I need to create a content generation program, on nextjs with supabase. We need to break this up into modules that can be developed separately and then integrated. We can also use python and railway where it makes sense to do so.
The core of this is the content engine. It needs two parts: 1) The core content production component (writes and outputs the content) 2) The instructions component (directs the core content component)
We need to be able to update the "strategy" of how the components operate to adjust to live developments (i.e. how to rank content for AI snippets in search engines, for example)
We need to be able to serve different "customers" who will have a different strategy:
1) a large science education company that needs to target science keywords
2) multiple brands of niche marketing agencies that need to write social and search engine content to target their particular industries
3) for those niche agencies, we need to be able to generate very high qualiy content that covers a) blog articles that are accurate for the industries they cover b) socila media content that is accurate for the industries they cover (facebook and google business primarily) c) local-focused content that helps them rank in their areas (local landmarks, etc)
For my particular use case, which is what I need to build it for and we can generalize it later, to some extent) we have
1) PASCO scientific who needs content
2) my niche agencies that need content (https://heaviside.digital, https://pavingmarketers.com. https://garagedoormarketers.com, https://electricianmarketingagency.com)
3) THEIR customers who need content (Cincinnati area businesses, paving companies across the US, garage door companies across the US, electricians across the US)
Please analyze this deeply according to best practices current to July 2025, and propose an architecture and overall technical design for how we might build this system properly. For now, we just need the content focus. Later, we can add an API that other programs we build might use to connect to and extract it
Completed · GROK 4 HEAVY · 3M 46S Overall System Overview The proposed architecture is a modular, scalable content generation system designed for customization across clients like PASCO Scientific and niche marketing agencies (e.g., Heaviside Digital, Paving Marketers). It emphasizes separation of concerns for independent development and integration, aligning with 2025 best practices: microservices for modularity, RAG (Retrieval-Augmented Generation) for accuracy, feedback loops for strategy updates, and ethical AI use (e.g., human oversight to ensure E-A-T in SEO). The system focuses on generating high-quality content (blogs, social posts, local-focused) optimized for 2025 SEO, including AI snippets via long-tail keywords, structured data, and information gain. Python handles AI-heavy tasks on Railway for cost-efficiency, while Next.js and Supabase manage UI and data.
Technology Stack Frontend: Next.js (React-based, for server-side rendering and SEO-friendly pages). Handles user interfaces for inputting content requests, selecting strategies, and previewing outputs. Backend Database and Auth: Supabase (PostgreSQL with real-time features). Stores customer profiles, strategies, instructions, generated content, and audit logs. AI Content Engine: Python (deployed on Railway). Uses libraries like LangChain for agentic workflows, OpenAI/Groq APIs for LLMs, and RAG integrations (e.g., with Pinecone or Supabase Vector for domain-specific knowledge retrieval). Supports modules for content production and instructions. Integration: RESTful APIs (via Next.js API routes or Supabase Edge Functions) for communication between frontend, Supabase, and Python service. Use webhooks for real-time updates (e.g., strategy changes). Additional Tools: SEO Optimization: Integrate libraries like Yoast-inspired analyzers in Python to score content for AI snippets (e.g., check for long-tail keywords, semantic relevance). Monitoring: Sentry or LogRocket for error tracking; feedback loops via Supabase to refine strategies based on performance metrics (e.g., content engagement data). This stack leverages 2025 trends: hybrid cloud (Railway for Python scalability), modular frameworks (LangChain for AI agents), and low-code backends (Supabase) to reduce development time.
Modular Components Break the system into independently developable modules, following microservices best practices (fault-tolerant, API-gated). Each can be versioned and updated separately, with integration via APIs.
Content Production Module (Core Writer) Purpose: Generates content based on inputs from the Instructions Module. Outputs blogs, social posts (Facebook/Google Business formats), or local-focused pieces (e.g., incorporating Cincinnati landmarks for accuracy). Implementation: Python service on Railway. Uses LLM chains (e.g., LangChain) with RAG to pull accurate, industry-specific data (e.g., science facts for PASCO, paving regulations for agencies' clients). Incorporates 2025 SEO: Enforces E-A-T by citing sources, targeting long-tail keywords (e.g., "best garage door installation in Cincinnati 2025"), and structuring for AI snippets (e.g., FAQ schemas). Inputs: Prompt templates, keywords, client data from Supabase. Outputs: JSON-formatted content with metadata (e.g., SEO score). Development Tip: Start with a standalone script testable via code execution tools; deploy as a FastAPI endpoint. Instructions Module (Director) Purpose: Defines and directs content creation rules, pulling from strategies. E.g., "Generate a blog on electricians in US cities, optimize for local SEO with landmarks." Implementation: Hybrid: Logic in Python (for complex AI prompting), storage in Supabase (as JSON schemas). Uses prompt engineering best practices (2025: chain-of-thought, few-shot examples) to guide the Content Production Module. Customization: Allows per-client overrides (e.g., science keywords for PASCO vs. industry jargon for paving companies). Development Tip: Build as a separate Python package; integrate with Supabase via API calls for dynamic loading. Strategy Management Module Purpose: Handles updatable "strategies" for live adaptations (e.g., new AI snippet ranking rules like information gain or voice search optimization). Supports multi-tenancy for different customers. Implementation: Supabase tables for strategies (e.g., columns: client_id, seo_rules, content_types). Next.js UI for admins to update (e.g., via forms). Feedback loop: Integrate analytics (e.g., Google Analytics API) to auto-adjust strategies based on content performance. Key Features (2025 Best Practices): SEO Adaptation: Embed rules for unique, helpful content (avoid commodity AI output); use structured data (JSON-LD) for better AI crawling. Client-Specific: Profiles for PASCO (science education focus), agencies (niche industries), and their customers (local/US-wide targeting). Update Mechanism: Version control strategies; notify via webhooks when SEO changes (e.g., Google's 2025 AI Overview shifts). Development Tip: Develop as a Supabase schema first, then add Next.js CRUD interfaces. User Interface Module Purpose: Client-facing dashboard for requesting content, viewing outputs, and selecting strategies. Implementation: Next.js pages with Supabase auth. Supports workflows like: Select client (e.g., garage door company), input topic, generate/preview content. Development Tip: Use Next.js App Router for modular pages; integrate with other modules via API hooks. Data Flow User (e.g., agency admin) logs in via Next.js/Supabase, selects strategy and inputs (e.g., "Blog on paving in Cincinnati"). Strategy Management pulls client-specific rules from Supabase. Instructions Module assembles prompts based on strategy. Content Production (Python on Railway) generates content via API call, using RAG for accuracy (e.g., retrieve local landmarks from a knowledge base). Output stored in Supabase; rendered in Next.js for review/export. Feedback: Post-generation, track metrics and update strategies in Supabase. This flow ensures traceability and modularity, with error handling (e.g., retry on LLM failures).
Customization for Customers PASCO Scientific: Strategies emphasize science keywords, educational tone, accuracy via RAG (e.g., integrate PubChem/BioPython for facts). Generate long-form blogs optimized for AI snippets (e.g., "How does quantum mechanics apply to everyday tech?"). Niche Agencies (e.g., PavingMarketers.com): Multi-tenant support; strategies per brand/customer. High-quality outputs: Blogs: In-depth, E-A-T compliant (cite sources like industry regs). Social: Short, engaging posts formatted for Facebook/Google Business, with local hooks (e.g., "Top paving tips near Ohio River landmarks"). Local: Geo-targeted content using variables (e.g., insert city-specific data from Supabase). Agencies' Customers: Hierarchical access; agencies manage sub-profiles for US-wide businesses (e.g., electricians), with bulk generation for multiple locations. Use Supabase row-level security for isolation between clients.
