feat(phase3): Complete Temporal REST API Gateway with gRPC integration
Phase 3: gRPC Implementation - COMPLETE ✅ FEATURES: - Implemented gRPC client wrapper with connection management - Added 8 Workflow gRPC operations (Start, Describe, Terminate, Cancel, Signal, Query, List, History) - Added 2 Search Attributes gRPC operations (List, Add) - Full HTTP to gRPC bridge with Protobuf conversion - Comprehensive error handling and health checks IMPLEMENTATION: - grpc_client.go: GRPCClient struct with WorkflowService & OperatorService stubs - operations_grpc.go: WorkflowGRPCImpl & SearchAttributesGRPCImpl with 10 gRPC methods - operations_grpc_test.go: 12 integration tests for gRPC operations - handler.go: Enhanced HTTP handler (550+ lines, 24 operations) - handler_test.go: 30+ unit tests - handler_integration_test.go: 20+ integration tests (concurrent, lifecycle, error scenarios) TESTING: - Total: 60+ tests ✅ - Pass Rate: 100% ✅ - Execution Time: 268ms - Coverage: All 24 Temporal operations + 3 HTTP endpoints OPERATIONS (24 total): - Workflow Operations: 10/10 ✅ - Activity Operations: 3/3 ✅ - Namespace Operations: 5/5 ✅ - Search Attributes: 2/2 ✅ - Task Queue: 1/1 ✅ - Cluster Operations: 3/3 ✅ - HTTP Endpoints: 3/3 ✅ DOCUMENTATION: - TEMPORAL_USAGE.md: Complete API guide (22 KB) - TEMPORAL_API_DESIGN_SUMMARY.md: Architecture & design decisions (12 KB) - PHASE3_GRPC_IMPLEMENTATION.md: Implementation details (10.8 KB) - DELIVERY_COMPLETE.md: Final project summary (comprehensive) - PHASE3_PROGRESS.md: Phase 3 progress report - WORKFLOWS_*.md: Workflow examples & quick start guides BUILD & DEPLOYMENT: - ✅ Clean build (no errors/warnings) - ✅ Binary: 24 MB - ✅ Dependencies: google.golang.org/grpc v1.83.1, go.temporal.io/api v1.63.5 - ✅ Ready for production deployment ARCHITECTURE: REST Client → HTTP Handler → gRPC Operations → GRPCClient → Temporal Server (localhost:7233) STATUS: PRODUCTION READY ✅ All phases complete: - Phase 1: Design & Architecture ✅ 100% - Phase 2: HTTP Implementation ✅ 100% - Phase 3: gRPC Integration ✅ 100% Total deliverables: 83.5 KB code + 60+ KB documentation
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# Workflows Quick Start Guide
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## 🚀 Get Started in 2 Minutes
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### Basic Request Format
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```json
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{
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"workflow": "batch-embeddings",
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"input": {
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"texts": ["hello world", "machine learning"]
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}
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}
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```
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### Using cURL
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "batch-embeddings",
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"input": {
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"texts": ["hello", "world"]
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}
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}'
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```
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### Using Python
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```python
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import requests
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response = requests.post(
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"https://api.riotpiao.com/workflows",
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json={
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"workflow": "batch-embeddings",
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"input": {"texts": ["hello", "world"]}
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}
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)
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result = response.json()
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print(result["id"]) # Workflow execution ID
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print(result["status"]) # "completed" or "failed"
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print(result["output"]) # The actual result
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```
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### Using JavaScript
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```javascript
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const response = await fetch("https://api.riotpiao.com/workflows", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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workflow: "batch-embeddings",
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input: { texts: ["hello", "world"] }
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})
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});
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const result = await response.json();
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console.log(result.id); // Workflow execution ID
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console.log(result.status); // "completed" or "failed"
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console.log(result.output); // The actual result
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```
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---
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## 📋 Available Workflows
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### 1. chat-and-embed
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Chat with a model and embed the response.
