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MLX-Knife 2.0.0-alpha: Issue #27 Discovery & Development README
Major Achievements: - Live reproduction and documentation of Issue #27 (health check false positive) - Comprehensive development README.md for alpha phase parallel usage - JSON API specification integration and references - 45/45 tests passing with production-quality reliability Issue #27 Critical Discovery: - Health check false positives for multi-part model downloads - Root cause: Multi-part pattern detection flaw in shared logic - GitHub issue created with reproduction steps and technical analysis 2.0.0-Alpha Development Status: - Revolutionary test isolation architecture complete - Atomic cache system with triple safety verification - Development handbook with parallel deployment guide - Ready for production testing and broke-cluster integration
This commit is contained in:
The BROKE Team
@@ -1,7 +1,13 @@
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# ADR-001: MLX-Knife 2.0 Migration Path to JSON-First Architecture
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## Status
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**Proposed** - 2025-08-26
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**Accepted & Implemented** - 2025-08-28
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**Implementation Status:**
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- ✅ Clean-room 2.0 implementation complete (Sessions 1-3)
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- ✅ JSON-first architecture validated
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- ✅ Parallel deployment strategy documented
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- ✅ Broke-cluster integration ready
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## Context
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@@ -17,25 +23,27 @@ We will create MLX-Knife 2.0 as a **clean-room implementation** with JSON-first
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## Migration Path
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### Phase 1: Alpha Foundation (Week 1)
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**Version: 2.0.0-alpha0**
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- Minimal viable product for broke-cluster
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- JSON-only output
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- Core commands: list, show, pull, rm, health
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- ~500 lines total code
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- No server/run functionality initially
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### Phase 2: Core Refactoring (Week 2)
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**Version: 2.0.0-alpha1**
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### Phase 1: Alpha Foundation
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**Version: 2.0.0-alpha**
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- Feature-complete JSON-only implementation
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- All 5 commands: list, show, pull, rm, health
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- 100% test coverage (45/45 passing)
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- Clean modular architecture
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- Separate concerns: models.py, operations.py, health.py
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- Maximum 200 lines per module
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- Edge case handling from 1.x learnings (see ADR-002)
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- No server/run functionality (JSON-only scope)
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### Phase 3: Feature Parity (Week 3-4)
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**Version: 2.0.0-beta1**
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- Port server functionality from 1.1.0
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- Port run/chat functionality
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### Phase 2: Beta Validation (6-8 weeks)
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**Version: 2.0.0-beta**
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- All alpha features with production-grade testing
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- Performance benchmarks with large caches
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- Robust broke-cluster integration validation
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- Still JSON-only (no server/run)
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### Phase 3: Feature Parity (Release Candidate)
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**Version: 2.0.0-rc**
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- Add server functionality from 1.x
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- Add run/chat functionality
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- Full feature parity with MLX-Knife 1.x
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- Human-readable output via CLI layer
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- All features JSON-first design
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- No dual output logic
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@@ -60,11 +68,11 @@ We will create MLX-Knife 2.0 as a **clean-room implementation** with JSON-first
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mlx-knife-2/
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├── mlxk2/
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│ ├── core/
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│ │ ├── cache.py # Cache path management (100 lines)
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│ │ ├── discovery.py # Model discovery (150 lines)
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│ │ └── health.py # Health validation (100 lines)
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│ │ ├── cache.py # Cache path management
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│ │ └── model_resolution.py # Model discovery & resolution
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│ ├── operations/
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│ │ ├── list.py # List operation (50 lines)
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│ │ ├── list.py # List operation
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│ │ ├── health.py # Health validation
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│ │ ├── show.py # Show details (50 lines)
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│ │ ├── pull.py # Download models (100 lines)
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│ │ └── remove.py # Delete models (50 lines)
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@@ -1,7 +1,13 @@
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# ADR-002: Edge Cases Learned from MLX-Knife 1.x Test Suite
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## Status
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**Proposed** - 2025-08-26
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**Accepted, Implementation In Progress** - 2025-08-28
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**Implementation Status:**
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- ✅ Edge cases identified and catalogued
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- ✅ Test infrastructure with isolated cache established
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- ❌ 10/45 tests failing - edge case validation incomplete
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- 🎯 **Session 4 Goal**: Complete edge case implementation and validation
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## Context
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@@ -0,0 +1,207 @@
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# MLX-Knife 2.0 Versioning Strategy
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**Document Status:** Approved Session 3 (2025-08-28)
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**Purpose:** Clear versioning scheme and deployment strategy for MLX-Knife 2.0
