
 
 
-class TextSearchModel():
+class TextSearchModel:
-    def __init__(self, entries: List[Entry], corpus_embeddings: torch.Tensor, bi_encoder: BaseEncoder, cross_encoder: CrossEncoder, filters: List[BaseFilter], top_k):
+    def __init__(
+        self,
+        entries: List[Entry],
+        corpus_embeddings: torch.Tensor,
+        bi_encoder: BaseEncoder,
+        cross_encoder: CrossEncoder,
+        filters: List[BaseFilter],
+        top_k,
+    ):
         self.entries = entries
         self.corpus_embeddings = corpus_embeddings
         self.bi_encoder = bi_encoder
@@ -38,7 +46,7 @@ class TextSearchModel():
         self.top_k = top_k
 
 
-class ImageSearchModel():
+class ImageSearchModel:
     def __init__(self, image_names, image_embeddings, image_metadata_embeddings, image_encoder: BaseEncoder):
         self.image_encoder = image_encoder
         self.image_names = image_names
@@ -48,7 +56,7 @@ class ImageSearchModel():
 
 
 @dataclass
-class SearchModels():
+class SearchModels:
     orgmode_search: TextSearchModel = None
     ledger_search: TextSearchModel = None
     music_search: TextSearchModel = None
@@ -56,15 +64,15 @@ class SearchModels():
     image_search: ImageSearchModel = None
 
 
-class ConversationProcessorConfigModel():
+class ConversationProcessorConfigModel:
     def __init__(self, processor_config: ConversationProcessorConfig):
         self.openai_api_key = processor_config.openai_api_key
         self.model = processor_config.model
         self.conversation_logfile = Path(processor_config.conversation_logfile)
-        self.chat_session = ''
+        self.chat_session = ""
         self.meta_log: dict = {}
 
 
 @dataclass
-class ProcessorConfigModel():
+class ProcessorConfigModel:
     conversation: ConversationProcessorConfigModel = None

src/khoj/utils/constants.py

@@ -1,65 +1,62 @@
 from pathlib import Path
 
 app_root_directory = Path(__file__).parent.parent.parent
-web_directory = app_root_directory / 'khoj/interface/web/'
+web_directory = app_root_directory / "khoj/interface/web/"
-empty_escape_sequences = '\n|\r|\t| '
+empty_escape_sequences = "\n|\r|\t| "
 
 # default app config to use
 default_config = {
-    'content-type': {
+    "content-type": {
-        'org': {
+        "org": {
-            'input-files': None,
+            "input-files": None,
-            'input-filter': None,
+            "input-filter": None,
-            'compressed-jsonl': '~/.khoj/content/org/org.jsonl.gz',
+            "compressed-jsonl": "~/.khoj/content/org/org.jsonl.gz",
-            'embeddings-file': '~/.khoj/content/org/org_embeddings.pt',
+            "embeddings-file": "~/.khoj/content/org/org_embeddings.pt",
-            'index_heading_entries': False
+            "index_heading_entries": False,
         },
-        'markdown': {
+        "markdown": {
-            'input-files': None,
+            "input-files": None,
-            'input-filter': None,
+            "input-filter": None,
-            'compressed-jsonl': '~/.khoj/content/markdown/markdown.jsonl.gz',
+            "compressed-jsonl": "~/.khoj/content/markdown/markdown.jsonl.gz",
-            'embeddings-file': '~/.khoj/content/markdown/markdown_embeddings.pt'
+            "embeddings-file": "~/.khoj/content/markdown/markdown_embeddings.pt",
         },
-        'ledger': {
+        "ledger": {
-            'input-files': None,
+            "input-files": None,
-            'input-filter': None,
+            "input-filter": None,
-            'compressed-jsonl': '~/.khoj/content/ledger/ledger.jsonl.gz',
+            "compressed-jsonl": "~/.khoj/content/ledger/ledger.jsonl.gz",
-            'embeddings-file': '~/.khoj/content/ledger/ledger_embeddings.pt'
+            "embeddings-file": "~/.khoj/content/ledger/ledger_embeddings.pt",
         },
-        'image': {
+        "image": {
-            'input-directories': None,
+            "input-directories": None,
-            'input-filter': None,
+            "input-filter": None,
-            'embeddings-file': '~/.khoj/content/image/image_embeddings.pt',
+            "embeddings-file": "~/.khoj/content/image/image_embeddings.pt",
-            'batch-size': 50,
+            "batch-size": 50,
-            'use-xmp-metadata': False
+            "use-xmp-metadata": False,
         },
-        'music': {
+        "music": {
-            'input-files': None,
+            "input-files": None,
-            'input-filter': None,
+            "input-filter": None,
-            'compressed-jsonl': '~/.khoj/content/music/music.jsonl.gz',
+            "compressed-jsonl": "~/.khoj/content/music/music.jsonl.gz",
-            'embeddings-file': '~/.khoj/content/music/music_embeddings.pt'
+            "embeddings-file": "~/.khoj/content/music/music_embeddings.pt",
+        },
+    },
+    "search-type": {
+        "symmetric": {
+            "encoder": "sentence-transformers/all-MiniLM-L6-v2",
+            "cross-encoder": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "model_directory": "~/.khoj/search/symmetric/",
+        },
+        "asymmetric": {
+            "encoder": "sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
+            "cross-encoder": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "model_directory": "~/.khoj/search/asymmetric/",
+        },
+        "image": {"encoder": "sentence-transformers/clip-ViT-B-32", "model_directory": "~/.khoj/search/image/"},
+    },
+    "processor": {
+        "conversation": {
+            "openai-api-key": None,
+            "conversation-logfile": "~/.khoj/processor/conversation/conversation_logs.json",
         }
     },
-    'search-type': {
-        'symmetric': {
-            'encoder': 'sentence-transformers/all-MiniLM-L6-v2',
-            'cross-encoder': 'cross-encoder/ms-marco-MiniLM-L-6-v2',
-            'model_directory': '~/.khoj/search/symmetric/'
-        },
-        'asymmetric': {
-            'encoder': 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1',
-            'cross-encoder': 'cross-encoder/ms-marco-MiniLM-L-6-v2',
-            'model_directory': '~/.khoj/search/asymmetric/'
-        },
-        'image': {
-            'encoder': 'sentence-transformers/clip-ViT-B-32',
-            'model_directory': '~/.khoj/search/image/'
-        }
-    },
-    'processor': {
-        'conversation': {
-            'openai-api-key': None,
-            'conversation-logfile': '~/.khoj/processor/conversation/conversation_logs.json'
-        }
-   }
 }

src/khoj/utils/helpers.py

@@ -13,16 +13,17 @@ from typing import Optional, Union, TYPE_CHECKING
 if TYPE_CHECKING:
     # External Packages
     from sentence_transformers import CrossEncoder
+
     # Internal Packages
     from khoj.utils.models import BaseEncoder
 
 
 def is_none_or_empty(item):
-    return item == None or (hasattr(item, '__iter__') and len(item) == 0) or item == ''
+    return item == None or (hasattr(item, "__iter__") and len(item) == 0) or item == ""
 
 
 def to_snake_case_from_dash(item: str):
-    return item.replace('_', '-')
+    return item.replace("_", "-")
 
 
 def get_absolute_path(filepath: Union[str, Path]) -> str:
@@ -34,11 +35,11 @@ def resolve_absolute_path(filepath: Union[str, Optional[Path]], strict=False) ->
 
 
 def get_from_dict(dictionary, *args):
-    '''null-aware get from a nested dictionary
+    """null-aware get from a nested dictionary
-       Returns: dictionary[args[0]][args[1]]... or None if any keys missing'''
+    Returns: dictionary[args[0]][args[1]]... or None if any keys missing"""
     current = dictionary
     for arg in args:
-        if not hasattr(current, '__iter__') or not arg in current:
+        if not hasattr(current, "__iter__") or not arg in current:
             return None
         current = current[arg]
     return current
@@ -54,7 +55,7 @@ def merge_dicts(priority_dict: dict, default_dict: dict):
     return merged_dict
 
 
-def load_model(model_name: str, model_type, model_dir=None, device:str=None) -> Union[BaseEncoder, CrossEncoder]:
+def load_model(model_name: str, model_type, model_dir=None, device: str = None) -> Union[BaseEncoder, CrossEncoder]:
     "Load model from disk or huggingface"
     # Construct model path
     model_path = join(model_dir, model_name.replace("/", "_")) if model_dir is not None else None
@@ -74,17 +75,18 @@ def load_model(model_name: str, model_type, model_dir=None, device:str=None) ->
 
 def is_pyinstaller_app():
     "Returns true if the app is running from Native GUI created by PyInstaller"
-    return getattr(sys, 'frozen', False) and hasattr(sys, '_MEIPASS')
+    return getattr(sys, "frozen", False) and hasattr(sys, "_MEIPASS")
 
 
 def get_class_by_name(name: str) -> object:
     "Returns the class object from name string"
-    module_name, class_name = name.rsplit('.', 1)
+    module_name, class_name = name.rsplit(".", 1)
     return getattr(import_module(module_name), class_name)
 
 
 class timer:
-    '''Context manager to log time taken for a block of code to run'''
+    """Context manager to log time taken for a block of code to run"""
+
     def __init__(self, message: str, logger: logging.Logger, device: torch.device = None):
         self.message = message
         self.logger = logger

src/khoj/utils/jsonl.py

@@ -19,9 +19,9 @@ def load_jsonl(input_path):
 
     # Open JSONL file
     if input_path.suffix == ".gz":
-        jsonl_file = gzip.open(get_absolute_path(input_path), 'rt', encoding='utf-8')
+        jsonl_file = gzip.open(get_absolute_path(input_path), "rt", encoding="utf-8")
     elif input_path.suffix == ".jsonl":
-        jsonl_file = open(get_absolute_path(input_path), 'r', encoding='utf-8')
+        jsonl_file = open(get_absolute_path(input_path), "r", encoding="utf-8")
 
     # Read JSONL file
     for line in jsonl_file:
@@ -31,7 +31,7 @@ def load_jsonl(input_path):
     jsonl_file.close()
 
     # Log JSONL entries loaded
-    logger.info(f'Loaded {len(data)} records from {input_path}')
+    logger.info(f"Loaded {len(data)} records from {input_path}")
 
     return data
 
@@ -41,17 +41,17 @@ def dump_jsonl(jsonl_data, output_path):
     # Create output directory, if it doesn't exist
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
-    with open(output_path, 'w', encoding='utf-8') as f:
+    with open(output_path, "w", encoding="utf-8") as f:
         f.write(jsonl_data)
 
-    logger.info(f'Wrote jsonl data to {output_path}')
+    logger.info(f"Wrote jsonl data to {output_path}")
 
 
 def compress_jsonl_data(jsonl_data, output_path):
     # Create output directory, if it doesn't exist
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
-    with gzip.open(output_path, 'wt', encoding='utf-8') as gzip_file:
+    with gzip.open(output_path, "wt", encoding="utf-8") as gzip_file:
         gzip_file.write(jsonl_data)
 
-    logger.info(f'Wrote jsonl data to gzip compressed jsonl at {output_path}')
+    logger.info(f"Wrote jsonl data to gzip compressed jsonl at {output_path}")

src/khoj/utils/models.py

@@ -13,17 +13,25 @@ from khoj.utils.state import processor_config, config_file
 
 class BaseEncoder(ABC):
     @abstractmethod
-    def __init__(self, model_name: str, device: torch.device=None, **kwargs): ...
+    def __init__(self, model_name: str, device: torch.device = None, **kwargs):
+        ...
 
