codex-memory 3.0.15

use crate::error::Result;
use crate::models::{
    Memory, SearchParams, SearchResult, SearchResultWithMetadata, SearchStrategy, StorageStats,
};
use async_trait::async_trait;
use sqlx::{PgPool, Row};
use uuid::Uuid;

/// Storage trait defining the interface for memory storage operations
#[async_trait]
pub trait StorageInterface: Send + Sync {
    /// Store text content with context and summary
    async fn store(
        &self,
        content: &str,
        context: String,
        summary: String,
        tags: Option<Vec<String>>,
    ) -> Result<Uuid>;

    /// Store a chunk with parent reference
    async fn store_chunk(
        &self,
        content: &str,
        context: String,
        summary: String,
        tags: Option<Vec<String>>,
        chunk_index: i32,
        total_chunks: i32,
        parent_id: Uuid,
    ) -> Result<Uuid>;

    /// Get memory by ID
    async fn get(&self, id: Uuid) -> Result<Option<Memory>>;

    /// Delete memory by ID
    async fn delete(&self, id: Uuid) -> Result<bool>;

    /// Search memories with flexible parameters
    async fn search(&self, params: SearchParams) -> Result<Vec<SearchResult>>;

    /// Get storage statistics
    async fn stats(&self) -> Result<StorageStats>;

    /// List recent memories
    async fn list_recent(&self, limit: i64) -> Result<Vec<Memory>>;

    /// Get all chunks for a parent document
    async fn get_chunks(&self, parent_id: Uuid) -> Result<Vec<Memory>>;
}

/// Concrete storage implementation using PostgreSQL
pub struct Storage {
    pool: PgPool,
}

impl Storage {
    /// Create a new storage instance
    pub fn new(pool: PgPool) -> Self {
        Self { pool }
    }

    /// Store text with context and summary (deduplication by hash)
    pub async fn store(
        &self,
        content: &str,
        context: String,
        summary: String,
        tags: Option<Vec<String>>,
    ) -> Result<Uuid> {
        let memory = Memory::new(content.to_string(), context, summary, tags);

        // Simple content deduplication based on content hash
        let result: Uuid = sqlx::query_scalar(
            r#"
            INSERT INTO memories (id, content, content_hash, tags, context, summary, chunk_index, total_chunks, parent_id, created_at, updated_at)
            VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)
            ON CONFLICT (content_hash) DO UPDATE SET
                context = EXCLUDED.context,
                summary = EXCLUDED.summary,
                tags = EXCLUDED.tags,
                updated_at = EXCLUDED.updated_at
            RETURNING id
            "#
        )
        .bind(memory.id)
        .bind(memory.content)
        .bind(memory.content_hash)
        .bind(&memory.tags)
        .bind(&memory.context)
        .bind(&memory.summary)
        .bind(memory.chunk_index)
        .bind(memory.total_chunks)
        .bind(memory.parent_id)
        .bind(memory.created_at)
        .bind(memory.updated_at)
        .fetch_one(&self.pool)
        .await?;

        Ok(result)
    }

    /// Store a chunk with parent reference
    pub async fn store_chunk(
        &self,
        content: &str,
        context: String,
        summary: String,
        tags: Option<Vec<String>>,
        chunk_index: i32,
        total_chunks: i32,
        parent_id: Uuid,
    ) -> Result<Uuid> {
        let memory = Memory::new_chunk(
            content.to_string(),
            context,
            summary,
            tags,
            chunk_index,
            total_chunks,
            parent_id,
        );

        // Insert chunk (no deduplication for chunks to preserve order)
        let result: Uuid = sqlx::query_scalar(
            r#"
            INSERT INTO memories (id, content, content_hash, tags, context, summary, chunk_index, total_chunks, parent_id, created_at, updated_at)
            VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)
            RETURNING id
            "#
        )
        .bind(memory.id)
        .bind(memory.content)
        .bind(memory.content_hash)
        .bind(&memory.tags)
        .bind(&memory.context)
        .bind(&memory.summary)
        .bind(memory.chunk_index)
        .bind(memory.total_chunks)
        .bind(memory.parent_id)
        .bind(memory.created_at)
        .bind(memory.updated_at)
        .fetch_one(&self.pool)
        .await?;

