/**
 * HNSW (Hierarchical Navigable Small World) Index implementation
 * Based on the paper: "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs"
 */

import {
  DistanceFunction,
  HNSWConfig,
  HNSWNoun,
  Vector,
  VectorDocument
} from '../coreTypes.js'
import { euclideanDistance, calculateDistancesWithGPU } from '../utils/index.js'
import { executeInThread } from '../utils/workerUtils.js'

// Default HNSW parameters
const DEFAULT_CONFIG: HNSWConfig = {
  M: 16, // Max number of connections per noun
  efConstruction: 200, // Size of a dynamic candidate list during construction
  efSearch: 50, // Size of a dynamic candidate list during search
  ml: 16 // Max level
}

export class HNSWIndex {
  private nouns: Map<string, HNSWNoun> = new Map()
  private entryPointId: string | null = null
  private maxLevel = 0
  private config: HNSWConfig
  private distanceFunction: DistanceFunction
  private dimension: number | null = null
  private useParallelization: boolean = true // Whether to use parallelization for performance-critical operations

  constructor(
    config: Partial<HNSWConfig> = {},
    distanceFunction: DistanceFunction = euclideanDistance,
    options: { useParallelization?: boolean } = {}
  ) {
    this.config = { ...DEFAULT_CONFIG, ...config }
    this.distanceFunction = distanceFunction
    this.useParallelization =
      options.useParallelization !== undefined
        ? options.useParallelization
        : true
  }

  /**
   * Set whether to use parallelization for performance-critical operations
   */
  public setUseParallelization(useParallelization: boolean): void {
    this.useParallelization = useParallelization
  }

  /**
   * Get whether parallelization is enabled
   */
  public getUseParallelization(): boolean {
    return this.useParallelization
  }

  /**
   * Calculate distances between a query vector and multiple vectors in parallel
   * This is used to optimize performance for search operations
   * Uses GPU acceleration when available for optimal performance
   *
   * @param queryVector The query vector
   * @param vectors Array of vectors to compare against
   * @returns Array of distances
   */
  private async calculateDistancesInParallel(
    queryVector: Vector,
    vectors: Array<{ id: string; vector: Vector }>
  ): Promise<Array<{ id: string; distance: number }>> {
    // If parallelization is disabled or there are very few vectors, use sequential processing
    if (!this.useParallelization || vectors.length < 10) {
      return vectors.map((item) => ({
        id: item.id,
        distance: this.distanceFunction(queryVector, item.vector)
      }))
    }

    try {
      // Extract just the vectors from the input array
      const vectorsOnly = vectors.map((item) => item.vector)

      // Use GPU-accelerated distance calculation when possible
      const distances = await calculateDistancesWithGPU(
        queryVector,
        vectorsOnly,
        this.distanceFunction
      )

      // Map the distances back to their IDs
      return vectors.map((item, index) => ({
        id: item.id,
        distance: distances[index]
      }))
    } catch (error) {
      console.error(
        'Error in GPU-accelerated distance calculation, falling back to threaded CPU:',
        error
      )

      // Fall back to threaded CPU processing if GPU acceleration fails
      // Function to be executed in a worker thread
      const distanceCalculator = (args: {
        queryVector: Vector
        vectors: Array<{ id: string; vector: Vector }>
        distanceFnString: string
      }) => {
        const { queryVector, vectors, distanceFnString } = args

        // Recreate the distance function from its string representation
        const distanceFunction = new Function(
          'return ' + distanceFnString
        )() as DistanceFunction

        // Calculate distances for all items
        return vectors.map((item) => ({
          id: item.id,
          distance: distanceFunction(queryVector, item.vector)
        }))
      }

      try {
        // Convert the distance function to a string for serialization
        const distanceFnString = this.distanceFunction.toString()

        // Execute the distance calculation in a separate thread
        return await executeInThread<Array<{ id: string; distance: number }>>(
          distanceCalculator.toString(),
          { queryVector, vectors, distanceFnString }
        )
      } catch (threadError) {
        console.error(
          'Error in threaded distance calculation, falling back to sequential:',
          threadError
        )
        // Fall back to sequential processing if both GPU and threaded execution fail
        return vectors.map((item) => ({
          id: item.id,
          distance: this.distanceFunction(queryVector, item.vector)
        }))
      }
    }
  }

