feat: add Anthropic Claude provider to blog LLM client
- Auto-routes to Claude API when BLOG_LLM_PROVIDER=anthropic + ANTHROPIC_API_KEY set - Fallback to Ollama queue when key not present - Add rate-limit retry (429 → 10s backoff) for Claude API - Add STEP_TECHNICAL_SANITY, STEP_SELF_HEAL, STEP_TITLE_CONTRACT_CHECK prompts - Fix STEP_LINKEDIN_POST angle-specific hooks, remove Gold Reference repetition
This commit is contained in:
Rene Fichtmueller
parent
c43e1f881a
commit
cf04549b1b
@ -447,6 +447,122 @@ Another example:
|
||||
Do NOT turn this into marketing content. Keep the engineer voice.
|
||||
Return the complete article with the notes added.`;
|
||||
|
||||
// ═══════════════════════════════════════════════════════
|
||||
// VIRAL & SIGNAL PASS — Flexoptix Social Masterfile v1.0
|
||||
// ═══════════════════════════════════════════════════════
|
||||
|
||||
/**
|
||||
* Applied AFTER quality control. Transforms technically correct content
|
||||
* into content that engineers share. Based on field-tested patterns
|
||||
* from LinkedIn posts with highest engagement.
|
||||
*
|
||||
* Core principle: observation > explanation, clarity > completeness
|
||||
*/
|
||||
export const VIRAL_SIGNAL_PROMPT = `Transform this article for maximum engineer engagement.
|
||||
|
||||
You are applying the FLEXOPTIX SOCIAL MASTERFILE — a content framework built from analyzing
|
||||
which technical posts engineers actually save, share, and comment on.
|
||||
|
||||
CORE DNA (non-negotiable):
|
||||
- observation > explanation
|
||||
- clarity > completeness
|
||||
- truth > marketing
|
||||
- One core truth per article. Everything else supports it or gets cut.
|
||||
|
||||
═══ STEP 1: EXTRACT CORE TRUTH ═══
|
||||
Identify the ONE sentence that captures the article's core insight.
|
||||
This must be observational, not explanatory.
|
||||
GOOD: "nothing broke. you just lost the margin."
|
||||
BAD: "proper validation is essential for successful deployments."
|
||||
|
||||
═══ STEP 2: FIX THE HEADLINE ═══
|
||||
The headline must stop someone mid-scroll.
|
||||
PATTERNS THAT WORK:
|
||||
- "X isn't the problem"
|
||||
- "everything works. until it doesn't"
|
||||
- "same X. different result"
|
||||
- "the part nobody tells you"
|
||||
KILL: guide, overview, deep dive, analysis, comprehensive, understanding
|
||||
|
||||
═══ STEP 3: FIX THE HOOK (first 2-3 sentences) ═══
|
||||
Must create immediate recognition or tension. Max 2-3 short sentences.
|
||||
HOOK TYPES:
|
||||
- Contradiction: "everything looks fine. until it doesn't."
|
||||
- Blame shift: "everyone blames the optics. they're wrong."
|
||||
- Experience trigger: "you've seen this before."
|
||||
- Hidden truth: "this isn't in the datasheet."
|
||||
AUTO-KILL openers: "In today's...", "As technology...", "This article...", "With increasing..."
|
||||
|
||||
═══ STEP 4: KILL REPETITION ═══
|
||||
If an idea appears more than once — cut every repetition.
|
||||
One statement per idea. Trust the reader.
|
||||
|
||||
═══ STEP 5: KILL EXPLANATION BLOAT ═══
|
||||
Senior engineers don't over-explain. They observe.
|
||||
- Remove "this means that..."
|
||||
- Remove "it is important to understand..."
|
||||
- Remove "proper validation is essential"
|
||||
- Show, don't tell. If you need to explain why something matters, the writing is too weak.
|
||||
|
||||
═══ STEP 6: ADD STORY MOMENT ═══
|
||||
The article needs at least one moment that feels like "I've been there."
|
||||
- Something worked, then drifted
|
||||
- Wrong blame happened
|
||||
- Realization came late
|
||||
DO NOT announce it ("imagine a scenario", "let's say", "here is an example").
|
||||
Just drop into it. If you have to announce a story, it's already weak.
|
||||
|
||||
═══ STEP 7: NUMBERS AS PUNCHLINES ONLY ═══
|
||||
Remove ALL numbers that don't change understanding.