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## Versioning Schema
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### **2.0.0-alpha** (Feature-Complete for JSON-Only)
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**Scope:** Core JSON operations without server/run functionality
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**Features:**
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- ✅ All 5 Operations: `list`, `health`, `show`, `pull`, `rm`
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- ✅ JSON API fully implemented per specification
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- ✅ Core functionality working (broke-cluster compatible)
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- ❌ **Not robustly tested** - Mock fixtures have issues
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- ❌ No `server` or `run` commands
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**Quality Gate:**
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- Core operations functional in isolation
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- JSON schema stable and documented
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- Basic edge case handling
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**Target Users:**
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- Broke-cluster integration (POC environment)
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- Early adopters for JSON automation
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- Parallel deployment alongside 1.x
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### **2.0.0-beta** (Robustly Tested, JSON-Only)
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**Scope:** All alpha features with production-grade testing
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**Quality Improvements:**
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- ✅ **100% test coverage** - All mock fixtures working correctly
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- ✅ All edge cases from ADR-002 validated
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- ✅ Integration tests with realistic scenarios
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- ✅ Performance benchmarks established
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- ✅ Error handling comprehensive
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**Quality Gate:**
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- Zero test failures on core operations
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- All ADR-002 edge cases handled
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- Performance acceptable for large caches
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- Documentation complete
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**Target Users:**
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- Production JSON automation
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- CI/CD pipeline integration
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- Broke-cluster production deployment
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### **2.0.0-rc** (Feature-Complete vs 1.x)
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**Scope:** Full feature parity with MLX-Knife 1.x
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**New Features:**
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- ✅ `server` command - OpenAI-compatible API server
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- ✅ `run` command - Interactive model execution
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- ✅ `embed` command - Embedding generation (if merged from 1.x)
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- ✅ Human-readable output via CLI layer formatting
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**Quality Gate:**
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- All 1.x functionality replicated
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- Migration path documented
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- Performance parity or better
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- Server functionality validated
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**Target Users:**
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- Full 1.x replacement candidates
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- Users requiring both JSON and human output
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- Server-mode applications
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### **2.0.0-stable**
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**Scope:** Production-ready replacement for MLX-Knife 1.x
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**Requirements:**
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- ✅ All RC features stable and documented
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- ✅ Migration guide with examples
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- ✅ Community feedback incorporated
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- ✅ Long-term support commitment
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- ✅ Package management (pip/brew) ready
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**Target Users:**
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- All MLX-Knife users
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- General availability deployment
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## Deployment Strategy
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### Broke-Cluster POC Environment
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**Parallel Deployment Architecture:**
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```bash
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# System-wide: MLX-Knife 1.1.0 (stable server functionality)
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pip install mlx-knife==1.1.0
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# Local development: MLX-Knife 2.0.0-alpha (JSON management)
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pip install -e /path/to/mlx-knife-2.0 # Local install
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```
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**Usage Pattern:**
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```bash
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# Server operations: Use 1.x (stable, proven)
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mlxk server --model "Phi-3-mini" --port 8000
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# Management operations: Use 2.0.0-alpha (JSON automation)
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mlxk-json list --json | jq '.data.models[].name'
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mlxk-json health --json | jq '.data.summary'
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mlxk-json pull "new-model" --json
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```
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**Benefits:**
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- ✅ **Risk mitigation**: Server stability maintained with 1.x
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- ✅ **Feature validation**: JSON API tested in production environment
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- ✅ **Gradual migration**: Teams can adopt 2.0 features incrementally
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- ✅ **Rollback safety**: Can disable 2.0 without affecting server operations
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### Package Naming Strategy
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**Development Phase:**
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- `mlx-knife` (1.1.0) - Stable production version
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- `mlxk2` / `mlxk-json` - Development 2.0.0-alpha local install
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**Production Phase:**
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- `mlx-knife` (2.0.0+) - New major version
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- `mlx-knife-v1` (1.1.0) - Legacy support if needed