  /**
   * Add a vector to the index
   */
  public async addItem(item: VectorDocument): Promise<string> {
    // Check if item is defined
    if (!item) {
      throw new Error('Item is undefined or null')
    }

    const { id, vector } = item

    // Check if vector is defined
    if (!vector) {
      throw new Error('Vector is undefined or null')
    }

    // Set dimension on first insert
    if (this.dimension === null) {
      this.dimension = vector.length
    } else if (vector.length !== this.dimension) {
      throw new Error(
        `Vector dimension mismatch: expected ${this.dimension}, got ${vector.length}`
      )
    }

    // Generate random level for this noun
    const nounLevel = this.getRandomLevel()

    // Create new noun
    const noun: HNSWNoun = {
      id,
      vector,
      connections: new Map()
    }

    // Initialize empty connection sets for each level
    for (let level = 0; level <= nounLevel; level++) {
      noun.connections.set(level, new Set<string>())
    }

    // If this is the first noun, make it the entry point
    if (this.nouns.size === 0) {
      this.entryPointId = id
      this.maxLevel = nounLevel
      this.nouns.set(id, noun)
      return id
    }

    // Find entry point
    if (!this.entryPointId) {
      console.error('Entry point ID is null')
      // If there's no entry point, this is the first noun, so we should have returned earlier
      // This is a safety check
      this.entryPointId = id
      this.maxLevel = nounLevel
      this.nouns.set(id, noun)
      return id
    }

    const entryPoint = this.nouns.get(this.entryPointId)
    if (!entryPoint) {
      console.error(`Entry point with ID ${this.entryPointId} not found`)
      // If the entry point doesn't exist, treat this as the first noun
      this.entryPointId = id
      this.maxLevel = nounLevel
      this.nouns.set(id, noun)
      return id
    }

    let currObj = entryPoint
    let currDist = this.distanceFunction(vector, entryPoint.vector)

    // Traverse the graph from top to bottom to find the closest noun
    for (let level = this.maxLevel; level > nounLevel; level--) {
      let changed = true
      while (changed) {
        changed = false

        // Check all neighbors at current level
        const connections = currObj.connections.get(level) || new Set<string>()

        for (const neighborId of connections) {
          const neighbor = this.nouns.get(neighborId)
          if (!neighbor) {
            // Skip neighbors that don't exist (expected during rapid additions/deletions)
            continue
          }
          const distToNeighbor = this.distanceFunction(vector, neighbor.vector)

          if (distToNeighbor < currDist) {
            currDist = distToNeighbor
            currObj = neighbor
            changed = true
          }
        }
      }
    }

    // For each level from nounLevel down to 0
    for (let level = Math.min(nounLevel, this.maxLevel); level >= 0; level--) {
      // Find ef nearest elements using greedy search
      const nearestNouns = await this.searchLayer(
        vector,
        currObj,
        this.config.efConstruction,
        level
      )

      // Select M nearest neighbors
      const neighbors = this.selectNeighbors(
        vector,
        nearestNouns,
        this.config.M
      )

      // Add bidirectional connections
      for (const [neighborId, _] of neighbors) {
        const neighbor = this.nouns.get(neighborId)
        if (!neighbor) {
          // Skip neighbors that don't exist (expected during rapid additions/deletions)
          continue
        }

        noun.connections.get(level)!.add(neighborId)

        // Add reverse connection
        if (!neighbor.connections.has(level)) {
          neighbor.connections.set(level, new Set<string>())
        }
        neighbor.connections.get(level)!.add(id)

        // Ensure neighbor doesn't have too many connections
        if (neighbor.connections.get(level)!.size > this.config.M) {
          this.pruneConnections(neighbor, level)
        }
      }

