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* fix: unify SettingsManagers, fix env var bugs, delete duplicate Two parallel SettingsManager implementations existed (settings/manager.py and web/services/settings_manager.py) that diverged accidentally, each with different bugs. This unifies them into a single implementation. Bug fixes in settings/manager.py: - get_setting() now checks env vars when setting is not in DB (was jumping straight to return default, ignoring env override) - get_all_settings() now type-converts env overrides through get_typed_setting_value() (was storing raw strings like "true" instead of True) - create_or_update_setting() now correctly checks db_setting.editable (was checking input dict's .editable which caused AttributeError) - Added missing ui_element types: textarea, multiselect Features added to settings/manager.py: - get_bool_setting() method (required by rag_routes.py) - default_settings now loads all 18 JSON files via rglob (was only loading 1 file with 370 settings, now loads 526) All production and test imports updated from web.services.settings_manager to settings.manager. Duplicate web/services/settings_manager.py deleted. 314 tests pass across 7 test files. 9 new tests cover bug fixes. * test: add 29 tests for unified SettingsManager coverage gaps (#2071) Cover create_or_update_setting (8 tests), default_settings property (4), _ensure_settings_initialized (2), new UI element types textarea/multiselect/ range (4), _emit_settings_changed error resilience (3), plus edge cases for get_setting check_env=False, get_all_settings with locked settings, get_bool_setting with integers, parse_boolean edge cases, and env override type conversion for text settings. * fix: add missing abstract methods, env var defaults override, and type bug (#2074) - Add get_bool_setting() and get_settings_snapshot() abstract methods to ISettingsManager base class so the interface contract is complete - Fix create_or_update_setting: use setting_obj.type directly instead of SettingType[setting_obj.type.upper()] which fails when type is already a SettingType enum from the Pydantic model - Add env var override in get_all_settings() defaults loop so settings not yet in DB can still be overridden via LDR_* environment variables - Fix test_get_all_settings_db_error to expect defaults on DB failure (graceful degradation after unification) * refactor: deduplicate provider availability checks and settings wrapper (#2054) (#2068) - Delegate 5 provider availability functions in llm_config.py to their existing provider class is_available() methods (OpenAI, Anthropic, CustomOpenAIEndpoint, Ollama, LMStudio) - Extract _get_or_create_status() helper in queue_service.py to eliminate duplicated QueueStatus lookup-or-create pattern - Centralize get_llm_setting_from_snapshot() in thread_settings.py, replacing 6 identical copy-pasted wrappers across provider files - Update test mock targets to reflect new delegation pattern * fix: add missing abstract method implementations to InMemorySettingsManager InMemorySettingsManager was missing get_bool_setting() and get_settings_snapshot() implementations required by the ISettingsManager ABC, causing TypeError on instantiation and cascading failures in LLM unit tests, REST API tests, and Puppeteer auth tests. * fix: convert web SettingType to database SettingType in create_or_update_setting The PR changed `type=SettingType[setting_obj.type.upper()]` to `type=setting_obj.type`, but setting_obj.type is a web model SettingType (str, Enum) while Setting.type expects the database SettingType (enum.Enum). This causes a 500 error when creating new settings via PUT endpoint. Use `.name` for cleaner enum-to-enum conversion instead of `.upper()`. * fix: add multiselect type conversion and warn on untyped env overrides (#2080) Address review feedback from @djpetti on PR #2070: 1. Replace multiselect `lambda x: x` with `_parse_multiselect()` that properly handles env var strings — parses JSON arrays (e.g. '["markdown","latex"]') and comma-separated values (e.g. 'markdown,latex') while passing through lists from SQLAlchemy unchanged. 2. Log a warning when get_setting() encounters an env var override for a setting not in defaults, returning the raw string without type conversion. This surfaces settings that should be added to a defaults JSON file to get proper type information. Tests: 14 new tests (111 total in test_settings_manager.py, 0 failures) * test: add tests for consolidated UI element-to-type mapping Verifies single canonical _UI_ELEMENT_TO_SETTING_TYPE is reused by both InMemorySettingsManager and SettingsManager.
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Local Deep Research API Examples
This directory contains examples for using LDR through different interfaces.
