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docs: generate comprehensive CLAUDE.md reference documentation across codebase
Create a hierarchical CLAUDE.md documentation system for the entire Open Notebook codebase with focus on concise, pattern-driven reference cards rather than comprehensive tutorials. ## Changes ### Core Documentation System - Updated `.claude/commands/build-claude-md.md` to distinguish between leaf and parent modules, with special handling for prompt/template modules - Established clear patterns: * Leaf modules (40-70 lines): Components, hooks, API clients * Parent modules (50-150 lines): Architecture, cross-layer patterns, data flows * Template modules: Pattern focus, not catalog listings ### Generated Documentation Created 15 CLAUDE.md reference files across the project: **Frontend (React/Next.js)** - frontend/src/CLAUDE.md: Architecture overview, data flow, three-tier design - frontend/src/lib/hooks/CLAUDE.md: React Query patterns, state management - frontend/src/lib/api/CLAUDE.md: Axios client, FormData handling, interceptors - frontend/src/lib/stores/CLAUDE.md: Zustand state persistence, auth patterns - frontend/src/components/ui/CLAUDE.md: Radix UI primitives, CVA styling **Backend (Python/FastAPI)** - open_notebook/CLAUDE.md: System architecture, layer interactions - open_notebook/ai/CLAUDE.md: Model provisioning, Esperanto integration - open_notebook/domain/CLAUDE.md: Data models, ObjectModel/RecordModel patterns - open_notebook/database/CLAUDE.md: Repository pattern, async migrations - open_notebook/graphs/CLAUDE.md: LangGraph workflows, async orchestration - open_notebook/utils/CLAUDE.md: Cross-cutting utilities, context building - open_notebook/podcasts/CLAUDE.md: Episode/speaker profiles, job tracking **API & Other** - api/CLAUDE.md: REST layer, service architecture - commands/CLAUDE.md: Async command handlers, job queue patterns - prompts/CLAUDE.md: Jinja2 templates, prompt engineering patterns (refactored) **Project Root** - CLAUDE.md: Project overview, three-tier architecture, tech stack, getting started ### Key Features - Zero duplication: Parent modules reference child CLAUDE.md files, don't repeat them - Pattern-focused: Emphasizes how components work together, not component catalogs - Scannable: Short bullets, code examples only when necessary (1-2 per file) - Practical: "How to extend" guides, quirks/gotchas for each module - Navigation: Root CLAUDE.md acts as hub pointing to specialized documentation ### Cleanup - Removed unused `batch_fix_services.py` - Removed deprecated `open_notebook/plugins/podcasts.py` - Updated .gitignore for documentation consistency ## Impact New contributors can now: 1. Read root CLAUDE.md for system architecture (5 min) 2. Jump to specific layer documentation (frontend, api, open_notebook) 3. Dive into module-specific patterns in child CLAUDE.md files (1 min per module) All documentation is lean, reference-focused, and avoids duplication.
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open_notebook/utils/CLAUDE.md
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# Utils Module
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Utility functions and helpers for context building, text processing, tokenization, and versioning.
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## Purpose
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Provides cross-cutting concerns: building LLM context from sources/insights, text utilities (truncation, cleaning), token counting, and version management.
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## Architecture Overview
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**Four core utilities**:
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1. **context_builder.py**: Flexible context assembly from sources, notes, insights with token budgeting
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2. **text_utils.py**: Text truncation, whitespace cleaning, formatting helpers
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3. **token_utils.py**: Token counting for LLM context windows (wrapper around encoding library)
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4. **version_utils.py**: Version parsing, comparison, and schema compatibility checks
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Each utility is stateless and can be imported independently.