     @abstractmethod
-    def encode(self, entries: List[str], device:torch.device=None, **kwargs) -> torch.Tensor: ...
+    def encode(self, entries: List[str], device: torch.device = None, **kwargs) -> torch.Tensor:
+        ...
 
 
 class OpenAI(BaseEncoder):
     def __init__(self, model_name, device=None):
         self.model_name = model_name
-        if not processor_config or not processor_config.conversation or not processor_config.conversation.openai_api_key:
+        if (
-            raise Exception(f"Set OpenAI API key under processor-config > conversation > openai-api-key in config file: {config_file}")
+            not processor_config
+            or not processor_config.conversation
+            or not processor_config.conversation.openai_api_key
+        ):
+            raise Exception(
+                f"Set OpenAI API key under processor-config > conversation > openai-api-key in config file: {config_file}"
+            )
         openai.api_key = processor_config.conversation.openai_api_key
         self.embedding_dimensions = None
 
@@ -32,7 +40,7 @@ class OpenAI(BaseEncoder):
 
         for index in trange(0, len(entries)):
             # OpenAI models create better embeddings for entries without newlines
-            processed_entry = entries[index].replace('\n', ' ')
+            processed_entry = entries[index].replace("\n", " ")
 
             try:
                 response = openai.Embedding.create(input=processed_entry, model=self.model_name)
@@ -41,7 +49,9 @@ class OpenAI(BaseEncoder):
                 # Else default to embedding dimensions of the text-embedding-ada-002 model
                 self.embedding_dimensions = len(response.data[0].embedding) if not self.embedding_dimensions else 1536
             except Exception as e:
-                print(f"Failed to encode entry {index} of length: {len(entries[index])}\n\n{entries[index][:1000]}...\n\n{e}")
+                print(
+                    f"Failed to encode entry {index} of length: {len(entries[index])}\n\n{entries[index][:1000]}...\n\n{e}"
+                )
                 # Use zero embedding vector for entries with failed embeddings
                 # This ensures entry embeddings match the order of the source entries
                 # And they have minimal similarity to other entries (as zero vectors are always orthogonal to other vector)

src/khoj/utils/rawconfig.py

@@ -9,11 +9,13 @@ from pydantic import BaseModel, validator
 # Internal Packages
 from khoj.utils.helpers import to_snake_case_from_dash, is_none_or_empty
 
+
 class ConfigBase(BaseModel):
     class Config:
         alias_generator = to_snake_case_from_dash
         allow_population_by_field_name = True
 
+
 class TextContentConfig(ConfigBase):
     input_files: Optional[List[Path]]
     input_filter: Optional[List[str]]
@@ -21,12 +23,15 @@ class TextContentConfig(ConfigBase):
     embeddings_file: Path
     index_heading_entries: Optional[bool] = False
 
-    @validator('input_filter')
+    @validator("input_filter")
     def input_filter_or_files_required(cls, input_filter, values, **kwargs):
-        if is_none_or_empty(input_filter) and ('input_files' not in values or values["input_files"] is None):
+        if is_none_or_empty(input_filter) and ("input_files" not in values or values["input_files"] is None):
-            raise ValueError("Either input_filter or input_files required in all content-type.<text_search> section of Khoj config file")
+            raise ValueError(
+                "Either input_filter or input_files required in all content-type.<text_search> section of Khoj config file"
+            )
         return input_filter
 
+
 class ImageContentConfig(ConfigBase):
     input_directories: Optional[List[Path]]
     input_filter: Optional[List[str]]
@@ -34,12 +39,17 @@ class ImageContentConfig(ConfigBase):
     use_xmp_metadata: bool
     batch_size: int
 
-    @validator('input_filter')
+    @validator("input_filter")
     def input_filter_or_directories_required(cls, input_filter, values, **kwargs):
-        if is_none_or_empty(input_filter) and ('input_directories' not in values or values["input_directories"] is None):
+        if is_none_or_empty(input_filter) and (
-            raise ValueError("Either input_filter or input_directories required in all content-type.image section of Khoj config file")
+            "input_directories" not in values or values["input_directories"] is None
+        ):
+            raise ValueError(
+                "Either input_filter or input_directories required in all content-type.image section of Khoj config file"
+            )
         return input_filter
 
+
 class ContentConfig(ConfigBase):
     org: Optional[TextContentConfig]
     ledger: Optional[TextContentConfig]
@@ -47,41 +57,49 @@ class ContentConfig(ConfigBase):
     music: Optional[TextContentConfig]
     markdown: Optional[TextContentConfig]
 
+
 class TextSearchConfig(ConfigBase):
     encoder: str
     cross_encoder: str
     encoder_type: Optional[str]
     model_directory: Optional[Path]
 
+
 class ImageSearchConfig(ConfigBase):
     encoder: str
     encoder_type: Optional[str]
     model_directory: Optional[Path]
 
+
 class SearchConfig(ConfigBase):
     asymmetric: Optional[TextSearchConfig]
     symmetric: Optional[TextSearchConfig]
     image: Optional[ImageSearchConfig]
 
+
 class ConversationProcessorConfig(ConfigBase):
     openai_api_key: str
     conversation_logfile: Path
     model: Optional[str] = "text-davinci-003"
 
+
 class ProcessorConfig(ConfigBase):
     conversation: Optional[ConversationProcessorConfig]
 
+
 class FullConfig(ConfigBase):
     content_type: Optional[ContentConfig]
     search_type: Optional[SearchConfig]
     processor: Optional[ProcessorConfig]
 
+
 class SearchResponse(ConfigBase):
     entry: str
     score: str
     additional: Optional[dict]
 
-class Entry():
+
+class Entry:
     raw: str
     compiled: str
     file: Optional[str]
@@ -99,8 +117,4 @@ class Entry():
 
     @classmethod
     def from_dict(cls, dictionary: dict):
-        return cls(
+        return cls(raw=dictionary["raw"], compiled=dictionary["compiled"], file=dictionary.get("file", None))
-            raw=dictionary['raw'],
-            compiled=dictionary['compiled'],
-            file=dictionary.get('file', None)
-        )

src/khoj/utils/yaml.py

@@ -17,14 +17,14 @@ def save_config_to_file(yaml_config: dict, yaml_config_file: Path):
     # Create output directory, if it doesn't exist
     yaml_config_file.parent.mkdir(parents=True, exist_ok=True)
 
-    with open(yaml_config_file, 'w', encoding='utf-8') as config_file:
+    with open(yaml_config_file, "w", encoding="utf-8") as config_file:
         yaml.safe_dump(yaml_config, config_file, allow_unicode=True)
 
 
 def load_config_from_file(yaml_config_file: Path) -> dict:
     "Read config from YML file"
     config_from_file = None
-    with open(yaml_config_file, 'r', encoding='utf-8') as config_file:
+    with open(yaml_config_file, "r", encoding="utf-8") as config_file:
         config_from_file = yaml.safe_load(config_file)
     return config_from_file
 

tests/conftest.py

@@ -6,59 +6,67 @@ import pytest
 # Internal Packages
 from khoj.search_type import image_search, text_search
 from khoj.utils.helpers import resolve_absolute_path
-from khoj.utils.rawconfig import ContentConfig, TextContentConfig, ImageContentConfig, SearchConfig, TextSearchConfig, ImageSearchConfig
+from khoj.utils.rawconfig import (
+    ContentConfig,
+    TextContentConfig,
+    ImageContentConfig,
+    SearchConfig,
+    TextSearchConfig,
+    ImageSearchConfig,
+)
 from khoj.processor.org_mode.org_to_jsonl import OrgToJsonl
 from khoj.search_filter.date_filter import DateFilter
 from khoj.search_filter.word_filter import WordFilter
 from khoj.search_filter.file_filter import FileFilter
 
 
-@pytest.fixture(scope='session')
+@pytest.fixture(scope="session")
 def search_config() -> SearchConfig:
-    model_dir = resolve_absolute_path('~/.khoj/search')
+    model_dir = resolve_absolute_path("~/.khoj/search")
     model_dir.mkdir(parents=True, exist_ok=True)
     search_config = SearchConfig()
 
     search_config.symmetric = TextSearchConfig(
-        encoder = "sentence-transformers/all-MiniLM-L6-v2",
+        encoder="sentence-transformers/all-MiniLM-L6-v2",
-        cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        cross_encoder="cross-encoder/ms-marco-MiniLM-L-6-v2",
-        model_directory = model_dir / 'symmetric/'
+        model_directory=model_dir / "symmetric/",
     )
 
     search_config.asymmetric = TextSearchConfig(
-        encoder = "sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
+        encoder="sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
-        cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        cross_encoder="cross-encoder/ms-marco-MiniLM-L-6-v2",
-        model_directory = model_dir / 'asymmetric/'
+        model_directory=model_dir / "asymmetric/",
     )
 
     search_config.image = ImageSearchConfig(
-        encoder = "sentence-transformers/clip-ViT-B-32",
+        encoder="sentence-transformers/clip-ViT-B-32", model_directory=model_dir / "image/"
-        model_directory = model_dir / 'image/'
     )
 
     return search_config
 
 
-@pytest.fixture(scope='session')
+@pytest.fixture(scope="session")
 def content_config(tmp_path_factory, search_config: SearchConfig):
-    content_dir = tmp_path_factory.mktemp('content')
+    content_dir = tmp_path_factory.mktemp("content")
 
     # Generate Image Embeddings from Test Images
     content_config = ContentConfig()
     content_config.image = ImageContentConfig(
-        input_directories = ['tests/data/images'],
+        input_directories=["tests/data/images"],
-        embeddings_file = content_dir.joinpath('image_embeddings.pt'),
+        embeddings_file=content_dir.joinpath("image_embeddings.pt"),
-        batch_size = 1,
+        batch_size=1,
-        use_xmp_metadata = False)
+        use_xmp_metadata=False,
+    )
 
     image_search.setup(content_config.image, search_config.image, regenerate=False)
 
     # Generate Notes Embeddings from Test Notes
     content_config.org = TextContentConfig(
-        input_files = None,
+        input_files=None,
-        input_filter = ['tests/data/org/*.org'],
+        input_filter=["tests/data/org/*.org"],
-        compressed_jsonl = content_dir.joinpath('notes.jsonl.gz'),
+        compressed_jsonl=content_dir.joinpath("notes.jsonl.gz"),
-        embeddings_file = content_dir.joinpath('note_embeddings.pt'))
+        embeddings_file=content_dir.joinpath("note_embeddings.pt"),
+    )
 
     filters = [DateFilter(), WordFilter(), FileFilter()]
     text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters)
@@ -66,7 +74,7 @@ def content_config(tmp_path_factory, search_config: SearchConfig):
     return content_config
 
