Move runtime configuration endpoint from /api/runtime-config to /_config to avoid
conflicts with reverse proxies that route all /api/* requests to the FastAPI backend.
This fixes an issue where users with reverse proxies would see port 5055 incorrectly
appended to their API_URL even when explicitly set via environment variable.
Changes:
- Move frontend/src/app/api/runtime-config/route.ts to frontend/src/app/_config/route.ts
- Update config.ts to fetch from /_config instead of /api/runtime-config
- Add troubleshooting documentation for reverse proxy users
- Update all reverse proxy examples to show correct routing (catch-all handles /_config)
- Bump version to 1.0.11
The new /_config endpoint is automatically handled by standard reverse proxy catch-all
rules (location / { proxy_pass http://frontend; }), requiring no additional configuration
for most users.
Fixes issue where API_URL environment variable was being ignored in reverse proxy setups,
causing CORS errors with "Status code: (null)" and incorrect port 5055 being added.
* fix: increase API client timeouts for transformation operations
- Increase frontend timeout from 30s to 300s (5 minutes)
- Increase Streamlit API client timeout from 30s to 300s
- Add API_CLIENT_TIMEOUT environment variable for configurability
- Add ESPERANTO_LLM_TIMEOUT environment variable documentation
- Update .env.example with comprehensive timeout documentation
Fixes#131 - API timeout errors during transformation generation
Transformations now have sufficient time to complete on slower
hardware (Ollama, LM Studio) without frontend timeout errors.
Users can now configure timeouts for both the API client layer
(API_CLIENT_TIMEOUT) and the LLM provider layer (ESPERANTO_LLM_TIMEOUT)
to accommodate their specific hardware and network conditions.
* docs: add timeout configuration documentation
- Add comprehensive timeout troubleshooting section to common-issues.md
- Add FAQ entry about timeout errors during transformations
- Document API_CLIENT_TIMEOUT and ESPERANTO_LLM_TIMEOUT usage
- Provide specific timeout recommendations for different hardware/network scenarios
- Link to GitHub issue #131 for reference
* chore: bump
* refactor: improve timeout configuration with validation and consistency
Based on PR review feedback, this commit addresses several improvements:
**Timeout Validation:**
- Add validation to ensure timeout values are between 30s and 3600s
- Invalid values fall back to default 300s with warning logs
- Handles edge cases (negative, zero, invalid strings)
**Fix Hard-coded Timeouts:**
- Replace all hard-coded timeout values in api/client.py
- ask_simple: 300s → self.timeout
- execute_transformation: 120s → self.timeout
- embed_content: 120s → self.timeout
- create_source: 300s → self.timeout
- rebuild_embeddings: Uses smart logic (2x timeout, max 3600s)
**Improved Documentation:**
- Add clarifying comments about ms vs seconds (frontend vs backend)
- Document that frontend uses 300000ms = backend 300s
- Add inline documentation for rebuild_embeddings timeout logic
**Development Dependencies:**
- Add pytest>=8.0.0 to dev dependencies for future test coverage
This makes timeout configuration more robust, consistent, and user-friendly
while maintaining backward compatibility.
* fix text
* remove lint from docker publish workflow
* gemini base url docs
* feat: add multimodal support for openai-compatible providers
- Add helper function to check OpenAI-compatible provider availability per mode
- Update provider detection to support language, embedding, STT, and TTS modalities
- Implement mode-specific environment variable detection (LLM, EMBEDDING, STT, TTS)
- Maintain backward compatibility with generic OPENAI_COMPATIBLE_BASE_URL
- Add comprehensive unit tests for all configuration scenarios
- Update .env.example with mode-specific environment variables
- Update provider support matrix in ai-models.md
- Create comprehensive openai-compatible.md setup guide
This enables users to configure different OpenAI-compatible endpoints for
different AI capabilities (e.g., LM Studio for language models, dedicated
server for embeddings) while maintaining full backward compatibility.
* upgrade
* chore: change docker release strategy
- Add Docker image registry section explaining both Docker Hub and GHCR options
- Include GHCR alternative in Quick Start examples
- Add comments showing how to use GHCR in docker-compose examples
- Help users understand they can use either registry interchangeably
New front-end
Launch Chat API
Manage Sources
Enable re-embedding of all contents
Sources can be added without a notebook now
Improved settings
Enable model selector on all chats
Background processing for better experience
Dark mode
Improved Notes
Improved Docs:
- Remove all Streamlit references from documentation
- Update deployment guides with React frontend setup
- Fix Docker environment variables format (SURREAL_URL, SURREAL_PASSWORD)
- Update docker image tag from :latest to :v1-latest
- Change navigation references (Settings → Models to just Models)
- Update development setup to include frontend npm commands
- Add MIGRATION.md guide for users upgrading from Streamlit
- Update quick-start guide with correct environment variables
- Add port 5055 documentation for API access
- Update project structure to reflect frontend/ directory
- Remove outdated source-chat documentation files
* fix: prevent project failing to start when cannot talk to github - fixes#128
* improve ollama documentation - see #127
* chore: update esperanto library to enable gpt-5 - see #107; update podcast-creator library to enable TTS_BATCH_SIZE - fixes#125
* add info on ollama env variables
* chore: ignore dev logs
* chore: bump
Creates the API layer for Open Notebook
Creates a services API gateway for the Streamlit front-end
Migrates the SurrealDB SDK to the official one
Change all database calls to async
New podcast framework supporting multiple speaker configurations
Implement the surreal-commands library for async processing
Improve docker image and docker-compose configurations