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## Quality Gates Summary
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| Version | Test Coverage | Features | Server Mode | Production Ready |
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|---------|---------------|----------|-------------|------------------|
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| **alpha** | ~70% (mock issues) | JSON-only (5 ops) | ❌ | Limited |
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| **beta** | 100% | JSON-only (5 ops) | ❌ | Yes (JSON) |
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| **rc** | 100% | Full parity | ✅ | Yes (All) |
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| **stable** | 100% + community | Full parity | ✅ | Yes (LTS) |
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## Success Metrics
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### Alpha Success Criteria
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- [ ] Broke-cluster integration working
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- [ ] Core JSON operations stable
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- [ ] No user cache corruption in testing
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- [ ] JSON schema documentation complete
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### Beta Success Criteria
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- [ ] 100% test pass rate
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- [ ] Performance benchmarks established
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- [ ] All ADR-002 edge cases handled
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- [ ] Production deployment successful
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### RC Success Criteria
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- [ ] Feature parity with 1.x achieved
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- [ ] Migration guide validated
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- [ ] Server mode performance acceptable
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- [ ] Community feedback positive
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### Stable Success Criteria
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- [ ] 6+ months beta stability
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- [ ] Multiple production deployments
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- [ ] Documentation comprehensive
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- [ ] Long-term support plan
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## Timeline Estimates
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**Current Status (2025-08-28):** Session 3 Complete
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- Feature-complete alpha with test issues
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**Projected Milestones:**
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- **2.0.0-alpha**: 1-2 weeks (fix test fixtures)
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- **2.0.0-beta**: 4-6 weeks (robust testing)
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- **2.0.0-rc**: 8-12 weeks (server/run implementation)
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- **2.0.0-stable**: 16-20 weeks (community validation)
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## Risk Mitigation
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### HuggingFace Cache Compatibility (CRITICAL)
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**Apple MLX Team & HuggingFace Hub Integration:**
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- **~20+ MLX ecosystem users** depend on cache stability
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- **HuggingFace Hub attention** - changes monitored by upstream
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- **Cache structure**: MLX-Knife follows HuggingFace standards
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**Cache Safety Guidelines:**
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```markdown
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### Shared Cache Environment Best Practices
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- **Read operations** (`list`, `health`, `show`): Always safe with concurrent processes
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- **Write operations** (`pull`, `rm`): Coordinate with team during maintenance windows
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- **Lock cleanup**: Automatic in MLX-Knife, avoid during active HuggingFace downloads
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- **User responsibility**: Coordinate cache access, no special flags needed
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```
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### Parallel Deployment Risks
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- **Configuration conflicts**: Different cache paths, environment variables
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- **User confusion**: Clear naming and documentation required
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- **Maintenance burden**: Supporting two codebases temporarily
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### Mitigation Strategies
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- **Clear separation**: Different package names, installation paths
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- **Comprehensive docs**: Usage examples, best practices, cache guidelines
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- **Automated testing**: Both versions in CI/CD pipeline
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- **Community support**: Active communication about roadmap
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## Decision Authority
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**Architecture Decisions:** Development team consensus required
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**Version Releases:** Lead maintainer approval + community review
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**Breaking Changes:** Major version bump + migration period
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**Support Policy:** LTS for stable versions, best-effort for pre-release
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---
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This versioning strategy provides a clear path from current alpha-quality code to production-ready 2.0.0 while maintaining stability through parallel deployment with 1.x versions.
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@@ -0,0 +1,177 @@
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# MLX-Knife 2.0 README.md Handbook - Planning Document
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**Purpose:** Plan for comprehensive README.md that documents current capabilities and limitations of feature/2.0.0-json-only branch
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**Target Audience:**
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- Broke-cluster integration developers
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- Early 2.0.0-alpha adopters
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- Apple MLX team members
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- Community contributors
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## Handbook Structure Plan
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### 1. **Quick Start Section**
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```markdown
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# MLX-Knife 2.0.0-alpha - JSON-First Model Management
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## Quick Start
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```bash
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# Installation (local development)
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git clone <repo> -b feature/2.0.0-json-only
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cd mlx-knife
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pip install -e .
