// Package vectord owns the vector-search surface — HNSW indexes
// keyed by string IDs with optional opaque JSON metadata. The
// underlying library is github.com/coder/hnsw (pure Go, no cgo).
//
// G1 scope: in-memory only. Persistence to storaged + rehydrate
// across restart is the next piece — keeping it out of this layer
// makes the index API easier to test and keeps the storaged
// dependency optional for downstream tooling.
package vectord

import (
	"encoding/json"
	"errors"
	"fmt"
	"math"
	"sync"

	"github.com/coder/hnsw"
)

// Distance names accepted by IndexParams.Distance.
const (
	DistanceCosine    = "cosine"
	DistanceEuclidean = "euclidean"
)

// Default HNSW parameters — match coder/hnsw's NewGraph defaults
// which are tuned for OpenAI-shaped embeddings (1536-d, but the
// hyperparameters generalize).
const (
	DefaultM        = 16
	DefaultEfSearch = 20
)

// IndexParams describes one vector index. Once an Index is built,
// these are fixed — changing M / dimension / distance requires a
// rebuild.
type IndexParams struct {
	Name      string `json:"name"`
	Dimension int    `json:"dimension"`
	M         int    `json:"m"`
	EfSearch  int    `json:"ef_search"`
	Distance  string `json:"distance"`
}

// Result is one search hit. Distance semantics depend on the
// configured distance function — for cosine it's `1 - cos(a,b)`
// where smaller = closer; for euclidean it's the L2 norm of
// `a - b`. Either way, smaller = closer and the result list is
// sorted ascending.
type Result struct {
	ID       string          `json:"id"`
	Distance float32         `json:"distance"`
	Metadata json.RawMessage `json:"metadata,omitempty"`
}

// Index wraps a coder/hnsw graph plus a side map of opaque JSON
// metadata per ID. Concurrency: read-heavy via Search (read-lock);
// Add and Delete take the write lock.
type Index struct {
	params IndexParams
	g      *hnsw.Graph[string]
	meta   map[string]json.RawMessage
	mu     sync.RWMutex
}

// Errors surfaced to HTTP handlers. Sentinel-based so the wire
// layer can map to status codes via errors.Is.
var (
	ErrDimensionMismatch = errors.New("vectord: vector dimension mismatch")
	ErrUnknownDistance   = errors.New("vectord: unknown distance function")
	ErrInvalidParams     = errors.New("vectord: invalid index params")
)

// NewIndex builds a fresh index from validated params.
func NewIndex(p IndexParams) (*Index, error) {
	if p.Name == "" {
		return nil, fmt.Errorf("%w: empty name", ErrInvalidParams)
	}
	if p.Dimension <= 0 {
		return nil, fmt.Errorf("%w: dimension must be > 0 (got %d)", ErrInvalidParams, p.Dimension)
	}
	if p.M <= 0 {
		p.M = DefaultM
	}
	if p.EfSearch <= 0 {
		p.EfSearch = DefaultEfSearch
	}
	if p.Distance == "" {
		p.Distance = DistanceCosine
	}
	dist, err := distanceFn(p.Distance)
	if err != nil {
		return nil, err
	}

	g := hnsw.NewGraph[string]()
	g.M = p.M
	g.EfSearch = p.EfSearch
	g.Distance = dist
	// Ml stays at the library default (0.25); exposing it as a knob
	// is a G2 concern when we have real tuning data.

	return &Index{
		params: p,
		g:      g,
		meta:   make(map[string]json.RawMessage),
	}, nil
}

// distanceFn maps the string name to the underlying function.
// Easier to unit-test than calling out to coder/hnsw's registry.
func distanceFn(name string) (hnsw.DistanceFunc, error) {
	switch name {
	case DistanceCosine, "":
		return hnsw.CosineDistance, nil
	case DistanceEuclidean:
		return hnsw.EuclideanDistance, nil
	}
	return nil, fmt.Errorf("%w: %q (want cosine or euclidean)", ErrUnknownDistance, name)
}

// Params returns a copy of the immutable index params.
func (i *Index) Params() IndexParams { return i.params }

// Len returns the number of vectors currently in the index.
func (i *Index) Len() int {
	i.mu.RLock()
	defer i.mu.RUnlock()
	return i.g.Len()
}