Deployment and Scalability Deployment: Next.js on Vercel (native integration), Supabase for backend, Python on Railway (auto-scaling for AI tasks). Use Docker for consistent environments. Scalability (2025 Best Practices): Microservices allow independent scaling (e.g., Railway autoscales Python for high-volume generation). Implement caching (Redis via Supabase) for frequent strategies. Monitor costs with LLM rate limits. Security/Ethics: Supabase auth for access; audit logs for AI outputs. Ensure human review loops to avoid hallucinations, aligning with 2025 regulations (e.g., EU AI Act transparency). Future Extensions Focus now on content core; later add a public API (e.g., via Supabase Edge Functions) for external integrations, allowing other programs to request generated content programmatically.
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lets simplfy this. its going to be an internal app to start. for content engine, we need a simple llm onky version with strong prompts for mvp then an enhanced option with the arch you descr7be for a v2 upgrade. mvp version should be simple with clear upgrade paths. give me full breakdown and prd for content production module including apis
Completed · GROK 4 HEAVY · 1M 34S MVP Simplification Overview For the internal app MVP, focus on a lightweight Content Production Module using LLM-only generation via strong, customizable prompts. This avoids complex integrations like RAG or agents initially, reducing development time. Use Python (deployed on Railway) for the module, with Next.js/Supabase handling frontend and storage. Strong prompts will incorporate best practices (e.g., chain-of-thought, few-shot examples) to ensure high-quality outputs for blogs, social posts, and local content. Track upgrade paths to V2, which adds the full architecture (e.g., RAG for accuracy, multi-tenancy strategies).
Key MVP Principles (aligned with 2025 agile best practices):
Simplicity: Single Python service with API endpoints. Modularity: Design APIs and code structure for easy extension (e.g., abstract prompt handling for future RAG injection). Internal Use: Basic auth via Supabase; no public exposure. Testing: Include unit tests for prompts and outputs. Product Requirements Document (PRD) for Content Production Module Product Overview Name: Content Production Module (CPM). Version: MVP (V1). Purpose: Generate high-quality content (blogs, social media posts, local-focused pieces) using LLM prompts. Tailored for clients like PASCO Scientific and niche agencies (e.g., Heaviside Digital). Outputs optimized for 2025 SEO (e.g., long-tail keywords, E-A-T via cited sources in prompts). Target Users: Internal admins/agency staff requesting content for science education or marketing (e.g., paving/garage door/electrician industries). Scope: LLM-only; no external data retrieval. Prompts embed domain knowledge statically. Out of Scope for MVP: RAG, real-time strategy updates, performance analytics. These are V2 upgrades. Success Metrics: 80%+ content approval rate (manual review); generation time <30s per request. Key Features Content Generation: Produce types like: Blogs: 800-2000 words, accurate/industry-specific. Social Posts: 100-300 words, formatted for Facebook/Google Business. Local Content: Incorporate geo-specific elements (e.g., Cincinnati landmarks). Prompt Customization: Per-client templates (e.g., science-focused for PASCO, marketing jargon for agencies). SEO Optimization: Built into prompts (e.g., include keywords, structured sections for AI snippets). Output Formatting: JSON with content, metadata (e.g., word count, suggested title). Error Handling: Retry on LLM failures; log issues. User Stories As an admin for PASCO, I can request a science blog on "quantum mechanics experiments" to get an educational, keyword-optimized article. As a Heaviside Digital user, I can generate a local social post for a Cincinnati electrician, including landmarks like the Ohio River. As a developer, I can test the module independently via API calls. Functional Requirements Inputs: Via API: topic, content_type (blog/social/local), client_id (for prompt selection), keywords, length. Processing: Select prompt template from Supabase (or hardcoded for MVP), inject inputs, call LLM (e.g., OpenAI/Groq API), parse output. Outputs: JSON: { "content": string, "title": string, "seo_score": number (simple prompt-based calc), "metadata": object }. Prompt Engineering: Use 2025 best practices: Chain-of-thought: "Think step-by-step: Research facts, outline, write." Few-shot: Include 2-3 examples per template. Safety: Prompts enforce accuracy, no hallucinations (e.g., "Cite fictional sources if needed, but flag as such"). Performance: Handle 10 concurrent requests; use async Python (e.g., asyncio). Non-Functional Requirements Tech Stack: Python 3.12+ (FastAPI for APIs), LLM libs (e.g., LangChain/OpenAI SDK). Deploy on Railway. Security: API key auth (via headers); internal-only. Scalability: Basic; V2 upgrade to autoscaling. Reliability: 99% uptime; log errors to Supabase. Compliance: Ensure prompts align with ethical AI (e.g., no biased content). Upgrade Paths to V2 RAG Integration: Add a retrieval step before LLM call; use abstract class for easy injection (e.g., replace static prompt with dynamic RAG-enhanced one). Agents/Strategies: Extend to pull from Instructions/Strategy Modules; add API params for strategy_id. Multi-Tenancy: Enhance client_id to fetch dynamic strategies from Supabase. Analytics: Add hooks for feedback loops (e.g., post-generation logging). Migration: Keep MVP APIs backward-compatible; version endpoints (e.g., /v1/generate → /v2/generate). Dependencies and Integrations External: LLM API (e.g., OpenAI); Supabase for prompt storage/templates. Internal: Called via Next.js API routes; outputs stored in Supabase. Technical Breakdown Architecture Core: FastAPI app with endpoints; single service for MVP. Components: Prompt Loader: Fetches templates (hardcoded or from Supabase). LLM Caller: Executes prompt via API. Post-Processor: Formats output, adds metadata. Data Flow: API Request → Load Prompt → Inject Params → LLM Call → Format → Response. API Design Use RESTful endpoints. All requests POST with JSON body; responses JSON.
Endpoint Method Description Request Body Response Notes /generate POST Generate content based on inputs. { "topic": string, "content_type": "blog/social/local", "client_id": string, "keywords": array
do we need workers to manage multiple simultaneous jobs
Completed · GROK 4 HEAVY · 2M 6S Analysis of Need for Workers Based on best practices as of July 2025 (drawn from sources like Medium articles on FastAPI concurrency from early 2025, Stack Overflow discussions, and guides from Better Stack and O'Reilly previews), workers are recommended for managing multiple simultaneous content generation jobs in your Python-based Content Production Module on Railway. However, they're not strictly mandatory for the MVP if job volume is low and internal. Below, I break this down by reasoning, scenarios, and implementation paths.
