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refactor: extract analysis prompts from content-analyzer (224/115 lines)

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Move system prompt, user prompt, and performance context builders into
PRAutoBlogger_Analysis_Prompts. Analyzer retains orchestration, data
fetch, parsing, and storage. Backward-compatible private proxies kept.
Both files well under 300-line limit.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
peptiderepo
2026-04-17 02:54:33 +07:00
parent 8de7b7f68a
commit dd10b43aab
2 changed files with 155 additions and 123 deletions
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163
includes/core/class-content-analyzer.php
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@@ -7,14 +7,16 @@ declare(strict_types=1);
*
* Uses a cheap model (configurable) to keep analysis costs low.
* Batches source data into manageable chunks for the LLM context window.
* Prompt construction is delegated to PRAutoBlogger_Analysis_Prompts.
*
* Triggered by: PRAutoBlogger::run_generation_pipeline() (step 2).
* Dependencies: LLM_Provider_Interface, Cost_Tracker, WordPress $wpdb.
* Dependencies: LLM_Provider_Interface, Cost_Tracker, PRAutoBlogger_Analysis_Prompts, WordPress $wpdb.
*
* @see providers/interface-llm-provider.php — LLM used for analysis.
* @see core/class-cost-tracker.php — Logs API costs.
* @see core/class-idea-scorer.php — Consumes analysis results.
* @see ARCHITECTURE.md — Data flow step 2.
* @see core/class-analysis-prompts.php — System/user prompt builders + performance context.
* @see providers/interface-llm-provider.php — LLM used for analysis.
* @see core/class-cost-tracker.php — Logs API costs.
* @see core/class-idea-scorer.php — Consumes analysis results.
* @see ARCHITECTURE.md — Data flow step 2.
*/
class PRAutoBlogger_Content_Analyzer {
@@ -37,11 +39,9 @@ class PRAutoBlogger_Content_Analyzer {
*
* Side effects: LLM API call, database reads/writes.
*
* @return PRAutoBlogger_Analysis_Result[] Detected patterns.
*/
/**
* @param int $target_idea_count How many distinct topic ideas the LLM should produce.
* Defaults to 6 so the scorer has enough to pick from.
* @return PRAutoBlogger_Analysis_Result[] Detected patterns.
*/
public function analyze_recent_data( int $target_idea_count = 6 ): array {
$source_items = $this->fetch_recent_source_data();
@@ -54,14 +54,10 @@ class PRAutoBlogger_Content_Analyzer {
$niche = get_option( 'prautoblogger_niche_description', '' );
$model = get_option( 'prautoblogger_analysis_model', PRAUTOBLOGGER_DEFAULT_ANALYSIS_MODEL );
// Build the source data summary for the LLM (trim to avoid huge contexts).
$summary = $this->build_source_summary( $source_items );
// Include past performance data for self-improvement.
$performance_context = $this->get_performance_context();
$system_prompt = $this->build_system_prompt( $niche, $performance_context, $target_idea_count );
$user_prompt = $this->build_user_prompt( $summary, $target_idea_count );
$summary = $this->build_source_summary( $source_items );
$performance_context = PRAutoBlogger_Analysis_Prompts::get_performance_context();
$system_prompt = PRAutoBlogger_Analysis_Prompts::build_system_prompt( $niche, $performance_context, $target_idea_count );
$user_prompt = PRAutoBlogger_Analysis_Prompts::build_user_prompt( $summary, $target_idea_count );
$response = $this->llm->send_chat_completion(
[
@@ -76,7 +72,6 @@ class PRAutoBlogger_Content_Analyzer {
]
);
// Log the cost.
$this->cost_tracker->log_api_call(
null,
'analysis',
@@ -86,15 +81,38 @@ class PRAutoBlogger_Content_Analyzer {
$response['completion_tokens']
);
// Parse LLM response into Analysis_Result objects.
$results = $this->parse_analysis_response( $response['content'], $source_items );
// Store in database.
$this->store_results( $results );
return $results;
}
// ── Backward-compatible proxies (prompt building moved to Analysis_Prompts) ──
/**
* @deprecated Use PRAutoBlogger_Analysis_Prompts::build_system_prompt() directly.
* @see core/class-analysis-prompts.php
*/
private function build_system_prompt( string $niche, string $performance_context, int $target_count = 6 ): string {
return PRAutoBlogger_Analysis_Prompts::build_system_prompt( $niche, $performance_context, $target_count );
}
/**
* @deprecated Use PRAutoBlogger_Analysis_Prompts::build_user_prompt() directly.
* @see core/class-analysis-prompts.php
*/
private function build_user_prompt( string $summary, int $target_count = 6 ): string {
return PRAutoBlogger_Analysis_Prompts::build_user_prompt( $summary, $target_count );
}
/**
* @deprecated Use PRAutoBlogger_Analysis_Prompts::get_performance_context() directly.
* @see core/class-analysis-prompts.php
*/
private function get_performance_context(): string {
return PRAutoBlogger_Analysis_Prompts::get_performance_context();
}
/**
* Fetch source data collected in the last 24 hours.