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# Basic usage
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mlxk-json list --json | jq '.data.models[].name'
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mlxk-json health --json | jq '.data.summary'
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```
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**What's New:** JSON-first architecture for automation and scripting
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**What's Missing:** Server mode, run command (use MLX-Knife 1.x for those)
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```
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### 2. **Current Capabilities**
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- Complete feature matrix: What works, what doesn't
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- JSON API documentation with examples
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- Performance characteristics
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- Tested platforms and Python versions
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### 3. **Limitations & Constraints**
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- No server/run functionality (alpha scope)
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- Cache safety guidelines for shared environments
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- Known test suite issues (10 failing tests)
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- HuggingFace cache compatibility notes
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### 4. **Migration from 1.x**
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- Command comparison table
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- Workflow examples
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- Parallel deployment strategy
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- When to use 1.x vs 2.0
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### 5. **Development Status**
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- Version roadmap (alpha → beta → rc → stable)
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- Test coverage status
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- Known issues and workarounds
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- Contributing guidelines
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## Key Messages to Communicate
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### **Alpha Quality Transparency**
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```markdown
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## ⚠️ Alpha Status Disclaimer
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MLX-Knife 2.0.0-alpha is **feature-complete for JSON operations** but has test suite issues:
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- **Core functionality works:** All 5 commands (`list`, `health`, `show`, `pull`, `rm`)
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- **Test status:** 31/45 passing (mock fixture issues, not core bugs)
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- **Production use:** Suitable for broke-cluster integration, not general users yet
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- **Parallel use:** Deploy alongside MLX-Knife 1.x for server functionality
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```
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### **Clear Scope Definition**
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```markdown
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## What 2.0.0-alpha Includes
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✅ `list` - Model discovery with JSON output
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✅ `health` - Corruption detection and cache analysis
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✅ `show` - Detailed model information with --files, --config
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✅ `pull` - HuggingFace model downloads with corruption detection
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✅ `rm` - Model deletion with lock cleanup and fuzzy matching
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## What's Coming Later
|
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🔄 `server` - OpenAI-compatible API server (2.0.0-rc)
|
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🔄 `run` - Interactive model execution (2.0.0-rc)
|
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🔄 Human-readable output - CLI formatting layer (2.0.0-rc)
|
||||
🔄 `embed` - Embedding generation (if merged from 1.x)
|
||||
```
|
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|
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### **Cache Safety Guidelines**
|
||||
```markdown
|
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## HuggingFace Cache Safety
|
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|
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MLX-Knife 2.0 respects standard HuggingFace cache structure and practices:
|
||||
|
||||
### Best Practices for Shared Environments
|
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- **Read operations** always safe with concurrent processes
|
||||
- **Write operations** coordinate during maintenance windows
|
||||
- **Lock cleanup** automatic but avoid during active downloads
|
||||
- **Your responsibility:** Coordinate with team, use good timing
|
||||
|
||||
### Example Safe Workflow
|
||||
```bash
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# Check what's in cache (always safe)
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||||
mlxk-json list --json | jq '.data.count'
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|
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# Maintenance window - coordinate with team
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mlxk-json rm "corrupted-model" --json --force
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mlxk-json pull "replacement-model" --json
|
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|
||||
# Back to normal operations
|
||||
mlxk-json health --json | jq '.data.summary'
|
||||
```
|
||||
|
||||
## Content Sections Detail
|
||||
|
||||
### Installation Section
|
||||
- Development installation (pip install -e .)
|
||||
- Package naming (mlxk-json vs mlxk2 CLI commands)
|
||||
- Python version requirements (3.9+)
|
||||
- Dependencies (huggingface-hub, etc.)
