b5666c4d68
* 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.
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# API CONFIGURATION
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# URL where the API can be accessed by the browser
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# This setting allows the frontend to connect to the API at runtime (no rebuild needed!)
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#
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# IMPORTANT: Do NOT include /api at the end - it will be added automatically!
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#
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# Common scenarios:
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# - Docker on localhost: http://localhost:5055 (default, works for most cases)
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# - Docker on LAN/remote server: http://192.168.1.100:5055 or http://your-server-ip:5055
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# - Behind reverse proxy with custom domain: https://your-domain.com
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# - Behind reverse proxy with subdomain: https://api.your-domain.com
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#
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# Examples for reverse proxy users:
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# - API_URL=https://notebook.example.com (frontend will call https://notebook.example.com/api/*)
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# - API_URL=https://api.example.com (frontend will call https://api.example.com/api/*)
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#
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# Note: If not set, the system will auto-detect based on the incoming request.
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# Only set this if you need to override the auto-detection (e.g., reverse proxy scenarios).
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API_URL=http://localhost:5055
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# API CLIENT TIMEOUT (in seconds)
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# Controls how long the frontend/Streamlit UI waits for API responses
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# Increase this if you're using slow AI providers or hardware (Ollama on CPU, remote LM Studio, etc.)
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# Default: 300 seconds (5 minutes) - sufficient for most transformation/insight operations
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#
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# Common scenarios:
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# - Fast cloud APIs (OpenAI, Anthropic): 300 seconds is more than enough
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# - Local Ollama on GPU: 300 seconds should work fine
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# - Local Ollama on CPU: Consider 600 seconds (10 minutes) or more
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# - Remote LM Studio over slow network: Consider 900 seconds (15 minutes)
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# - Very large documents: May need 900+ seconds
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#
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# API_CLIENT_TIMEOUT=300
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# ESPERANTO LLM TIMEOUT (in seconds)
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# Controls the timeout for AI model API calls at the Esperanto library level
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# This is separate from API_CLIENT_TIMEOUT and applies to the actual LLM provider requests
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# Only increase this if you're experiencing timeouts during model inference itself
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# Default: 60 seconds (built into Esperanto)
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#
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# Important: This should generally be LOWER than API_CLIENT_TIMEOUT to allow proper error handling
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#
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# Common scenarios:
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# - Fast cloud APIs (OpenAI, Anthropic, Groq): 60 seconds is sufficient
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# - Local Ollama with small models: 120-180 seconds may help
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# - Local Ollama with large models on CPU: 300+ seconds
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# - Remote or self-hosted LLMs: 180-300 seconds depending on hardware
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#
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# Note: If transformations complete but you see timeout errors, increase API_CLIENT_TIMEOUT first.
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# Only increase ESPERANTO_LLM_TIMEOUT if the model itself is timing out during inference.
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#
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# ESPERANTO_LLM_TIMEOUT=60
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# SECURITY
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# Set this to protect your Open Notebook instance with a password (for public hosting)
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# OPEN_NOTEBOOK_PASSWORD=
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# OPENAI
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# OPENAI_API_KEY=
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# ANTHROPIC
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# ANTHROPIC_API_KEY=
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# GEMINI
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# this is the best model for long context and podcast generation
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# GOOGLE_API_KEY=
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# GEMINI_API_BASE_URL= # Optional: Override default endpoint (for Vertex AI, proxies, etc.)
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# VERTEXAI
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# VERTEX_PROJECT=my-google-cloud-project-name
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# GOOGLE_APPLICATION_CREDENTIALS=./google-credentials.json
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# VERTEX_LOCATION=us-east5
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# MISTRAL
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# MISTRAL_API_KEY=
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# DEEPSEEK
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# DEEPSEEK_API_KEY=
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# OLLAMA
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# OLLAMA_API_BASE="http://10.20.30.20:11434"
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# OPEN ROUTER
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# OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"
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# OPENROUTER_API_KEY=
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# GROQ
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# GROQ_API_KEY=
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# XAI
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# XAI_API_KEY=
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# ELEVENLABS
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# Used only by the podcast feature
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# ELEVENLABS_API_KEY=
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# TTS BATCH SIZE
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# Controls concurrent TTS requests for podcast generation (default: 5)
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# Lower values reduce provider load but increase generation time
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# Recommended: OpenAI=5, ElevenLabs=2, Google=4, Custom=1
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# TTS_BATCH_SIZE=2
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# VOYAGE AI
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# VOYAGE_API_KEY=
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# OPENAI COMPATIBLE ENDPOINTS
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# Generic configuration (applies to all modalities: language, embedding, STT, TTS)
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# OPENAI_COMPATIBLE_BASE_URL=http://localhost:1234/v1
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# OPENAI_COMPATIBLE_API_KEY=
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# Mode-specific configuration (overrides generic if set)
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# Use these when you want different endpoints for different capabilities
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# OPENAI_COMPATIBLE_BASE_URL_LLM=http://localhost:1234/v1
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# OPENAI_COMPATIBLE_API_KEY_LLM=
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# OPENAI_COMPATIBLE_BASE_URL_EMBEDDING=http://localhost:8080/v1
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# OPENAI_COMPATIBLE_API_KEY_EMBEDDING=
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# OPENAI_COMPATIBLE_BASE_URL_STT=http://localhost:9000/v1
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# OPENAI_COMPATIBLE_API_KEY_STT=
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# OPENAI_COMPATIBLE_BASE_URL_TTS=http://localhost:9000/v1
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# OPENAI_COMPATIBLE_API_KEY_TTS=
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# AZURE OPENAI
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# AZURE_OPENAI_API_KEY=
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# AZURE_OPENAI_ENDPOINT=
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# AZURE_OPENAI_API_VERSION="2024-12-01-preview"
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# AZURE_OPENAI_DEPLOYMENT_NAME=
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# USE THIS IF YOU WANT TO DEBUG THE APP ON LANGSMITH
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# LANGCHAIN_TRACING_V2=true
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# LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
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# LANGCHAIN_API_KEY=
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# LANGCHAIN_PROJECT="Open Notebook"
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# CONNECTION DETAILS FOR YOUR SURREAL DB
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# New format (preferred) - WebSocket URL
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SURREAL_URL="ws://surrealdb/rpc:8000"
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SURREAL_USER="root"
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SURREAL_PASSWORD="root"
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SURREAL_NAMESPACE="open_notebook"
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SURREAL_DATABASE="staging"
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# OPEN_NOTEBOOK_PASSWORD=
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# FIRECRAWL - Get a key at https://firecrawl.dev/
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FIRECRAWL_API_KEY=
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# JINA - Get a key at https://jina.ai/
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JINA_API_KEY= |