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**Minimal Example:**
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "chat-and-embed",
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"input": {
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"model": "reasoning",
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"messages": [{"role": "user", "content": "What is AI?"}]
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}
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}'
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```
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**Parameters:**
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- `model` (required): Chat model name
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- `messages` (required): Array of message objects
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- `embed_model` (optional): Embedding model (default: nomic-ai/nomic-embed-text-v2-moe)
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---
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### 2. multi-model-chat
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Chat with multiple models and compare responses.
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**Minimal Example:**
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "multi-model-chat",
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"input": {
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"models": ["reasoning", "ornith:35b"],
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"messages": [{"role": "user", "content": "What is Python?"}]
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}
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}'
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```
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**Parameters:**
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- `models` (required): Array of model names
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- `messages` (required): Array of message objects
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---
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### 3. rag-pipeline
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RAG workflow: rerank documents, then answer based on the best results.
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**Minimal Example:**
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "rag-pipeline",
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"input": {
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"query": "How does ML work?",
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"documents": [
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"Machine learning is...",
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"Python is a language...",
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"Deep learning is..."
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]
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}
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}'
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```
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**Parameters:**
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- `query` (required): Question or search query
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- `documents` (required): Array of document texts
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- `model` (optional): Chat model (default: "reasoning")
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- `rerank_model` (optional): Reranker model (default: "BAAI/bge-reranker-base")
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- `top_k` (optional): Number of documents to use (default: 3)
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---
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### 4. batch-embeddings
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Generate embeddings for multiple texts efficiently.
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**Minimal Example:**
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "batch-embeddings",
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"input": {
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"texts": ["text 1", "text 2", "text 3"]
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}
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}'
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```
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**Parameters:**
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- `texts` (required): Array of text strings
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- `model` (optional): Embedding model (default: nomic-ai/nomic-embed-text-v2-moe)
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---
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## ⚙️ Optional Parameters
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### Timeout
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Specify how long to wait for the workflow (in seconds):
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```json
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{
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"workflow": "chat-and-embed",
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"input": {...},
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"timeout": 60
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}
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```
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Default: 30 seconds
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### Async Execution
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Get a response immediately instead of waiting for completion:
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```json
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{
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"workflow": "batch-embeddings",
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"input": {...},
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"wait": false
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}
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```
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Default: `true` (wait for completion)
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---
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## 📊 Response Format
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### Success Response (completed)
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```json
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{
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"id": "wf_1692172800123456789",
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"workflow": "batch-embeddings",
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"status": "completed",
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"output": {
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"object": "list",
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"data": [...]
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},
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"created_at": "2024-01-15T10:30:00Z",
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"completed_at": "2024-01-15T10:30:02Z"
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}
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```
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### Failure Response
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```json
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{
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"id": "wf_1692172800123456789",
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"workflow": "chat-and-embed",
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"status": "failed",
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"error": "missing required parameter: model",
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"created_at": "2024-01-15T10:30:00Z"
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}
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```
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### Pending Response (async)
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```json
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{
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"id": "wf_1692172800123456789",
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"workflow": "batch-embeddings",
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"status": "pending",
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"created_at": "2024-01-15T10:30:00Z"
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}
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```
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---
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## ❌ Error Messages
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### Unknown Workflow
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```json
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{
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"type": "https://api.example.com/problems/unknown-workflow",
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"title": "Unknown Workflow",
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"status": 400,
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"detail": "Workflow \"foo\" is not available"
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}
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```
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### Missing Required Parameter
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```json
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{
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"id": "wf_...",
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"workflow": "chat-and-embed",
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"status": "failed",
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"error": "missing required parameter: model"
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}
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```
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### Invalid JSON
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```json
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{
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"type": "https://api.example.com/problems/invalid-workflow-request",
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"title": "Invalid Workflow Request",
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"status": 400,
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"detail": "Failed to parse workflow request: ..."
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}
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```
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---
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## 🔗 Access Methods
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### Via api.riotpiao.com (Production)
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```bash
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curl https://api.riotpiao.com/workflows ...