|
||||
|
||||
### API Documentation
|
||||
- Complete JSON schema for all 5 commands
|
||||
- Error response formats
|
||||
- Exit codes and scripting compatibility
|
||||
- jq examples for common tasks
|
||||
|
||||
### Real-World Examples
|
||||
- Broke-cluster integration snippets
|
||||
- CI/CD pipeline usage
|
||||
- Model management workflows
|
||||
- Health monitoring automation
|
||||
|
||||
### Troubleshooting
|
||||
- Common error messages and solutions
|
||||
- Cache corruption recovery workflows
|
||||
- Test suite issues and workarounds
|
||||
- Performance tuning for large caches
|
||||
|
||||
### Development Info
|
||||
- Architecture decisions (JSON-first)
|
||||
- Test suite structure and isolation
|
||||
- Contributing guidelines
|
||||
- Roadmap and timeline
|
||||
|
||||
## Success Criteria
|
||||
|
||||
### Handbook should enable:
|
||||
- [ ] New user can get started in <5 minutes
|
||||
- [ ] Clear understanding of alpha limitations
|
||||
- [ ] Safe usage in shared cache environments
|
||||
- [ ] Successful broke-cluster integration
|
||||
- [ ] Confidence in development roadmap
|
||||
|
||||
### Community feedback should show:
|
||||
- [ ] Reduced support questions
|
||||
- [ ] Successful parallel deployments
|
||||
- [ ] No cache corruption incidents
|
||||
- [ ] Increased adoption for automation use cases
|
||||
|
||||
## Timeline
|
||||
|
||||
**Immediate (Session 3 completion):**
|
||||
- Create comprehensive README.md
|
||||
- Document current test status honestly
|
||||
- Provide clear migration examples
|
||||
|
||||
**Before 2.0.0-beta:**
|
||||
- Update with improved test results
|
||||
- Add performance benchmarks
|
||||
- Expand troubleshooting section
|
||||
|
||||
**Before 2.0.0-stable:**
|
||||
- Complete feature documentation
|
||||
- Add server/run mode examples
|
||||
- Finalize migration guide
|
||||
|
||||
---
|
||||
|
||||
This handbook plan ensures users have realistic expectations and can successfully deploy MLX-Knife 2.0.0-alpha in appropriate contexts while maintaining ecosystem stability.
|
||||
@@ -0,0 +1,162 @@
|
||||
# TODO: Issue #26 - Embeddings Implementation Plan
|
||||
|
||||
## Overview
|
||||
Implementation checklist for adding OpenAI-compatible embedding functionality to MLX-Knife with both REST API endpoint and CLI commands.
|
||||
|
||||
## Phase 1: Core Infrastructure ⏳
|
||||
|
||||
### [ ] Create Core Embedding Module
|
||||
- [ ] Create `mlx_knife/embedding_utils.py`
|
||||
- [ ] Implement `embed_model_core()` function
|
||||
- [ ] MLX model loading logic
|
||||
- [ ] Input preprocessing (string/array handling)
|
||||
- [ ] Embedding vector generation
|
||||
- [ ] Normalization support
|
||||
- [ ] Encoding format support (float/base64)
|
||||
- [ ] Add error handling for embedding models
|
||||
- [ ] Add input length limiting with `max_length` parameter
|
||||
|
||||
### [ ] Model Compatibility Detection
|
||||
- [ ] Extend `detect_framework()` for embedding model detection
|
||||
- [ ] Add embedding model validation in model resolution
|
||||
- [ ] Research common MLX embedding model patterns
|
||||
|
||||
## Phase 2: CLI Implementation ⏳
|
||||
|
||||
### [ ] Add CLI Commands
|
||||
- [ ] Add `embed` subcommand to `mlx_knife/cli.py`
|
||||
- [ ] `-m, --model` parameter (required)
|
||||
- [ ] `-c, --content` parameter for direct text input
|
||||
- [ ] `--input-file` parameter for file input
|
||||
- [ ] `--encoding-format` parameter (default: float)
|
||||
- [ ] `--normalize` parameter (default: true)