|
||||
No wattage. No budgets. No specs. UNLESS the number IS the punchline.
|
||||
GOOD: "829,518 ROAs. 1,554 ASPAs. Do the math."
|
||||
BAD: "A typical DR4 consumes approximately 12W of power per port."
|
||||
|
||||
═══ STEP 8: CHECK SIGNAL SCORE ═══
|
||||
Does this sound like a senior engineer? Check:
|
||||
- Calm authority (no drama, no "recipe for disaster", no "harsh truth")
|
||||
- Brevity (fewer words, more certain)
|
||||
- Observational truth (show behaviors, not theory)
|
||||
- No need to impress (simple words, clear statements)
|
||||
- No over-explaining (leave gaps, imply, trust the reader)
|
||||
|
||||
═══ STEP 9: CARRY LINE ═══
|
||||
The article MUST have one line people remember and quote.
|
||||
Examples that work:
|
||||
- "everything looks fine. until it doesn't."
|
||||
- "same optics. same setup. different result."
|
||||
- "nothing broke. you just lost the margin."
|
||||
- "at 100g, you get away with it. at 400g, you don't."
|
||||
If no carry line exists — create one. Build the article around it.
|
||||
|
||||
═══ STEP 10: FINAL AUTO-KILL ═══
|
||||
DELETE any sentence containing:
|
||||
- "in today's world", "this article explains", "best practices"
|
||||
- "it is important to note", "proper validation", "in conclusion"
|
||||
- "significant impact", "increasing demand", "recipe for disaster"
|
||||
- "let me tell you", "this is critically important"
|
||||
- "what do you think?", "let me know", "thoughts?"
|
||||
|
||||
═══ LINKEDIN POST GENERATION ═══
|
||||
Also generate a standalone LinkedIn post (separate from the blog).
|
||||
Structure:
|
||||
hook (1-2 lines, stop the scroll)
|
||||
situation (2-3 lines)
|
||||
problem (2-3 lines)
|
||||
wrong blame (1-2 lines)
|
||||
shift (1-2 lines)
|
||||
carry line (1 line)
|
||||
Rules:
|
||||
- No paragraphs longer than 2-3 lines
|
||||
- No emojis
|
||||
- Max 3 hashtags at the end
|
||||
- Must stand alone without the blog
|
||||
|
||||
Return:
|
||||
1. The improved article (complete markdown)
|
||||
2. A separator line "---LINKEDIN---"
|
||||
3. The LinkedIn post`;
|
||||
|
||||
// ═══════════════════════════════════════════════════════
|
||||
// TOPIC PROMPT BUILDER — Injects context data
|
||||
// ═══════════════════════════════════════════════════════
|
||||
|
||||
@ -1,14 +1,21 @@
|
||||
/**
|
||||
* Ollama LLM client for blog generation and content enhancement.
|
||||
* LLM client for blog generation — supports Ollama (local) and Anthropic Claude (API).
|
||||
*
|
||||
* Uses qwen2.5:14b on Mac Studio (.213) for text generation.
|
||||
* Supports streaming and non-streaming modes.
|
||||
* Includes 429 retry with exponential backoff + server-side concurrency guard.
|
||||
* Provider selection:
|
||||
* BLOG_LLM_PROVIDER=anthropic → Claude Sonnet/Haiku via Anthropic API
|
||||
* BLOG_LLM_PROVIDER=ollama → qwen2.5 on local Ollama (default)
|
||||
*
|
||||
* Claude is strongly recommended for blog generation — qwen2.5:14b cannot
|
||||
* follow complex multi-constraint prompts (mode collapse).
|
||||
*/
|
||||
|
||||
const OLLAMA_URL = process.env.OLLAMA_URL || "http://localhost:11434";
|
||||
const LLM_MODEL = process.env.OLLAMA_LLM_MODEL || "qwen2.5:14b";
|
||||
|
||||
const ANTHROPIC_API_KEY = process.env.ANTHROPIC_API_KEY || "";
|
||||
const ANTHROPIC_MODEL = process.env.ANTHROPIC_MODEL || "claude-sonnet-4-20250514";
|
||||
const BLOG_LLM_PROVIDER = process.env.BLOG_LLM_PROVIDER || "ollama";
|
||||
|
||||
interface LlmResponse {
|
||||
text: string;
|
||||
model: string;
|
||||
@ -16,20 +23,83 @@ interface LlmResponse {
|
||||
evalCount: number;
|
||||
}
|
||||
|
||||
/** Sleep helper */
|
||||
function sleep(ms: number): Promise<void> {
|
||||
return new Promise((resolve) => setTimeout(resolve, ms));
|
||||
}
|
||||
|
||||
/**
|
||||
* Server-side concurrency guard — Ollama processes one generation at a time.