*
@@ -120,7 +138,6 @@ class PRAutoBlogger_Content_Analyzer {
* Prioritizes high-engagement content and keeps total text under ~8000 tokens.
*
* @param array<int, array<string, mixed>> $items Source data rows.
*
* @return string Formatted text summary.
*/
private function build_source_summary( array $items ): string {
@@ -150,109 +167,11 @@ class PRAutoBlogger_Content_Analyzer {
return implode( "\n", $parts );
}
/**
* Get past content performance data to feed into analysis for self-improvement.
*
* @return string Summary of what topics performed well/poorly.
*/
private function get_performance_context(): string {
global $wpdb;
$scores_table = $wpdb->prefix . 'prautoblogger_content_scores';
// phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery
$top_posts = $wpdb->get_results(
"SELECT cs.post_id, cs.composite_score, p.post_title
FROM {$scores_table} cs
JOIN {$wpdb->posts} p ON p.ID = cs.post_id
WHERE cs.composite_score > 0
ORDER BY cs.composite_score DESC
LIMIT 5",
ARRAY_A
);
if ( empty( $top_posts ) ) {
return '';
}
$lines = [ 'Top performing past articles (learn from these):' ];
foreach ( $top_posts as $post ) {
$lines[] = sprintf(
'- "%s" (score: %.1f)',
$post['post_title'],
(float) $post['composite_score']
);
}
return implode( "\n", $lines );
}
/**
* Build the system prompt for content analysis.
*
* @param string $niche Niche description from settings.
* @param string $performance_context Past performance data.
*
* @return string
*/
private function build_system_prompt( string $niche, string $performance_context, int $target_count = 6 ): string {
$prompt = "You are a content strategist analyzing social media discussions to find article ideas for a blog";
if ( '' !== $niche ) {
$prompt .= " in the {$niche} niche";
}
$prompt .= ".\n\n";
$prompt .= "IMPORTANT: You must identify exactly {$target_count} DISTINCT article ideas. ";
$prompt .= "Each idea must cover a substantially different topic — no two ideas should overlap ";
$prompt .= "in their main subject. Aim for diversity across these categories:\n";
$prompt .= "1. QUESTIONS: Recurring questions people ask (\"How do I...\", \"What is...\", \"Is it safe to...\")\n";
$prompt .= "2. COMPLAINTS: Pain points, frustrations, or problems people report\n";
$prompt .= "3. COMPARISONS: Product/method comparisons people discuss (\"X vs Y\", \"Which is better\")\n";
$prompt .= "4. NEWS: Recent developments, rule changes, or trends people are discussing\n";
$prompt .= "5. GUIDES: How-to topics, best practices, or educational content people need\n\n";
$prompt .= "Spread your {$target_count} ideas across multiple categories. ";
$prompt .= "Be specific — \"peptide dosing for BPC-157\" is better than \"peptide information\". ";
$prompt .= "Each idea should be narrow enough to be a single focused blog post.\n\n";
$prompt .= "For each idea, provide:\n";
$prompt .= "- type: 'question', 'complaint', 'comparison', 'news', or 'guide'\n";
$prompt .= "- topic: A clear, specific, narrow topic description\n";
$prompt .= "- summary: 1-2 sentence summary of why this topic matters\n";
$prompt .= "- frequency: How many of the provided posts relate to this topic\n";
$prompt .= "- relevance_score: 0.0 to 1.0 indicating how relevant and article-worthy this is\n";
$prompt .= "- suggested_title: A compelling, unique blog post title\n";
$prompt .= "- key_points: Array of 3-5 key points the article should cover\n";
$prompt .= "- target_keywords: Array of SEO keywords for this topic\n\n";
if ( '' !== $performance_context ) {
$prompt .= $performance_context . "\n\n";
}
$prompt .= "Respond with valid JSON containing exactly {$target_count} patterns:\n";
$prompt .= '{"patterns": [{"type": "...", "topic": "...", "summary": "...", "frequency": N, "relevance_score": 0.X, "suggested_title": "...", "key_points": [...], "target_keywords": [...]}]}';
return $prompt;
}
/**
* Build the user prompt containing the actual source data.
*
* @param string $summary Formatted source data summary.
*
* @return string
*/
private function build_user_prompt( string $summary, int $target_count = 6 ): string {
return "Here are the recent social media posts and comments to analyze. "
. "Find {$target_count} distinct, diverse article ideas from this data:\n\n"
. $summary;
}
/**
* Parse the LLM's JSON response into Analysis_Result objects.
*
* @param string $content LLM response content (JSON).
* @param array<int, array<string, mixed>> $source_items Original source data for ID mapping.
*
* @param string $content LLM response content (JSON).
* @param array<int, array<string, mixed>> $source_items Original source data for ID mapping.
* @return PRAutoBlogger_Analysis_Result[]
*/
private function parse_analysis_response( string $content, array $source_items ): array {
@@ -291,8 +210,6 @@ class PRAutoBlogger_Content_Analyzer {
* Side effects: database inserts.
*
* @param PRAutoBlogger_Analysis_Result[] $results
*
* @return void
*/
private function store_results( array $results ): void {
global $wpdb;