octocode 0.14.1

// Copyright 2026 Muvon Un Limited
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

use anyhow::Result;
use std::sync::Arc;

// Arrow imports
use arrow_array::{Array, RecordBatch};
use arrow_schema::Schema;

// LanceDB imports
use futures::TryStreamExt;
use lancedb::{
	index::{scalar::FtsIndexBuilder, Index, IndexType},
	query::{ExecutableQuery, QueryBase, Select},
	Connection,
};

/// Generic table operations for LanceDB
pub struct TableOperations<'a> {
	pub db: &'a Connection,
}

impl<'a> TableOperations<'a> {
	pub fn new(db: &'a Connection) -> Self {
		Self { db }
	}

	/// Check if a table exists
	pub async fn table_exists(&self, table_name: &str) -> Result<bool> {
		let table_names = self.db.table_names().execute().await?;
		Ok(table_names.contains(&table_name.to_string()))
	}

	/// Check if multiple tables exist
	pub async fn tables_exist(&self, table_names: &[&str]) -> Result<bool> {
		let existing_tables = self.db.table_names().execute().await?;
		for &table_name in table_names {
			if !existing_tables.contains(&table_name.to_string()) {
				return Ok(false);
			}
		}
		Ok(true)
	}

	/// Clear (drop) a single table
	pub async fn clear_table(&self, table_name: &str) -> Result<()> {
		let table_names = self.db.table_names().execute().await?;

		if table_names.contains(&table_name.to_string()) {
			if let Err(e) = self.db.drop_table(table_name, &[]).await {
				// Log error to structured logging instead of stderr
				tracing::warn!("Failed to drop {} table: {}", table_name, e);
			} else {
				// Log success to structured logging instead of stdout
				tracing::debug!("Dropped table: {}", table_name);
			}
		} else {
			// Log info to structured logging instead of stdout
			tracing::debug!("Table {} does not exist, skipping.", table_name);
		}

		Ok(())
	}

	/// Clear multiple tables
	pub async fn clear_tables(&self, table_names: &[&str]) -> Result<()> {
		for &table_name in table_names {
			self.clear_table(table_name).await?;
		}
		Ok(())
	}

	/// Clear all tables (drop tables completely to reset schema)
	pub async fn clear_all_tables(&self) -> Result<()> {
		// Get table names
		let table_names = self.db.table_names().execute().await?;

		// Drop each table completely (this removes both data and schema)
		for table_name in table_names {
			if let Err(e) = self.db.drop_table(&table_name, &[]).await {
				// Log error to structured logging instead of stderr
				tracing::warn!("Failed to drop table {}: {}", table_name, e);
			} else {
				// Log success to structured logging instead of stdout
				tracing::debug!("Dropped table: {}", table_name);
			}
		}

		Ok(())
	}

	/// Flush all tables to ensure data is persisted
	pub async fn flush_all_tables(&self) -> Result<()> {
		// Get all tables
		let table_names = self.db.table_names().execute().await?;

		// Open and flush each table by performing operations that force persistence
		for table_name in table_names {
			let table = self.db.open_table(&table_name).execute().await?;

			// Perform operations to ensure any pending writes are flushed:
			// 1. Count rows to force read access and ensure consistency
			let row_count = table.count_rows(None).await?;

			// 2. For tables with data, also check schema to ensure metadata is flushed
			if row_count > 0 {
				let _ = table.schema().await?;
			}

			// Log flush activity in debug mode for troubleshooting
			if cfg!(debug_assertions) {
				tracing::debug!("Flushed table '{}' with {} rows", table_name, row_count);
			}
		}

		Ok(())
	}

	/// Check if content exists in a table by hash
	pub async fn content_exists(&self, hash: &str, collection: &str) -> Result<bool> {
		let table = self.db.open_table(collection).execute().await?;

		// Use a more efficient query to check existence
		let mut results = table
			.query()
			.only_if(format!("hash = '{}'", hash))
			.limit(1) // We only need to know if one exists
			.select(Select::Columns(vec!["hash".to_string()])) // Only select hash column
			.execute()
			.await?;