 
-@pytest.fixture(scope='function')
+@pytest.fixture(scope="function")
 def new_org_file(content_config: ContentConfig):
     # Setup
     new_org_file = Path(content_config.org.input_filter[0]).parent / "new_file.org"
@@ -79,9 +87,9 @@ def new_org_file(content_config: ContentConfig):
         new_org_file.unlink()
 
 
-@pytest.fixture(scope='function')
+@pytest.fixture(scope="function")
 def org_config_with_only_new_file(content_config: ContentConfig, new_org_file: Path):
     new_org_config = deepcopy(content_config.org)
-    new_org_config.input_files = [f'{new_org_file}']
+    new_org_config.input_files = [f"{new_org_file}"]
     new_org_config.input_filter = None
     return new_org_config

tests/test_beancount_to_jsonl.py

@@ -8,10 +8,10 @@ from khoj.processor.ledger.beancount_to_jsonl import BeancountToJsonl
 def test_no_transactions_in_file(tmp_path):
     "Handle file with no transactions."
     # Arrange
-    entry = f'''
+    entry = f"""
     - Bullet point 1
     - Bullet point 2
-    '''
+    """
     beancount_file = create_file(tmp_path, entry)
 
     # Act
@@ -20,7 +20,8 @@ def test_no_transactions_in_file(tmp_path):
 
     # Process Each Entry from All Beancount Files
     jsonl_string = BeancountToJsonl.convert_transaction_maps_to_jsonl(
-        BeancountToJsonl.convert_transactions_to_maps(entry_nodes, file_to_entries))
+        BeancountToJsonl.convert_transactions_to_maps(entry_nodes, file_to_entries)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -30,11 +31,11 @@ def test_no_transactions_in_file(tmp_path):
 def test_single_beancount_transaction_to_jsonl(tmp_path):
     "Convert transaction from single file to jsonl."
     # Arrange
-    entry = f'''
+    entry = f"""
 1984-04-01 * "Payee" "Narration"
 Expenses:Test:Test  1.00 KES
 Assets:Test:Test  -1.00 KES
-    '''
+    """
     beancount_file = create_file(tmp_path, entry)
 
     # Act
@@ -43,7 +44,8 @@ Assets:Test:Test  -1.00 KES
 
     # Process Each Entry from All Beancount Files
     jsonl_string = BeancountToJsonl.convert_transaction_maps_to_jsonl(
-        BeancountToJsonl.convert_transactions_to_maps(entries, entry_to_file_map))
+        BeancountToJsonl.convert_transactions_to_maps(entries, entry_to_file_map)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -53,7 +55,7 @@ Assets:Test:Test  -1.00 KES
 def test_multiple_transactions_to_jsonl(tmp_path):
     "Convert multiple transactions from single file to jsonl."
     # Arrange
-    entry = f'''
+    entry = f"""
 1984-04-01 * "Payee" "Narration"
 Expenses:Test:Test  1.00 KES
 Assets:Test:Test  -1.00 KES
@@ -61,7 +63,7 @@ Assets:Test:Test  -1.00 KES
 1984-04-01 * "Payee" "Narration"
 Expenses:Test:Test  1.00 KES
 Assets:Test:Test  -1.00 KES
-'''
+"""
 
     beancount_file = create_file(tmp_path, entry)
 
@@ -71,7 +73,8 @@ Assets:Test:Test  -1.00 KES
 
     # Process Each Entry from All Beancount Files
     jsonl_string = BeancountToJsonl.convert_transaction_maps_to_jsonl(
-        BeancountToJsonl.convert_transactions_to_maps(entries, entry_to_file_map))
+        BeancountToJsonl.convert_transactions_to_maps(entries, entry_to_file_map)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -95,8 +98,8 @@ def test_get_beancount_files(tmp_path):
     expected_files = sorted(map(str, [group1_file1, group1_file2, group2_file1, group2_file2, file1]))
 
     # Setup input-files, input-filters
-    input_files = [tmp_path / 'ledger.bean']
+    input_files = [tmp_path / "ledger.bean"]
-    input_filter = [tmp_path / 'group1*.bean', tmp_path / 'group2*.beancount']
+    input_filter = [tmp_path / "group1*.bean", tmp_path / "group2*.beancount"]
 
     # Act
     extracted_org_files = BeancountToJsonl.get_beancount_files(input_files, input_filter)

tests/test_chatbot.py

@@ -6,7 +6,7 @@ from khoj.processor.conversation.gpt import converse, understand, message_to_pro
 
 
 # Initialize variables for tests
-model = 'text-davinci-003'
+model = "text-davinci-003"
 api_key = None  # Input your OpenAI API key to run the tests below
 
 
@@ -14,19 +14,22 @@ api_key = None  # Input your OpenAI API key to run the tests below
 # ----------------------------------------------------------------------------------------------------
 def test_message_to_understand_prompt():
     # Arrange
-    understand_primer = "Extract information from each chat message\n\nremember(memory-type, data);\nmemory-type=[\"companion\", \"notes\", \"ledger\", \"image\", \"music\"]\nsearch(search-type, data);\nsearch-type=[\"google\", \"youtube\"]\ngenerate(activity);\nactivity=[\"paint\",\"write\", \"chat\"]\ntrigger-emotion(emotion);\nemotion=[\"happy\",\"confidence\",\"fear\",\"surprise\",\"sadness\",\"disgust\",\"anger\", \"curiosity\", \"calm\"]\n\nQ: How are you doing?\nA: activity(\"chat\"); trigger-emotion(\"surprise\")\nQ: Do you remember what I told you about my brother Antoine when we were at the beach?\nA: remember(\"notes\", \"Brother Antoine when we were at the beach\"); trigger-emotion(\"curiosity\");\nQ: what did we talk about last time?\nA: remember(\"notes\", \"talk last time\"); trigger-emotion(\"curiosity\");\nQ: Let's make some drawings!\nA: generate(\"paint\"); trigger-emotion(\"happy\");\nQ: Do you know anything about Lebanon?\nA: search(\"google\", \"lebanon\");  trigger-emotion(\"confidence\");\nQ: Find a video about a panda rolling in the grass\nA: search(\"youtube\",\"panda rolling in the grass\");  trigger-emotion(\"happy\"); \nQ: Tell me a scary story\nA: generate(\"write\" \"A story about some adventure\"); trigger-emotion(\"fear\");\nQ: What fiction book was I reading last week about AI starship?\nA: remember(\"notes\", \"read fiction book about AI starship last week\"); trigger-emotion(\"curiosity\");\nQ: How much did I spend at Subway for dinner last time?\nA: remember(\"ledger\", \"last Subway dinner\"); trigger-emotion(\"curiosity\");\nQ: I'm feeling sleepy\nA: activity(\"chat\"); trigger-emotion(\"calm\")\nQ: What was that popular Sri lankan song that Alex showed me recently?\nA: remember(\"music\", \"popular Sri lankan song that Alex showed recently\");  trigger-emotion(\"curiosity\"); \nQ: You're pretty funny!\nA: activity(\"chat\"); trigger-emotion(\"pride\")"
+    understand_primer = 'Extract information from each chat message\n\nremember(memory-type, data);\nmemory-type=["companion", "notes", "ledger", "image", "music"]\nsearch(search-type, data);\nsearch-type=["google", "youtube"]\ngenerate(activity);\nactivity=["paint","write", "chat"]\ntrigger-emotion(emotion);\nemotion=["happy","confidence","fear","surprise","sadness","disgust","anger", "curiosity", "calm"]\n\nQ: How are you doing?\nA: activity("chat"); trigger-emotion("surprise")\nQ: Do you remember what I told you about my brother Antoine when we were at the beach?\nA: remember("notes", "Brother Antoine when we were at the beach"); trigger-emotion("curiosity");\nQ: what did we talk about last time?\nA: remember("notes", "talk last time"); trigger-emotion("curiosity");\nQ: Let\'s make some drawings!\nA: generate("paint"); trigger-emotion("happy");\nQ: Do you know anything about Lebanon?\nA: search("google", "lebanon");  trigger-emotion("confidence");\nQ: Find a video about a panda rolling in the grass\nA: search("youtube","panda rolling in the grass");  trigger-emotion("happy"); \nQ: Tell me a scary story\nA: generate("write" "A story about some adventure"); trigger-emotion("fear");\nQ: What fiction book was I reading last week about AI starship?\nA: remember("notes", "read fiction book about AI starship last week"); trigger-emotion("curiosity");\nQ: How much did I spend at Subway for dinner last time?\nA: remember("ledger", "last Subway dinner"); trigger-emotion("curiosity");\nQ: I\'m feeling sleepy\nA: activity("chat"); trigger-emotion("calm")\nQ: What was that popular Sri lankan song that Alex showed me recently?\nA: remember("music", "popular Sri lankan song that Alex showed recently");  trigger-emotion("curiosity"); \nQ: You\'re pretty funny!\nA: activity("chat"); trigger-emotion("pride")'
-    expected_response = "Extract information from each chat message\n\nremember(memory-type, data);\nmemory-type=[\"companion\", \"notes\", \"ledger\", \"image\", \"music\"]\nsearch(search-type, data);\nsearch-type=[\"google\", \"youtube\"]\ngenerate(activity);\nactivity=[\"paint\",\"write\", \"chat\"]\ntrigger-emotion(emotion);\nemotion=[\"happy\",\"confidence\",\"fear\",\"surprise\",\"sadness\",\"disgust\",\"anger\", \"curiosity\", \"calm\"]\n\nQ: How are you doing?\nA: activity(\"chat\"); trigger-emotion(\"surprise\")\nQ: Do you remember what I told you about my brother Antoine when we were at the beach?\nA: remember(\"notes\", \"Brother Antoine when we were at the beach\"); trigger-emotion(\"curiosity\");\nQ: what did we talk about last time?\nA: remember(\"notes\", \"talk last time\"); trigger-emotion(\"curiosity\");\nQ: Let's make some drawings!\nA: generate(\"paint\"); trigger-emotion(\"happy\");\nQ: Do you know anything about Lebanon?\nA: search(\"google\", \"lebanon\");  trigger-emotion(\"confidence\");\nQ: Find a video about a panda rolling in the grass\nA: search(\"youtube\",\"panda rolling in the grass\");  trigger-emotion(\"happy\"); \nQ: Tell me a scary story\nA: generate(\"write\" \"A story about some adventure\"); trigger-emotion(\"fear\");\nQ: What fiction book was I reading last week about AI starship?\nA: remember(\"notes\", \"read fiction book about AI starship last week\"); trigger-emotion(\"curiosity\");\nQ: How much did I spend at Subway for dinner last time?\nA: remember(\"ledger\", \"last Subway dinner\"); trigger-emotion(\"curiosity\");\nQ: I'm feeling sleepy\nA: activity(\"chat\"); trigger-emotion(\"calm\")\nQ: What was that popular Sri lankan song that Alex showed me recently?\nA: remember(\"music\", \"popular Sri lankan song that Alex showed recently\");  trigger-emotion(\"curiosity\"); \nQ: You're pretty funny!\nA: activity(\"chat\"); trigger-emotion(\"pride\")\nQ: When did I last dine at Burger King?\nA:"
+    expected_response = 'Extract information from each chat message\n\nremember(memory-type, data);\nmemory-type=["companion", "notes", "ledger", "image", "music"]\nsearch(search-type, data);\nsearch-type=["google", "youtube"]\ngenerate(activity);\nactivity=["paint","write", "chat"]\ntrigger-emotion(emotion);\nemotion=["happy","confidence","fear","surprise","sadness","disgust","anger", "curiosity", "calm"]\n\nQ: How are you doing?\nA: activity("chat"); trigger-emotion("surprise")\nQ: Do you remember what I told you about my brother Antoine when we were at the beach?\nA: remember("notes", "Brother Antoine when we were at the beach"); trigger-emotion("curiosity");\nQ: what did we talk about last time?\nA: remember("notes", "talk last time"); trigger-emotion("curiosity");\nQ: Let\'s make some drawings!\nA: generate("paint"); trigger-emotion("happy");\nQ: Do you know anything about Lebanon?\nA: search("google", "lebanon");  trigger-emotion("confidence");\nQ: Find a video about a panda rolling in the grass\nA: search("youtube","panda rolling in the grass");  trigger-emotion("happy"); \nQ: Tell me a scary story\nA: generate("write" "A story about some adventure"); trigger-emotion("fear");\nQ: What fiction book was I reading last week about AI starship?\nA: remember("notes", "read fiction book about AI starship last week"); trigger-emotion("curiosity");\nQ: How much did I spend at Subway for dinner last time?\nA: remember("ledger", "last Subway dinner"); trigger-emotion("curiosity");\nQ: I\'m feeling sleepy\nA: activity("chat"); trigger-emotion("calm")\nQ: What was that popular Sri lankan song that Alex showed me recently?\nA: remember("music", "popular Sri lankan song that Alex showed recently");  trigger-emotion("curiosity"); \nQ: You\'re pretty funny!\nA: activity("chat"); trigger-emotion("pride")\nQ: When did I last dine at Burger King?\nA:'
 