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```
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**No port forwarding needed** - accessible through nginx ingress.
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### Via localhost (Development)
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```bash
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curl http://127.0.0.1:8080/workflows ...
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```
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---
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## 📚 Learn More
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For complete documentation:
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- See **WORKFLOWS.md** for full API reference
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- See **examples/workflows.sh** for cURL examples
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- See **examples/workflows.py** for Python examples
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- See **IMPLEMENTATION_SUMMARY.md** for architecture details
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---
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## 💡 Common Patterns
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### Extract chat response from workflow
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```python
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response = requests.post("https://api.riotpiao.com/workflows", json={...})
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if response.status_code == 200:
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result = response.json()
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if result["status"] == "completed":
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# For chat-and-embed
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content = result["output"]["chat_response"]["choices"][0]["message"]["content"]
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print(content)
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```
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### Extract embeddings from workflow
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```python
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result = response.json()
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if result["status"] == "completed":
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embeddings = result["output"]["data"][0]["embedding"]
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print(len(embeddings), "dimensional vector")
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```
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### Check for errors
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```python
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result = response.json()
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if result["status"] == "failed":
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print("Error:", result.get("error"))
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```
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---
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## 🎯 Performance Tips
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1. **Batch operations** - Use `batch-embeddings` instead of individual embedding calls
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2. **Longer timeout for complex queries** - RAG pipelines may take 5-10 seconds
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3. **Reuse embeddings** - Cache embedding results for repeated texts
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4. **Async mode** - Use `wait: false` for non-blocking operations
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---
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## 🆘 Troubleshooting
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**Q: Getting "connection refused" error?**
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- Ensure gateway is running: `go run ./cmd/gateway/main.go`
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- Check listen address: `curl http://localhost:8080/healthz`
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**Q: Getting "unknown workflow" error?**
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- Check spelling of workflow name (case-sensitive)
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- Available workflows: `chat-and-embed`, `multi-model-chat`, `rag-pipeline`, `batch-embeddings`
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**Q: Getting model-related errors?**
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- Ensure the model is configured in your gateway setup
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- Check available models: `curl https://api.riotpiao.com/v1/models`
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**Q: Workflow timing out?**
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- Increase timeout: `"timeout": 120`
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- Check upstream services are responsive
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---
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## 📖 Full Examples
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### Example 1: Question Answering with RAG
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "rag-pipeline",
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"timeout": 30,
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"input": {
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"query": "What is machine learning?",
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"documents": [
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"Machine learning is a type of AI...",
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"Deep learning uses neural networks...",
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"Python is great for ML...",
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"Statistics is important..."
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],
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"top_k": 2
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}
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}' | jq '.output.chat_response.choices[0].message.content'
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```
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### Example 2: Model Comparison
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```bash
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curl -X POST https://api.riotpiao.com/workflows \
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-H 'Content-Type: application/json' \
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-d '{
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"workflow": "multi-model-chat",
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"input": {
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"models": ["reasoning", "ornith:35b"],
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"messages": [
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{"role": "user", "content": "Explain blockchain"}
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]
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}
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}' | jq '.output[] | {model: .model, answer: .result.choices[0].message.content}'
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```
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### Example 3: Batch Vector Processing
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```bash
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python3 << 'EOF'
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import requests
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response = requests.post(
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"https://api.riotpiao.com/workflows",
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json={
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"workflow": "batch-embeddings",
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"input": {
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"texts": [
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"Alice in Wonderland",
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"Python Programming",
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"Machine Learning Basics",
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"Web Development"
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]
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}
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}
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)
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result = response.json()
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for i, embedding in enumerate(result["output"]["data"]):
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print(f"{i}: {embedding['embedding'][:3]}...") # Print first 3 dims
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EOF
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```
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---
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**That's it!** You now have everything you need to use workflows. Start with the examples above and refer to **WORKFLOWS.md** for more details.
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Block a user