init

.opencode/skills/ai-artist/scripts/core.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+AI Artist Core - BM25 search engine for prompt engineering resources
+"""
+
+import csv
+import re
+from pathlib import Path
+from math import log
+from collections import defaultdict
+
+# ============ CONFIGURATION ============
+DATA_DIR = Path(__file__).parent.parent / "data"
+MAX_RESULTS = 3
+
+CSV_CONFIG = {
+    "use-case": {
+        "file": "use-cases.csv",
+        "search_cols": ["Use Case", "Category", "Keywords", "Best Platforms"],
+        "output_cols": ["Use Case", "Category", "Keywords", "Prompt Template", "Key Elements", "Best Platforms", "Aspect Ratios", "Tips", "Example"]
+    },
+    "style": {
+        "file": "styles.csv",
+        "search_cols": ["Style Name", "Category", "Keywords", "Description", "Best For"],
+        "output_cols": ["Style Name", "Category", "Description", "Key Characteristics", "Color Palette", "Best For", "Platforms", "Prompt Keywords"]
+    },
+    "platform": {
+        "file": "platforms.csv",
+        "search_cols": ["Platform", "Type", "Keywords", "Strengths"],
+        "output_cols": ["Platform", "Type", "Prompt Style", "Key Parameters", "Strengths", "Limitations", "Aspect Ratios", "Best Practices"]
+    },
+    "technique": {
+        "file": "techniques.csv",
+        "search_cols": ["Technique", "Category", "Keywords", "Description", "When to Use"],
+        "output_cols": ["Technique", "Category", "Description", "When to Use", "Syntax Example", "Platforms", "Tips"]
+    },
+    "lighting": {
+        "file": "lighting.csv",
+        "search_cols": ["Lighting Type", "Category", "Keywords", "Description", "Mood", "Best For"],
+        "output_cols": ["Lighting Type", "Category", "Description", "Mood", "Best For", "Prompt Keywords", "Technical Notes"]
+    },
+    "template": {
+        "file": "nano-banana-templates.csv",
+        "search_cols": ["Category", "Template Name", "Keywords"],
+        "output_cols": ["Category", "Template Name", "Keywords", "Prompt Template", "Aspect Ratio", "Tips"]
+    },
+    "awesome": {
+        "file": "awesome-prompts.csv",
+        "search_cols": ["title", "description", "prompt"],
+        "output_cols": ["id", "title", "category", "description", "prompt", "author", "source"]
+    }
+}
+
+
+# ============ BM25 IMPLEMENTATION ============
+class BM25:
+    """BM25 ranking algorithm for text search"""
+
+    def __init__(self, k1=1.5, b=0.75):
+        self.k1 = k1
+        self.b = b
+        self.corpus = []
+        self.doc_lengths = []
+        self.avgdl = 0
+        self.idf = {}
+        self.doc_freqs = defaultdict(int)
+        self.N = 0
+
+    def tokenize(self, text):
+        """Lowercase, split, remove punctuation, filter short words"""
+        text = re.sub(r'[^\w\s]', ' ', str(text).lower())
+        return [w for w in text.split() if len(w) > 2]
+
+    def fit(self, documents):
+        """Build BM25 index from documents"""
+        self.corpus = [self.tokenize(doc) for doc in documents]
+        self.N = len(self.corpus)
+        if self.N == 0:
+            return
+        self.doc_lengths = [len(doc) for doc in self.corpus]
+        self.avgdl = sum(self.doc_lengths) / self.N
+
+        for doc in self.corpus:
+            seen = set()
+            for word in doc:
+                if word not in seen:
+                    self.doc_freqs[word] += 1
+                    seen.add(word)
+
+        for word, freq in self.doc_freqs.items():
+            self.idf[word] = log((self.N - freq + 0.5) / (freq + 0.5) + 1)
+
+    def score(self, query):
+        """Score all documents against query"""
+        query_tokens = self.tokenize(query)
+        scores = []
+
+        for idx, doc in enumerate(self.corpus):
+            score = 0
+            doc_len = self.doc_lengths[idx]