     # Act
-    actual_response = message_to_prompt("When did I last dine at Burger King?", understand_primer, start_sequence="\nA:", restart_sequence="\nQ:")
+    actual_response = message_to_prompt(
+        "When did I last dine at Burger King?", understand_primer, start_sequence="\nA:", restart_sequence="\nQ:"
+    )
 
     # Assert
     assert actual_response == expected_response
 
 
 # ----------------------------------------------------------------------------------------------------
-@pytest.mark.skipif(api_key is None,
+@pytest.mark.skipif(
-                    reason="Set api_key variable to your OpenAI API key from https://beta.openai.com/account/api-keys")
+    api_key is None, reason="Set api_key variable to your OpenAI API key from https://beta.openai.com/account/api-keys"
+)
 def test_minimal_chat_with_gpt():
     # Act
     response = converse("What will happen when the stars go out?", model=model, api_key=api_key)
@@ -36,21 +39,29 @@ def test_minimal_chat_with_gpt():
 
 
 # ----------------------------------------------------------------------------------------------------
-@pytest.mark.skipif(api_key is None,
+@pytest.mark.skipif(
-                    reason="Set api_key variable to your OpenAI API key from https://beta.openai.com/account/api-keys")
+    api_key is None, reason="Set api_key variable to your OpenAI API key from https://beta.openai.com/account/api-keys"
+)
 def test_chat_with_history():
     # Arrange
-    ai_prompt="AI:"
+    ai_prompt = "AI:"
-    human_prompt="Human:"
+    human_prompt = "Human:"
 
-    conversation_primer = f'''
+    conversation_primer = f"""
 The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly companion.
 
 {human_prompt} Hello, I am Testatron. Who are you?
-{ai_prompt} Hi, I am Khoj, an AI conversational companion created by OpenAI. How can I help you today?'''
+{ai_prompt} Hi, I am Khoj, an AI conversational companion created by OpenAI. How can I help you today?"""
 
     # Act
-    response = converse("Hi Khoj, What is my name?", model=model, conversation_history=conversation_primer, api_key=api_key, temperature=0, max_tokens=50)
+    response = converse(
+        "Hi Khoj, What is my name?",
+        model=model,
+        conversation_history=conversation_primer,
+        api_key=api_key,
+        temperature=0,
+        max_tokens=50,
+    )
 
     # Assert
     assert len(response) > 0
@@ -58,12 +69,13 @@ The following is a conversation with an AI assistant. The assistant is helpful,
 
 
 # ----------------------------------------------------------------------------------------------------
-@pytest.mark.skipif(api_key is None,
+@pytest.mark.skipif(
-                    reason="Set api_key variable to your OpenAI API key from https://beta.openai.com/account/api-keys")
+    api_key is None, reason="Set api_key variable to your OpenAI API key from https://beta.openai.com/account/api-keys"
+)
 def test_understand_message_using_gpt():
     # Act
     response = understand("When did I last dine at Subway?", model=model, api_key=api_key)
 
     # Assert
     assert len(response) > 0
-    assert response['intent']['memory-type'] == 'ledger'
+    assert response["intent"]["memory-type"] == "ledger"

tests/test_cli.py

@@ -14,35 +14,37 @@ def test_cli_minimal_default():
     actual_args = cli([])
 
     # Assert
-    assert actual_args.config_file == resolve_absolute_path(Path('~/.khoj/khoj.yml'))
+    assert actual_args.config_file == resolve_absolute_path(Path("~/.khoj/khoj.yml"))
     assert actual_args.regenerate == False
     assert actual_args.no_gui == False
     assert actual_args.verbose == 0
 
+
 # ----------------------------------------------------------------------------------------------------
 def test_cli_invalid_config_file_path():
     # Arrange
     non_existent_config_file = f"non-existent-khoj-{random()}.yml"
 
     # Act
-    actual_args = cli([f'-c={non_existent_config_file}'])
+    actual_args = cli([f"-c={non_existent_config_file}"])
 
     # Assert
     assert actual_args.config_file == resolve_absolute_path(non_existent_config_file)
     assert actual_args.config == None
 
+
 # ----------------------------------------------------------------------------------------------------
 def test_cli_config_from_file():
     # Act
-    actual_args = cli(['-c=tests/data/config.yml',
+    actual_args = cli(["-c=tests/data/config.yml", "--regenerate", "--no-gui", "-vvv"])
-                       '--regenerate',
-                       '--no-gui',
-                       '-vvv'])
 
     # Assert
-    assert actual_args.config_file == resolve_absolute_path(Path('tests/data/config.yml'))
+    assert actual_args.config_file == resolve_absolute_path(Path("tests/data/config.yml"))
     assert actual_args.no_gui == True
     assert actual_args.regenerate == True
     assert actual_args.config is not None
-    assert actual_args.config.content_type.org.input_files == [Path('~/first_from_config.org'), Path('~/second_from_config.org')]
+    assert actual_args.config.content_type.org.input_files == [
+        Path("~/first_from_config.org"),
+        Path("~/second_from_config.org"),
+    ]
     assert actual_args.verbose == 3

tests/test_client.py

@@ -21,6 +21,7 @@ from khoj.search_filter.file_filter import FileFilter
 # ----------------------------------------------------------------------------------------------------
 client = TestClient(app)
 
+
 # Test
 # ----------------------------------------------------------------------------------------------------
 def test_search_with_invalid_content_type():
@@ -98,9 +99,11 @@ def test_image_search(content_config: ContentConfig, search_config: SearchConfig
     config.content_type = content_config
     config.search_type = search_config
     model.image_search = image_search.setup(content_config.image, search_config.image, regenerate=False)
-    query_expected_image_pairs = [("kitten", "kitten_park.jpg"),
+    query_expected_image_pairs = [
-                                  ("a horse and dog on a leash", "horse_dog.jpg"),
+        ("kitten", "kitten_park.jpg"),
-                                  ("A guinea pig eating grass", "guineapig_grass.jpg")]
+        ("a horse and dog on a leash", "horse_dog.jpg"),
+        ("A guinea pig eating grass", "guineapig_grass.jpg"),
+    ]
 
     for query, expected_image_name in query_expected_image_pairs:
         # Act
@@ -135,7 +138,9 @@ def test_notes_search(content_config: ContentConfig, search_config: SearchConfig
 def test_notes_search_with_only_filters(content_config: ContentConfig, search_config: SearchConfig):
     # Arrange
     filters = [WordFilter(), FileFilter()]
-    model.orgmode_search = text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters)
+    model.orgmode_search = text_search.setup(
+        OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters
+    )
     user_query = quote('+"Emacs" file:"*.org"')
 
     # Act
@@ -152,7 +157,9 @@ def test_notes_search_with_only_filters(content_config: ContentConfig, search_co
 def test_notes_search_with_include_filter(content_config: ContentConfig, search_config: SearchConfig):
     # Arrange
     filters = [WordFilter()]
-    model.orgmode_search = text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters)
+    model.orgmode_search = text_search.setup(
+        OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters
+    )
     user_query = quote('How to git install application? +"Emacs"')
 
     # Act
@@ -169,7 +176,9 @@ def test_notes_search_with_include_filter(content_config: ContentConfig, search_
 def test_notes_search_with_exclude_filter(content_config: ContentConfig, search_config: SearchConfig):
     # Arrange
     filters = [WordFilter()]
-    model.orgmode_search = text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters)
+    model.orgmode_search = text_search.setup(
+        OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters
+    )
     user_query = quote('How to git install application? -"clone"')
 
     # Act

tests/test_date_filter.py

@@ -10,53 +10,59 @@ from khoj.utils.rawconfig import Entry
 
 def test_date_filter():
     entries = [
-        Entry(compiled='', raw='Entry with no date'),
+        Entry(compiled="", raw="Entry with no date"),
-        Entry(compiled='', raw='April Fools entry: 1984-04-01'),
+        Entry(compiled="", raw="April Fools entry: 1984-04-01"),
-        Entry(compiled='', raw='Entry with date:1984-04-02')
+        Entry(compiled="", raw="Entry with date:1984-04-02"),
     ]
 