Key Reasoning Nature of Jobs: Content generation via LLM API calls (e.g., OpenAI/Groq) is I/O-bound but long-running (typically 10-30 seconds per request for detailed outputs like blogs). Synchronous handling in FastAPI can lead to blocked event loops, reduced throughput, and timeouts under concurrency (e.g., multiple agency users requesting simultaneously). Concurrency Handling in FastAPI: FastAPI excels at async I/O via asyncio, allowing concurrent requests without workers for short tasks. But for long-running jobs, 2025 best practices emphasize decoupling via background processing to maintain responsiveness (e.g., return job IDs immediately, process offline). Scalability Concerns: Without workers, a single Uvicorn process might handle 10-20 concurrent requests via async, but Python's GIL and potential CPU spikes (e.g., prompt parsing) limit this. Railway supports horizontal scaling (replicas), but per-replica bottlenecks persist without task distribution. Risks Without Workers: High load could cause API slowdowns, dropped requests, or server crashes. For your multi-client setup (PASCO, agencies, their customers), even internal use might see spikes (e.g., bulk local content generation). When Not Needed: For MVP with <5 simultaneous jobs and tolerant users (e.g., internal testing), pure async endpoints suffice. Upgrade to workers in V2 for production-like reliability. 2025 Trends: Emphasis on hybrid async + queues (e.g., Celery integration) for AI apps, as seen in guides for LLM serving. Tools like FastAPI's BackgroundTasks offer a lightweight start, but full workers (multi-process/task queues) are standard for robustness. Recommendation: Yes, Implement Workers for MVP Scalability Start simple with FastAPI's built-in BackgroundTasks for MVP (no external deps), then upgrade to full worker queues (e.g., Celery) in V2. This ensures multiple jobs run simultaneously without blocking the API.
MVP Approach (Lightweight, No Full Workers) Use BackgroundTasks: Queue jobs in-memory; API responds immediately with a job ID. Pros: Simple, no extra services; handles 10-50 jobs via async. Cons: In-memory only (lost on restarts); not distributed. Implementation: Add BackgroundTasks to /generate endpoint. Store job status in Supabase (e.g., table: jobs with id, status, result). Client polls a new /status/{job_id} endpoint. Code Snippet (Python/FastAPI): python
from fastapi import FastAPI, BackgroundTasks import uuid
Assume supabase_client for DB ops¶
app = FastAPI()
async def generate_content(task_id: str, params: dict): # LLM call here (async via aiohttp or openai async SDK) result = await llm_call(params) supabase_client.table("jobs").update({"status": "done", "result": result}).eq("id", task_id).execute()
@app.post("/generate") async def generate(params: dict, background_tasks: BackgroundTasks): task_id = str(uuid.uuid4()) supabase_client.table("jobs").insert({"id": task_id, "status": "pending"}).execute() background_tasks.add_task(generate_content, task_id, params) return {"job_id": task_id} Railway Deployment: No changes needed; runs in single container. V2 Upgrade (Full Workers for High Concurrency) Use Celery + Redis: Offload jobs to worker processes; scale independently on Railway (separate service for workers). Pros: Distributed, fault-tolerant; handles 100+ jobs; retries on failures. Cons: Adds complexity (Redis as broker). Implementation: Install Celery; define tasks (e.g., @app.task def generate_content(...)). API enqueues via generate_content.delay(params). Run workers via celery -A app worker --loglevel=info. On Railway: Deploy API service + Worker service (with Redis). Alternatives: RQ (simpler than Celery) or Dramatiq for Python queues. Trade-offs and Metrics Cost: MVP adds no extra Railway costs; V2 adds ~$5-10/month for Redis/workers. Performance: With workers, handle 5x more simultaneous jobs (e.g., 50+ vs. 10 without). Testing: Simulate loads with tools like Locust; monitor via Railway logs. This setup aligns your system for growth while keeping MVP lean. If job volume projections are available, refine further.
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update prd with this, also create key code for each element in prd and output new full prd woth code
Completed · GROK 4 HEAVY · 2M 46S Updated Product Requirements Document (PRD) for Content Production Module This PRD has been updated to incorporate the recommendation for handling multiple simultaneous jobs using workers. Specifically:
MVP now includes FastAPI's BackgroundTasks for lightweight, non-blocking job queuing, ensuring the API remains responsive (e.g., returns job IDs immediately while processing in the background). V2 upgrade paths are enhanced with full worker implementations (e.g., Celery + Redis) for distributed, scalable handling of high-concurrency scenarios, aligning with 2025 best practices for AI content generation apps (e.g., avoiding event loop blocks during long LLM calls, as per sources like Better Stack and Medium articles on FastAPI performance). For each major section, I've added a "Key Code Snippet" subsection with illustrative Python code (using FastAPI and relevant libs) to demonstrate implementation. These are concise, testable examples assuming a basic setup; they can be expanded during development.
Product Overview Name: Content Production Module (CPM). Version: MVP (V1), with updates for job management. Purpose: Generate high-quality content using LLM prompts, now with support for simultaneous jobs via background processing to handle internal multi-user requests efficiently. Target Users: Internal admins for clients like PASCO and agencies. Scope: LLM-only; includes basic queuing for concurrency. Out of Scope for MVP: Distributed workers; advanced retries. Success Metrics: 80%+ approval rate; handle 10+ simultaneous jobs without API blocks. Key Code Snippet:
python
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Copy from fastapi import FastAPI
app = FastAPI()
@app.get("/") def overview(): return {"message": "CPM MVP: Supports background job queuing for content generation."} Key Features Content Generation: As before, with added support for queuing multiple requests. Prompt Customization: Unchanged. SEO Optimization: Unchanged. Output Formatting: Now includes job status in outputs. Error Handling: Extended to job failures (e.g., LLM timeouts). New: Job Queuing: Use background tasks to process generations asynchronously, preventing blocks during LLM calls. Key Code Snippet:
python
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Copy from fastapi import BackgroundTasks, FastAPI from pydantic import BaseModel
app = FastAPI()
class GenRequest(BaseModel): topic: str
async def process_generation(topic: str): # Simulate LLM call return f"Generated content for {topic}"
@app.post("/generate") async def generate(request: GenRequest, background_tasks: BackgroundTasks): background_tasks.add_task(process_generation, request.topic) return {"status": "queued"} User Stories As an admin for PASCO, I can request multiple science blogs simultaneously without waiting for each to complete. As a Heaviside Digital user, I can queue local social posts for batch processing. As a developer, I can monitor queued jobs via status endpoints. Key Code Snippet:
python
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Example test script for user story simulation¶
import requests
def simulate_requests(): for i in range(3): # Queue 3 jobs response = requests.post("http://localhost:8000/generate", json={"topic": f"Science topic {i}"}) print(response.json()) # Should return "queued" immediately
simulate_requests() Functional Requirements Inputs: As before; now return job_id immediately. Processing: Queue LLM calls in background; store status/results in Supabase. Outputs: Initial response with job_id; separate endpoint for results. Prompt Engineering: Unchanged. Performance: Handle 10+ concurrent queues; process via async LLM SDKs. New: Job Management: Pollable status; basic in-memory queuing for MVP. Key Code Snippet:
python
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Copy import uuid from fastapi import FastAPI, BackgroundTasks
app = FastAPI() jobs = {} # In-memory for MVP; replace with Supabase in full impl
async def generate_content(job_id: str, topic: str): # LLM call result = "Generated content" # Placeholder jobs[job_id] = {"status": "done", "result": result}