      // Update entry point for the next level
      if (nearestNouns.size > 0) {
        const [nearestId, nearestDist] = [...nearestNouns][0]
        if (nearestDist < currDist) {
          currDist = nearestDist
          const nearestNoun = this.nouns.get(nearestId)
          if (!nearestNoun) {
            console.error(
              `Nearest noun with ID ${nearestId} not found in addItem`
            )
            // Keep the current object as is
          } else {
            currObj = nearestNoun
          }
        }
      }
    }

    // Update max level and entry point if needed
    if (nounLevel > this.maxLevel) {
      this.maxLevel = nounLevel
      this.entryPointId = id
    }

    // Add noun to the index
    this.nouns.set(id, noun)
    return id
  }

  /**
   * Search for nearest neighbors
   */
  public async search(
    queryVector: Vector,
    k: number = 10
  ): Promise<Array<[string, number]>> {
    if (this.nouns.size === 0) {
      return []
    }

    // Check if query vector is defined
    if (!queryVector) {
      throw new Error('Query vector is undefined or null')
    }

    if (this.dimension !== null && queryVector.length !== this.dimension) {
      throw new Error(
        `Query vector dimension mismatch: expected ${this.dimension}, got ${queryVector.length}`
      )
    }

    // Start from the entry point
    if (!this.entryPointId) {
      console.error('Entry point ID is null')
      return []
    }

    const entryPoint = this.nouns.get(this.entryPointId)
    if (!entryPoint) {
      console.error(`Entry point with ID ${this.entryPointId} not found`)
      return []
    }

    let currObj = entryPoint
    let currDist = this.distanceFunction(queryVector, currObj.vector)

    // Traverse the graph from top to bottom to find the closest noun
    for (let level = this.maxLevel; level > 0; level--) {
      let changed = true
      while (changed) {
        changed = false

        // Check all neighbors at current level
        const connections = currObj.connections.get(level) || new Set<string>()

        // If we have enough connections, use parallel distance calculation
        if (this.useParallelization && connections.size >= 10) {
          // Prepare vectors for parallel calculation
          const vectors: Array<{ id: string; vector: Vector }> = []
          for (const neighborId of connections) {
            const neighbor = this.nouns.get(neighborId)
            if (!neighbor) continue
            vectors.push({ id: neighborId, vector: neighbor.vector })
          }

          // Calculate distances in parallel
          const distances = await this.calculateDistancesInParallel(
            queryVector,
            vectors
          )

          // Find the closest neighbor
          for (const { id, distance } of distances) {
            if (distance < currDist) {
              currDist = distance
              const neighbor = this.nouns.get(id)
              if (neighbor) {
                currObj = neighbor
                changed = true
              }
            }
          }
        } else {
          // Use sequential processing for small number of connections
          for (const neighborId of connections) {
            const neighbor = this.nouns.get(neighborId)
            if (!neighbor) {
              // Skip neighbors that don't exist (expected during rapid additions/deletions)
              continue
            }
            const distToNeighbor = this.distanceFunction(
              queryVector,
              neighbor.vector
            )

            if (distToNeighbor < currDist) {
              currDist = distToNeighbor
              currObj = neighbor
              changed = true
            }
          }
        }
      }
    }

    // Search at level 0 with ef = k
    const nearestNouns = await this.searchLayer(
      queryVector,
      currObj,
      Math.max(this.config.efSearch, k),
      0
    )

    // Convert to array and sort by distance
    return [...nearestNouns].slice(0, k)
  }

  /**
   * Remove an item from the index
   */
  public removeItem(id: string): boolean {
    if (!this.nouns.has(id)) {
      return false
    }

    const noun = this.nouns.get(id)!