// Add inserts a vector with optional metadata, with replace
// semantics for the vector: if id already exists, the prior
// vector is removed first. Dim must match the index dim or
// ErrDimensionMismatch is returned.
//
// Metadata semantics (post-scrum K-B1): nil meta is "leave
// existing alone" (upsert-style); to clear metadata, pass an
// empty `{}` or Delete+Add. This avoids silent metadata loss
// when the JSON `metadata` field is omitted on re-add.
//
// Validates that all vector components are finite (post-scrum
// O-W3). NaN/Inf in any component poisons HNSW: distance
// comparisons return false for both `<` and `>`, breaking the
// search heap invariants. Zero-norm vectors are also rejected
// under cosine distance — cos(0,x) = NaN.
//
// Note: coder/hnsw's Graph.Add panics on re-adding an existing
// key (internal "node not added" length-invariant check). We
// pre-Delete to make Add idempotent on re-insert.
func (i *Index) Add(id string, vec []float32, meta json.RawMessage) error {
	if id == "" {
		return errors.New("vectord: empty id")
	}
	if len(vec) != i.params.Dimension {
		return fmt.Errorf("%w: index dim=%d, got=%d", ErrDimensionMismatch, i.params.Dimension, len(vec))
	}
	if err := validateVector(vec, i.params.Distance); err != nil {
		return err
	}
	i.mu.Lock()
	defer i.mu.Unlock()
	// coder/hnsw has two sharp edges on re-add:
	//   1. Add of an existing key panics with "node not added"
	//      (length-invariant fires because internal delete+re-add
	//      doesn't change Len). Pre-Delete fixes this for n>1.
	//   2. Delete of the LAST node leaves layers[0] non-empty but
	//      entryless; the next Add SIGSEGVs in Dims() because
	//      entry().Value is nil. We rebuild the graph in that case.
	_, exists := i.g.Lookup(id)
	if exists {
		if i.g.Len() == 1 {
			i.resetGraphLocked()
		} else {
			i.g.Delete(id)
		}
	}
	i.g.Add(hnsw.MakeNode(id, vec))
	if meta != nil {
		// Per scrum K-B1 (Kimi): only OVERWRITE on explicit non-nil.
		// nil = "leave existing meta alone" (upsert). To clear, the
		// caller should send an empty `{}` body or Delete the id.
		i.meta[id] = meta
	}
	return nil
}

// resetGraphLocked recreates the underlying coder/hnsw Graph with
// the same params. Caller MUST hold i.mu (write-lock). Used to
// dodge the library's "delete the last node, then segfault on
// next Add" bug — see Add for details. Metadata map is preserved
// because the only entry it could affect is the one being
// re-added, which Add overwrites.
func (i *Index) resetGraphLocked() {
	g := hnsw.NewGraph[string]()
	g.M = i.params.M
	g.EfSearch = i.params.EfSearch
	g.Distance = i.g.Distance
	i.g = g
}

// validateVector rejects vectors that would poison the HNSW
// graph or produce NaN distances. Per scrum O-W3 (Opus).
func validateVector(vec []float32, distance string) error {
	var sumSq float64
	for j, v := range vec {
		f := float64(v)
		if math.IsNaN(f) || math.IsInf(f, 0) {
			return fmt.Errorf("vectord: vec[%d] is non-finite (got %v)", j, v)
		}
		sumSq += f * f
	}
	if distance == DistanceCosine && sumSq == 0 {
		return errors.New("vectord: zero-norm vector under cosine distance")
	}
	return nil
}

// Delete removes id from the index. Returns true if present.
func (i *Index) Delete(id string) bool {
	i.mu.Lock()
	defer i.mu.Unlock()
	delete(i.meta, id)
	return i.g.Delete(id)
}

// Search returns the k nearest neighbors of query, sorted
// ascending by distance.
//
// Note: coder/hnsw's Search returns `[]Node[K]` without distances —
// they're computed internally in the search candidate heap but
// dropped from the public API. We recompute distance from the
// returned vectors. O(k·dim) per search, negligible at typical
// k=10 / dim<2048.
func (i *Index) Search(query []float32, k int) ([]Result, error) {
	if len(query) != i.params.Dimension {
		return nil, fmt.Errorf("%w: index dim=%d, got=%d", ErrDimensionMismatch, i.params.Dimension, len(query))
	}
	if k <= 0 {
		return nil, errors.New("vectord: k must be > 0")
	}
	i.mu.RLock()
	defer i.mu.RUnlock()

	// Per scrum O-I2 (Opus): use the stored distance function
	// directly rather than re-resolving the string name on every
	// search. The graph's Distance is set once at NewIndex.
	dist := i.g.Distance
	hits := i.g.Search(query, k)
	out := make([]Result, len(hits))
	for j, n := range hits {
		out[j] = Result{
			ID:       n.Key,
			Distance: dist(query, n.Value),
			Metadata: i.meta[n.Key],
		}
	}
	return out, nil
}

// Lookup returns the stored vector + metadata for an id.
//
// Per scrum O-W1 (Opus): the vector is COPIED before return.
// coder/hnsw's Lookup hands back the underlying graph slice;
// caller mutation would corrupt the index without locking.
func (i *Index) Lookup(id string) (vec []float32, meta json.RawMessage, ok bool) {
	i.mu.RLock()
	defer i.mu.RUnlock()
	v, found := i.g.Lookup(id)
	if !found {
		return nil, nil, false
	}
	out := make([]float32, len(v))
	copy(out, v)
	return out, i.meta[id], true
}