Important: Authentication Required (v2.0+)
Since LDR v2.0, all API access requires authentication due to per-user encrypted databases. You must:
- Create a user account through the web interface
- Authenticate before making API calls
- Pass settings_snapshot for programmatic access
Directory Structure
-
programmatic/- Direct Python API usage (import fromlocal_deep_research.api)programmatic_access.ipynb- Jupyter notebook with comprehensive examplesretriever_usage_example.py- Using LangChain retrievers with LDR
-
http/- HTTP REST API usage (requires running server)simple_working_example.py- ✅ BEST WORKING EXAMPLE - Clean, tested, and ready to usesimple_http_example.py- Quick start example (needs updating for auth)http_api_examples.py- Comprehensive examples including batch processing
Quick Start
Programmatic API (Python Package)
from local_deep_research.api import quick_summary
from local_deep_research.settings import SettingsManager
from local_deep_research.database.session_context import get_user_db_session
# Authenticate and get settings
with get_user_db_session(username="your_username", password="your_password") as session:
settings_manager = SettingsManager(session)
settings_snapshot = settings_manager.get_all_settings()
# Use the API
result = quick_summary(
"What is quantum computing?",
settings_snapshot=settings_snapshot
)
print(result["summary"])
HTTP API (REST)
🎯 Quick Start - Works Completely Out of the Box!
Our tested working example requires zero manual setup:
# 1. Start the server
python -m local_deep_research.web.app
# 2. Run the working example (creates user automatically!)
python examples/api_usage/http/simple_working_example.py
# 3. Done! ✅ No other steps required
The example will:
- ✅ Create a unique test user automatically
- ✅ Test authentication with proper CSRF handling
- ✅ Execute a research query using the correct API endpoint
- ✅ Provide credentials for manual testing (if desired)
- ✅ Show results with direct links to view them
📋 Manual API Usage:
If you want to integrate the API into your own code:
import requests
from bs4 import BeautifulSoup
# Create session for cookie persistence
session = requests.Session()
# Login - get CSRF token first
login_page = session.get("http://localhost:5000/auth/login")
soup = BeautifulSoup(login_page.text, 'html.parser')
csrf_input = soup.find('input', {'name': 'csrf_token'})
login_csrf = csrf_input.get('value')
# Login with form data
session.post(
"http://localhost:5000/auth/login",
data={
"username": "your_username",
"password": "your_password",
"csrf_token": login_csrf
}
)
# Get CSRF token
csrf_token = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]
# Make API request
response = session.post(
"http://localhost:5000/api/start_research",
json={"query": "What is quantum computing?"},
headers={"X-CSRF-Token": csrf_token, "Content-Type": "application/json"}
)
print(response.json())
⚠️ Important Notes:
- Use the correct endpoint:
/api/start_research(not/research/api/start) - Login with form data (not JSON)
- Handle CSRF tokens properly
- User must be created through web interface first
Which API Should I Use?
-
Programmatic API: Use when integrating LDR into your Python application
- ✅ Direct access, no HTTP overhead
- ✅ Full access to all features and parameters
- ✅ Can pass Python objects (like LangChain retrievers)
- ❌ Requires LDR to be installed in your environment
- ❌ Requires database session and settings snapshot
-
HTTP API: Use when accessing LDR from other languages or remote systems
- ✅ Language agnostic - works with any HTTP client
- ✅ Can run LDR on a separate server
- ✅ Easy to scale and deploy
- ❌ Limited to JSON-serializable parameters
- ❌ Requires running the web server
- ❌ Requires authentication and CSRF tokens
API Changes in v2.0
Breaking Changes
- Authentication Required: All endpoints now require login
- Settings Snapshot: Programmatic API needs
settings_snapshotparameter - New Endpoints: API routes moved (e.g.,
/api/v1/quick_summary→/api/start_research) - CSRF Protection: POST/PUT/DELETE requests need CSRF token
Migration Guide
Old (v1.x):
# Programmatic
from local_deep_research.api import quick_summary
result = quick_summary("query")
# HTTP
curl -X POST http://localhost:5000/api/v1/quick_summary \
-d '{"query": "test"}'
New (v2.0+):
# Programmatic - with authentication and settings
with get_user_db_session(username, password) as session:
settings_manager = SettingsManager(session)
settings_snapshot = settings_manager.get_all_settings()
result = quick_summary("query", settings_snapshot=settings_snapshot)
# HTTP - with authentication and CSRF
# See examples above
Running the Examples
Prerequisites
- Install LDR:
pip install local-deep-research - Create a user account:
- Start server:
python -m local_deep_research.web.app - Open http://localhost:5000 and register
- Start server:
- Configure your LLM provider in settings
Programmatic Examples
# Update credentials in the example files first!
python examples/api_usage/programmatic/retriever_usage_example.py
# Or use the Jupyter notebook
jupyter notebook examples/api_usage/programmatic/programmatic_access.ipynb
HTTP Examples
# First, start the LDR server
python -m local_deep_research.web.app
# In another terminal, run the examples
# Note: These need to be updated for v2.0 authentication!
python examples/api_usage/http/simple_http_example.py
python examples/api_usage/http/http_api_examples.py
Need Help?
- See the API Quick Start Guide
- Check the FAQ
- Join our Discord for support