+            term_freqs = defaultdict(int)
+            for word in doc:
+                term_freqs[word] += 1
+
+            for token in query_tokens:
+                if token in self.idf:
+                    tf = term_freqs[token]
+                    idf = self.idf[token]
+                    numerator = tf * (self.k1 + 1)
+                    denominator = tf + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl)
+                    score += idf * numerator / denominator
+
+            scores.append((idx, score))
+
+        return sorted(scores, key=lambda x: x[1], reverse=True)
+
+
+# ============ SEARCH FUNCTIONS ============
+def _load_csv(filepath):
+    """Load CSV and return list of dicts"""
+    with open(filepath, 'r', encoding='utf-8') as f:
+        return list(csv.DictReader(f))
+
+
+def _search_csv(filepath, search_cols, output_cols, query, max_results):
+    """Core search function using BM25"""
+    if not filepath.exists():
+        return []
+
+    data = _load_csv(filepath)
+
+    # Build documents from search columns
+    documents = [" ".join(str(row.get(col, "")) for col in search_cols) for row in data]
+
+    # BM25 search
+    bm25 = BM25()
+    bm25.fit(documents)
+    ranked = bm25.score(query)
+
+    # Get top results with score > 0
+    results = []
+    for idx, score in ranked[:max_results]:
+        if score > 0:
+            row = data[idx]
+            results.append({col: row.get(col, "") for col in output_cols if col in row})
+
+    return results
+
+
+def detect_domain(query):
+    """Auto-detect the most relevant domain from query"""
+    query_lower = query.lower()
+
+    domain_keywords = {
+        "use-case": ["avatar", "profile", "thumbnail", "poster", "social", "youtube", "instagram", "marketing", "product", "e-commerce", "infographic", "comic", "game", "app", "web", "header", "banner"],
+        "style": ["style", "aesthetic", "photorealistic", "anime", "manga", "3d", "render", "illustration", "pixel", "watercolor", "oil", "cyberpunk", "vaporwave", "minimalist", "vintage", "retro"],
+        "platform": ["midjourney", "dalle", "dall-e", "stable diffusion", "flux", "nano banana", "gemini", "imagen", "ideogram", "leonardo", "firefly", "platform", "tool"],
+        "technique": ["prompt", "technique", "weight", "emphasis", "negative", "json", "structured", "iteration", "reference", "identity", "multi-panel", "search grounding"],
+        "lighting": ["lighting", "light", "shadow", "golden hour", "blue hour", "rembrandt", "butterfly", "neon", "volumetric", "softbox", "rim light", "studio"]
+    }
+
+    scores = {domain: sum(1 for kw in keywords if kw in query_lower) for domain, keywords in domain_keywords.items()}
+    best = max(scores, key=scores.get)
+    return best if scores[best] > 0 else "style"
+
+
+def search(query, domain=None, max_results=MAX_RESULTS):
+    """Main search function with auto-domain detection"""
+    if domain is None:
+        domain = detect_domain(query)
+
+    config = CSV_CONFIG.get(domain, CSV_CONFIG["style"])
+    filepath = DATA_DIR / config["file"]
+
+    if not filepath.exists():
+        return {"error": f"File not found: {filepath}", "domain": domain}
+
+    results = _search_csv(filepath, config["search_cols"], config["output_cols"], query, max_results)
+
+    return {
+        "domain": domain,
+        "query": query,
+        "file": config["file"],
+        "count": len(results),
+        "results": results
+    }
+
+
+def search_all_domains(query, max_per_domain=2):
+    """Search across all domains for comprehensive results"""
+    all_results = {}
+    for domain in CSV_CONFIG.keys():
+        result = search(query, domain, max_per_domain)
+        if result.get("count", 0) > 0:
+            all_results[domain] = result
+    return all_results