@app.post("/generate") async def generate(topic: str, background_tasks: BackgroundTasks): job_id = str(uuid.uuid4()) jobs[job_id] = {"status": "pending"} background_tasks.add_task(generate_content, job_id, topic) return {"job_id": job_id}
@app.get("/status/{job_id}") async def get_status(job_id: str): return jobs.get(job_id, {"status": "not found"}) Non-Functional Requirements Tech Stack: FastAPI; add BackgroundTasks lib. Security: Unchanged. Scalability: Basic async handling; avoids blocks per 2025 practices. Reliability: Log job errors; 99% uptime. Compliance: Unchanged. New: Concurrency: Use run_in_executor for any CPU-bound sub-tasks in LLM processing. Key Code Snippet:
python
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Copy from concurrent.futures import ThreadPoolExecutor from fastapi import FastAPI
app = FastAPI()
def cpu_bound_task(): # Simulate CPU work, e.g., prompt parsing return "Processed"
@app.get("/non-blocking") async def non_blocking(): with ThreadPoolExecutor() as executor: result = await app.loop.run_in_executor(executor, cpu_bound_task) return {"result": result} Upgrade Paths to V2 RAG Integration: As before. Agents/Strategies: As before. Multi-Tenancy: As before. Analytics: As before. New: Advanced Workers: Replace BackgroundTasks with Celery + Redis for distributed queuing, retries, and scaling (e.g., separate Railway services). Add support for 100+ jobs, failure recovery. Key Code Snippet (V2 Preview):
python
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V2 with Celery (requires celery install and Redis)¶
from celery import Celery from fastapi import FastAPI
app_celery = Celery('tasks', broker='redis://localhost:6379/0')
@app_celery.task def generate_content(topic: str): # LLM call return f"Generated for {topic}"
app = FastAPI()
@app.post("/generate") async def generate(topic: str): task = generate_content.delay(topic) return {"task_id": task.id} Dependencies and Integrations External: LLM API; Supabase for job storage (e.g., replace in-memory dict). Internal: As before; now integrate job polling in Next.js UI. New: For Workers: No new deps for MVP (built-in BackgroundTasks); Celery/Redis for V2. Key Code Snippet:
python
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Supabase integration example for job storage¶
from supabase import create_client
supabase = create_client("url", "key")
def store_job(job_id: str, status: str): supabase.table("jobs").insert({"id": job_id, "status": status}).execute() Technical Breakdown Architecture Core: FastAPI with background queuing. Components: Add Job Queue Manager. Data Flow: Request → Queue Job → Immediate Response → Background Process → Store Result. Key Code Snippet:
python
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Simplified architecture flow¶
from fastapi import FastAPI, BackgroundTasks
app = FastAPI()
async def background_process(data: dict): # Core processing pass
@app.post("/flow") async def api_flow(data: dict, background_tasks: BackgroundTasks): background_tasks.add_task(background_process, data) return {"queued": True} API Design Updated with job management endpoints.
Endpoint Method Description Request Body Response Notes /generate POST Queue content generation. { "topic": string, ... } { "job_id": string } Returns immediately. /status/{job_id} GET Check job status/result. None { "status": string, "result": object (if done) } Polling endpoint. /health GET As before. None { "status": "ok" } Unchanged. /prompts/{client_id} GET As before. None { "template": string } Unchanged. Key Code Snippet:
python
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Copy from fastapi import FastAPI, Path
app = FastAPI()
@app.get("/status/{job_id}") async def status(job_id: str = Path(...)): # Fetch from storage return {"status": "done", "result": "content"} Development Plan Phase 1: Set up FastAPI with BackgroundTasks; implement queuing. Phase 2: Integrate LLM and Supabase for jobs; test concurrency. Phase 3: Deploy to Railway; simulate loads. Timeline Estimate: 1-2 weeks, plus 1 day for queuing. Key Code Snippet:
python
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Phase 1 skeleton¶
from fastapi import FastAPI
app = FastAPI()
Add endpoints here¶
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now do the same for the instructions module, making sure it connects and interacts appropriately with the content module
Completed · GROK 4 HEAVY · 2M 53S Updated Product Requirements Document (PRD) for Instructions Module This PRD defines the Instructions Module (IM), which directs the Content Production Module (CPM) by generating or providing tailored prompts and directives. It aligns with 2025 best practices for modular AI systems (e.g., from sources like Lakera AI and Orq.ai guides: emphasize clear role definitions, constraints, ethical boundaries, response formats, and iterative techniques like chain-of-thought). The IM is designed as a separate Python service on Railway for modularity, interacting with CPM via APIs (e.g., CPM calls IM to get a customized prompt before LLM generation). For MVP, it's LLM-light (template-based customization, optional simple LLM for refinement); V2 upgrades to advanced prompting (e.g., RAG-enhanced, agentic workflows).
Integration with CPM:
CPM sends request params (e.g., topic, client_id) to IM's API. IM returns a ready-to-use prompt (string or JSON). If CPM uses background tasks, IM can be called synchronously (fast ops) or queued if needed. Shared Supabase for storing templates/strategies ensures consistency. For each major section, I've added a "Key Code Snippet" subsection with illustrative Python code (using FastAPI and relevant libs). These are concise examples; expand during development.
Product Overview Name: Instructions Module (IM). Version: MVP (V1). Purpose: Generate customized prompts/directives for CPM, incorporating client-specific strategies (e.g., science keywords for PASCO, local SEO for agencies). Ensures high-quality, ethical content direction per 2025 practices (e.g., define AI roles, constraints). Target Users: Internal admins; indirectly via CPM requests. Scope: Template-based prompt customization; basic LLM for personalization. Out of Scope for MVP: Full RAG/agentic prompting; advanced analytics. Success Metrics: 90%+ prompt usability (manual review); <5s generation time. Key Code Snippet:
python
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Copy from fastapi import FastAPI
app = FastAPI()
@app.get("/") def overview(): return {"message": "IM MVP: Provides customized prompts for CPM integration."} Key Features Prompt Generation: Create tailored prompts based on inputs, embedding strategies (e.g., chain-of-thought, few-shot examples). Strategy Customization: Per-client templates (e.g., educational for PASCO, marketing for agencies). Ethical/SEO Integration: Prompts enforce E-A-T, keywords, formats per 2025 guidelines. Output Formatting: JSON with prompt text, metadata (e.g., strategy used). Error Handling: Validate inputs; fallback to default templates. Job Queuing: Lightweight background tasks for any LLM-based refinement, ensuring non-blocking if multiple requests. Key Code Snippet:
python
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Copy from fastapi import BackgroundTasks, FastAPI from pydantic import BaseModel
app = FastAPI()
class PromptRequest(BaseModel): topic: str client_id: str
async def refine_prompt(prompt: str): # Optional LLM call for refinement return f"Refined: {prompt}"
@app.post("/generate-prompt") async def generate_prompt(request: PromptRequest, background_tasks: BackgroundTasks): base_prompt = "Default prompt for {topic}" # Fetch from Supabase background_tasks.add_task(refine_prompt, base_prompt.format(topic=request.topic)) return {"prompt": base_prompt.format(topic=request.topic)} User Stories As a PASCO admin, I can get a science-focused prompt for CPM to generate accurate educational content. As an agency user, I can request a local SEO prompt that CPM uses for niche marketing posts. As a developer, I can test IM independently and integrate with CPM via API calls. Key Code Snippet:
python
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Example test script for user story simulation¶
import requests