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## Component Catalog
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### context_builder.py
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- **ContextItem**: Dataclass for individual context piece (id, type, content, priority, token_count)
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- **ContextConfig**: Configuration for context building (sources/notes/insights selection, max tokens, priority weights)
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- **ContextBuilder**: Main class assembling context
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- `add_source()`: Include source by ID with inclusion level
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- `add_note()`: Include note by ID
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- `add_insight()`: Include insight by ID
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- `build()`: Assemble context respecting token budget and priorities
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- Uses vector_search to fetch source/insight content from SurrealDB
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- Returns list of ContextItem objects sorted by priority
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**Key behavior**:
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- Token counting is automatic (calculated in ContextItem.__post_init__)
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- Max token enforcement via priority weighting (higher priority items included first)
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- Type-specific fetching: sources → Source.full_text, notes → Note.content, insights → SourceInsight.content
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- Raises DatabaseOperationError if source/note fetch fails
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### text_utils.py
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- **truncate_text(text, max_chars, suffix="...")**: Truncates string, adds ellipsis
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- **clean_text(text)**: Removes extra whitespace, normalizes newlines
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- **extract_sentences(text, max_count)**: Splits text into sentences up to limit
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- **normalize_whitespace(text)**: Collapse multiple spaces/newlines into single
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- **format_for_llm(text)**: Combines cleaning + normalization for LLM consumption
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**Key behavior**: All functions are pure (no side effects); safe for high-volume processing
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### token_utils.py
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- **token_count(text)**: Returns estimated token count for string (via encoding library)
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- **remaining_tokens(max_tokens, used)**: Returns remaining tokens in budget
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- **fits_in_context(text, max_tokens)**: Boolean check if text fits token budget
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**Key behavior**: Uses fixed encoding (cl100k_base for GPT models); may differ slightly from actual model tokenization
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### version_utils.py
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- **parse_version(version_string)**: Parses "1.2.3" format; returns Version namedtuple
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- **compare_versions(v1, v2)**: Returns -1 (v1 < v2), 0 (equal), 1 (v1 > v2)
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- **is_compatible(current, required)**: Checks if current version meets requirement (e.g., current >= required)
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- **schema_version_check()**: Validates database schema version on startup
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**Key behavior**: Assumes semantic versioning (MAJOR.MINOR.PATCH); non-standard formats raise ValueError
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## Common Patterns
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- **Dataclass-driven config**: ContextConfig used by ContextBuilder (immutable after init)
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- **Token budgeting**: ContextBuilder respects max_tokens constraint; prioritizes high-priority items
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- **Error handling resilience**: token_count() returns estimate; context_builder catches DB errors gracefully
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- **Pure text functions**: text_utils functions are stateless utilities (no class needed)
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- **Lazy evaluation**: ContextBuilder doesn't fetch items until build() called
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- **Type hints throughout**: All functions use Optional, List, Dict for clarity
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## Key Dependencies
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- `open_notebook.domain.notebook`: Source, Note, SourceInsight models; vector_search function
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- `open_notebook.exceptions`: DatabaseOperationError, NotFoundError
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- `tiktoken` (via token_utils.py): Token encoding for GPT models
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- `loguru`: Logging in context_builder (debug-level)
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## Important Quirks & Gotchas
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- **Token count estimation**: Uses cl100k_base encoding; may differ 5-10% from actual model tokens
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- **Priority weights default**: If not specified, ContextConfig uses default weights (source=1, note=0.8, insight=1.2)
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- **Vector search required**: ContextBuilder assumes vector_search is available on Notebook model; fails if not
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- **Source.full_text vs content**: Uses full_text field (may include extracted text + metadata)
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- **Type-specific fetch logic**: ContextItem.content stores raw dict; caller must parse (e.g., dict["content"])
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- **Circular import risk**: context_builder imports from domain.notebook; avoid domain importing utils
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- **Max tokens hard limit**: ContextBuilder stops adding items once max_tokens exceeded (not prorated)
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- **No caching**: Every build() call re-fetches from database (use cache layer if needed)
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- **Whitespace normalization lossy**: clean_text() may change intended formatting (code blocks, poetry, etc.)
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## How to Extend
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1. **Add new context source type**: Create fetch method in ContextBuilder; update ContextConfig.sources dict
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2. **Add text preprocessing**: Add new function to text_utils (e.g., remove_urls, extract_keywords)
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3. **Change tokenization**: Replace tiktoken with alternative library in token_utils; update all calls
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4. **Add context filtering**: Extend ContextConfig with filter_by_date, filter_by_topic fields
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5. **Implement caching**: Wrap ContextBuilder.build() with functools.lru_cache (be aware of mutability)
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## Usage Example
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```python
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from open_notebook.utils.context_builder import ContextBuilder, ContextConfig
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config = ContextConfig(
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sources={"source:123": "full", "source:456": "summary"},
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max_tokens=2000,
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)
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builder = ContextBuilder(notebook, config)
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context_items = await builder.build()
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# context_items is List[ContextItem] sorted by priority
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for item in context_items:
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print(f"{item.type}:{item.id} ({item.token_count} tokens)")
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```
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