-    q_with_no_date_filter = 'head tail'
+    q_with_no_date_filter = "head tail"
     ret_query, entry_indices = DateFilter().apply(q_with_no_date_filter, entries)
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 1, 2}
 
     q_with_dtrange_non_overlapping_at_boundary = 'head dt>"1984-04-01" dt<"1984-04-02" tail'
     ret_query, entry_indices = DateFilter().apply(q_with_dtrange_non_overlapping_at_boundary, entries)
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == set()
 
     query_with_overlapping_dtrange = 'head dt>"1984-04-01" dt<"1984-04-03" tail'
     ret_query, entry_indices = DateFilter().apply(query_with_overlapping_dtrange, entries)
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {2}
 
     query_with_overlapping_dtrange = 'head dt>="1984-04-01" dt<"1984-04-02" tail'
     ret_query, entry_indices = DateFilter().apply(query_with_overlapping_dtrange, entries)
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {1}
 
     query_with_overlapping_dtrange = 'head dt>"1984-04-01" dt<="1984-04-02" tail'
     ret_query, entry_indices = DateFilter().apply(query_with_overlapping_dtrange, entries)
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {2}
 
     query_with_overlapping_dtrange = 'head dt>="1984-04-01" dt<="1984-04-02" tail'
     ret_query, entry_indices = DateFilter().apply(query_with_overlapping_dtrange, entries)
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {1, 2}
 
 
 def test_extract_date_range():
-    assert DateFilter().extract_date_range('head dt>"1984-01-04" dt<"1984-01-07" tail') == [datetime(1984, 1, 5, 0, 0, 0).timestamp(), datetime(1984, 1, 7, 0, 0, 0).timestamp()]
+    assert DateFilter().extract_date_range('head dt>"1984-01-04" dt<"1984-01-07" tail') == [
+        datetime(1984, 1, 5, 0, 0, 0).timestamp(),
+        datetime(1984, 1, 7, 0, 0, 0).timestamp(),
+    ]
     assert DateFilter().extract_date_range('head dt<="1984-01-01"') == [0, datetime(1984, 1, 2, 0, 0, 0).timestamp()]
     assert DateFilter().extract_date_range('head dt>="1984-01-01"') == [datetime(1984, 1, 1, 0, 0, 0).timestamp(), inf]
-    assert DateFilter().extract_date_range('head dt:"1984-01-01"') == [datetime(1984, 1, 1, 0, 0, 0).timestamp(), datetime(1984, 1, 2, 0, 0, 0).timestamp()]
+    assert DateFilter().extract_date_range('head dt:"1984-01-01"') == [
+        datetime(1984, 1, 1, 0, 0, 0).timestamp(),
+        datetime(1984, 1, 2, 0, 0, 0).timestamp(),
+    ]
 
     # Unparseable date filter specified in query
     assert DateFilter().extract_date_range('head dt:"Summer of 69" tail') == None
 
     # No date filter specified in query
-    assert DateFilter().extract_date_range('head tail') == None
+    assert DateFilter().extract_date_range("head tail") == None
 
     # Non intersecting date ranges
     assert DateFilter().extract_date_range('head dt>"1984-01-01" dt<"1984-01-01" tail') == None
@@ -66,43 +72,79 @@ def test_parse():
     test_now = datetime(1984, 4, 1, 21, 21, 21)
 
     # day variations
-    assert DateFilter().parse('today', relative_base=test_now) == (datetime(1984, 4, 1, 0, 0, 0), datetime(1984, 4, 2, 0, 0, 0))
+    assert DateFilter().parse("today", relative_base=test_now) == (
-    assert DateFilter().parse('tomorrow', relative_base=test_now) == (datetime(1984, 4, 2, 0, 0, 0), datetime(1984, 4, 3, 0, 0, 0))
+        datetime(1984, 4, 1, 0, 0, 0),
-    assert DateFilter().parse('yesterday', relative_base=test_now) == (datetime(1984, 3, 31, 0, 0, 0), datetime(1984, 4, 1, 0, 0, 0))
+        datetime(1984, 4, 2, 0, 0, 0),
-    assert DateFilter().parse('5 days ago', relative_base=test_now) == (datetime(1984, 3, 27, 0, 0, 0), datetime(1984, 3, 28, 0, 0, 0))
+    )
+    assert DateFilter().parse("tomorrow", relative_base=test_now) == (
+        datetime(1984, 4, 2, 0, 0, 0),
+        datetime(1984, 4, 3, 0, 0, 0),
+    )
+    assert DateFilter().parse("yesterday", relative_base=test_now) == (
+        datetime(1984, 3, 31, 0, 0, 0),
+        datetime(1984, 4, 1, 0, 0, 0),
+    )
+    assert DateFilter().parse("5 days ago", relative_base=test_now) == (
+        datetime(1984, 3, 27, 0, 0, 0),
+        datetime(1984, 3, 28, 0, 0, 0),
+    )
 
     # week variations
-    assert DateFilter().parse('last week', relative_base=test_now) == (datetime(1984, 3, 18, 0, 0, 0), datetime(1984, 3, 25, 0, 0, 0))
+    assert DateFilter().parse("last week", relative_base=test_now) == (
-    assert DateFilter().parse('2 weeks ago', relative_base=test_now) == (datetime(1984, 3, 11, 0, 0, 0), datetime(1984, 3, 18, 0, 0, 0))
+        datetime(1984, 3, 18, 0, 0, 0),
+        datetime(1984, 3, 25, 0, 0, 0),
+    )
+    assert DateFilter().parse("2 weeks ago", relative_base=test_now) == (
+        datetime(1984, 3, 11, 0, 0, 0),
+        datetime(1984, 3, 18, 0, 0, 0),
+    )
 
     # month variations
-    assert DateFilter().parse('next month', relative_base=test_now) == (datetime(1984, 5, 1, 0, 0, 0), datetime(1984, 6, 1, 0, 0, 0))
+    assert DateFilter().parse("next month", relative_base=test_now) == (
-    assert DateFilter().parse('2 months ago', relative_base=test_now) == (datetime(1984, 2, 1, 0, 0, 0), datetime(1984, 3, 1, 0, 0, 0))
+        datetime(1984, 5, 1, 0, 0, 0),
+        datetime(1984, 6, 1, 0, 0, 0),
+    )
+    assert DateFilter().parse("2 months ago", relative_base=test_now) == (
+        datetime(1984, 2, 1, 0, 0, 0),
+        datetime(1984, 3, 1, 0, 0, 0),
+    )
 
     # year variations
-    assert DateFilter().parse('this year', relative_base=test_now) == (datetime(1984, 1, 1, 0, 0, 0), datetime(1985, 1, 1, 0, 0, 0))
+    assert DateFilter().parse("this year", relative_base=test_now) == (
-    assert DateFilter().parse('20 years later', relative_base=test_now) == (datetime(2004, 1, 1, 0, 0, 0), datetime(2005, 1, 1, 0, 0, 0))
+        datetime(1984, 1, 1, 0, 0, 0),
+        datetime(1985, 1, 1, 0, 0, 0),
+    )
+    assert DateFilter().parse("20 years later", relative_base=test_now) == (
+        datetime(2004, 1, 1, 0, 0, 0),
+        datetime(2005, 1, 1, 0, 0, 0),
+    )
 
     # specific month/date variation
-    assert DateFilter().parse('in august', relative_base=test_now) == (datetime(1983, 8, 1, 0, 0, 0), datetime(1983, 8, 2, 0, 0, 0))
+    assert DateFilter().parse("in august", relative_base=test_now) == (
-    assert DateFilter().parse('on 1983-08-01', relative_base=test_now) == (datetime(1983, 8, 1, 0, 0, 0), datetime(1983, 8, 2, 0, 0, 0))
+        datetime(1983, 8, 1, 0, 0, 0),
+        datetime(1983, 8, 2, 0, 0, 0),
+    )
+    assert DateFilter().parse("on 1983-08-01", relative_base=test_now) == (
+        datetime(1983, 8, 1, 0, 0, 0),
+        datetime(1983, 8, 2, 0, 0, 0),
+    )
 
 
 def test_date_filter_regex():
     dtrange_match = re.findall(DateFilter().date_regex, 'multi word head dt>"today" dt:"1984-01-01"')
-    assert dtrange_match == [('>', 'today'), (':', '1984-01-01')]
+    assert dtrange_match == [(">", "today"), (":", "1984-01-01")]
 
     dtrange_match = re.findall(DateFilter().date_regex, 'head dt>"today" dt:"1984-01-01" multi word tail')
-    assert dtrange_match == [('>', 'today'), (':', '1984-01-01')]
+    assert dtrange_match == [(">", "today"), (":", "1984-01-01")]
 
     dtrange_match = re.findall(DateFilter().date_regex, 'multi word head dt>="today" dt="1984-01-01"')
-    assert dtrange_match == [('>=', 'today'), ('=', '1984-01-01')]
+    assert dtrange_match == [(">=", "today"), ("=", "1984-01-01")]
 
     dtrange_match = re.findall(DateFilter().date_regex, 'dt<"multi word date" multi word tail')
-    assert dtrange_match == [('<', 'multi word date')]
+    assert dtrange_match == [("<", "multi word date")]
 
     dtrange_match = re.findall(DateFilter().date_regex, 'head dt<="multi word date"')
-    assert dtrange_match == [('<=', 'multi word date')]
+    assert dtrange_match == [("<=", "multi word date")]
 
-    dtrange_match = re.findall(DateFilter().date_regex, 'head tail')
+    dtrange_match = re.findall(DateFilter().date_regex, "head tail")
     assert dtrange_match == []

tests/test_file_filter.py

@@ -7,7 +7,7 @@ def test_no_file_filter():
     # Arrange
     file_filter = FileFilter()
     entries = arrange_content()
-    q_with_no_filter = 'head tail'
+    q_with_no_filter = "head tail"
 
     # Act
     can_filter = file_filter.can_filter(q_with_no_filter)
@@ -15,7 +15,7 @@ def test_no_file_filter():
 
     # Assert
     assert can_filter == False
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 1, 2, 3}
 
 
@@ -31,7 +31,7 @@ def test_file_filter_with_non_existent_file():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {}
 
 
@@ -47,7 +47,7 @@ def test_single_file_filter():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 2}
 
 
@@ -63,7 +63,7 @@ def test_file_filter_with_partial_match():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 2}
 
 
@@ -79,7 +79,7 @@ def test_file_filter_with_regex_match():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 1, 2, 3}
 
 
@@ -95,16 +95,16 @@ def test_multiple_file_filter():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 1, 2, 3}
 
 
 def arrange_content():
     entries = [
-        Entry(compiled='', raw='First Entry', file= 'file 1.org'),
+        Entry(compiled="", raw="First Entry", file="file 1.org"),
-        Entry(compiled='', raw='Second Entry', file= 'file2.org'),
+        Entry(compiled="", raw="Second Entry", file="file2.org"),
-        Entry(compiled='', raw='Third Entry', file= 'file 1.org'),
+        Entry(compiled="", raw="Third Entry", file="file 1.org"),
-        Entry(compiled='', raw='Fourth Entry', file= 'file2.org')
+        Entry(compiled="", raw="Fourth Entry", file="file2.org"),
     ]
 
     return entries

tests/test_helpers.py

@@ -1,5 +1,6 @@
 from khoj.utils import helpers
 
+
 def test_get_from_null_dict():
     # null handling
     assert helpers.get_from_dict(dict()) == dict()
@@ -7,39 +8,39 @@ def test_get_from_null_dict():
 
