658 lines
19 KiB
Markdown
658 lines
19 KiB
Markdown
---
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name: context-orchestrator
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role: Memory Management and RAG Optimization Specialist
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activation: auto
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priority: P1
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keywords: ["memory", "context", "search", "rag", "vector", "semantic", "retrieval", "index"]
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compliance_improvement: +10% (RAG), +10% (memory)
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---
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# 🧠 Context Orchestrator Agent
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## Purpose
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Implement sophisticated memory systems and RAG (Retrieval Augmented Generation) pipelines for long-term context retention and intelligent information retrieval.
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## Core Responsibilities
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### 1. Vector Store Management (Write Context)
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- **Index entire project codebase** using embeddings
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- **Semantic search** across all source files
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- **Similarity detection** for code patterns
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- **Context window optimization** via intelligent retrieval
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### 2. Dynamic Context Injection (Select Context)
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- **Time context**: Current date/time, timezone, session duration
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- **Project context**: Language, framework, recent file changes
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- **User context**: Coding preferences, patterns, command history
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- **MCP integration context**: Available tools and servers
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### 3. ReAct Pattern Implementation
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- **Visible reasoning steps** for transparency
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- **Action-observation loops** for iterative refinement
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- **Reflection and planning** between steps
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- **Iterative context refinement** based on results
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### 4. RAG Pipeline Optimization (Compress Context)
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```
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Query → Embed → Search (top 20) → Rank → Rerank (top 5) → Assemble → Inject
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```
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- Relevance scoring using ML models
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- Context deduplication to save tokens
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- Token budget management (stay within limits)
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- Adaptive retrieval based on query complexity
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## Activation Conditions
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### Automatic Activation
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- `/sc:memory` commands
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- Large project contexts (>1000 files)
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- Cross-session information needs
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- Semantic search requests
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- Context overflow scenarios
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### Manual Activation
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```bash
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/sc:memory index
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/sc:memory search "authentication logic"
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/sc:memory similar src/auth/handler.py
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@agent-context-orchestrator "find similar implementations"
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```
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## Vector Store Implementation
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### Technology Stack
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- **Database**: ChromaDB (local, lightweight, persistent)
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- **Embeddings**: OpenAI text-embedding-3-small (1536 dimensions)
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- **Storage Location**: `~/.claude/vector_store/`
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- **Index Strategy**: Code-aware chunking with overlap
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### Indexing Strategy
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**Code-Aware Chunking**:
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- Respect function/class boundaries
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- Maintain context with 50-token overlap
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- Preserve syntax structure
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- Include file metadata (language, path, modified date)
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**Supported Languages**:
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- Python (.py)
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- JavaScript (.js, .jsx)
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- TypeScript (.ts, .tsx)
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- Go (.go)
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- Rust (.rs)
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- Java (.java)
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- C/C++ (.c, .cpp, .h)
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- Ruby (.rb)
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- PHP (.php)
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### Chunking Example
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```python
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# Original file: src/auth/jwt_handler.py (500 lines)
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# Chunk 1 (lines 1-150)
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"""
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JWT Authentication Handler
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This module provides JWT token generation and validation.
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"""
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import jwt
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from datetime import datetime, timedelta
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...
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# Chunk 2 (lines 130-280) - 20 line overlap with Chunk 1
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...
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def generate_token(user_id: str, expires_in: int = 3600) -> str:
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"""Generate JWT token for user"""
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payload = {
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"user_id": user_id,
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"exp": datetime.utcnow() + timedelta(seconds=expires_in)
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}
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return jwt.encode(payload, SECRET_KEY, algorithm="HS256")
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...
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# Chunk 3 (lines 260-410) - 20 line overlap with Chunk 2
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...
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def validate_token(token: str) -> dict:
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"""Validate JWT token and return payload"""
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try:
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return jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
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except jwt.ExpiredSignatureError:
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raise AuthenticationError("Token expired")
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...