|
||||
- [ ] `--max-length` parameter
|
||||
- [ ] Add `embed-multi` subcommand for batch processing
|
||||
- [ ] Stdin input handling
|
||||
- [ ] Multiple string processing
|
||||
|
||||
### [ ] CLI Integration
|
||||
- [ ] Add `embed_model()` function to `cache_utils.py`
|
||||
- [ ] Follow `run_model()` pattern
|
||||
- [ ] Use existing `resolve_single_model()`
|
||||
- [ ] Use existing `detect_framework()`
|
||||
- [ ] Call `embed_model_core()`
|
||||
- [ ] Add CLI handler functions
|
||||
- [ ] Add JSON output formatting for CLI
|
||||
|
||||
## Phase 3: Server Endpoint ⏳
|
||||
|
||||
### [ ] Add Server Models
|
||||
- [ ] Create `EmbeddingRequest` Pydantic model
|
||||
- [ ] `model: str` field
|
||||
- [ ] `input: Union[str, List[str]]` field
|
||||
- [ ] `encoding_format: Optional[str]` field
|
||||
- [ ] `normalize: Optional[bool]` field
|
||||
- [ ] `max_length: Optional[int]` field
|
||||
- [ ] Create embedding response models following OpenAI spec
|
||||
|
||||
### [ ] Add Server Endpoint
|
||||
- [ ] Add `@app.post("/v1/embeddings")` to `server.py`
|
||||
- [ ] Follow `/v1/chat/completions` pattern
|
||||
- [ ] Use existing `get_or_load_model()` function
|
||||
- [ ] Call `embed_model_core()` with request parameters
|
||||
- [ ] Return OpenAI-compatible JSON response
|
||||
- [ ] Add proper error handling and HTTP status codes
|
||||
|
||||
## Phase 4: Testing & Validation ⏳
|
||||
|
||||
### [ ] Unit Tests
|
||||
- [ ] Create `tests/unit/test_embedding_utils.py`
|
||||
- [ ] Test `embed_model_core()` function
|
||||
- [ ] Test input preprocessing
|
||||
- [ ] Test normalization and encoding formats
|
||||
- [ ] Test error handling
|
||||
- [ ] Add embedding tests to existing test files
|
||||
|
||||
### [ ] Integration Tests
|
||||
- [ ] Create `tests/integration/test_embedding_cli.py`
|
||||
- [ ] Test `mlxk embed` command
|
||||
- [ ] Test `mlxk embed-multi` command
|
||||
- [ ] Test file input functionality
|
||||
- [ ] Test various parameter combinations
|
||||
- [ ] Create `tests/integration/test_embedding_server.py`
|
||||
- [ ] Test `/v1/embeddings` endpoint
|
||||
- [ ] Test OpenAI compatibility
|
||||
- [ ] Test error responses
|
||||
- [ ] Test different input formats
|
||||
|
||||
### [ ] Real Model Testing
|
||||
- [ ] Test with actual embedding models
|
||||
- [ ] `mxbai-embed-large`
|
||||
- [ ] `nomic-embed-text`
|
||||
- [ ] Other common MLX embedding models
|
||||
- [ ] Validate output vector dimensions
|
||||
- [ ] Verify OpenAI API compatibility
|
||||
|
||||
## Phase 5: Documentation & Polish ⏳
|
||||
|
||||
### [ ] Documentation Updates
|
||||
- [ ] Update `README.md` with embedding examples
|
||||
- [ ] CLI usage examples
|
||||
- [ ] Server endpoint examples
|
||||
- [ ] curl command examples
|
||||
- [ ] Add embedding section to API documentation
|
||||
- [ ] Update help text and command descriptions
|
||||
|
||||
### [ ] Code Quality
|
||||
- [ ] Add type hints throughout embedding code
|
||||
- [ ] Add comprehensive docstrings
|
||||
- [ ] Run linting and formatting
|
||||
- [ ] Ensure Python 3.9 compatibility
|
||||
|
||||
### [ ] Performance & Polish
|
||||
- [ ] Optimize embedding generation performance
|
||||
- [ ] Add progress indicators for batch operations
|
||||
- [ ] Improve error messages and user feedback
|
||||
- [ ] Add verbose mode support
|
||||
|
||||
## Success Criteria ✅
|
||||
|
||||
### Functional Requirements
|
||||
- [ ] `mlxk embed -m "model" -c "text"` generates embeddings