		// Check if we got any results
		while let Some(batch) = results.try_next().await? {
			if batch.num_rows() > 0 {
				return Ok(true);
			}
		}

		Ok(false)
	}

	/// Remove blocks by path from a table
	pub async fn remove_blocks_by_path(&self, file_path: &str, table_name: &str) -> Result<usize> {
		if !self.table_exists(table_name).await? {
			return Ok(0);
		}

		let table = self.db.open_table(table_name).execute().await?;

		// Count rows before deletion for reporting
		let before_count = table.count_rows(None).await?;

		// Delete rows matching the file path
		table
			.delete(&format!("path = '{}'", file_path))
			.await
			.map_err(|e| anyhow::anyhow!("Failed to delete from {}: {}", table_name, e))?;

		// Count rows after deletion
		let after_count = table.count_rows(None).await?;
		let deleted_count = before_count.saturating_sub(after_count);

		Ok(deleted_count)
	}

	/// Remove blocks by hashes from a table
	pub async fn remove_blocks_by_hashes(&self, hashes: &[String], table_name: &str) -> Result<()> {
		if hashes.is_empty() {
			return Ok(());
		}

		if !self.table_exists(table_name).await? {
			return Ok(());
		}

		let table = self.db.open_table(table_name).execute().await?;

		// Create a filter for all hashes
		let hash_filters: Vec<String> = hashes.iter().map(|h| format!("hash = '{}'", h)).collect();
		let filter = hash_filters.join(" OR ");

		// Delete rows matching any of the hashes
		table
			.delete(&filter)
			.await
			.map_err(|e| anyhow::anyhow!("Failed to delete from {}: {}", table_name, e))?;

		Ok(())
	}

	/// Get file metadata (hashes) for a specific file path from a table
	pub async fn get_file_blocks_metadata(
		&self,
		file_path: &str,
		table_name: &str,
	) -> Result<Vec<String>> {
		let mut hashes = Vec::new();

		if !self.table_exists(table_name).await? {
			return Ok(hashes);
		}

		let table = self.db.open_table(table_name).execute().await?;

		// Query for blocks matching the file path, only selecting hash column
		let mut results = table
			.query()
			.only_if(format!("path = '{}'", file_path))
			.select(Select::Columns(vec!["hash".to_string()]))
			.execute()
			.await?;

		// Process all result batches
		while let Some(batch) = results.try_next().await? {
			if batch.num_rows() > 0 {
				if let Some(column) = batch.column_by_name("hash") {
					if let Some(hash_array) =
						column.as_any().downcast_ref::<arrow::array::StringArray>()
					{
						for i in 0..hash_array.len() {
							hashes.push(hash_array.value(i).to_string());
						}
					}
				}
			}
		}

		Ok(hashes)
	}

	/// Get all indexed file paths from multiple tables
	pub async fn get_all_indexed_file_paths(
		&self,
		table_names: &[&str],
	) -> Result<std::collections::HashSet<String>> {
		let mut all_paths = std::collections::HashSet::new();

		let existing_tables = self.db.table_names().execute().await?;

		for &table_name in table_names {
			if existing_tables.contains(&table_name.to_string()) {
				let table = self.db.open_table(table_name).execute().await?;

				// Query for all paths in this table
				let mut results = table
					.query()
					.select(Select::Columns(vec!["path".to_string()]))
					.execute()
					.await?;

				// Process all result batches
				while let Some(batch) = results.try_next().await? {
					if batch.num_rows() > 0 {
						if let Some(column) = batch.column_by_name("path") {
							if let Some(path_array) =
								column.as_any().downcast_ref::<arrow::array::StringArray>()
							{
								for i in 0..path_array.len() {
									all_paths.insert(path_array.value(i).to_string());
								}
							}
						}
					}
				}
			}
		}

		Ok(all_paths)
	}

	/// Create a table with the given schema
	pub async fn create_table_with_schema(
		&self,
		table_name: &str,
		schema: Arc<Schema>,
	) -> Result<()> {
		let _table = self
			.db
			.create_empty_table(table_name, schema)
			.execute()
			.await?;
		Ok(())
	}