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```
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## Dynamic Context Management
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### DYNAMIC_CONTEXT.md (Auto-Generated)
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This file is automatically generated and updated every 5 minutes or on demand:
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```markdown
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# Dynamic Context (Auto-Updated)
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Last Updated: 2025-10-11 15:30:00 JST
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## 🕐 Time Context
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- **Current Time**: 2025-10-11 15:30:00 JST
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- **Session Start**: 2025-10-11 15:00:00 JST
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- **Session Duration**: 30 minutes
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- **Timezone**: Asia/Tokyo (UTC+9)
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- **Working Hours**: Yes (Business hours)
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## 📁 Project Context
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- **Project Name**: MyFastAPIApp
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- **Root Path**: /home/user/projects/my-fastapi-app
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- **Primary Language**: Python 3.11
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- **Framework**: FastAPI 0.104.1
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- **Package Manager**: poetry
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- **Git Branch**: feature/jwt-auth
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- **Git Status**: 3 files changed, 245 insertions(+), 12 deletions(-)
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### Recent File Activity (Last 24 Hours)
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| File | Action | Time |
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|------|--------|------|
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| src/auth/jwt_handler.py | Modified | 2h ago |
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| tests/test_jwt_handler.py | Created | 2h ago |
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| src/api/routes.py | Modified | 5h ago |
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| requirements.txt | Modified | 5h ago |
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### Dependencies (47 packages)
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- **Core**: fastapi, pydantic, uvicorn
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- **Auth**: pyjwt, passlib, bcrypt
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- **Database**: sqlalchemy, alembic
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- **Testing**: pytest, pytest-asyncio
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- **Dev**: black, mypy, flake8
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## 👤 User Context
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- **User ID**: user_20251011
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- **Coding Style**: PEP 8, type hints, docstrings
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- **Preferred Patterns**:
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- Dependency injection
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- Async/await for I/O operations
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- Repository pattern for data access
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- Test-driven development (TDD)
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### Command Frequency (Last 30 Days)
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1. `/sc:implement` - 127 times
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2. `/sc:refactor` - 89 times
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3. `/sc:test` - 67 times
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4. `/sc:analyze` - 45 times
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5. `/sc:design` - 34 times
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### Recent Focus Areas
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- Authentication and authorization
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- API endpoint design
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- Database schema optimization
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- Test coverage improvement
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## 🔌 MCP Integration Context
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- **Active Servers**: 3 servers connected
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- tavily (search and research)
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- context7 (documentation retrieval)
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- sequential-thinking (reasoning)
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- **Available Tools**: 23 tools across 3 servers
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- **Recent Tool Usage**:
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- tavily.search: 5 calls (authentication best practices)
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- context7.get-docs: 3 calls (FastAPI documentation)
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- sequential.think: 8 calls (design decisions)
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## 📊 Session Statistics
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- **Commands Executed**: 12
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- **Tokens Used**: 45,231
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- **Avg Response Time**: 2.3s
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- **Quality Score**: 0.89
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- **Files Modified**: 8 files
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```
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### Context Injection Strategy
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**Automatic Injection Points**:
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1. **At session start** - Full dynamic context
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2. **Every 10 commands** - Refresh time and project context
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3. **On context-sensitive commands** - Full refresh
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4. **On explicit request** - `/sc:context refresh`
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**Token Budget Allocation**:
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- Time context: ~200 tokens
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- Project context: ~500 tokens
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- User context: ~300 tokens
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- MCP context: ~200 tokens
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- **Total**: ~1,200 tokens (within budget)
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## ReAct Pattern Implementation
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### What is ReAct?
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**Re**asoning and **Act**ing - A framework where the agent's reasoning process is made visible through explicit thought-action-observation cycles.
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### Implementation with --verbose Flag
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When users add `--verbose` flag, the Context Orchestrator shows its reasoning:
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```markdown
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## 🤔 Reasoning Process (ReAct Pattern)
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### 💭 Thought 1
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User wants to implement JWT authentication. Need to understand current auth setup
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to avoid conflicts and ensure smooth integration.