    // Remove connections to this noun from all neighbors
    for (const [level, connections] of noun.connections.entries()) {
      for (const neighborId of connections) {
        const neighbor = this.nouns.get(neighborId)
        if (!neighbor) {
          // Skip neighbors that don't exist (expected during rapid additions/deletions)
          continue
        }
        if (neighbor.connections.has(level)) {
          neighbor.connections.get(level)!.delete(id)

          // Prune connections after removing this noun to ensure consistency
          this.pruneConnections(neighbor, level)
        }
      }
    }

    // Also check all other nouns for references to this noun and remove them
    for (const [nounId, otherNoun] of this.nouns.entries()) {
      if (nounId === id) continue // Skip the noun being removed

      for (const [level, connections] of otherNoun.connections.entries()) {
        if (connections.has(id)) {
          connections.delete(id)

          // Prune connections after removing this reference
          this.pruneConnections(otherNoun, level)
        }
      }
    }

    // Remove the noun
    this.nouns.delete(id)

    // If we removed the entry point, find a new one
    if (this.entryPointId === id) {
      if (this.nouns.size === 0) {
        this.entryPointId = null
        this.maxLevel = 0
      } else {
        // Find the noun with the highest level
        let maxLevel = 0
        let newEntryPointId = null

        for (const [nounId, noun] of this.nouns.entries()) {
          if (noun.connections.size === 0) continue // Skip nouns with no connections

          const nounLevel = Math.max(...noun.connections.keys())
          if (nounLevel >= maxLevel) {
            maxLevel = nounLevel
            newEntryPointId = nounId
          }
        }

        this.entryPointId = newEntryPointId
        this.maxLevel = maxLevel
      }
    }

    return true
  }

  /**
   * Get all nouns in the index
   */
  public getNouns(): Map<string, HNSWNoun> {
    return new Map(this.nouns)
  }

  /**
   * Clear the index
   */
  public clear(): void {
    this.nouns.clear()
    this.entryPointId = null
    this.maxLevel = 0
  }

  /**
   * Get the size of the index
   */
  public size(): number {
    return this.nouns.size
  }

  /**
   * Get the distance function used by the index
   */
  public getDistanceFunction(): DistanceFunction {
    return this.distanceFunction
  }

  /**
   * Get the entry point ID
   */
  public getEntryPointId(): string | null {
    return this.entryPointId
  }

  /**
   * Get the maximum level
   */
  public getMaxLevel(): number {
    return this.maxLevel
  }

  /**
   * Get the dimension
   */
  public getDimension(): number | null {
    return this.dimension
  }

  /**
   * Get the configuration
   */
  public getConfig(): HNSWConfig {
    return { ...this.config }
  }

  /**
   * Search within a specific layer
   * Returns a map of noun IDs to distances, sorted by distance
   */
  private async searchLayer(
    queryVector: Vector,
    entryPoint: HNSWNoun,
    ef: number,
    level: number
  ): Promise<Map<string, number>> {
    // Set of visited nouns
    const visited = new Set<string>([entryPoint.id])

    // Priority queue of candidates (closest first)
    const candidates = new Map<string, number>()
    candidates.set(
      entryPoint.id,
      this.distanceFunction(queryVector, entryPoint.vector)
    )

    // Priority queue of nearest neighbors found so far (closest first)
    const nearest = new Map<string, number>()
    nearest.set(
      entryPoint.id,
      this.distanceFunction(queryVector, entryPoint.vector)
    )

    // While there are candidates to explore
    while (candidates.size > 0) {
      // Get closest candidate
      const [closestId, closestDist] = [...candidates][0]
      candidates.delete(closestId)

      // If this candidate is farther than the farthest in our result set, we're done
      const farthestInNearest = [...nearest][nearest.size - 1]
      if (nearest.size >= ef && closestDist > farthestInNearest[1]) {
        break
      }

      // Explore neighbors of the closest candidate
      const noun = this.nouns.get(closestId)
      if (!noun) {
        console.error(`Noun with ID ${closestId} not found in searchLayer`)
        continue
      }
      const connections = noun.connections.get(level) || new Set<string>()

      // If we have enough connections and parallelization is enabled, use parallel distance calculation
      if (this.useParallelization && connections.size >= 10) {
        // Collect unvisited neighbors
        const unvisitedNeighbors: Array<{ id: string; vector: Vector }> = []
        for (const neighborId of connections) {
          if (!visited.has(neighborId)) {
            visited.add(neighborId)
            const neighbor = this.nouns.get(neighborId)
            if (!neighbor) continue
            unvisitedNeighbors.push({ id: neighborId, vector: neighbor.vector })
          }
        }

        if (unvisitedNeighbors.length > 0) {
          // Calculate distances in parallel
          const distances = await this.calculateDistancesInParallel(
            queryVector,
            unvisitedNeighbors
          )