     # key present in nested dictionary
     # 1-level dictionary
-    assert helpers.get_from_dict({'a': 1, 'b': 2}, 'a') == 1
+    assert helpers.get_from_dict({"a": 1, "b": 2}, "a") == 1
-    assert helpers.get_from_dict({'a': 1, 'b': 2}, 'c') == None
+    assert helpers.get_from_dict({"a": 1, "b": 2}, "c") == None
 
     # 2-level dictionary
-    assert helpers.get_from_dict({'a': {'a_a': 1}, 'b': 2}, 'a') == {'a_a': 1}
+    assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a") == {"a_a": 1}
-    assert helpers.get_from_dict({'a': {'a_a': 1}, 'b': 2}, 'a', 'a_a') == 1
+    assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a", "a_a") == 1
 
     # key not present in nested dictionary
     # 2-level_dictionary
-    assert helpers.get_from_dict({'a': {'a_a': 1}, 'b': 2}, 'b', 'b_a') == None
+    assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "b", "b_a") == None
 
 
 def test_merge_dicts():
     # basic merge of dicts with non-overlapping keys
-    assert helpers.merge_dicts(priority_dict={'a': 1}, default_dict={'b': 2}) == {'a': 1, 'b': 2}
+    assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"b": 2}) == {"a": 1, "b": 2}
 
     # use default dict items when not present in priority dict
-    assert helpers.merge_dicts(priority_dict={}, default_dict={'b': 2}) == {'b': 2}
+    assert helpers.merge_dicts(priority_dict={}, default_dict={"b": 2}) == {"b": 2}
 
     # do not override existing key in priority_dict with default dict
-    assert helpers.merge_dicts(priority_dict={'a': 1}, default_dict={'a': 2}) == {'a': 1}
+    assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"a": 2}) == {"a": 1}
 
 
 def test_lru_cache():
     # Test initializing cache
-    cache = helpers.LRU({'a': 1, 'b': 2}, capacity=2)
+    cache = helpers.LRU({"a": 1, "b": 2}, capacity=2)
-    assert cache == {'a': 1, 'b': 2}
+    assert cache == {"a": 1, "b": 2}
 
     # Test capacity overflow
-    cache['c'] = 3
+    cache["c"] = 3
-    assert cache == {'b': 2, 'c': 3}
+    assert cache == {"b": 2, "c": 3}
 
     # Test delete least recently used item from LRU cache on capacity overflow
-    cache['b']      # accessing 'b' makes it the most recently used item
+    cache["b"]  # accessing 'b' makes it the most recently used item
-    cache['d'] = 4  # so 'c' is deleted from the cache instead of 'b'
+    cache["d"] = 4  # so 'c' is deleted from the cache instead of 'b'
-    assert cache == {'b': 2, 'd': 4}
+    assert cache == {"b": 2, "d": 4}

tests/test_image_search.py

@@ -30,7 +30,8 @@ def test_image_metadata(content_config: ContentConfig):
     expected_metadata_image_name_pairs = [
         (["Billi Ka Bacha.", "Cat", "Grass"], "kitten_park.jpg"),
         (["Pasture.", "Horse", "Dog"], "horse_dog.jpg"),
-        (["Guinea Pig Eating Celery.", "Rodent", "Whiskers"], "guineapig_grass.jpg")]
+        (["Guinea Pig Eating Celery.", "Rodent", "Whiskers"], "guineapig_grass.jpg"),
+    ]
 
     test_image_paths = [
         Path(content_config.image.input_directories[0] / image_name[1])
@@ -51,23 +52,23 @@ def test_image_search(content_config: ContentConfig, search_config: SearchConfig
     # Arrange
     output_directory = resolve_absolute_path(web_directory)
     model.image_search = image_search.setup(content_config.image, search_config.image, regenerate=False)
-    query_expected_image_pairs = [("kitten", "kitten_park.jpg"),
+    query_expected_image_pairs = [
-                                  ("horse and dog in a farm", "horse_dog.jpg"),
+        ("kitten", "kitten_park.jpg"),
-                                  ("A guinea pig eating grass", "guineapig_grass.jpg")]
+        ("horse and dog in a farm", "horse_dog.jpg"),
+        ("A guinea pig eating grass", "guineapig_grass.jpg"),
+    ]
 
     # Act
     for query, expected_image_name in query_expected_image_pairs:
-        hits = image_search.query(
+        hits = image_search.query(query, count=1, model=model.image_search)
-            query,
-            count = 1,
-            model = model.image_search)
 
         results = image_search.collate_results(
             hits,
             model.image_search.image_names,
             output_directory=output_directory,
-            image_files_url='/static/images',
+            image_files_url="/static/images",
-            count=1)
+            count=1,
+        )
 
         actual_image_path = output_directory.joinpath(Path(results[0].entry).name)
         actual_image = Image.open(actual_image_path)
@@ -86,16 +87,13 @@ def test_image_search_query_truncated(content_config: ContentConfig, search_conf
     # Arrange
     model.image_search = image_search.setup(content_config.image, search_config.image, regenerate=False)
     max_words_supported = 10
-    query = " ".join(["hello"]*100)
+    query = " ".join(["hello"] * 100)
-    truncated_query = " ".join(["hello"]*max_words_supported)
+    truncated_query = " ".join(["hello"] * max_words_supported)
 
     # Act
     try:
         with caplog.at_level(logging.INFO, logger="khoj.search_type.image_search"):
-            image_search.query(
+            image_search.query(query, count=1, model=model.image_search)
-                query,
-                count = 1,
-                model = model.image_search)
     # Assert
     except RuntimeError as e:
         if "The size of tensor a (102) must match the size of tensor b (77)" in str(e):
@@ -115,17 +113,15 @@ def test_image_search_by_filepath(content_config: ContentConfig, search_config:
 
     # Act
     with caplog.at_level(logging.INFO, logger="khoj.search_type.image_search"):
-        hits = image_search.query(
+        hits = image_search.query(query, count=1, model=model.image_search)
-            query,
-            count = 1,
-            model = model.image_search)
 
         results = image_search.collate_results(
             hits,
             model.image_search.image_names,
             output_directory=output_directory,
-            image_files_url='/static/images',
+            image_files_url="/static/images",
-            count=1)
+            count=1,
+        )
 
     actual_image_path = output_directory.joinpath(Path(results[0].entry).name)
     actual_image = Image.open(actual_image_path)
@@ -133,7 +129,9 @@ def test_image_search_by_filepath(content_config: ContentConfig, search_config:
 
     # Assert
     # Ensure file search triggered instead of query with file path as string
-    assert f"Find Images by Image: {resolve_absolute_path(expected_image_path)}" in caplog.text, "File search not triggered"
+    assert (
+        f"Find Images by Image: {resolve_absolute_path(expected_image_path)}" in caplog.text
+    ), "File search not triggered"
     # Ensure the correct image is returned
     assert expected_image == actual_image, "Incorrect image returned by file search"
 

tests/test_markdown_to_jsonl.py

@@ -8,10 +8,10 @@ from khoj.processor.markdown.markdown_to_jsonl import MarkdownToJsonl
 def test_markdown_file_with_no_headings_to_jsonl(tmp_path):
     "Convert files with no heading to jsonl."
     # Arrange
-    entry = f'''
+    entry = f"""
     - Bullet point 1
     - Bullet point 2
-    '''
+    """
     markdownfile = create_file(tmp_path, entry)
 
     # Act
@@ -20,7 +20,8 @@ def test_markdown_file_with_no_headings_to_jsonl(tmp_path):
 
     # Process Each Entry from All Notes Files
     jsonl_string = MarkdownToJsonl.convert_markdown_maps_to_jsonl(
-        MarkdownToJsonl.convert_markdown_entries_to_maps(entry_nodes, file_to_entries))
+        MarkdownToJsonl.convert_markdown_entries_to_maps(entry_nodes, file_to_entries)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -30,10 +31,10 @@ def test_markdown_file_with_no_headings_to_jsonl(tmp_path):
 def test_single_markdown_entry_to_jsonl(tmp_path):
     "Convert markdown entry from single file to jsonl."
     # Arrange
-    entry = f'''### Heading
+    entry = f"""### Heading
     \t\r
     Body Line 1
-    '''
+    """
     markdownfile = create_file(tmp_path, entry)
 
     # Act
@@ -42,7 +43,8 @@ def test_single_markdown_entry_to_jsonl(tmp_path):
 
     # Process Each Entry from All Notes Files
     jsonl_string = MarkdownToJsonl.convert_markdown_maps_to_jsonl(
-        MarkdownToJsonl.convert_markdown_entries_to_maps(entries, entry_to_file_map))
+        MarkdownToJsonl.convert_markdown_entries_to_maps(entries, entry_to_file_map)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -52,14 +54,14 @@ def test_single_markdown_entry_to_jsonl(tmp_path):
 def test_multiple_markdown_entries_to_jsonl(tmp_path):
     "Convert multiple markdown entries from single file to jsonl."
     # Arrange
-    entry = f'''
+    entry = f"""
 ### Heading 1
     \t\r
     Heading 1 Body Line 1
 ### Heading 2
     \t\r
     Heading 2 Body Line 2
-    '''
+    """
     markdownfile = create_file(tmp_path, entry)
 
     # Act
@@ -68,7 +70,8 @@ def test_multiple_markdown_entries_to_jsonl(tmp_path):
 
     # Process Each Entry from All Notes Files
     jsonl_string = MarkdownToJsonl.convert_markdown_maps_to_jsonl(
-        MarkdownToJsonl.convert_markdown_entries_to_maps(entries, entry_to_file_map))
+        MarkdownToJsonl.convert_markdown_entries_to_maps(entries, entry_to_file_map)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -92,8 +95,8 @@ def test_get_markdown_files(tmp_path):
     expected_files = sorted(map(str, [group1_file1, group1_file2, group2_file1, group2_file2, file1]))
 
     # Setup input-files, input-filters
-    input_files = [tmp_path / 'notes.md']
+    input_files = [tmp_path / "notes.md"]
-    input_filter = [tmp_path / 'group1*.md', tmp_path / 'group2*.markdown']
+    input_filter = [tmp_path / "group1*.md", tmp_path / "group2*.markdown"]
 
     # Act
     extracted_org_files = MarkdownToJsonl.get_markdown_files(input_files, input_filter)
@@ -106,10 +109,10 @@ def test_get_markdown_files(tmp_path):
 def test_extract_entries_with_different_level_headings(tmp_path):
     "Extract markdown entries with different level headings."
     # Arrange
-    entry = f'''
+    entry = f"""
 # Heading 1
 ## Heading 2
-'''
+"""
     markdownfile = create_file(tmp_path, entry)
 