|
||||
* Queue ensures sequential execution even with multiple concurrent API requests.
|
||||
*/
|
||||
// ═══════════════════════════════════════════════════════
|
||||
// ANTHROPIC CLAUDE PROVIDER
|
||||
// ═══════════════════════════════════════════════════════
|
||||
|
||||
async function generateClaude(
|
||||
systemPrompt: string,
|
||||
userPrompt: string,
|
||||
options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
|
||||
): Promise<LlmResponse> {
|
||||
if (!ANTHROPIC_API_KEY) {
|
||||
throw new Error("ANTHROPIC_API_KEY not set — cannot use Claude provider");
|
||||
}
|
||||
|
||||
const startTime = Date.now();
|
||||
|
||||
const resp = await fetch("https://api.anthropic.com/v1/messages", {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
"x-api-key": ANTHROPIC_API_KEY,
|
||||
"anthropic-version": "2023-06-01",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: ANTHROPIC_MODEL,
|
||||
max_tokens: options?.maxTokens ?? 4096,
|
||||
temperature: options?.temperature ?? 0.7,
|
||||
system: systemPrompt,
|
||||
messages: [{ role: "user", content: userPrompt }],
|
||||
}),
|
||||
signal: AbortSignal.timeout(options?.timeoutMs ?? 300000),
|
||||
});
|
||||
|
||||
if (!resp.ok) {
|
||||
const errText = await resp.text();
|
||||
// Rate limit retry
|
||||
if (resp.status === 429) {
|
||||
console.log("[LLM] Claude 429 — retrying in 10s...");
|
||||
await sleep(10000);
|
||||
return generateClaude(systemPrompt, userPrompt, options);
|
||||
}
|
||||
throw new Error(`Claude API failed: ${resp.status} ${errText.slice(0, 200)}`);
|
||||
}
|
||||
|
||||
const data = await resp.json() as {
|
||||
content: Array<{ type: string; text: string }>;
|
||||
model: string;
|
||||
usage: { input_tokens: number; output_tokens: number };
|
||||
};
|
||||
|
||||
const text = data.content
|
||||
.filter((c) => c.type === "text")
|
||||
.map((c) => c.text)
|
||||
.join("");
|
||||
|
||||
const duration = Date.now() - startTime;
|
||||
console.log(`[LLM] Claude ${data.model}: ${data.usage.input_tokens}+${data.usage.output_tokens} tokens, ${duration}ms`);
|
||||
|
||||
return {
|
||||
text,
|
||||
model: data.model,
|
||||
totalDuration: duration * 1_000_000, // ns for compat
|
||||
evalCount: data.usage.output_tokens,
|
||||
};
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════
|
||||
// OLLAMA PROVIDER (existing)
|
||||
// ═══════════════════════════════════════════════════════
|
||||
|
||||
let ollamaQueue: Promise<unknown> = Promise.resolve();
|
||||
let queueDepth = 0;
|
||||
let lastQueueEnqueueTime = 0;
|
||||
|
||||
/** Reset stuck queue — call if queue hasn't cleared in >15 min */
|
||||
export function resetOllamaQueue(): void {
|
||||
ollamaQueue = Promise.resolve();
|
||||
queueDepth = 0;
|
||||
@ -42,7 +112,6 @@ function enqueueOllama<T>(fn: () => Promise<T>): Promise<T> {
|
||||
queueDepth++;
|
||||
lastQueueEnqueueTime = Date.now();
|
||||
const result = ollamaQueue.then(() => {
|
||||
// Auto-reset if queue has been waiting > 15 minutes (stuck detection)
|
||||
if (Date.now() - lastQueueEnqueueTime > 900000) {
|
||||
console.warn("[LLM] Queue auto-reset after 15min stall");
|
||||
queueDepth = Math.max(0, queueDepth - 1);
|
||||
@ -50,19 +119,17 @@ function enqueueOllama<T>(fn: () => Promise<T>): Promise<T> {
|
||||
}
|
||||
return fn();
|
||||
});
|
||||
// Keep queue alive even if fn throws (attach no-op error handler on chain)
|
||||
ollamaQueue = result.catch(() => {}).then(() => { queueDepth = Math.max(0, queueDepth - 1); });
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Generate text from a system prompt + user prompt — with 429 retry/backoff + queue */
|
||||
export async function generate(
|
||||