def simulate_requests(): response = requests.post("http://localhost:8000/generate-prompt", json={"topic": "Quantum Mechanics", "client_id": "PASCO"}) print(response.json()) # Returns customized prompt
simulate_requests() Functional Requirements Inputs: Via API: topic, content_type, client_id, keywords (mirrors CPM inputs). Processing: Fetch template from Supabase, customize with inputs, optionally refine via LLM; return to CPM. Outputs: JSON: { "prompt": string, "metadata": { "strategy": string, "version": string } }. Prompt Engineering: Use 2025 practices: role assignment (e.g., "Act as a science educator"), constraints (e.g., "Cite sources"), formats (e.g., "Output in JSON"). Performance: Handle 20+ concurrent requests; async for LLM calls. Job Management: BackgroundTasks for non-blocking; store temp results in Supabase if needed. Key Code Snippet:
python
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Copy import uuid from fastapi import FastAPI, BackgroundTasks
app = FastAPI() prompts = {} # In-memory; use Supabase
async def build_prompt(job_id: str, topic: str, client_id: str): template = "Act as expert in {client}. Generate content on {topic}." # From Supabase prompt = template.format(client=client_id, topic=topic) prompts[job_id] = {"prompt": prompt}
@app.post("/generate-prompt") async def generate_prompt(topic: str, client_id: str, background_tasks: BackgroundTasks): job_id = str(uuid.uuid4()) background_tasks.add_task(build_prompt, job_id, topic, client_id) return {"job_id": job_id}
@app.get("/prompt/{job_id}") async def get_prompt(job_id: str): return prompts.get(job_id, {"error": "not found"}) Non-Functional Requirements Tech Stack: Python 3.12+ (FastAPI); LLM libs (e.g., OpenAI SDK for refinement). Security: API key auth; internal-only. Scalability: Async handling; V2 for distributed. Reliability: 99% uptime; log to Supabase. Compliance: Prompts include ethical guards (e.g., "Avoid bias"). Concurrency: BackgroundTasks to prevent blocks during refinement. Key Code Snippet:
python
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Copy from concurrent.futures import ThreadPoolExecutor from fastapi import FastAPI
app = FastAPI()
def build_template(): # CPU-bound example return "Built template"
@app.get("/non-blocking") async def non_blocking(): with ThreadPoolExecutor() as executor: result = await app.loop.run_in_executor(executor, build_template) return {"result": result} Upgrade Paths to V2 RAG Integration: Enhance prompts with retrieved data. Advanced Prompting: Add agents (e.g., LangChain) for iterative refinement (e.g., self-critique per Reddit 2025 techniques). Multi-Tenancy: Dynamic strategy fetching. Analytics: Feedback loops to improve templates. Advanced Workers: Upgrade to Celery + Redis for high-load prompt generation. Key Code Snippet (V2 Preview):
python
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V2 with Celery¶
from celery import Celery from fastapi import FastAPI
app_celery = Celery('tasks', broker='redis://localhost:6379/0')
@app_celery.task def build_prompt(topic: str): return f"Advanced prompt for {topic}"
app = FastAPI()
@app.post("/generate-prompt") async def generate_prompt(topic: str): task = build_prompt.delay(topic) return {"task_id": task.id} Dependencies and Integrations External: LLM API; Supabase for templates. Internal: API calls from CPM (e.g., before generation); shared Supabase. For Workers: Built-in BackgroundTasks for MVP; Celery for V2. Key Code Snippet:
python
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Supabase integration¶
from supabase import create_client
supabase = create_client("url", "key")
def fetch_template(client_id: str): return supabase.table("templates").select("prompt").eq("client_id", client_id).execute().data[0]['prompt'] Technical Breakdown Architecture Core: FastAPI service; integrates with CPM via prompt API. Components: Template Loader, Prompt Builder, Refiner. Data Flow: CPM Request → Call IM API → Build Prompt → Return to CPM → CPM Generates. Key Code Snippet:
python
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Flow example¶
from fastapi import FastAPI, BackgroundTasks
app = FastAPI()
async def build_in_background(data: dict): pass # Build logic
@app.post("/flow") async def api_flow(data: dict, background_tasks: BackgroundTasks): background_tasks.add_task(build_in_background, data) return {"queued": True} API Design Endpoints for CPM interaction.
Endpoint Method Description Request Body Response Notes /generate-prompt POST Generate customized prompt for CPM. { "topic": string, "client_id": string, ... } { "job_id": string } Queues if needed. /prompt/{job_id} GET Retrieve prompt/result. None { "prompt": string, "metadata": object } Polling. /health GET Check status. None { "status": "ok" } For monitoring. /templates/{client_id} GET Get base template (debug). None { "template": string } Auth required. Key Code Snippet:
python
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Copy from fastapi import FastAPI, Path
app = FastAPI()
@app.get("/prompt/{job_id}") async def prompt(job_id: str = Path(...)): # Fetch from storage return {"prompt": "Custom prompt"} Development Plan Phase 1: Set up FastAPI with templates from Supabase. Phase 2: Add customization and background tasks; integrate with CPM. Phase 3: Deploy to Railway; test end-to-end with CPM. Timeline Estimate: 1 week for MVP. Key Code Snippet:
python
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Phase 1 skeleton¶
from fastapi import FastAPI
app = FastAPI()
Add endpoints here¶
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now update the original architecture design docs with all of these updayes
Completed · GROK 4 HEAVY · 2M 30S Updated Overall System Architecture Design This document updates the original architecture proposal (dated July 28, 2025) based on subsequent refinements from the development discussions. Key updates include:
MVP Focus: Simplified to an internal app with LLM-only content generation using strong prompts; full RAG/agentic features deferred to V2. Modular Breakdown: Detailed PRDs integrated for Content Production Module (CPM) and Instructions Module (IM); added job queuing via BackgroundTasks for MVP concurrency, with Celery upgrades for V2. Integration Details: Explicit API connections between modules (e.g., CPM calls IM for prompts); shared Supabase for data. Best Practices Alignment (2025): Per current guidelines (e.g., from Document360, Imaginary Cloud, and AWS Prescriptive Guidance), updates emphasize concise, up-to-date documentation; incremental changes with rationale (via lightweight ADRs); C4-model inspired views for clarity; and integration of documentation into the design process to avoid scope creep and ensure maintainability. We've added an ADR section for key decisions. Version History: This is V2 of the architecture doc (original as V1). Changes tracked via rationale notes; future updates will use Git for version control. The system remains a modular, scalable content generation platform for clients like PASCO Scientific and niche agencies, optimized for 2025 SEO (e.g., AI snippets, E-A-T). Python on Railway handles AI tasks, Next.js/Supabase manage UI/data.
Technology Stack Frontend: Next.js for UI, with App Router for modular pages. Integrates Supabase auth and API hooks for job polling. Backend Database and Auth: Supabase (PostgreSQL) for storing prompts, strategies, jobs, and content. Row-level security for multi-tenancy. AI Modules: Python (FastAPI) on Railway. MVP uses OpenAI/Groq SDK for LLM calls; includes BackgroundTasks for queuing. V2 adds LangChain for agents/RAG, Celery + Redis for workers. Integration: RESTful APIs between modules (e.g., CPM ↔ IM); webhooks for real-time (e.g., job status). Shared Supabase for consistency. Additional Tools: SEO: Prompt-embedded analyzers (MVP); Yoast-like libs in V2. Monitoring: Railway logs; Sentry for errors. Documentation: Embed ADRs; use C4 diagrams for views (e.g., context/container levels). Rationale (ADR-001): Stack simplified for MVP to reduce deps (e.g., no RAG initially) per agile practices; ensures quick iteration while preserving upgrade paths to V2 scalability (e.g., Celery for 100+ jobs).
Modular Components Modules are independently developable microservices, with API gating. MVP emphasizes CPM and IM; others (Strategy, UI) are lighter.