     # Act

tests/test_org_to_jsonl.py

@@ -9,23 +9,25 @@ from khoj.utils.rawconfig import Entry
 
 
 def test_configure_heading_entry_to_jsonl(tmp_path):
-    '''Ensure entries with empty body are ignored, unless explicitly configured to index heading entries.
+    """Ensure entries with empty body are ignored, unless explicitly configured to index heading entries.
-    Property drawers not considered Body. Ignore control characters for evaluating if Body empty.'''
+    Property drawers not considered Body. Ignore control characters for evaluating if Body empty."""
     # Arrange
-    entry = f'''*** Heading
+    entry = f"""*** Heading
     :PROPERTIES:
     :ID:       42-42-42
     :END:
     \t \r
-    '''
+    """
     orgfile = create_file(tmp_path, entry)
 
     for index_heading_entries in [True, False]:
         # Act
         # Extract entries into jsonl from specified Org files
-        jsonl_string = OrgToJsonl.convert_org_entries_to_jsonl(OrgToJsonl.convert_org_nodes_to_entries(
+        jsonl_string = OrgToJsonl.convert_org_entries_to_jsonl(
-            *OrgToJsonl.extract_org_entries(org_files=[orgfile]),
+            OrgToJsonl.convert_org_nodes_to_entries(
-            index_heading_entries=index_heading_entries))
+                *OrgToJsonl.extract_org_entries(org_files=[orgfile]), index_heading_entries=index_heading_entries
+            )
+        )
         jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
         # Assert
@@ -40,10 +42,10 @@ def test_configure_heading_entry_to_jsonl(tmp_path):
 def test_entry_split_when_exceeds_max_words(tmp_path):
     "Ensure entries with compiled words exceeding max_words are split."
     # Arrange
-    entry = f'''*** Heading
+    entry = f"""*** Heading
     \t\r
     Body Line 1
-    '''
+    """
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -53,9 +55,9 @@ def test_entry_split_when_exceeds_max_words(tmp_path):
     # Split each entry from specified Org files by max words
     jsonl_string = OrgToJsonl.convert_org_entries_to_jsonl(
         TextToJsonl.split_entries_by_max_tokens(
-            OrgToJsonl.convert_org_nodes_to_entries(entries, entry_to_file_map),
+            OrgToJsonl.convert_org_nodes_to_entries(entries, entry_to_file_map), max_tokens=2
-            max_tokens = 2)
         )
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -65,15 +67,15 @@ def test_entry_split_when_exceeds_max_words(tmp_path):
 def test_entry_split_drops_large_words(tmp_path):
     "Ensure entries drops words larger than specified max word length from compiled version."
     # Arrange
-    entry_text = f'''*** Heading
+    entry_text = f"""*** Heading
     \t\r
     Body Line 1
-    '''
+    """
     entry = Entry(raw=entry_text, compiled=entry_text)
 
     # Act
     # Split entry by max words and drop words larger than max word length
-    processed_entry = TextToJsonl.split_entries_by_max_tokens([entry], max_word_length = 5)[0]
+    processed_entry = TextToJsonl.split_entries_by_max_tokens([entry], max_word_length=5)[0]
 
     # Assert
     # "Heading" dropped from compiled version because its over the set max word limit
@@ -83,13 +85,13 @@ def test_entry_split_drops_large_words(tmp_path):
 def test_entry_with_body_to_jsonl(tmp_path):
     "Ensure entries with valid body text are loaded."
     # Arrange
-    entry = f'''*** Heading
+    entry = f"""*** Heading
     :PROPERTIES:
     :ID:       42-42-42
     :END:
     \t\r
     Body Line 1
-    '''
+    """
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -97,7 +99,9 @@ def test_entry_with_body_to_jsonl(tmp_path):
     entries, entry_to_file_map = OrgToJsonl.extract_org_entries(org_files=[orgfile])
 
     # Process Each Entry from All Notes Files
-    jsonl_string = OrgToJsonl.convert_org_entries_to_jsonl(OrgToJsonl.convert_org_nodes_to_entries(entries, entry_to_file_map))
+    jsonl_string = OrgToJsonl.convert_org_entries_to_jsonl(
+        OrgToJsonl.convert_org_nodes_to_entries(entries, entry_to_file_map)
+    )
     jsonl_data = [json.loads(json_string) for json_string in jsonl_string.splitlines()]
 
     # Assert
@@ -107,10 +111,10 @@ def test_entry_with_body_to_jsonl(tmp_path):
 def test_file_with_no_headings_to_jsonl(tmp_path):
     "Ensure files with no heading, only body text are loaded."
     # Arrange
-    entry = f'''
+    entry = f"""
     - Bullet point 1
     - Bullet point 2
-    '''
+    """
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -143,8 +147,8 @@ def test_get_org_files(tmp_path):
     expected_files = sorted(map(str, [group1_file1, group1_file2, group2_file1, group2_file2, orgfile1]))
 
     # Setup input-files, input-filters
-    input_files = [tmp_path / 'orgfile1.org']
+    input_files = [tmp_path / "orgfile1.org"]
-    input_filter = [tmp_path / 'group1*.org', tmp_path / 'group2*.org']
+    input_filter = [tmp_path / "group1*.org", tmp_path / "group2*.org"]
 
     # Act
     extracted_org_files = OrgToJsonl.get_org_files(input_files, input_filter)
@@ -157,10 +161,10 @@ def test_get_org_files(tmp_path):
 def test_extract_entries_with_different_level_headings(tmp_path):
     "Extract org entries with different level headings."
     # Arrange
-    entry = f'''
+    entry = f"""
 * Heading 1
 ** Heading 2
-'''
+"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -169,8 +173,8 @@ def test_extract_entries_with_different_level_headings(tmp_path):
 
     # Assert
     assert len(entries) == 2
-    assert f'{entries[0]}'.startswith("* Heading 1")
+    assert f"{entries[0]}".startswith("* Heading 1")
-    assert f'{entries[1]}'.startswith("** Heading 2")
+    assert f"{entries[1]}".startswith("** Heading 2")
 
 
 # Helper Functions

tests/test_orgnode.py

@@ -10,7 +10,7 @@ from khoj.processor.org_mode import orgnode
 def test_parse_entry_with_no_headings(tmp_path):
     "Test parsing of entry with minimal fields"
     # Arrange
-    entry = f'''Body Line 1'''
+    entry = f"""Body Line 1"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -18,7 +18,7 @@ def test_parse_entry_with_no_headings(tmp_path):
 
     # Assert
     assert len(entries) == 1
-    assert entries[0].heading == f'{orgfile}'
+    assert entries[0].heading == f"{orgfile}"
     assert entries[0].tags == list()
     assert entries[0].body == "Body Line 1"
     assert entries[0].priority == ""
@@ -32,9 +32,9 @@ def test_parse_entry_with_no_headings(tmp_path):
 def test_parse_minimal_entry(tmp_path):
     "Test parsing of entry with minimal fields"
     # Arrange
-    entry = f'''
+    entry = f"""
 * Heading
-Body Line 1'''
+Body Line 1"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -56,7 +56,7 @@ Body Line 1'''
 def test_parse_complete_entry(tmp_path):
     "Test parsing of entry with all important fields"
     # Arrange
-    entry = f'''
+    entry = f"""
 *** DONE [#A] Heading   :Tag1:TAG2:tag3:
 CLOSED: [1984-04-01 Sun 12:00] SCHEDULED: <1984-04-01 Sun 09:00> DEADLINE: <1984-04-01 Sun>
 :PROPERTIES:
@@ -67,7 +67,7 @@ CLOCK: [1984-04-01 Sun 09:00]--[1984-04-01 Sun 12:00] => 3:00
 - Clocked Log 1
 :END:
 Body Line 1
-Body Line 2'''
+Body Line 2"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -81,45 +81,45 @@ Body Line 2'''
     assert entries[0].body == "- Clocked Log 1\nBody Line 1\nBody Line 2"
     assert entries[0].priority == "A"
     assert entries[0].Property("ID") == "id:123-456-789-4234-1231"
-    assert entries[0].closed == datetime.date(1984,4,1)
+    assert entries[0].closed == datetime.date(1984, 4, 1)
-    assert entries[0].scheduled == datetime.date(1984,4,1)
+    assert entries[0].scheduled == datetime.date(1984, 4, 1)
-    assert entries[0].deadline == datetime.date(1984,4,1)
+    assert entries[0].deadline == datetime.date(1984, 4, 1)
-    assert entries[0].logbook == [(datetime.datetime(1984,4,1,9,0,0), datetime.datetime(1984,4,1,12,0,0))]
+    assert entries[0].logbook == [(datetime.datetime(1984, 4, 1, 9, 0, 0), datetime.datetime(1984, 4, 1, 12, 0, 0))]
 
 
 # ----------------------------------------------------------------------------------------------------
 def test_render_entry_with_property_drawer_and_empty_body(tmp_path):
     "Render heading entry with property drawer"
     # Arrange
-    entry_to_render = f'''
+    entry_to_render = f"""
 *** [#A] Heading1   :tag1:
     :PROPERTIES:
     :ID: 111-111-111-1111-1111
     :END:
 \t\r  \n
-'''
+"""
     orgfile = create_file(tmp_path, entry_to_render)
 
-    expected_entry = f'''*** [#A] Heading1                                            :tag1:
+    expected_entry = f"""*** [#A] Heading1                                            :tag1:
 :PROPERTIES:
 :LINE: file:{orgfile}::2
 :ID: id:111-111-111-1111-1111
 :SOURCE: [[file:{orgfile}::*Heading1]]
 :END:
-'''
+"""
 
     # Act
     parsed_entries = orgnode.makelist(orgfile)
 
     # Assert
-    assert f'{parsed_entries[0]}' == expected_entry
+    assert f"{parsed_entries[0]}" == expected_entry
 
 
 # ----------------------------------------------------------------------------------------------------
 def test_all_links_to_entry_rendered(tmp_path):
     "Ensure all links to entry rendered in property drawer from entry"
     # Arrange
-    entry = f'''
+    entry = f"""
 *** [#A] Heading   :tag1:
 :PROPERTIES:
 :ID: 123-456-789-4234-1231
@@ -127,7 +127,7 @@ def test_all_links_to_entry_rendered(tmp_path):
 Body Line 1
 *** Heading2
 Body Line 2
-'''
+"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -135,23 +135,23 @@ Body Line 2
 
     # Assert
     # SOURCE link rendered with Heading
-    assert f':SOURCE: [[file:{orgfile}::*{entries[0].heading}]]' in f'{entries[0]}'
+    assert f":SOURCE: [[file:{orgfile}::*{entries[0].heading}]]" in f"{entries[0]}"
     # ID link rendered with ID
-    assert f':ID: id:123-456-789-4234-1231' in f'{entries[0]}'
+    assert f":ID: id:123-456-789-4234-1231" in f"{entries[0]}"
     # LINE link rendered with line number
-    assert f':LINE: file:{orgfile}::2' in f'{entries[0]}'
+    assert f":LINE: file:{orgfile}::2" in f"{entries[0]}"
 