        Ok(result)
    }

    /// Get memory by ID
    pub async fn get(&self, id: Uuid) -> Result<Option<Memory>> {
        let memory = sqlx::query_as::<_, Memory>(
            r#"
            SELECT 
                id,
                content,
                content_hash,
                tags,
                context,
                summary,
                chunk_index,
                total_chunks,
                parent_id,
                created_at,
                updated_at
            FROM memories
            WHERE id = $1
            "#,
        )
        .bind(id)
        .fetch_optional(&self.pool)
        .await?;

        Ok(memory)
    }

    /// Get all chunks for a parent document, ordered by chunk index
    pub async fn get_chunks(&self, parent_id: Uuid) -> Result<Vec<Memory>> {
        let memories = sqlx::query_as::<_, Memory>(
            r#"
            SELECT 
                id,
                content,
                content_hash,
                tags,
                context,
                summary,
                chunk_index,
                total_chunks,
                parent_id,
                created_at,
                updated_at
            FROM memories
            WHERE parent_id = $1
            ORDER BY chunk_index ASC
            "#,
        )
        .bind(parent_id)
        .fetch_all(&self.pool)
        .await?;

        Ok(memories)
    }

    /// Delete memory by ID
    pub async fn delete(&self, id: Uuid) -> Result<bool> {
        let result = sqlx::query("DELETE FROM memories WHERE id = $1")
            .bind(id)
            .execute(&self.pool)
            .await?;

        Ok(result.rows_affected() > 0)
    }

    /// Get basic storage statistics
    pub async fn stats(&self) -> Result<StorageStats> {
        let row = sqlx::query(
            r#"
            SELECT 
                COUNT(*) as total_memories,
                pg_size_pretty(pg_total_relation_size('memories')) as table_size,
                MAX(created_at) as last_memory_created
            FROM memories
            "#,
        )
        .fetch_one(&self.pool)
        .await?;

        let stats = StorageStats {
            total_memories: row.get("total_memories"),
            table_size: row.get("table_size"),
            last_memory_created: row.get("last_memory_created"),
        };

        Ok(stats)
    }

    /// List recent memories (for basic browsing)
    pub async fn list_recent(&self, limit: i64) -> Result<Vec<Memory>> {
        let memories = sqlx::query_as::<_, Memory>(
            r#"
            SELECT 
                id,
                content,
                content_hash,
                tags,
                context,
                summary,
                chunk_index,
                total_chunks,
                parent_id,
                created_at,
                updated_at
            FROM memories
            ORDER BY created_at DESC
            LIMIT $1
            "#,
        )
        .bind(limit)
        .fetch_all(&self.pool)
        .await?;

        Ok(memories)
    }

    /// Find memories with similar content but different contexts
    /// Implements transfer appropriate processing - matching content with varying contexts
    pub async fn find_similar_content(
        &self,
        content_hash: &str,
        limit: i64,
    ) -> Result<Vec<Memory>> {
        let memories = sqlx::query_as::<_, Memory>(
            r#"
            SELECT 
                id,
                content,
                content_hash,
                tags,
                context,
                summary,
                chunk_index,
                total_chunks,
                parent_id,
                created_at,
                updated_at
            FROM memories
            WHERE content_hash = $1
            ORDER BY created_at DESC
            LIMIT $2
            "#,
        )
        .bind(content_hash)
        .bind(limit)
        .fetch_all(&self.pool)
        .await?;

        Ok(memories)
    }

    /// Check if a specific content already exists
    /// Simplified deduplication based on content hash only
    pub async fn exists_with_content(&self, content_hash: &str) -> Result<bool> {
        let count: i64 =
            sqlx::query_scalar("SELECT COUNT(*) FROM memories WHERE content_hash = $1")
                .bind(content_hash)
                .fetch_one(&self.pool)
                .await?;

        Ok(count > 0)
    }

    /// Get content statistics showing duplicate content
    /// Useful for understanding deduplication effectiveness
    pub async fn get_content_stats(&self) -> Result<Vec<(String, i64)>> {
        let rows = sqlx::query(
            r#"
            SELECT 
                content_hash,
                COUNT(*) as total_count
            FROM memories 
            GROUP BY content_hash
            HAVING COUNT(*) > 1
            ORDER BY total_count DESC
            LIMIT 50
            "#,
        )
        .fetch_all(&self.pool)
        .await?;

        let stats = rows
            .into_iter()
            .map(|row| {
                (
                    row.get::<String, _>("content_hash"),
                    row.get::<i64, _>("total_count"),
                )
            })
            .collect();

