Long markdown files (>~8KB) silently failed to embed because nomic-embed-text on iguana has a 2048-token context. embed sync logged errors=1 every cycle with no useful body until #37 added per-item logging — three files exceed the ceiling: finbert source (8 KB), koala-machine-state (7.1 KB), litellm-absorption (8.8 KB). Curated knowledge entries should never be vector-blind. Approach: chunk-before-embed, no schema change. vectorstore/chunk.go (new) - ChunkMarkdown splits at H1/H2 boundaries; sections over maxBytes are further split at paragraph boundaries, packing greedily under budget. - NumberChunks assigns "<parent>#NNNN" storage paths (1-based, zero-padded to 4 digits — handles files with up to ~10k sections in stable sort order). - ParentPath strips the chunk suffix for retrieval-side dedup. vectorstore/sync.go - After ChunkMarkdown produces N pieces, each is embedded + upserted as a separate brain_embeddings row at "<parent>#NNNN". maxChunkBytes = 4000 (≈1000 nomic tokens, well under the 2048 ceiling with headroom for unicode/code blocks). - "Already embedded?" check now reduces known paths to parent set via ParentPath, so the first chunk hit short-circuits the file. - Delete walk also reduces via ParentPath; when a parent file disappears, every chunk row (and any pre-existing bare-path row, for backward compatibility with rows written before this change) gets dropped. search/search.go - hybridMerge collapses chunk-path vector hits to parent via ParentPath before scope check, RRF accumulation, and hydration. A file with three chunk hits returns one result row, not three. Backward compatibility: pre-existing bare-path rows in brain_embeddings keep working — ParentPath returns them unchanged, knownParents handles them as if they were "wiki/foo.md#NNNN" hits, sync skips re-embed, and search dedup is a no-op for them. No migration required to ship. Tests: - chunk_test.go covers short / heading split / oversized section / content preservation / chunk numbering / parent-path stripping. - sync_test.go adds long-file chunking, single-chunk-row short file, skip-if-any-chunk-known, delete-all-chunks-of-disappeared-file. Existing tests updated for #NNNN paths. - search_test.go adds chunk-paths-dedupe-to-parent. Closes gitea/mathias/infra#38.
349 lines
9.6 KiB
Go
349 lines
9.6 KiB
Go
// ingestion/internal/search/search.go
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package search
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import (
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"bufio"
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"context"
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"fmt"
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"log/slog"
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"os"
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"path/filepath"
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"sort"
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"strings"
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"github.com/mathiasbq/hyperguild/ingestion/internal/brain"
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"github.com/mathiasbq/hyperguild/ingestion/internal/vectorstore"
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)
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// VectorSearcher returns the top-limit nearest paths by cosine
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// distance. The vectorstore package implements this against pgvector.
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type VectorSearcher interface {
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Search(ctx context.Context, query []float32, limit int) ([]VectorHit, error)
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}
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// VectorHit is a single path + distance pair from a vector search.
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// Re-declared here (rather than imported) to keep search package
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// free of vectorstore/embed deps and to make stubbing trivial in tests.
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type VectorHit struct {
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Path string
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Distance float64
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}
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// Embedder turns a query string into a dense vector. The embed package
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// implements this against Ollama's /api/embed.
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type Embedder interface {
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Embed(ctx context.Context, text string) ([]float32, error)
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}
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// Result is a single search hit from the brain wiki.
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type Result struct {
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Path string `json:"path"`
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Title string `json:"title"`
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Excerpt string `json:"excerpt"`
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Score int `json:"score"`
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Wing string `json:"wing,omitempty"`
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Hall string `json:"hall,omitempty"`
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}
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// QueryOptions configures a search.
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//
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// When Wing is set, the walk is restricted to brain/wiki/<wing>/.
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// When Hall is additionally set, the walk is restricted to
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// brain/wiki/<wing>/<hall>/. Without either, the legacy walk over
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// brain/knowledge/ and brain/wiki/ is used.
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//
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// When both Vector and Embedder are non-nil, results are computed
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// hybridly: BM25 and vector candidate lists are merged via Reciprocal
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// Rank Fusion. With either nil the function falls back to BM25 only,
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// keeping behaviour unchanged for callers that have not opted in.
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type QueryOptions struct {
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Query string
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Limit int
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Wing string
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Hall string
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Vector VectorSearcher
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Embedder Embedder
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}
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// Query searches the brain. Returns up to opts.Limit results sorted by
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// score descending. Empty query returns nil.