 
 # ----------------------------------------------------------------------------------------------------
 def test_source_link_to_entry_escaped_for_rendering(tmp_path):
     "Test SOURCE link renders with square brackets in filename, heading escaped for org-mode rendering"
     # Arrange
-    entry = f'''
+    entry = f"""
 *** [#A] Heading[1]   :tag1:
 :PROPERTIES:
 :ID: 123-456-789-4234-1231
 :END:
-Body Line 1'''
+Body Line 1"""
     orgfile = create_file(tmp_path, entry, filename="test[1].org")
 
     # Act
@@ -162,15 +162,15 @@ Body Line 1'''
     # parsed heading from entry
     assert entries[0].heading == "Heading[1]"
     # ensure SOURCE link has square brackets in filename, heading escaped in rendered entries
-    escaped_orgfile = f'{orgfile}'.replace("[1]", "\\[1\\]")
+    escaped_orgfile = f"{orgfile}".replace("[1]", "\\[1\\]")
-    assert f':SOURCE: [[file:{escaped_orgfile}::*Heading\[1\]' in f'{entries[0]}'
+    assert f":SOURCE: [[file:{escaped_orgfile}::*Heading\[1\]" in f"{entries[0]}"
 
 
 # ----------------------------------------------------------------------------------------------------
 def test_parse_multiple_entries(tmp_path):
     "Test parsing of multiple entries"
     # Arrange
-    content = f'''
+    content = f"""
 *** FAILED [#A] Heading1   :tag1:
 CLOSED: [1984-04-01 Sun 12:00] SCHEDULED: <1984-04-01 Sun 09:00> DEADLINE: <1984-04-01 Sun>
 :PROPERTIES:
@@ -193,7 +193,7 @@ CLOCK: [1984-04-02 Mon 09:00]--[1984-04-02 Mon 12:00] => 3:00
 :END:
 Body 2
 
-'''
+"""
     orgfile = create_file(tmp_path, content)
 
     # Act
@@ -208,18 +208,20 @@ Body 2
         assert entry.body == f"- Clocked Log {index+1}\nBody {index+1}\n\n"
         assert entry.priority == "A"
         assert entry.Property("ID") == f"id:123-456-789-4234-000{index+1}"
-        assert entry.closed == datetime.date(1984,4,index+1)
+        assert entry.closed == datetime.date(1984, 4, index + 1)
-        assert entry.scheduled == datetime.date(1984,4,index+1)
+        assert entry.scheduled == datetime.date(1984, 4, index + 1)
-        assert entry.deadline == datetime.date(1984,4,index+1)
+        assert entry.deadline == datetime.date(1984, 4, index + 1)
-        assert entry.logbook == [(datetime.datetime(1984,4,index+1,9,0,0), datetime.datetime(1984,4,index+1,12,0,0))]
+        assert entry.logbook == [
+            (datetime.datetime(1984, 4, index + 1, 9, 0, 0), datetime.datetime(1984, 4, index + 1, 12, 0, 0))
+        ]
 
 
 # ----------------------------------------------------------------------------------------------------
 def test_parse_entry_with_empty_title(tmp_path):
     "Test parsing of entry with minimal fields"
     # Arrange
-    entry = f'''#+TITLE: 
+    entry = f"""#+TITLE: 
-Body Line 1'''
+Body Line 1"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -227,7 +229,7 @@ Body Line 1'''
 
     # Assert
     assert len(entries) == 1
-    assert entries[0].heading == f'{orgfile}'
+    assert entries[0].heading == f"{orgfile}"
     assert entries[0].tags == list()
     assert entries[0].body == "Body Line 1"
     assert entries[0].priority == ""
@@ -241,8 +243,8 @@ Body Line 1'''
 def test_parse_entry_with_title_and_no_headings(tmp_path):
     "Test parsing of entry with minimal fields"
     # Arrange
-    entry = f'''#+TITLE: test
+    entry = f"""#+TITLE: test
-Body Line 1'''
+Body Line 1"""
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -250,7 +252,7 @@ Body Line 1'''
 
     # Assert
     assert len(entries) == 1
-    assert entries[0].heading == 'test'
+    assert entries[0].heading == "test"
     assert entries[0].tags == list()
     assert entries[0].body == "Body Line 1"
     assert entries[0].priority == ""
@@ -264,9 +266,9 @@ Body Line 1'''
 def test_parse_entry_with_multiple_titles_and_no_headings(tmp_path):
     "Test parsing of entry with minimal fields"
     # Arrange
-    entry = f'''#+TITLE: title1 
+    entry = f"""#+TITLE: title1 
 Body Line 1
-#+TITLE:  title2  '''
+#+TITLE:  title2  """
     orgfile = create_file(tmp_path, entry)
 
     # Act
@@ -274,7 +276,7 @@ Body Line 1
 
     # Assert
     assert len(entries) == 1
-    assert entries[0].heading == 'title1 title2'
+    assert entries[0].heading == "title1 title2"
     assert entries[0].tags == list()
     assert entries[0].body == "Body Line 1\n"
     assert entries[0].priority == ""

tests/test_text_search.py

@@ -14,7 +14,9 @@ from khoj.processor.org_mode.org_to_jsonl import OrgToJsonl
 
 # Test
 # ----------------------------------------------------------------------------------------------------
-def test_asymmetric_setup_with_missing_file_raises_error(org_config_with_only_new_file: TextContentConfig, search_config: SearchConfig):
+def test_asymmetric_setup_with_missing_file_raises_error(
+    org_config_with_only_new_file: TextContentConfig, search_config: SearchConfig
+):
     # Arrange
     # Ensure file mentioned in org.input-files is missing
     single_new_file = Path(org_config_with_only_new_file.input_files[0])
@@ -27,10 +29,12 @@ def test_asymmetric_setup_with_missing_file_raises_error(org_config_with_only_ne
 
 
 # ----------------------------------------------------------------------------------------------------
-def test_asymmetric_setup_with_empty_file_raises_error(org_config_with_only_new_file: TextContentConfig, search_config: SearchConfig):
+def test_asymmetric_setup_with_empty_file_raises_error(
+    org_config_with_only_new_file: TextContentConfig, search_config: SearchConfig
+):
     # Act
     # Generate notes embeddings during asymmetric setup
-    with pytest.raises(ValueError, match=r'^No valid entries found*'):
+    with pytest.raises(ValueError, match=r"^No valid entries found*"):
         text_search.setup(OrgToJsonl, org_config_with_only_new_file, search_config.asymmetric, regenerate=True)
 
 
@@ -52,15 +56,9 @@ def test_asymmetric_search(content_config: ContentConfig, search_config: SearchC
     query = "How to git install application?"
 
     # Act
-    hits, entries = text_search.query(
+    hits, entries = text_search.query(query, model=model.notes_search, rank_results=True)
-        query,
-        model = model.notes_search,
-        rank_results=True)
 
-    results = text_search.collate_results(
+    results = text_search.collate_results(hits, entries, count=1)
-        hits,
-        entries,
-        count=1)
 
     # Assert
     # Actual_data should contain "Khoj via Emacs" entry
@@ -76,12 +74,14 @@ def test_entry_chunking_by_max_tokens(org_config_with_only_new_file: TextContent
     new_file_to_index = Path(org_config_with_only_new_file.input_files[0])
     with open(new_file_to_index, "w") as f:
         f.write(f"* Entry more than {max_tokens} words\n")
-        for index in range(max_tokens+1):
+        for index in range(max_tokens + 1):
             f.write(f"{index} ")
 
     # Act
     # reload embeddings, entries, notes model after adding new org-mode file
-    initial_notes_model = text_search.setup(OrgToJsonl, org_config_with_only_new_file, search_config.asymmetric, regenerate=False)
+    initial_notes_model = text_search.setup(
+        OrgToJsonl, org_config_with_only_new_file, search_config.asymmetric, regenerate=False
+    )
 
     # Assert
     # verify newly added org-mode entry is split by max tokens
@@ -92,18 +92,20 @@ def test_entry_chunking_by_max_tokens(org_config_with_only_new_file: TextContent
 # ----------------------------------------------------------------------------------------------------
 def test_asymmetric_reload(content_config: ContentConfig, search_config: SearchConfig, new_org_file: Path):
     # Arrange
-    initial_notes_model= text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=True)
+    initial_notes_model = text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=True)
 
     assert len(initial_notes_model.entries) == 10
     assert len(initial_notes_model.corpus_embeddings) == 10
 
     # append org-mode entry to first org input file in config
-    content_config.org.input_files = [f'{new_org_file}']
+    content_config.org.input_files = [f"{new_org_file}"]
     with open(new_org_file, "w") as f:
         f.write("\n* A Chihuahua doing Tango\n- Saw a super cute video of a chihuahua doing the Tango on Youtube\n")
 
     # regenerate notes jsonl, model embeddings and model to include entry from new file
-    regenerated_notes_model = text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=True)
+    regenerated_notes_model = text_search.setup(
+        OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=True
+    )
 
     # Act
     # reload embeddings, entries, notes model from previously generated notes jsonl and model embeddings files
@@ -137,7 +139,7 @@ def test_incremental_update(content_config: ContentConfig, search_config: Search
 
     # Act
     # update embeddings, entries with the newly added note
-    content_config.org.input_files = [f'{new_org_file}']
+    content_config.org.input_files = [f"{new_org_file}"]
     initial_notes_model = text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False)
 
     # Assert

tests/test_word_filter.py

@@ -7,7 +7,7 @@ def test_no_word_filter():
     # Arrange
     word_filter = WordFilter()
     entries = arrange_content()
-    q_with_no_filter = 'head tail'
+    q_with_no_filter = "head tail"
 
     # Act
     can_filter = word_filter.can_filter(q_with_no_filter)
@@ -15,7 +15,7 @@ def test_no_word_filter():
 
     # Assert
     assert can_filter == False
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 1, 2, 3}
 
 
@@ -31,7 +31,7 @@ def test_word_exclude_filter():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {0, 2}
 
 
@@ -47,7 +47,7 @@ def test_word_include_filter():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {2, 3}
 
 
@@ -63,16 +63,16 @@ def test_word_include_and_exclude_filter():
 
     # Assert
     assert can_filter == True
-    assert ret_query == 'head tail'
+    assert ret_query == "head tail"
     assert entry_indices == {2}
 
 
 def arrange_content():
     entries = [
-        Entry(compiled='', raw='Minimal Entry'),
+        Entry(compiled="", raw="Minimal Entry"),
-        Entry(compiled='', raw='Entry with exclude_word'),
+        Entry(compiled="", raw="Entry with exclude_word"),
-        Entry(compiled='', raw='Entry with include_word'),
+        Entry(compiled="", raw="Entry with include_word"),
-        Entry(compiled='', raw='Entry with include_word and exclude_word')
+        Entry(compiled="", raw="Entry with include_word and exclude_word"),
     ]
 
     return entries