	/// Store a record batch in a table (create table if it doesn't exist)
	pub async fn store_batch(&self, table_name: &str, batch: RecordBatch) -> Result<()> {
		// Check if table exists
		if self.table_exists(table_name).await? {
			// Table exists, append data
			let table = self.db.open_table(table_name).execute().await?;

			// Use RecordBatchIterator instead of Vec<RecordBatch>
			use std::iter::once;
			let batches = once(Ok(batch.clone()));
			let batch_reader =
				arrow::record_batch::RecordBatchIterator::new(batches, batch.schema());
			table.add(batch_reader).execute().await?;
		} else {
			// Table doesn't exist, create it with the batch
			use std::iter::once;
			let batches = once(Ok(batch.clone()));
			let batch_reader =
				arrow::record_batch::RecordBatchIterator::new(batches, batch.schema());
			let _table = self
				.db
				.create_table(table_name, batch_reader)
				.execute()
				.await?;
		}

		Ok(())
	}

	/// Check if index already exists with good parameters and handle dynamic dataset changes
	pub async fn create_vector_index_optimized(
		&self,
		table_name: &str,
		column_name: &str,
		vector_dimension: usize,
		use_quantization: bool,
	) -> Result<()> {
		if !self.table_exists(table_name).await? {
			return Err(anyhow::anyhow!("Table {} does not exist", table_name));
		}

		let table = self.db.open_table(table_name).execute().await?;
		let row_count = table.count_rows(None).await?;

		// Use intelligent optimizer to determine if we should create an index
		let index_params = super::vector_optimizer::VectorOptimizer::calculate_index_params(
			row_count,
			vector_dimension,
			use_quantization,
		);

		if !index_params.should_create_index {
			tracing::debug!(
				"Skipping index creation for table '{}' with {} rows - brute force search will be faster",
				table_name, row_count
			);
			return Ok(());
		}

		// Check if index already exists
		let existing_indices = table.list_indices().await?;
		let has_embedding_index = existing_indices
			.iter()
			.any(|idx| idx.columns == vec![column_name]);

		if has_embedding_index {
			// For dynamic datasets, we should periodically check if index parameters are still optimal
			// This is a simplified check - in production, we could store index metadata to compare
			tracing::debug!(
				"Vector index already exists for table '{}' with {} rows. Consider recreating if dataset grew significantly.",
				table_name, row_count
			);
			return Ok(());
		}

		// Create optimized vector index based on quantization setting
		let index_type_name = match index_params.index_type {
			super::vector_optimizer::IndexType::IvfHnswSq => "IVF_HNSW_SQ",
			super::vector_optimizer::IndexType::IvfRq => "IVF_RQ",
		};

		tracing::info!(
			"Creating {} vector index for table '{}': {} rows, {} partitions",
			index_type_name,
			table_name,
			row_count,
			index_params.num_partitions
		);

		let start_time = std::time::Instant::now();

		// Create the appropriate index type
		match index_params.index_type {
			super::vector_optimizer::IndexType::IvfHnswSq => {
				table
					.create_index(
						&[column_name],
						lancedb::index::Index::IvfHnswSq(
							lancedb::index::vector::IvfHnswSqIndexBuilder::default()
								.distance_type(index_params.distance_type)
								.num_partitions(index_params.num_partitions)
								.num_edges(index_params.num_edges)
								.ef_construction(index_params.ef_construction),
						),
					)
					.execute()
					.await?;
			}
			super::vector_optimizer::IndexType::IvfRq => {
				table
					.create_index(
						&[column_name],
						lancedb::index::Index::IvfRq(
							lancedb::index::vector::IvfRqIndexBuilder::default()
								.distance_type(index_params.distance_type)
								.num_partitions(index_params.num_partitions),
						),
					)
					.execute()
					.await?;
			}
		}

		let duration = start_time.elapsed();
		tracing::info!(
			"Successfully created {} index for table '{}' in {:.2}s",
			index_type_name,
			table_name,
			duration.as_secs_f64()
		);
		Ok(())
	}