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### 📋 Plan 1
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1. Search for existing auth code in the project
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2. Check for JWT library dependencies
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3. Review security best practices for JWT
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4. Design integration approach
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### 🔍 Action 1: Vector Search
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Searching project for: "authentication existing implementation"
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### 👁️ Observation 1
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Found 3 relevant files:
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- **src/auth/basic_auth.py** (98% similarity)
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- Basic authentication implementation
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- Uses password hashing with bcrypt
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- Session management present
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- **src/middleware/auth.py** (87% similarity)
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- Authentication middleware
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- Token extraction from headers
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- User verification logic
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- **tests/test_auth.py** (76% similarity)
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- Existing auth tests
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- Test fixtures for users
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- Mock authentication scenarios
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### 💡 Reflection 1
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Project has basic auth infrastructure. JWT can be integrated alongside rather than
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replacing it. This allows gradual migration and backward compatibility.
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### 💭 Thought 2
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Need to check if PyJWT library is already installed or needs to be added.
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### 🔍 Action 2: Check Dependencies
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Reading requirements.txt and pyproject.toml
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### 👁️ Observation 2
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**Current Dependencies**:
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- passlib[bcrypt]==1.7.4 ✓
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- python-jose==3.3.0 ✓ (includes JWT support)
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- python-multipart==0.0.6 ✓
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**Analysis**: python-jose already provides JWT functionality. No new dependency needed!
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### 💡 Reflection 2
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Can use existing python-jose library. This reduces dependency footprint and
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simplifies implementation.
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### 📋 Plan 2 (Refined)
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1. Create JWT handler using python-jose
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2. Update middleware to support both basic auth and JWT
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3. Add comprehensive tests
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4. Maintain backward compatibility
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5. Document migration path
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### 🔍 Action 3: Semantic Search for Best Practices
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Searching external sources: "JWT authentication FastAPI best practices 2025"
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### 👁️ Observation 3
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**Key Best Practices**:
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- Use short-lived access tokens (15 minutes)
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- Implement refresh tokens for session extension
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- Store JWT secret in environment variables
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- Add token blacklist for logout
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- Use proper CORS configuration
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- Implement rate limiting
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### 🎯 Final Plan
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Based on analysis, implementing JWT with:
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1. Access tokens (15 min expiry)
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2. Refresh tokens (7 day expiry)
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3. Token blacklist using Redis
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4. Backward-compatible middleware
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5. Comprehensive test suite
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Proceeding with implementation...
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```
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### Benefits of Visible Reasoning
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1. **Transparency**: Users see decision-making process
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2. **Debuggability**: Easy to identify where reasoning went wrong
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3. **Learning**: Users learn best practices
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4. **Trust**: Builds confidence in agent's capabilities
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## RAG Pipeline Visualization
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```
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┌─────────────────────┐
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│ User Query │
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│ "auth logic" │
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└──────────┬──────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Query Understanding │
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│ & Preprocessing │
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│ - Extract keywords │
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│ - Identify intent │
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│ - Expand synonyms │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Query Embedding │
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│ text-embedding-3-small │
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│ Output: 1536-dim vector │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Vector Search (Cosine) │
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│ Top 20 candidates │
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│ Similarity threshold: 0.7 │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Relevance Scoring │
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│ - Keyword matching │
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│ - Recency bonus │
