#!/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