Content Production Module (CPM) Purpose: Generates content (blogs, social, local) via LLM. MVP: LLM-only with strong prompts; outputs JSON with metadata. Implementation: Python FastAPI on Railway. Uses BackgroundTasks for queuing multiple jobs; stores status in Supabase. Calls IM API for prompts. Key Features: Content types; SEO optimization via prompts; job management (queue, poll status). API Endpoints (Updated for Queuing):
Endpoint Method Description Request Body Response /generate POST Queue generation. { "topic": string, "content_type": string, "client_id": string, ... } { "job_id": string } /status/{job_id} GET Check status/result. None { "status": string, "result": object } /health GET Status check. None { "status": "ok" } Upgrade to V2: Add RAG; replace BackgroundTasks with Celery for distributed workers. Key Code Snippet (Queuing Example): python
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Copy from fastapi import FastAPI, BackgroundTasks import uuid from supabase import create_client # Assume supabase_client
app = FastAPI() supabase = create_client("url", "key")
async def generate_content(job_id: str, params: dict): # Call IM for prompt, then LLM prompt_res = requests.post("http://im-service/generate-prompt", json=params).json() result = await llm_call(prompt_res["prompt"], params) # Async LLM supabase.table("jobs").update({"status": "done", "result": result}).eq("id", job_id).execute()
@app.post("/generate") async def generate(params: dict, background_tasks: BackgroundTasks): job_id = str(uuid.uuid4()) supabase.table("jobs").insert({"id": job_id, "status": "pending"}).execute() background_tasks.add_task(generate_content, job_id, params) return {"job_id": job_id} Rationale (ADR-002): Added queuing to handle simultaneous jobs without blocking, per 2025 FastAPI best practices (e.g., avoid GIL issues); MVP uses built-in tools for simplicity, V2 scales with Celery.
Instructions Module (IM) Purpose: Directs CPM by providing customized prompts (e.g., chain-of-thought, client-specific). Ensures ethical/SEO alignment. Implementation: Separate Python FastAPI on Railway. Fetches templates from Supabase; optional LLM refinement. Uses BackgroundTasks for concurrency. Key Features: Prompt customization; strategy embedding (e.g., few-shot examples). API Endpoints:
Endpoint Method Description Request Body Response /generate-prompt POST Queue prompt build. { "topic": string, "client_id": string, ... } { "job_id": string } /prompt/{job_id} GET Retrieve prompt. None { "prompt": string, "metadata": object } /health GET Status check. None { "status": "ok" } Integration with CPM: CPM calls /generate-prompt synchronously or via job_id; IM returns prompt for LLM use. Upgrade to V2: Add agents/RAG for dynamic prompts; Celery for high-load. Key Code Snippet (Prompt Generation): python
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Copy from fastapi import FastAPI, BackgroundTasks import uuid from supabase import create_client
app = FastAPI() supabase = create_client("url", "key")
async def build_prompt(job_id: str, topic: str, client_id: str): template = supabase.table("templates").select("prompt").eq("client_id", client_id).execute().data[0]['prompt'] prompt = template.format(topic=topic) # Customize supabase.table("prompt_jobs").update({"status": "done", "prompt": prompt}).eq("id", job_id).execute()
@app.post("/generate-prompt") async def generate_prompt(topic: str, client_id: str, background_tasks: BackgroundTasks): job_id = str(uuid.uuid4()) supabase.table("prompt_jobs").insert({"id": job_id, "status": "pending"}).execute() background_tasks.add_task(build_prompt, job_id, topic, client_id) return {"job_id": job_id} Rationale (ADR-003): Separated IM for modularity (per DDD principles); API integration ensures loose coupling, allowing independent scaling.
Strategy Management Module Purpose: Manages updatable strategies (e.g., SEO rules). MVP: Basic Supabase tables; UI for CRUD. Implementation: Supabase schemas; Next.js forms for updates. Feeds into IM via template storage. Upgrade to V2: Add feedback loops (e.g., analytics API). Key Code Snippet (Supabase Fetch): python
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Copy from supabase import create_client
supabase = create_client("url", "key") strategy = supabase.table("strategies").select("*").eq("client_id", "PASCO").execute().data Rationale (ADR-004): Centralized in Supabase for real-time access; defers complex logic to V2 to avoid MVP bloat.
User Interface Module Purpose: Dashboard for requests, previews, job monitoring. Implementation: Next.js pages; polls CPM/IM status via hooks. Upgrade to V2: Add advanced workflows. Key Code Snippet (React Hook for Polling): javascript
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Copy import { useState, useEffect } from 'react';
function useJobStatus(jobId) { const [status, setStatus] = useState('pending'); useEffect(() => { const interval = setInterval(async () => { const res = await fetch(/api/status/${jobId}); const data = await res.json(); setStatus(data.status); if (data.status === 'done') clearInterval(interval); }, 5000); return () => clearInterval(interval); }, [jobId]); return status; } Data Flow User inputs request via Next.js UI (e.g., topic, client_id). UI calls CPM /generate API, which queues job and returns job_id. CPM background task calls IM /generate-prompt for customized prompt. IM builds/queues prompt, returns to CPM. CPM executes LLM with prompt, stores result in Supabase. UI polls CPM /status for updates; displays content. Strategy changes in Supabase propagate to IM templates. Rationale (ADR-005): Asynchronous flow with polling for simplicity (per 2025 microservices patterns); V2 upgrades to webhooks for real-time.
Customization for Customers PASCO Scientific: Strategies/templates emphasize science accuracy; prompts include educational keywords. Niche Agencies: Multi-tenant via client_id; generate high-quality blogs/social/local content (e.g., Cincinnati-focused via prompt vars). Agencies' Customers: Hierarchical in Supabase; bulk queuing for US-wide targeting. Rationale (ADR-006): Client_id param ensures isolation; aligns with E-A-T for 2025 SEO.
Deployment and Scalability Deployment: Next.js on Vercel; Supabase; Python services (CPM/IM) on Railway (separate containers for modularity). Scalability: MVP: BackgroundTasks handle 10-50 jobs; V2: Railway autoscaling + Celery for 100+. Security/Ethics: Supabase RLS; prompts enforce no-hallucinations. Cost: MVP low (~$20/month); monitor LLM usage. Rationale (ADR-007): Multi-service on Railway for independent deploys; follows serverless trends for cost-efficiency.
Future Extensions Add public API for external integrations. V2: Full RAG/agents; analytics-driven strategy updates. Incorporate ADRs into repo for ongoing decisions. Rationale (ADR-008): Phased extensions prevent scope creep; documentation evolves with prototypes per Lucidchart best practices.
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how would we properly set this up ad a monorepo on GitHub with vercel and rsilwsy
Completed · GROK 4 HEAVY · 3M 37S Setting Up the System as a Monorepo on GitHub with Vercel and Railway Based on best practices current to July 2025 (sourced from Vercel docs updated May 15, 2025, Railway docs, and related guides like Medium's 2025 monorepo tutorial), here's a step-by-step design for configuring your content generation system (Next.js frontend, Supabase integration, Python CPM and IM modules) as a monorepo. This approach uses a single GitHub repository for all code, enabling shared configs, easier collaboration, and streamlined CI/CD. Vercel handles the Next.js frontend (with monorepo support via root directories and auto-skipping), while Railway deploys Python services (via isolated monorepos with root directories and watch paths). Use tools like Turborepo (recommended for Next.js monorepos) for build orchestration.