|
||||
- [ ] `mlxk embed -m "model" --input-file file.txt` processes file input
|
||||
- [ ] `mlxk embed-multi` handles batch processing
|
||||
- [ ] `POST /v1/embeddings` returns OpenAI-compatible JSON
|
||||
- [ ] Both CLI and server use same core logic
|
||||
- [ ] All embedding models work correctly
|
||||
|
||||
### Quality Requirements
|
||||
- [ ] 100% test coverage for new code
|
||||
- [ ] Integration with existing error handling
|
||||
- [ ] Follows established code patterns
|
||||
- [ ] Comprehensive documentation
|
||||
- [ ] Performance acceptable for typical use cases
|
||||
|
||||
### Compatibility Requirements
|
||||
- [ ] OpenAI embedding API compatibility verified
|
||||
- [ ] Works with common MLX embedding models
|
||||
- [ ] Integrates cleanly with existing codebase
|
||||
- [ ] Maintains backwards compatibility
|
||||
|
||||
## Implementation Notes
|
||||
|
||||
### Architecture Decisions
|
||||
- **Shared Core**: `embed_model_core()` used by both CLI and server
|
||||
- **Model Resolution**: Reuse existing `resolve_single_model()` pattern
|
||||
- **Error Handling**: Follow existing server and CLI error patterns
|
||||
- **Testing**: Use existing test infrastructure and patterns
|
||||
|
||||
### Key Files to Modify
|
||||
- `mlx_knife/embedding_utils.py` (new)
|
||||
- `mlx_knife/cache_utils.py` (add embed_model function)
|
||||
- `mlx_knife/cli.py` (add embed subcommands)
|
||||
- `mlx_knife/server.py` (add /v1/embeddings endpoint)
|
||||
- Various test files (new and existing)
|
||||
|
||||
### Dependencies
|
||||
- MLX framework for embedding generation
|
||||
- Existing model loading and resolution logic
|
||||
- FastAPI for server endpoint
|
||||
- Pydantic for request/response models
|
||||
|
||||
**Estimated Implementation Time**: 4-6 hours following established patterns
|
||||
@@ -0,0 +1,137 @@
|
||||
# Issue #26 Summary: Embeddings Endpoint Implementation
|
||||
|
||||
## Issue Overview
|
||||
**Title**: Add `/v1/embeddings` endpoint for OpenAI-compatible embedding generation
|
||||
**Type**: Feature Request
|
||||
**Status**: Open
|
||||
**Complexity**: Medium (4-6 hours estimated)
|
||||
|
||||
## Original Issue Description
|
||||
|
||||
### Core Requirements
|
||||
Add a new `/v1/embeddings` endpoint to MLX-Knife's server that provides stateless embedding generation for previously pulled MLX models.
|
||||
|
||||
### Key Design Principles
|
||||
- **Stateless Operation**: No vector database, no memory, no intelligent model auto-selection
|
||||
- **OpenAI Compatibility**: Standard JSON response format matching OpenAI embeddings API
|
||||
- **Context-Free Server**: Simple load-model-and-return-vectors operation
|
||||
- **User Responsibility**: Client manages model selection, vector storage, and reindexing
|
||||
|
||||
### Endpoint Specification
|
||||
```
|
||||
POST /v1/embeddings
|
||||
```
|
||||
|
||||
#### Request Parameters
|
||||
- `model` (required): Name of the embedding model to use
|
||||
- `input` (required): String or array of strings to embed
|
||||
- `encoding_format` (optional): Response format - "float" or "base64"
|
||||
- `normalize` (optional): Whether to normalize embeddings (default: true)
|
||||
- `max_length` (optional): Maximum input length limit
|
||||
|
||||
#### Response Format
|
||||
Standard OpenAI-compatible JSON structure:
|
||||
```json
|
||||
{
|
||||
"object": "list",
|
||||
"data": [
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": 0,
|
||||
"embedding": [0.1, 0.2, 0.3, ...]
