Adds an opt-in cross-encoder rerank step between BM25 retrieval and LLM synthesis. With BRAIN_RERANKER_URL set, brain_answer retrieves BM25 top-20, scores each excerpt against the query via Qwen3-Reranker on Ollama, drops the "no" answers, and forwards up to 5 surviving sources to the LLM. Unset, behaviour is unchanged (BM25 top-10 → LLM). The reranker is a *filter*, not a re-ranker: Qwen3-Reranker emits a binary yes/no token under its native chat template, and ties within the "yes" set are broken by BM25 rank — what got retrieved first stays ahead. New package ingestion/internal/reranker: - Client with URL, Model, HTTP fields. - New(url, model) returns nil on empty url so callers can treat "feature disabled" as a single nil check. - Score(ctx, query, docs) issues one /api/generate call per doc using the Qwen3-Reranker yes/no chat template (verbatim, because the model was trained on this exact wording). Parses the first non-think token. Wiring: - mcp.Server gains a WithReranker fluent setter to keep NewServer signature stable. - brain_answer's BM25 limit jumps to 20 only when a reranker is wired, to give the filter something to do. - cmd/server/main.go reads BRAIN_RERANKER_URL (+ optional BRAIN_RERANKER_MODEL, default dengcao/Qwen3-Reranker-0.6B:F16). Tests cover: nil-on-empty-url, ordered yes/no scoring, request shape (model, prompt contents, yes/no template), ambiguous response → 0, empty doc slice, upstream-error propagation, plus an end-to-end brain_answer integration that proves only the relevant note reaches the LLM when noise.md is rejected. Closes hyperguild#7.
153 lines
4.2 KiB
Go
153 lines
4.2 KiB
Go
package mcp
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import (
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"context"
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"encoding/json"
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"fmt"
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"strings"
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"github.com/mathiasbq/hyperguild/ingestion/internal/reranker"
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"github.com/mathiasbq/hyperguild/ingestion/internal/search"
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)
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// rerankResults scores each candidate's excerpt against the query and
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// returns up to top results whose score is positive, preserving the
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// caller's input order (BM25 rank) within the kept set. The reranker is
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// a filter: ties are broken by BM25, not by the reranker's binary score.
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func rerankResults(ctx context.Context, rr *reranker.Client, query string, results []search.Result, top int) ([]search.Result, error) {
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docs := make([]string, len(results))
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for i, r := range results {
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docs[i] = r.Excerpt
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}
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scores, err := rr.Score(ctx, query, docs)
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if err != nil {
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return nil, err
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}
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kept := make([]search.Result, 0, top)
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for i, r := range results {
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if scores[i] > 0 {
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kept = append(kept, r)
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}
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if len(kept) == top {
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break
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}
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}
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return kept, nil
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}
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const (
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answerSystemPrompt = `You are a knowledge assistant. Answer the question using ONLY the provided sources.
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Cite source file paths inline when referencing specific content.
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If the context does not contain enough information to answer, say so clearly.`
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classifySystemPrompt = `Classify the document. Respond with JSON only, no markdown fences.
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{"type":"...","title":"...","tags":["..."]}
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Valid types: spec, plan, decision, note, wiki, log, code, unknown.`
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)
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type brainAnswerArgs struct {
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Query string `json:"query"`
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}
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func (s *Server) brainAnswer(ctx context.Context, args json.RawMessage) (json.RawMessage, error) {
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if s.answerLLM == nil {
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return nil, fmt.Errorf("answer LLM not configured: set BRAIN_LLM_PRIMARY_URL")
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}
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var a brainAnswerArgs
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if err := json.Unmarshal(args, &a); err != nil {
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return nil, fmt.Errorf("parse args: %w", err)
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}
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if a.Query == "" {
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return nil, fmt.Errorf("query is required")
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}
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// With reranker disabled: BM25 top-10 straight to the LLM.
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// With reranker enabled: BM25 top-20 → cross-encoder filter → top-5.
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bm25Limit := 10
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if s.reranker != nil {
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bm25Limit = 20
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}
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results, err := search.Query(s.brainDir, search.QueryOptions{Query: a.Query, Limit: bm25Limit})
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if err != nil {
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return nil, fmt.Errorf("search: %w", err)
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}
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if s.reranker != nil && len(results) > 0 {
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results, err = rerankResults(ctx, s.reranker, a.Query, results, 5)
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if err != nil {
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return nil, fmt.Errorf("rerank: %w", err)
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}
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}
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if len(results) == 0 {
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return json.Marshal(map[string]any{
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"answer": "No relevant content found in brain.",
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"sources": []string{},
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})
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}
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var sb strings.Builder
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sources := make([]string, 0, len(results))
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for _, r := range results {
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fmt.Fprintf(&sb, "<source path=%q>\n%s\n</source>\n\n", r.Path, r.Excerpt)
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sources = append(sources, r.Path)
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}
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answer, err := s.answerLLM(ctx, answerSystemPrompt, sb.String()+"Question: "+a.Query)
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if err != nil {
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return nil, fmt.Errorf("llm: %w", err)
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}
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return json.Marshal(map[string]any{
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"answer": answer,
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"sources": sources,
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})
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}
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type brainClassifyArgs struct {
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Text string `json:"text"`
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}
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type classifyResult struct {
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Type string `json:"type"`
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Title string `json:"title"`
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Tags []string `json:"tags"`
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}
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func (s *Server) brainClassify(ctx context.Context, args json.RawMessage) (json.RawMessage, error) {
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if s.answerLLM == nil {
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return nil, fmt.Errorf("answer LLM not configured: set BRAIN_LLM_PRIMARY_URL")
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}
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var a brainClassifyArgs
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if err := json.Unmarshal(args, &a); err != nil {
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return nil, fmt.Errorf("parse args: %w", err)
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}
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if a.Text == "" {
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return nil, fmt.Errorf("text is required")
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}
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text := a.Text
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if len(text) > 3000 {
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text = text[:3000]
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}
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raw, err := s.answerLLM(ctx, classifySystemPrompt, text)
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if err != nil {
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return nil, fmt.Errorf("llm: %w", err)
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}
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// Strip markdown fences if model adds them despite the instruction.
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raw = strings.TrimSpace(raw)
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raw = strings.TrimPrefix(raw, "```json")
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raw = strings.TrimPrefix(raw, "```")
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raw = strings.TrimSuffix(raw, "```")
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raw = strings.TrimSpace(raw)
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var cr classifyResult
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if err := json.Unmarshal([]byte(raw), &cr); err != nil {
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return nil, fmt.Errorf("parse classify response %q: %w", raw, err)
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}
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if cr.Tags == nil {
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cr.Tags = []string{}
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}
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return json.Marshal(cr)
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}
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