        Ok(stats)
    }

    /// Semantic similarity search using existing embeddings from codex-dreams
    /// Implements progressive search strategy with automatic threshold relaxation
    pub async fn search_memories(&self, params: SearchParams) -> Result<Vec<SearchResult>> {
        // Use progressive search strategy for better results
        self.search_memories_progressive(params).await
    }

    /// Progressive search strategy that automatically retries with relaxed criteria
    /// Stage 1: Search with original parameters
    /// Stage 2: If no results, lower threshold by 0.25
    /// Stage 3: If still no results, do content-only similarity search
    async fn search_memories_progressive(&self, params: SearchParams) -> Result<Vec<SearchResult>> {
        let result_with_metadata = self
            .search_memories_progressive_with_metadata(params)
            .await?;
        Ok(result_with_metadata.results)
    }

    /// Progressive search with metadata about which stage was used
    pub async fn search_memories_progressive_with_metadata(
        &self,
        params: SearchParams,
    ) -> Result<SearchResultWithMetadata> {
        use crate::models::SearchMetadata;

        // Stage 1: Try with original parameters
        let stage1_results = self.search_memories_internal(params.clone()).await?;
        if !stage1_results.is_empty() {
            let metadata = SearchMetadata {
                stage_used: 1,
                stage_description: "Original parameters".to_string(),
                threshold_used: params.similarity_threshold,
                total_results: stage1_results.len(),
            };
            return Ok(SearchResultWithMetadata {
                results: stage1_results,
                metadata,
            });
        }

        // Stage 2: Lower threshold by 0.25 (minimum 0.1)
        let mut relaxed_params = params.clone();
        relaxed_params.similarity_threshold = (params.similarity_threshold - 0.25).max(0.1);

        let stage2_results = self
            .search_memories_internal(relaxed_params.clone())
            .await?;
        if !stage2_results.is_empty() {
            let metadata = SearchMetadata {
                stage_used: 2,
                stage_description: "Relaxed threshold".to_string(),
                threshold_used: relaxed_params.similarity_threshold,
                total_results: stage2_results.len(),
            };
            return Ok(SearchResultWithMetadata {
                results: stage2_results,
                metadata,
            });
        }

        // Stage 3: Content-only search with very low threshold
        let mut content_params = params.clone();
        content_params.similarity_threshold = 0.1;
        content_params.use_tag_embedding = false;
        content_params.search_strategy = SearchStrategy::ContentFirst;

        let stage3_results = self.search_memories_internal(content_params).await?;
        let metadata = SearchMetadata {
            stage_used: 3,
            stage_description: "Content-only similarity".to_string(),
            threshold_used: 0.1,
            total_results: stage3_results.len(),
        };

        Ok(SearchResultWithMetadata {
            results: stage3_results,
            metadata,
        })
    }

    /// Internal search implementation (original search_memories logic)
    async fn search_memories_internal(&self, params: SearchParams) -> Result<Vec<SearchResult>> {
        // Check if embedding columns exist (graceful degradation for test environments)
        let has_embeddings = self.check_embedding_columns_exist().await?;

        if !has_embeddings || (!params.use_tag_embedding && !params.use_content_embedding) {
            // Use fallback text search when embeddings unavailable or disabled
            return self.search_memories_fallback(params).await;
        }

        // Step 1: Generate query embedding by finding a similar memory first
        // This is a simplified approach - in production, you'd use the same embedding service as codex-dreams
        let query_memory_ids = if params.use_tag_embedding || params.use_content_embedding {
            // Find memories with similar text content for embedding reference
            let similar_text_rows = sqlx::query(
                r#"
                SELECT id, summary, content
                FROM memories 
                WHERE to_tsvector('english', summary || ' ' || content) @@ plainto_tsquery('english', $1)
                AND embedding_vector IS NOT NULL
                LIMIT 5
                "#
            )
            .bind(&params.query)
            .fetch_all(&self.pool)
            .await;

            match similar_text_rows {
                Ok(rows) => {
                    if rows.is_empty() {
                        // Fallback to basic text search if no embedding matches
                        return self.search_memories_fallback(params).await;
                    }
                    rows.into_iter()
                        .map(|row| row.get::<Uuid, _>("id"))
                        .collect::<Vec<_>>()
                }
                Err(_) => {
                    // Embedding columns don't exist, use fallback
                    return self.search_memories_fallback(params).await;
                }
            }
        } else {
            vec![]
        };