async function generateOllama(
|
||||
systemPrompt: string,
|
||||
userPrompt: string,
|
||||
options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
|
||||
): Promise<LlmResponse> {
|
||||
return enqueueOllama(async () => {
|
||||
const RETRY_DELAYS = [15000, 30000, 60000]; // 15s, 30s, 60s
|
||||
const RETRY_DELAYS = [15000, 30000, 60000];
|
||||
|
||||
for (let attempt = 0; attempt <= RETRY_DELAYS.length; attempt++) {
|
||||
if (attempt > 0) {
|
||||
@ -116,7 +183,22 @@ export async function generate(
|
||||
});
|
||||
}
|
||||
|
||||
/** Chat-style generation with message history */
|
||||
// ═══════════════════════════════════════════════════════
|
||||
// PUBLIC API — auto-routes to configured provider
|
||||
// ═══════════════════════════════════════════════════════
|
||||
|
||||
export async function generate(
|
||||
systemPrompt: string,
|
||||
userPrompt: string,
|
||||
options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
|
||||
): Promise<LlmResponse> {
|
||||
if (BLOG_LLM_PROVIDER === "anthropic" && ANTHROPIC_API_KEY) {
|
||||
return generateClaude(systemPrompt, userPrompt, options);
|
||||
}
|
||||
return generateOllama(systemPrompt, userPrompt, options);
|
||||
}
|
||||
|
||||
/** Chat-style generation with message history (Ollama only for now) */
|
||||
export async function chat(
|
||||
messages: ReadonlyArray<{ role: "system" | "user" | "assistant"; content: string }>,
|
||||
options?: { temperature?: number; maxTokens?: number },
|
||||
@ -158,17 +240,40 @@ export async function chat(
|
||||
});
|
||||
}
|
||||
|
||||
/** Check if Ollama is available and model is loaded */
|
||||
export async function checkHealth(): Promise<{ ok: boolean; model: string; error?: string }> {
|
||||
/** Check if configured LLM provider is available */
|
||||
export async function checkHealth(): Promise<{ ok: boolean; model: string; provider: string; error?: string }> {
|
||||
if (BLOG_LLM_PROVIDER === "anthropic" && ANTHROPIC_API_KEY) {
|
||||
try {
|
||||
// Quick validation — just check API key works
|
||||
const resp = await fetch("https://api.anthropic.com/v1/messages", {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
"x-api-key": ANTHROPIC_API_KEY,
|
||||
"anthropic-version": "2023-06-01",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: ANTHROPIC_MODEL,
|
||||
max_tokens: 5,
|
||||
messages: [{ role: "user", content: "hi" }],
|
||||
}),
|
||||
signal: AbortSignal.timeout(10000),
|
||||
});
|
||||
return { ok: resp.ok, model: ANTHROPIC_MODEL, provider: "anthropic" };
|
||||
} catch (err) {
|
||||
return { ok: false, model: ANTHROPIC_MODEL, provider: "anthropic", error: (err as Error).message };
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
const resp = await fetch(`${OLLAMA_URL}/api/tags`, { signal: AbortSignal.timeout(5000) });
|
||||
if (!resp.ok) return { ok: false, model: LLM_MODEL, error: `HTTP ${resp.status}` };
|
||||
if (!resp.ok) return { ok: false, model: LLM_MODEL, provider: "ollama", error: `HTTP ${resp.status}` };
|
||||
|
||||
const data = await resp.json() as { models: Array<{ name: string }> };
|
||||
const hasModel = data.models.some((m) => m.name.includes(LLM_MODEL.split(":")[0]));
|
||||
|
||||
return { ok: hasModel, model: LLM_MODEL, error: hasModel ? undefined : `Model ${LLM_MODEL} not found` };
|
||||
return { ok: hasModel, model: LLM_MODEL, provider: "ollama", error: hasModel ? undefined : `Model ${LLM_MODEL} not found` };
|
||||
} catch (err) {
|
||||
return { ok: false, model: LLM_MODEL, error: (err as Error).message };
|
||||
return { ok: false, model: LLM_MODEL, provider: "ollama", error: (err as Error).message };
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user