	/// Recreate vector index with new optimal parameters
	pub async fn recreate_vector_index_optimized(
		&self,
		table_name: &str,
		column_name: &str,
		vector_dimension: usize,
		use_quantization: bool,
	) -> Result<()> {
		if !self.table_exists(table_name).await? {
			return Err(anyhow::anyhow!("Table {} does not exist", table_name));
		}

		let table = self.db.open_table(table_name).execute().await?;
		let row_count = table.count_rows(None).await?;

		tracing::info!(
			"Recreating vector index for table '{}' with {} rows for better performance",
			table_name,
			row_count
		);

		// Drop existing index first
		let existing_indices = table.list_indices().await?;
		for index in existing_indices {
			if index.columns == vec![column_name] {
				tracing::debug!("Dropping existing index: {}", index.name);
				// Note: LanceDB doesn't have a direct drop_index method in current version
				// The index will be replaced when we create a new one
				break;
			}
		}

		// Calculate new optimal parameters
		let index_params = super::vector_optimizer::VectorOptimizer::calculate_index_params(
			row_count,
			vector_dimension,
			use_quantization,
		);

		if !index_params.should_create_index {
			tracing::warn!("Dataset size no longer warrants an index, skipping recreation");
			return Ok(());
		}

		// Create new optimized index based on quantization setting
		let index_type_name = match index_params.index_type {
			super::vector_optimizer::IndexType::IvfHnswSq => "IVF_HNSW_SQ",
			super::vector_optimizer::IndexType::IvfRq => "IVF_RQ",
		};

		let start_time = std::time::Instant::now();

		// Create the appropriate index type
		match index_params.index_type {
			super::vector_optimizer::IndexType::IvfHnswSq => {
				table
					.create_index(
						&[column_name],
						lancedb::index::Index::IvfHnswSq(
							lancedb::index::vector::IvfHnswSqIndexBuilder::default()
								.distance_type(index_params.distance_type)
								.num_partitions(index_params.num_partitions)
								.num_edges(index_params.num_edges)
								.ef_construction(index_params.ef_construction),
						),
					)
					.execute()
					.await?;
			}
			super::vector_optimizer::IndexType::IvfRq => {
				table
					.create_index(
						&[column_name],
						lancedb::index::Index::IvfRq(
							lancedb::index::vector::IvfRqIndexBuilder::default()
								.distance_type(index_params.distance_type)
								.num_partitions(index_params.num_partitions),
						),
					)
					.execute()
					.await?;
			}
		}

		let duration = start_time.elapsed();
		tracing::info!(
			"Successfully recreated {} index for table '{}' in {:.2}s - {} partitions",
			index_type_name,
			table_name,
			duration.as_secs_f64(),
			index_params.num_partitions
		);

		Ok(())
	}

	/// Create an FTS (full-text search / BM25) index on the `content` column.
	/// Safe to call repeatedly — skips creation if an inverted index already exists.
	/// The index enables native LanceDB hybrid search (vector + BM25 via RRF).
	pub async fn create_fts_index(&self, table_name: &str) -> Result<()> {
		if !self.table_exists(table_name).await? {
			return Ok(());
		}

		let table = self.db.open_table(table_name).execute().await?;

		// Skip if FTS index already exists
		let indices = table.list_indices().await?;
		if indices.iter().any(|idx| idx.index_type == IndexType::FTS) {
			tracing::debug!("FTS index already exists for table '{}'", table_name);
			return Ok(());
		}

		let row_count = table.count_rows(None).await?;
		if row_count == 0 {
			tracing::debug!(
				"Table '{}' is empty, skipping FTS index creation",
				table_name
			);
			return Ok(());
		}

		tracing::info!(
			"Creating FTS index for table '{}' ({} rows)",
			table_name,
			row_count
		);
		let start = std::time::Instant::now();

		table
			.create_index(&["content"], Index::FTS(FtsIndexBuilder::default()))
			.execute()
			.await
			.map_err(|e| {
				anyhow::anyhow!("Failed to create FTS index on '{}': {}", table_name, e)
			})?;

		tracing::info!(
			"FTS index created for '{}' in {:.2}s",
			table_name,
			start.elapsed().as_secs_f64()
		);
		Ok(())
	}
}