        // Step 2: Main search based on strategy
        let mut results = match params.search_strategy {
            SearchStrategy::TagsFirst => self.search_tags_first(&params, &query_memory_ids).await?,
            SearchStrategy::ContentFirst => {
                self.search_content_first(&params, &query_memory_ids)
                    .await?
            }
            SearchStrategy::Hybrid => self.search_hybrid(&params, &query_memory_ids).await?,
        };

        // Step 3: Apply recency boost if requested
        if params.boost_recent {
            self.apply_recency_boost(&mut results);
        }

        // Step 4: Sort by combined score and limit results
        results.sort_by(|a, b| b.combined_score.partial_cmp(&a.combined_score).unwrap());
        results.truncate(params.max_results);

        Ok(results)
    }

    /// Check if embedding columns exist in the database
    async fn check_embedding_columns_exist(&self) -> Result<bool> {
        let result = sqlx::query(
            r#"
            SELECT COUNT(*) as count
            FROM information_schema.columns 
            WHERE table_name = 'memories' 
            AND column_name IN ('embedding_vector', 'tag_embedding')
            "#,
        )
        .fetch_one(&self.pool)
        .await;

        match result {
            Ok(row) => {
                let count: i64 = row.get("count");
                Ok(count >= 2) // Both embedding columns should exist
            }
            Err(_) => Ok(false),
        }
    }

    /// Search using tag embeddings first, then content embeddings within results
    async fn search_tags_first(
        &self,
        params: &SearchParams,
        query_ids: &[Uuid],
    ) -> Result<Vec<SearchResult>> {
        if query_ids.is_empty() {
            return Ok(vec![]);
        }

        // Use the first similar memory's tag embedding as reference
        let tag_results = sqlx::query(
            r#"
            WITH query_embedding AS (
                SELECT tag_embedding as query_vector
                FROM memories 
                WHERE id = $1 AND tag_embedding IS NOT NULL
                LIMIT 1
            )
            SELECT m.*, 
                   (m.tag_embedding <=> q.query_vector) as tag_similarity,
                   m.semantic_cluster
            FROM memories m, query_embedding q
            WHERE m.tag_embedding IS NOT NULL
            AND ($2::text[] IS NULL OR m.tags && $2::text[])
            AND (m.tag_embedding <=> q.query_vector) <= $3
            ORDER BY m.tag_embedding <=> q.query_vector
            LIMIT $4
            "#,
        )
        .bind(query_ids[0])
        .bind(&params.tag_filter)
        .bind(1.0 - params.similarity_threshold) // Convert similarity to distance
        .bind((params.max_results * 3) as i64) // Get more candidates for content filtering
        .fetch_all(&self.pool)
        .await?;

        self.enhance_with_content_similarity(tag_results, query_ids, params)
            .await
    }

    /// Search using content embeddings first
    async fn search_content_first(
        &self,
        params: &SearchParams,
        query_ids: &[Uuid],
    ) -> Result<Vec<SearchResult>> {
        if query_ids.is_empty() {
            return Ok(vec![]);
        }

        let content_results = sqlx::query(
            r#"
            WITH query_embedding AS (
                SELECT embedding_vector as query_vector
                FROM memories 
                WHERE id = $1 AND embedding_vector IS NOT NULL
                LIMIT 1
            )
            SELECT m.*, 
                   (m.embedding_vector <=> q.query_vector) as content_similarity,
                   m.semantic_cluster
            FROM memories m, query_embedding q
            WHERE m.embedding_vector IS NOT NULL
            AND ($2::text[] IS NULL OR m.tags && $2::text[])
            AND (m.embedding_vector <=> q.query_vector) <= $3
            ORDER BY m.embedding_vector <=> q.query_vector
            LIMIT $4
            "#,
        )
        .bind(query_ids[0])
        .bind(&params.tag_filter)
        .bind(1.0 - params.similarity_threshold)
        .bind((params.max_results * 2) as i64)
        .fetch_all(&self.pool)
        .await?;

        self.enhance_with_tag_similarity(content_results, query_ids, params)
            .await
    }