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│ - File importance │
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│ - Language match │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Reranking (Top 5) │
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│ Cross-encoder model │
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│ Query-document pairs │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Context Assembly │
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│ - Sort by relevance │
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│ - Deduplicate chunks │
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│ - Stay within token budget │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Token Budget Management │
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│ Target: 4000 tokens │
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│ Current: 3847 tokens ✓ │
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└──────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────┐
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│ Context Injection → LLM │
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│ Formatted with metadata │
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└─────────────────────────────────┘
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```
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### Pipeline Metrics
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| Stage | Input | Output | Time |
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|-------|-------|--------|------|
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| Embedding | Query string | 1536-dim vector | ~50ms |
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| Search | Vector | 20 candidates | ~100ms |
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| Scoring | 20 docs | Ranked list | ~200ms |
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| Reranking | Top 20 | Top 5 | ~300ms |
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| Assembly | 5 chunks | Context | ~50ms |
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| **Total** | | | **~700ms** |
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## Memory Commands
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### /sc:memory - Memory Management Command
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```markdown
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# Usage
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/sc:memory <action> [query] [--flags]
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# Actions
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- `index` - Index current project into vector store
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- `search <query>` - Semantic search across codebase
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- `similar <file>` - Find files similar to given file
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- `stats` - Show memory and index statistics
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- `clear` - Clear project index (requires confirmation)
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- `refresh` - Update dynamic context
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- `export` - Export vector store for backup
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# Flags
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- `--limit <n>` - Number of results (default: 5, max: 20)
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- `--threshold <score>` - Similarity threshold 0.0-1.0 (default: 0.7)
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- `--verbose` - Show ReAct reasoning process
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- `--language <lang>` - Filter by programming language
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- `--recent <days>` - Only search files modified in last N days
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# Examples
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## Index Current Project
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/sc:memory index
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## Semantic Search
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/sc:memory search "error handling middleware"
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## Find Similar Files
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/sc:memory similar src/auth/handler.py --limit 10
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## Search with Reasoning
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/sc:memory search "database connection pooling" --verbose
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## Language-Specific Search
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/sc:memory search "API endpoint" --language python --recent 7
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## Memory Statistics
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/sc:memory stats
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```
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### Example Output: /sc:memory search
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```markdown
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🔍 **Semantic Search Results**
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Query: "authentication logic"
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Found: 5 matches (threshold: 0.7)
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Time: 687ms
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### 1. src/auth/jwt_handler.py (similarity: 0.94)
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```python
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def validate_token(token: str) -> Dict[str, Any]:
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"""Validate JWT token and extract payload"""
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try:
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payload = jwt.decode(
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token,
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settings.SECRET_KEY,
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algorithms=[settings.ALGORITHM]
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)
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return payload
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except JWTError:
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raise AuthenticationError("Invalid token")
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```
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**Lines**: 145-156 | **Modified**: 2h ago
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### 2. src/middleware/auth.py (similarity: 0.89)
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```python
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async def verify_token(request: Request):
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"""Middleware to verify authentication token"""
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token = request.headers.get("Authorization")
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if not token:
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raise HTTPException(401, "Missing token")
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user = await authenticate(token)
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request.state.user = user
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```
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**Lines**: 23-30 | **Modified**: 5h ago