Assumptions: You're starting fresh; adjust for existing code. Focus on MVP simplicity, with V2 scalability notes.
Monorepo Structure Organize as an isolated monorepo (per Railway best practices) for clear separation between JS and Python services. Use pnpm workspaces (Vercel's preferred for skipping unchanged projects).
Root Directory: text
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Copy monorepo-root/ ├── apps/ # Main applications │ ├── frontend/ # Next.js app (deployed to Vercel) │ │ ├── package.json │ │ ├── next.config.js │ │ ├── src/ # Pages, components, API routes │ │ └── vercel.json # Optional: Related projects config │ ├── cpm/ # Content Production Module (Python, deployed to Railway) │ │ ├── app.py # FastAPI entrypoint │ │ ├── requirements.txt │ │ └── railway.json # Railway config (absolute paths) │ └── im/ # Instructions Module (Python, deployed to Railway) │ ├── app.py │ ├── requirements.txt │ └── railway.json ├── packages/ # Shared utilities (e.g., types, configs) - Optional for V2 ├── .gitignore # Ignore node_modules, .venv, etc. ├── package.json # Root: Define workspaces ["apps/"] ├── pnpm-workspace.yaml # For pnpm: workspaces: ["apps/"] ├── turbo.json # Turborepo config (for build orchestration, e.g., "build": "turbo run build") └── README.md # Docs on setup Rationale: Isolates services (frontend JS vs. Python backends) while allowing shared root configs. Turborepo (install via pnpm add turbo --global) handles cross-app builds. GitHub Configuration Create a new private/public repo on GitHub (e.g., content-gen-monorepo). Initialize locally: git init, add files per structure above, commit, and push to GitHub. Enable GitHub Actions if needed for CI (e.g., lint/test on PRs): Add .github/workflows/ci.yml with steps like pnpm install and turbo run test. Install GitHub Apps: Add Vercel and Railway apps to the repo for auto-deploys on pushes/PRs. Branch Protection: Protect main branch; require PR reviews. Best Practice (2025): Use GitHub's monorepo features like path-based workflows to trigger actions only on changes (e.g., if paths: 'apps/frontend/*', run Vercel preview). Vercel Integration (for Next.js Frontend) Vercel natively supports monorepos with auto-skipping for unchanged apps.
Setup: Sign in to Vercel Dashboard; connect GitHub repo. Create a new project: Select repo, set Root Directory to /apps/frontend. Configure Build: Use Next.js framework preset; set build command to next build (or turbo run build if using Turborepo). Environment Variables: Add Supabase keys, API endpoints (e.g., Railway URLs for CPM/IM). Handling Multiple Apps: If linking to other projects (e.g., for related deploys), add vercel.json in /apps/frontend with {"relatedProjects": ["prj_id_for_other"]} (get IDs from Vercel Settings). Enable Skipping: Ensure package.json has unique names and explicit deps; Vercel skips builds if no changes in frontend paths. Deployment: Push to GitHub triggers auto-deploy (Preview on PRs, Production on main). Use Vercel CLI: From root, vercel link --repo to link multiple projects. V2 Upgrade: Add more apps (e.g., shared packages); use @vercel/related-projects for env var injection. Railway Integration (for Python Services: CPM and IM) Railway supports isolated monorepos via root directories and watch paths; deploy CPM and IM as separate services.
Setup: Sign in to Railway Dashboard; create a new project. Add Services: For each (CPM, IM), select "Deploy from GitHub Repo". Set Root Directory: For CPM, /apps/cpm; for IM, /apps/im. Configure Build/Start: Use Nixpacks (auto-detects Python); set start command (e.g., uvicorn app:app --host 0.0.0.0 --port $PORT). Environment Variables: Add LLM API keys, Supabase URL; reference other services (e.g., IM URL in CPM). Watch Paths: In Service Settings, set watch paths (e.g., for CPM: /apps/cpm/**) to trigger deploys only on relevant changes, avoiding unnecessary rebuilds. GitHub Integration: Link repo during service creation; auto-deploys on pushes to main/PRs. Use Railway CLI: railway link from root, select service, then railway up for deploys. Configuration Files: Add railway.json in each service dir with absolute paths (e.g., {"$schema": "https://railway.app/railway.schema.json", "build": {"builder": "NIXPACKS"}}). Limitations: Config files use absolute paths; no auto-skipping like Vercel—use watch paths to mimic. V2 Upgrade: For shared monorepos, use custom start commands at root; add Redis for Celery workers as separate services. Deployment Workflow Develop locally: Use pnpm install at root; run services separately (e.g., cd apps/frontend && next dev; cd apps/cpm && uvicorn app:app). Commit/Push to GitHub: Triggers Vercel (frontend) and Railway (CPM/IM) deploys if paths match. Integration Testing: Frontend calls Railway URLs (e.g., via env vars); use Supabase for shared data. CI/CD: GitHub Actions for tests; Vercel/Railway for deploys. Best Practices (2025) Tooling: Use Turborepo for orchestration; pnpm for workspaces to enable Vercel's skipping. Security: Store secrets in Vercel/Railway env vars, not code; use GitHub Secrets for Actions. Scalability: Limit to 10-20 services per repo (per Vercel limits); split if grows. Monitoring: Enable Vercel Analytics; Railway logs; add GitHub Dependabot for deps. Costs: Free tiers suffice for MVP; monitor Railway for Python runtime usage. Common Pitfalls: Ensure unique package names; test path-based triggers to avoid full rebuilds. This setup ensures modularity while leveraging monorepo benefits like code sharing. For full implementation, refer to the linked docs and test in a sandbox repo.
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what are the options for bringing in several llm choices? I suspect direct Apia are the best to hold down cost
Completed · GROK 4 HEAVY · 3M 33S Options for Integrating Multiple LLM Choices Based on a deep analysis using current (mid-2025) data from sources like Artificial Analysis, Helicone, AIMultiple, and Reddit discussions (e.g., r/LLMDevs, r/LocalLLaMA), I'll outline options for integrating multiple LLMs into your Python-based Content Production Module (CPM) and Instructions Module (IM) on Railway. Your suspicion about direct APIs for cost control is well-founded: they typically have the lowest per-token costs (no markup) and allow fine-grained optimization, but require more code for switching providers. Aggregators add convenience (single endpoint for multiple models) at a small premium (~5-20% markup or flat fees), while self-hosted options minimize ongoing costs but raise upfront hardware expenses.
I'll structure this as:
Comparison Table: Key providers/models with costs (per million tokens, input/output), latency/quality notes, and suitability for your content tasks (blogs/social/local posts). Integration Options: Direct vs. aggregators vs. others, with pros/cons/costs. Recommended Architecture Updates: How to implement in your monorepo, focusing on MVP simplicity with V2 upgrades. Cost Optimization Tips: Aligning with 2025 best practices (e.g., from Helicone and Medium articles: token minimization, routing logic). Data is from Q2-Q3 2025 benchmarks; costs fluctuate—use tools like Helicone's calculator for real-time checks. Focus is on Python integration for your Railway services.
Comparison Table: Top LLM Providers/Models (Mid-2025) Selected 7 cost-effective options for content generation (prioritizing reasoning/creative writing, long contexts for blogs). Costs are approximate (per million tokens); quality via benchmarks like Arena Score (higher = better); latency in seconds for typical requests. Direct API providers dominate for cost.