    /// Hybrid search combining both approaches
    async fn search_hybrid(
        &self,
        params: &SearchParams,
        query_ids: &[Uuid],
    ) -> Result<Vec<SearchResult>> {
        if query_ids.is_empty() {
            return Ok(vec![]);
        }

        let results = sqlx::query(
            r#"
            WITH query_embeddings AS (
                SELECT 
                    embedding_vector as content_query_vector,
                    tag_embedding as tag_query_vector
                FROM memories 
                WHERE id = $1 
                AND embedding_vector IS NOT NULL 
                AND tag_embedding IS NOT NULL
                LIMIT 1
            )
            SELECT m.*, 
                   (m.embedding_vector <=> q.content_query_vector) as content_similarity,
                   (m.tag_embedding <=> q.tag_query_vector) as tag_similarity,
                   m.semantic_cluster
            FROM memories m, query_embeddings q
            WHERE m.embedding_vector IS NOT NULL 
            AND m.tag_embedding IS NOT NULL
            AND ($2::text[] IS NULL OR m.tags && $2::text[])
            AND (
                (m.embedding_vector <=> q.content_query_vector) <= $3 OR
                (m.tag_embedding <=> q.tag_query_vector) <= $3
            )
            ORDER BY LEAST(
                m.embedding_vector <=> q.content_query_vector,
                m.tag_embedding <=> q.tag_query_vector
            )
            LIMIT $4
            "#,
        )
        .bind(query_ids[0])
        .bind(&params.tag_filter)
        .bind(1.0 - params.similarity_threshold)
        .bind((params.max_results * 2) as i64)
        .fetch_all(&self.pool)
        .await?;

        Ok(self.rows_to_search_results(results, params))
    }

    /// Enhance tag results with content similarity scores
    async fn enhance_with_content_similarity(
        &self,
        tag_results: Vec<sqlx::postgres::PgRow>,
        query_ids: &[Uuid],
        params: &SearchParams,
    ) -> Result<Vec<SearchResult>> {
        if query_ids.is_empty() || tag_results.is_empty() {
            return Ok(vec![]);
        }

        // Get content similarities for the tag results
        let memory_ids: Vec<Uuid> = tag_results.iter().map(|r| r.get("id")).collect();

        let content_similarities = if params.use_content_embedding {
            sqlx::query(
                r#"
                WITH query_embedding AS (
                    SELECT embedding_vector as query_vector
                    FROM memories 
                    WHERE id = $1 AND embedding_vector IS NOT NULL
                    LIMIT 1
                )
                SELECT m.id, (m.embedding_vector <=> q.query_vector) as content_similarity
                FROM memories m, query_embedding q
                WHERE m.id = ANY($2) AND m.embedding_vector IS NOT NULL
                "#,
            )
            .bind(query_ids[0])
            .bind(&memory_ids)
            .fetch_all(&self.pool)
            .await?
        } else {
            vec![]
        };

        // Create lookup map for content similarities
        let content_sim_map: std::collections::HashMap<Uuid, f64> = content_similarities
            .into_iter()
            .map(|row| (row.get("id"), 1.0 - row.get::<f64, _>("content_similarity")))
            .collect();

        // Combine results
        let mut results = vec![];
        for row in tag_results {
            let memory_id: Uuid = row.get("id");
            let tag_similarity = Some(1.0 - row.get::<f64, _>("tag_similarity"));
            let content_similarity = content_sim_map.get(&memory_id).copied();
            let semantic_cluster = row.get("semantic_cluster");

            let memory = self.row_to_memory(&row);
            let result = SearchResult::new(
                memory,
                tag_similarity,
                content_similarity,
                semantic_cluster,
                params.tag_weight,
                params.content_weight,
            );

            if result.combined_score >= params.similarity_threshold {
                results.push(result);
            }
        }