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func Query(brainDir string, opts QueryOptions) ([]Result, error) {
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return QueryContext(context.Background(), brainDir, opts)
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}
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// QueryContext is the cancellable variant of Query. Hybrid retrieval
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// requires a context because both the embedder and the vector store are
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// network calls.
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func QueryContext(ctx context.Context, brainDir string, opts QueryOptions) ([]Result, error) {
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if opts.Limit <= 0 {
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opts.Limit = 5
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}
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terms := strings.Fields(strings.ToLower(opts.Query))
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if len(terms) == 0 {
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return nil, nil
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}
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roots, err := resolveRoots(brainDir, opts.Wing, opts.Hall)
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if err != nil {
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return nil, err
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}
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var results []Result
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for _, dir := range roots {
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if _, statErr := os.Stat(dir); os.IsNotExist(statErr) {
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continue
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}
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err := filepath.WalkDir(dir, func(path string, d os.DirEntry, err error) error {
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if err != nil {
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slog.Warn("search: skipping path", "path", path, "err", err)
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return nil
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}
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if d.IsDir() || !strings.HasSuffix(path, ".md") {
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return nil
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}
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content, err := os.ReadFile(path)
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if err != nil {
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slog.Warn("search: skipping unreadable file", "path", path, "err", err)
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return nil
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}
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lower := strings.ToLower(string(content))
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score := 0
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for _, term := range terms {
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score += strings.Count(lower, term)
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}
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if score == 0 {
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return nil
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}
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rel, err := filepath.Rel(brainDir, path)
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if err != nil {
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return fmt.Errorf("rel path: %w", err)
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}
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rel = filepath.ToSlash(rel)
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wing, hall := extractWingHall(string(content), rel)
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results = append(results, Result{
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Path: rel,
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Title: extractTitle(string(content), d.Name()),
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Excerpt: excerpt(string(content), 300),
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Score: score,
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Wing: wing,
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Hall: hall,
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})
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return nil
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})
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if err != nil {
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return nil, err
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}
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}
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sort.Slice(results, func(i, j int) bool {
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return results[i].Score > results[j].Score
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})
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// Hybrid scoring kicks in only when both the embedder and the
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// vector store are wired and BM25 actually returned candidates.
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if opts.Vector != nil && opts.Embedder != nil && len(results) > 0 {
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merged, err := hybridMerge(ctx, brainDir, opts, results)
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if err != nil {
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slog.Warn("search: hybrid merge failed, falling back to BM25", "err", err)
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} else {
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results = merged
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}
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}
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if len(results) > opts.Limit {
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results = results[:opts.Limit]
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}
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return results, nil
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}
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// rrfK is the constant in the Reciprocal Rank Fusion formula. 60 is
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// standard (Cormack et al. 2009) and parameter-free in practice.
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const rrfK = 60.0
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// hybridMerge embeds the query, runs a vector search, and merges its
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// candidates with the BM25 list via Reciprocal Rank Fusion. Results
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// that came only from the vector side are hydrated by reading the
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// note's frontmatter for title/wing/hall and excerpting the body.
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//
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// rrf(d) = sum_r 1 / (k + rank_r(d)) over rankers r ∈ {BM25, vector}.
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func hybridMerge(ctx context.Context, brainDir string, opts QueryOptions, bm25 []Result) ([]Result, error) {
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q, err := opts.Embedder.Embed(ctx, opts.Query)
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if err != nil {
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return nil, fmt.Errorf("embed query: %w", err)
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}
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vectorLimit := opts.Limit * 4
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if vectorLimit < 20 {
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vectorLimit = 20
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}
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hits, err := opts.Vector.Search(ctx, q, vectorLimit)
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if err != nil {
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return nil, fmt.Errorf("vector search: %w", err)
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}
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rrf := make(map[string]float64)
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byPath := make(map[string]Result)
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for rank, r := range bm25 {
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rrf[r.Path] += 1.0 / (rrfK + float64(rank+1))
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byPath[r.Path] = r
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}
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for rank, h := range hits {
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// Vector store keys are chunk paths ("wiki/foo.md#0001"); collapse
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// back to the parent so multiple chunk hits from the same file
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// score against a single result row.