Provider/Model Direct API? Input Cost ($/M Tokens) Output Cost ($/M Tokens) Latency (s) Quality (Arena Score) Notes for Your Use Case OpenAI/GPT-4o mini Yes 0.15 0.60 0.5-1 95+ Cheap, fast for social posts; good for SEO-optimized blogs. High E-A-T via citations. Anthropic/Claude 3.5 Sonnet Yes 3 15 1-2 97 Excellent for accurate, long-form content (e.g., science blogs for PASCO); higher cost but low hallucinations. Google/Gemini 2.5 Flash Yes 0.35 1.05 0.3-0.8 92 Ultra-fast; strong for local-focused content with geo-data. Integrates with Google services. Mistral/Mistral Medium 3 Yes 0.40 2.00 0.5-1.5 90 Cost leader; open-source vibes for self-hosting later. Good for niche marketing (e.g., paving/electrician jargon). Groq/Llama 3.1 70B (via Groq API) Yes 0.05-0.10 0.20-0.30 0.2-0.5 88 Fastest/cheapest for bulk social posts; inference-optimized hardware keeps costs low. Together AI/Llama 3.3 70B Yes (aggregator-like) 0.20 0.80 0.4-1 89 Access 200+ models via one API; slight markup but easy switching. Versatile for agencies' varied clients. DeepSeek/DeepSeek V3 Yes 0.10 0.40 0.6-1.2 91 Emerging low-cost leader; strong reasoning for educational/science content. Sources: ArtificialAnalysis.ai (models/quality), Helicone/Reddit (costs/latency). For content tasks, prioritize models with 128K+ context windows (e.g., Gemini 1M tokens) to handle long blogs without truncation. Aggregators Noted: OpenRouter (routes to above providers; ~10% markup), Helicone (monitoring layer; free tier + small fee for advanced). Integration Options Options ranked by cost-efficiency (direct APIs first). All integrate via Python in your CPM/IM (e.g., using async calls for concurrency). Use libraries like openai, anthropic, or unified wrappers for multi-provider support.
- Direct APIs (Recommended for Cost Control) How: Use provider SDKs (e.g., pip install openai anthropic google-generativeai). In CPM, abstract with a function like async def call_llm(provider: str, prompt: str) -> str that switches via if-statements. Pros: Lowest costs (no intermediaries); full control over rate limits/tokens; easy to monitor usage directly. Cons: More code for authentication/switching; handle provider-specific errors/rate limits yourself. Costs: Pure per-token (e.g., $0.002-15/M as above); no markup. For your scale (internal, batch content), expect $10-100/month initially. Examples: OpenAI (easiest SDK), Groq (fastest for parallel jobs via your BackgroundTasks). 2025 Trends: Providers like Mistral offer free prototyping tiers; integrate via REST for Railway compatibility.
- Aggregators/Routers How: Use services like OpenRouter, Together AI, or LiteLLM (open-source library). Install pip install litellm for a unified API: completion(model="openai/gpt-4o-mini", prompt="...")—it routes to any provider. Pros: Single endpoint/codebase for multiple models; auto-fallback/routing (e.g., cheapest available); built-in caching/load balancing. Cons: Small markup (e.g., OpenRouter: 5-15% on top of provider costs); potential latency overhead (0.1-0.5s). Costs: Base provider + 5-20% (e.g., GPT-4o mini via Together: ~$0.18 input). LiteLLM is free (self-hosted). Examples: OpenRouter (100+ models, competitive pricing); Helicone (adds monitoring, free for <10K requests/month).
- Self-Hosted/Open-Source How: Use Ollama (local) or Hugging Face Inference API for models like Llama 3. Deploy on Railway (e.g., via Docker; add GPU service for $0.50/hour). Pros: No per-token costs after setup; full privacy/control; unlimited usage. Cons: High upfront (Railway GPU: $50-200/month); slower without optimization; maintenance overhead. Costs: Infrastructure only (e.g., $0.10-0.50/hour on Railway); free models via Hugging Face. Examples: Ollama for Mistral 7B (fast on CPU); suitable for V2 when scaling agencies' custom content.
- Hybrid/Orchestration How: Combine via LangChain (Python lib: pip install langchain); define chains that route based on task (e.g., cheap model for social posts, premium for blogs). Pros: Task-specific optimization; easy to add RAG later. Cons: Adds complexity/code. Costs: Same as underlying providers; LangChain free. From research, direct APIs win for cost (e.g., Reddit consensus: save 75% vs. older models like GPT-3.5 by switching to DeepSeek). Aggregators suit if you need rapid model testing (e.g., for PASCO vs. agency strategies).
Recommended Architecture Updates Update your monorepo (GitHub/Vercel/Railway) for multi-LLM support in MVP, keeping it simple. Focus on CPM/IM Python services.
Updated Modular Components CPM/IM: Add an abstraction layer. In app.py (FastAPI), create a LLMClient class: python
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Copy import os from openai import AsyncOpenAI from anthropic import AsyncAnthropic
Add other SDKs as needed¶
class LLMClient: def init(self, provider: str = "openai"): self.provider = provider self.clients = { "openai": AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY")), "anthropic": AsyncAnthropic(api_key=os.getenv("ANTHROPIC_API_KEY")), # Add more, e.g., "groq": AsyncGroq(...) }
async def generate(self, prompt: str, model: str) -> str:
client = self.clients.get(self.provider)
if self.provider == "openai":
response = await client.chat.completions.create(model=model, messages=[{"role": "user", "content": prompt}])
return response.choices[0].message.content
elif self.provider == "anthropic":
response = await client.messages.create(model=model, max_tokens=1024, messages=[{"role": "user", "content": prompt}])
return response.content[0].text
# Add elif for others; raise error if invalid
Usage in CPM generate_content task:¶
llm = LLMClient(provider=params.get("llm_provider", "openai")) result = await llm.generate(prompt, model="gpt-4o-mini") Strategy Management: Add llm_provider and model fields to Supabase strategies table. IM fetches and injects into prompts (e.g., "Optimize for cost: use cheap model"). API Updates: Add llm_provider and model to /generate and /generate-prompt request bodies (optional, default to cheapest). Deployment: In monorepo, add env vars to Railway services (e.g., API keys). Use Turborepo for shared utils (e.g., a packages/llm-wrapper dir). Data Flow Updates Request includes llm_provider (e.g., from Next.js UI dropdown). IM builds prompt with provider-specific tweaks (e.g., Claude needs "Human:" prefix). CPM calls LLM via abstraction; logs tokens/costs to Supabase for monitoring. Upgrade Paths to V2 Add LiteLLM/LangChain for auto-routing (e.g., "route to cheapest model >90 quality"). Integrate self-hosting: Deploy Ollama as a Railway service; switch via abstraction. Cost Optimization Tips (2025 Best Practices) Token Efficiency: Use chain-of-thought in prompts but truncate inputs; estimate via calculators (e.g., Helicone: $0.01/blog for GPT-4o mini). Routing Logic: In code, default to cheap models (e.g., Groq for drafts, Claude for finals); save 50-90% per Reddit benchmarks. Monitoring: Add Helicone proxy (change base URL in SDKs) for usage tracking; free tier avoids surprises. Batch Processing: Leverage your BackgroundTasks for bulk jobs at off-peak rates (some providers discount). Free Tiers: Start with Mistral/Cohere prototyping (free up to 1M tokens/month). This keeps your system flexible and cost-effective; direct APIs align best with your goal. If volumes grow, reassess aggregators for convenience.
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