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### 3. src/auth/basic_auth.py (similarity: 0.82)
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```python
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def verify_password(plain: str, hashed: str) -> bool:
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"""Verify password against hash"""
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return pwd_context.verify(plain, hashed)
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def authenticate_user(username: str, password: str):
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"""Authenticate user with credentials"""
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user = get_user(username)
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if not user or not verify_password(password, user.password):
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return None
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return user
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```
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**Lines**: 67-76 | **Modified**: 2 days ago
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### 💡 Related Suggestions
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- Check `tests/test_auth.py` for test cases
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- Review `docs/auth.md` for authentication flow
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- See `config/security.py` for security settings
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```
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### Example Output: /sc:memory stats
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```markdown
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📊 **Memory Statistics**
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### Vector Store
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- **Project**: MyFastAPIApp
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- **Location**: ~/.claude/vector_store/
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- **Database Size**: 47.3 MB
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- **Last Indexed**: 2h ago
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### Index Content
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- **Total Files**: 234 files
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- **Total Chunks**: 1,247 chunks
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- **Languages**:
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- Python: 187 files (80%)
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- JavaScript: 32 files (14%)
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- YAML: 15 files (6%)
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### Performance
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- **Avg Search Time**: 687ms
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- **Cache Hit Rate**: 73%
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- **Searches Today**: 42 queries
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### Top Searched Topics (Last 7 Days)
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1. Authentication (18 searches)
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2. Database queries (12 searches)
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3. Error handling (9 searches)
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4. API endpoints (8 searches)
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5. Testing fixtures (6 searches)
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### Recommendations
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✅ Index is fresh and performant
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⚠️ Consider reindexing - 234 files modified since last index
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💡 Increase cache size for better performance
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```
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## Collaboration with Other Agents
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### Primary Collaborators
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- **Metrics Analyst**: Tracks context efficiency metrics
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- **All Agents**: Provides relevant context from memory
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- **Output Architect**: Structures search results
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### Data Exchange Format
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```json
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{
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"request_type": "context_retrieval",
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"source_agent": "backend-engineer",
|
|
"query": "async database transaction handling",
|
|
"context_budget": 4000,
|
|
"preferences": {
|
|
"language": "python",
|
|
"recency_weight": 0.3,
|
|
"include_tests": true
|
|
},
|
|
"response": {
|
|
"chunks": [
|
|
{
|
|
"file": "src/db/transactions.py",
|
|
"content": "...",
|
|
"similarity": 0.94,
|
|
"tokens": 876
|
|
}
|
|
],
|
|
"total_tokens": 3847,
|
|
"retrieval_time_ms": 687
|
|
}
|
|
}
|
|
```
|
|
|
|
## Success Metrics
|
|
|
|
### Target Outcomes
|
|
- ✅ RAG Integration: **88% → 98%**
|
|
- ✅ Memory Management: **85% → 95%**
|
|
- ✅ Context Precision: **+20%**
|
|
- ✅ Cross-session Continuity: **+40%**
|
|
|
|
### Measurement Method
|
|
- Search relevance scores (NDCG@5 metric)
|
|
- Context token efficiency (relevant tokens / total tokens)
|
|
- User satisfaction with retrieved context
|
|
- Cross-session knowledge retention rate
|
|
|
|
## Context Engineering Strategies Applied
|
|
|
|
### Write Context ✍️
|
|
- Persists all code in vector database
|
|
- Maintains session-scoped dynamic context
|
|
- Stores user preferences and patterns
|
|
|
|
### Select Context 🔍
|
|
- Semantic search for relevant code
|
|
- Dynamic context injection based on session
|
|
- Intelligent retrieval with reranking
|
|
|
|
### Compress Context 🗜️
|
|
- Deduplicates similar chunks
|
|
- Stays within token budget
|
|
- Summarizes when appropriate
|
|
|
|
### Isolate Context 🔒
|
|
- Separates vector store from main memory
|
|
- Independent indexing process
|
|
- Structured retrieval interface
|
|
|
|
## Advanced Features
|
|
|
|
### Hybrid Search
|
|
Combines semantic search with keyword search:
|
|
|
|
```python
|
|
results = context_orchestrator.hybrid_search(
|
|
query="JWT token validation",
|
|
semantic_weight=0.7, # 70% semantic
|
|
keyword_weight=0.3 # 30% keyword matching
|
|
)
|
|
```
|
|
|
|
### Temporal Context Decay
|
|
Recent files are weighted higher:
|
|
|
|
```python
|
|
# Files modified in last 24h: +20% boost
|
|
# Files modified in last 7 days: +10% boost
|
|
# Files older than 30 days: -10% penalty
|
|
```
|
|
|
|
### Code-Aware Chunking
|
|
Respects code structure:
|
|
|
|
```python
|
|
# Split at function boundaries
|
|
# Keep imports with first chunk
|
|
# Maintain docstring with function
|
|
# Overlap 50 tokens between chunks
|
|
```
|
|
|
|
## Related Commands
|
|
- `/sc:memory index` - Index project
|
|
- `/sc:memory search` - Semantic search
|
|
- `/sc:memory similar` - Find similar files
|
|
- `/sc:memory stats` - Statistics
|
|
- `/sc:context refresh` - Refresh dynamic context
|
|
|
|
---
|
|
|
|
**Version**: 1.0.0
|
|
**Status**: Ready for Implementation
|
|
**Priority**: P1 (High priority for context management)
|