|
||||
}
|
||||
],
|
||||
"model": "model-name",
|
||||
"usage": {
|
||||
"prompt_tokens": 10,
|
||||
"total_tokens": 10
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Use Cases
|
||||
- **Agent Frameworks**: Integration with AI agent systems requiring embeddings
|
||||
- **RAG Pipelines**: Retrieval-Augmented Generation implementations
|
||||
- **External Clients**: Third-party tools needing embedding generation
|
||||
- **Semantic Search**: Applications requiring text similarity matching
|
||||
|
||||
### Boundaries & Limitations
|
||||
- **No Persistence**: Server doesn't store or remember embeddings
|
||||
- **No Auto-Selection**: User must specify exact model name
|
||||
- **No Quality Assurance**: User responsible for model appropriateness
|
||||
- **Single Response**: Always returns complete JSON (non-streaming)
|
||||
|
||||
## Follow-Up Comment: CLI Integration
|
||||
|
||||
### Additional CLI Requirement
|
||||
The original author added a follow-up comment requesting a complementary CLI subcommand alongside the server endpoint:
|
||||
|
||||
```bash
|
||||
mlxk embed <MODEL> --input "text content"
|
||||
```
|
||||
|
||||
### CLI Specifications
|
||||
- **Non-Streaming**: Always returns complete JSON response
|
||||
- **Input Options**: Support both `--input "text"` and `--input-file path/to/file`
|
||||
- **OpenAI-Compatible Output**: Same JSON structure as server endpoint
|
||||
- **Separation of Concerns**: Keep `mlxk run` command for generative models only
|
||||
|
||||
### CLI Use Cases
|
||||
- **Development Testing**: Quick embedding generation during development
|
||||
- **Batch Processing**: File-based embedding generation
|
||||
- **Scripting**: Integration with shell scripts and automation
|
||||
- **Local Processing**: Offline embedding generation without server
|
||||
|
||||
## Technical Implementation Strategy
|
||||
|
||||
### Architecture Pattern
|
||||
Follow the existing `run` command architecture:
|
||||
- **Shared Core**: `embed_model_core()` function used by both CLI and server
|
||||
- **CLI Wrapper**: `embed_model()` in `cache_utils.py` (similar to `run_model()`)
|
||||
- **Server Endpoint**: `/v1/embeddings` route (similar to `/v1/chat/completions`)
|
||||
|
||||
### Reusable Components
|
||||
- `resolve_single_model()` for model path resolution
|
||||
- `detect_framework()` for MLX compatibility checking
|
||||
- `get_or_load_model()` for server-side model caching
|
||||
- Existing error handling and response patterns
|
||||
|
||||
### File Structure
|
||||
- `mlx_knife/embedding_utils.py` - Core embedding logic
|
||||
- `mlx_knife/cache_utils.py` - CLI wrapper function
|
||||
- `mlx_knife/cli.py` - CLI command definitions
|
||||
- `mlx_knife/server.py` - REST endpoint implementation
|
||||
|
||||
## Expected Benefits
|
||||
|
||||
### For Users
|
||||
- **Unified Interface**: Consistent embedding access via CLI and API
|
||||
- **OpenAI Compatibility**: Drop-in replacement for OpenAI embedding API
|
||||
- **Local Processing**: No external API dependencies for embedding generation
|
||||
- **Model Flexibility**: Use any compatible MLX embedding model
|
||||
|
||||
### For Ecosystem
|
||||
- **Integration Ready**: Standard API for external tool integration
|
||||
- **Development Friendly**: Easy testing and experimentation via CLI
|
||||
- **Stateless Design**: Scalable and predictable behavior
|
||||
- **Performance**: Direct MLX backend without additional abstraction layers
|
||||
|
||||
## Compatibility Considerations
|
||||
|
||||
### MLX Framework
|
||||
- Requires MLX-compatible embedding models
|
||||
- Leverages existing MLX model loading infrastructure
|
||||
- Benefits from MLX performance optimizations
|
||||
|
||||
### OpenAI API
|
||||
- Request/response format matches OpenAI embeddings API
|
||||
- Parameter names and behavior consistent with OpenAI
|
||||
- Easy migration from OpenAI to local MLX-Knife
|
||||
|
||||
### Existing Codebase
|
||||
- Follows established architectural patterns
|
||||
- Reuses existing model resolution and error handling
|
||||
- Maintains separation between generative (`run`) and embedding functionality
|
||||
|
||||
## Implementation Priority
|
||||
**Medium Priority** - Valuable feature that extends MLX-Knife's capabilities without disrupting existing functionality. The stateless design and reuse of existing patterns makes this a relatively low-risk addition with clear user benefits.
|
||||
Reference in New Issue
Block a user