        Ok(results)
    }

    /// Enhance content results with tag similarity scores  
    async fn enhance_with_tag_similarity(
        &self,
        content_results: Vec<sqlx::postgres::PgRow>,
        query_ids: &[Uuid],
        params: &SearchParams,
    ) -> Result<Vec<SearchResult>> {
        if query_ids.is_empty() || content_results.is_empty() {
            return Ok(vec![]);
        }

        let memory_ids: Vec<Uuid> = content_results.iter().map(|r| r.get("id")).collect();

        let tag_similarities = if params.use_tag_embedding {
            sqlx::query(
                r#"
                WITH query_embedding AS (
                    SELECT tag_embedding as query_vector
                    FROM memories 
                    WHERE id = $1 AND tag_embedding IS NOT NULL
                    LIMIT 1
                )
                SELECT m.id, (m.tag_embedding <=> q.query_vector) as tag_similarity
                FROM memories m, query_embedding q
                WHERE m.id = ANY($2) AND m.tag_embedding IS NOT NULL
                "#,
            )
            .bind(query_ids[0])
            .bind(&memory_ids)
            .fetch_all(&self.pool)
            .await?
        } else {
            vec![]
        };

        let tag_sim_map: std::collections::HashMap<Uuid, f64> = tag_similarities
            .into_iter()
            .map(|row| (row.get("id"), 1.0 - row.get::<f64, _>("tag_similarity")))
            .collect();

        let mut results = vec![];
        for row in content_results {
            let memory_id: Uuid = row.get("id");
            let content_similarity = Some(1.0 - row.get::<f64, _>("content_similarity"));
            let tag_similarity = tag_sim_map.get(&memory_id).copied();
            let semantic_cluster = row.get("semantic_cluster");

            let memory = self.row_to_memory(&row);
            let result = SearchResult::new(
                memory,
                tag_similarity,
                content_similarity,
                semantic_cluster,
                params.tag_weight,
                params.content_weight,
            );

            if result.combined_score >= params.similarity_threshold {
                results.push(result);
            }
        }

        Ok(results)
    }

    /// Convert database rows to SearchResult objects
    fn rows_to_search_results(
        &self,
        rows: Vec<sqlx::postgres::PgRow>,
        params: &SearchParams,
    ) -> Vec<SearchResult> {
        rows.into_iter()
            .filter_map(|row| {
                let tag_similarity = row
                    .try_get::<f64, _>("tag_similarity")
                    .ok()
                    .map(|v| 1.0 - v);
                let content_similarity = row
                    .try_get::<f64, _>("content_similarity")
                    .ok()
                    .map(|v| 1.0 - v);
                let semantic_cluster = row.get("semantic_cluster");

                let memory = self.row_to_memory(&row);
                let result = SearchResult::new(
                    memory,
                    tag_similarity,
                    content_similarity,
                    semantic_cluster,
                    params.tag_weight,
                    params.content_weight,
                );

                if result.combined_score >= params.similarity_threshold {
                    Some(result)
                } else {
                    None
                }
            })
            .collect()
    }

    /// Convert database row to Memory object
    /// Note: This function is now mostly used for complex search queries with extra columns
    fn row_to_memory(&self, row: &sqlx::postgres::PgRow) -> Memory {
        Memory {
            id: row.get("id"),
            content: row.get("content"),
            content_hash: row.get("content_hash"),
            tags: row.get("tags"),
            context: row.get("context"),
            summary: row.get("summary"),
            chunk_index: row.get("chunk_index"),
            total_chunks: row.get("total_chunks"),
            parent_id: row.get("parent_id"),
            created_at: row.get("created_at"),
            updated_at: row.get("updated_at"),
        }
    }

    /// Apply recency boost to search results
    fn apply_recency_boost(&self, results: &mut [SearchResult]) {
        let now = chrono::Utc::now();
        for result in results.iter_mut() {
            let age_days = (now - result.memory.created_at).num_days() as f64;
            let recency_factor = (1.0 / (1.0 + age_days / 30.0)).max(0.1); // Boost recent memories
            result.combined_score *= recency_factor;
        }
    }