          // Process the results
          for (const { id, distance } of distances) {
            // If we haven't found ef nearest neighbors yet, or this neighbor is closer than the farthest one we've found
            if (nearest.size < ef || distance < farthestInNearest[1]) {
              candidates.set(id, distance)
              nearest.set(id, distance)

              // If we have more than ef neighbors, remove the farthest one
              if (nearest.size > ef) {
                const sortedNearest = [...nearest].sort((a, b) => a[1] - b[1])
                nearest.clear()
                for (let i = 0; i < ef; i++) {
                  nearest.set(sortedNearest[i][0], sortedNearest[i][1])
                }
              }
            }
          }
        }
      } else {
        // Use sequential processing for small number of connections
        for (const neighborId of connections) {
          if (!visited.has(neighborId)) {
            visited.add(neighborId)

            const neighbor = this.nouns.get(neighborId)
            if (!neighbor) {
              // Skip neighbors that don't exist (expected during rapid additions/deletions)
              continue
            }
            const distToNeighbor = this.distanceFunction(
              queryVector,
              neighbor.vector
            )

            // If we haven't found ef nearest neighbors yet, or this neighbor is closer than the farthest one we've found
            if (nearest.size < ef || distToNeighbor < farthestInNearest[1]) {
              candidates.set(neighborId, distToNeighbor)
              nearest.set(neighborId, distToNeighbor)

              // If we have more than ef neighbors, remove the farthest one
              if (nearest.size > ef) {
                const sortedNearest = [...nearest].sort((a, b) => a[1] - b[1])
                nearest.clear()
                for (let i = 0; i < ef; i++) {
                  nearest.set(sortedNearest[i][0], sortedNearest[i][1])
                }
              }
            }
          }
        }
      }
    }

    // Sort nearest by distance
    return new Map([...nearest].sort((a, b) => a[1] - b[1]))
  }

  /**
   * Select M nearest neighbors from the candidate set
   */
  private selectNeighbors(
    queryVector: Vector,
    candidates: Map<string, number>,
    M: number
  ): Map<string, number> {
    if (candidates.size <= M) {
      return candidates
    }

    // Simple heuristic: just take the M closest
    const sortedCandidates = [...candidates].sort((a, b) => a[1] - b[1])
    const result = new Map<string, number>()

    for (let i = 0; i < Math.min(M, sortedCandidates.length); i++) {
      result.set(sortedCandidates[i][0], sortedCandidates[i][1])
    }

    return result
  }

  /**
   * Ensure a noun doesn't have too many connections at a given level
   */
  private pruneConnections(noun: HNSWNoun, level: number): void {
    const connections = noun.connections.get(level)!
    if (connections.size <= this.config.M) {
      return
    }

    // Calculate distances to all neighbors
    const distances = new Map<string, number>()
    const validNeighborIds = new Set<string>()

    for (const neighborId of connections) {
      const neighbor = this.nouns.get(neighborId)
      if (!neighbor) {
        // Skip neighbors that don't exist (expected during rapid additions/deletions)
        continue
      }

      // Only add valid neighbors to the distances map
      distances.set(
        neighborId,
        this.distanceFunction(noun.vector, neighbor.vector)
      )
      validNeighborIds.add(neighborId)
    }

    // Only proceed if we have valid neighbors
    if (distances.size === 0) {
      // If no valid neighbors, clear connections at this level
      noun.connections.set(level, new Set())
      return
    }

    // Select M closest neighbors from valid ones
    const selectedNeighbors = this.selectNeighbors(
      noun.vector,
      distances,
      this.config.M
    )

    // Update connections with only valid neighbors
    noun.connections.set(level, new Set(selectedNeighbors.keys()))
  }

  /**
   * Generate a random level for a new noun
   * Uses the same distribution as in the original HNSW paper
   */
  private getRandomLevel(): number {
    const r = Math.random()
    return Math.floor(-Math.log(r) * (1.0 / Math.log(this.config.M)))
  }
}