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parent := vectorstore.ParentPath(h.Path)
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if opts.Wing != "" && !pathInScope(parent, opts.Wing, opts.Hall) {
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continue
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}
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rrf[parent] += 1.0 / (rrfK + float64(rank+1))
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if _, seen := byPath[parent]; !seen {
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r, err := hydrate(brainDir, parent)
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if err != nil {
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slog.Warn("search: hydrate failed for vector hit", "path", parent, "err", err)
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continue
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}
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byPath[parent] = r
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}
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}
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merged := make([]Result, 0, len(byPath))
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for p, r := range byPath {
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r.Score = int(rrf[p] * 1e6) // scale to int for stable JSON; relative order is what matters
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merged = append(merged, r)
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}
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sort.Slice(merged, func(i, j int) bool {
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return merged[i].Score > merged[j].Score
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})
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return merged, nil
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}
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// pathInScope reports whether a wiki path satisfies the wing/hall filter.
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func pathInScope(relPath, wing, hall string) bool {
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prefix := "wiki/" + brain.Sanitise(wing) + "/"
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if hall != "" {
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prefix += hall + "/"
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}
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return strings.HasPrefix(relPath, prefix)
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}
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// hydrate reads a single note from disk and returns a Result with title,
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// excerpt, wing, and hall populated. Used for paths that surface only
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// via vector search.
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func hydrate(brainDir, relPath string) (Result, error) {
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full := filepath.Join(brainDir, filepath.FromSlash(relPath))
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content, err := os.ReadFile(full)
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if err != nil {
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return Result{}, err
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}
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wing, hall := extractWingHall(string(content), relPath)
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return Result{
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Path: relPath,
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Title: extractTitle(string(content), filepath.Base(relPath)),
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Excerpt: excerpt(string(content), 300),
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Wing: wing,
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Hall: hall,
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}, nil
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}
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// resolveRoots returns the directories to walk for the given wing/hall
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// filters. Validates hall against the closed vocabulary when set.
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func resolveRoots(brainDir, wing, hall string) ([]string, error) {
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if hall != "" && !brain.IsValidHall(hall) {
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return nil, fmt.Errorf("invalid hall %q", hall)
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}
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if wing != "" {
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w := brain.Sanitise(wing)
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if w == "" {
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return nil, fmt.Errorf("invalid wing %q", wing)
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}
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if hall != "" {
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return []string{filepath.Join(brainDir, "wiki", w, hall)}, nil
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}
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return []string{filepath.Join(brainDir, "wiki", w)}, nil
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}
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if hall != "" {
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return nil, fmt.Errorf("hall filter requires wing")
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}
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return []string{
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filepath.Join(brainDir, "knowledge"),
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filepath.Join(brainDir, "wiki"),
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}, nil
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}
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// extractWingHall reads wing/hall from frontmatter first, falling back to
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// path segments brain/wiki/<wing>/<hall>/.
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func extractWingHall(content, relPath string) (wing, hall string) {
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scanner := bufio.NewScanner(strings.NewReader(content))
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inFrontmatter := false
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for scanner.Scan() {
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line := scanner.Text()
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if strings.TrimSpace(line) == "---" {
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if !inFrontmatter {
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inFrontmatter = true
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continue
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}
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break
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}
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if !inFrontmatter {
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continue
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}
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key, val, ok := strings.Cut(line, ":")
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if !ok {
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continue
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}
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v := strings.Trim(strings.TrimSpace(val), `"'`)
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switch strings.TrimSpace(key) {
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case "wing":
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wing = v
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case "hall":
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hall = v
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}
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}
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if wing != "" && hall != "" {
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return wing, hall
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}
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parts := strings.Split(relPath, "/")
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if len(parts) >= 4 && parts[0] == "wiki" {
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if wing == "" {
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wing = parts[1]
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}
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if hall == "" && brain.IsValidHall(parts[2]) {
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hall = parts[2]
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}
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}
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return wing, hall
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}
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func extractTitle(content, filename string) string {
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scanner := bufio.NewScanner(strings.NewReader(content))
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inFrontmatter := false
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for scanner.Scan() {
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line := scanner.Text()
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if strings.TrimSpace(line) == "---" {
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if !inFrontmatter {
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inFrontmatter = true
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continue
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}
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break
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}
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if inFrontmatter {
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key, val, ok := strings.Cut(line, ":")
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if ok && strings.TrimSpace(key) == "title" {
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return strings.Trim(strings.TrimSpace(val), `"'`)
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}
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}
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}
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return strings.TrimSuffix(filename, ".md")
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}
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func excerpt(content string, maxLen int) string {
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parts := strings.SplitN(content, "---", 3)
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body := content
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if len(parts) == 3 {
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body = strings.TrimSpace(parts[2])
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}
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if len(body) > maxLen {
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return body[:maxLen] + "…"
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}
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return body
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}
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