    /// Fallback search using simple pattern matching when embeddings unavailable
    async fn search_memories_fallback(&self, params: SearchParams) -> Result<Vec<SearchResult>> {
        // Use ILIKE for case-insensitive pattern matching as fallback
        let search_pattern = format!("%{}%", params.query);

        let query_sql = if let Some(ref _tag_filter) = params.tag_filter {
            r#"
            SELECT *, 
                   CAST(CASE 
                     WHEN content ILIKE $1 AND summary ILIKE $1 THEN 1.0
                     WHEN content ILIKE $1 OR summary ILIKE $1 THEN 0.8
                     WHEN context ILIKE $1 THEN 0.6
                     WHEN EXISTS (SELECT 1 FROM unnest(tags) AS tag WHERE tag ILIKE $1) THEN 0.5
                     ELSE 0.4
                   END AS FLOAT8) as rank
            FROM memories 
            WHERE (content ILIKE $1 OR summary ILIKE $1 OR context ILIKE $1 
                   OR EXISTS (SELECT 1 FROM unnest(tags) AS tag WHERE tag ILIKE $1))
            AND tags && $2::text[]
            ORDER BY rank DESC, created_at DESC
            LIMIT $3
            "#
        } else {
            r#"
            SELECT *, 
                   CAST(CASE 
                     WHEN content ILIKE $1 AND summary ILIKE $1 THEN 1.0
                     WHEN content ILIKE $1 OR summary ILIKE $1 THEN 0.8
                     WHEN context ILIKE $1 THEN 0.6
                     WHEN EXISTS (SELECT 1 FROM unnest(tags) AS tag WHERE tag ILIKE $1) THEN 0.5
                     ELSE 0.4
                   END AS FLOAT8) as rank
            FROM memories 
            WHERE content ILIKE $1 OR summary ILIKE $1 OR context ILIKE $1 
                  OR EXISTS (SELECT 1 FROM unnest(tags) AS tag WHERE tag ILIKE $1)
            ORDER BY rank DESC, created_at DESC
            LIMIT $2
            "#
        };

        let rows = if let Some(ref tag_filter) = params.tag_filter {
            sqlx::query(query_sql)
                .bind(&search_pattern)
                .bind(tag_filter)
                .bind(params.max_results as i64)
                .fetch_all(&self.pool)
                .await?
        } else {
            sqlx::query(query_sql)
                .bind(&search_pattern)
                .bind(params.max_results as i64)
                .fetch_all(&self.pool)
                .await?
        };

        let results = rows
            .into_iter()
            .map(|row| {
                let rank: f64 = row.get("rank");
                let memory = self.row_to_memory(&row);
                // For fallback search, use rank directly as it's already a final score
                SearchResult {
                    memory,
                    tag_similarity: None,
                    content_similarity: Some(rank),
                    combined_score: rank, // Use rank directly, not weighted
                    semantic_cluster: None,
                }
            })
            .filter(|result| result.combined_score >= params.similarity_threshold)
            .collect();

        Ok(results)
    }
}

#[async_trait]
impl StorageInterface for Storage {
    async fn store(
        &self,
        content: &str,
        context: String,
        summary: String,
        tags: Option<Vec<String>>,
    ) -> Result<Uuid> {
        self.store(content, context, summary, tags).await
    }

    async fn store_chunk(
        &self,
        content: &str,
        context: String,
        summary: String,
        tags: Option<Vec<String>>,
        chunk_index: i32,
        total_chunks: i32,
        parent_id: Uuid,
    ) -> Result<Uuid> {
        self.store_chunk(content, context, summary, tags, chunk_index, total_chunks, parent_id)
            .await
    }

    async fn get(&self, id: Uuid) -> Result<Option<Memory>> {
        self.get(id).await
    }

    async fn delete(&self, id: Uuid) -> Result<bool> {
        self.delete(id).await
    }

    async fn search(&self, params: SearchParams) -> Result<Vec<SearchResult>> {
        self.search_memories(params).await
    }

    async fn stats(&self) -> Result<StorageStats> {
        self.stats().await
    }

    async fn list_recent(&self, limit: i64) -> Result<Vec<Memory>> {
        self.list_recent(limit).await
    }

    async fn get_chunks(&self, parent_id: Uuid) -> Result<Vec<Memory>> {
        self.get_chunks(parent_id).await
    }
}