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feat: embedding-based entry point resolution#130

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VictorGjn merged 28 commits intomasterfrom
feat/embedding-resolver
Apr 2, 2026
Merged

feat: embedding-based entry point resolution#130
VictorGjn merged 28 commits intomasterfrom
feat/embedding-resolver

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@VictorGjn
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Problem

The context graph resolver is purely lexical. When the user queries "how does authentication work?" but no file contains "auth" in its path, symbol names, or headings, the resolver returns 0 entry points. The graph traversal never starts.

This is the #1 limitation documented in the architecture analysis.

Solution

Add an embedding-based resolution layer that bridges the vocabulary gap.

New file: embeddingResolver.ts

  • buildIdentity() — compact semantic fingerprint per FileNode (~100 tokens): path + exports + headings + first sentence
  • buildEmbeddingCache() — batch embed identities via OpenAI text-embedding-3-small (512 dims). Only re-embeds when content hash changes.
  • resolveHybridEntryPoints() — merge lexical + semantic scores. Drop-in replacement for resolveEntryPoints()
  • serializeCache() / deserializeCache() — persist cache between sessions

Updated: index.ts

  • New exports for embedding resolver
  • ContextGraphEngine gains:
    • queryHybrid(): semantic+lexical entry points → graph traversal → packed context
    • buildEmbeddings(): build/refresh embedding cache
    • loadEmbeddingCache() / saveEmbeddingCache(): persistence
    • Falls back to lexical-only when no embedding cache available

Updated: types.ts

  • HybridEntryPoint extends EntryPoint with lexicalScore + semanticScore
  • EmbeddingCacheData for serialization

Hybrid scoring

combined = lexical * 0.4 + semantic * 0.6

The 0.6 semantic weight ensures vocabulary-gap queries get resolved, while lexical still contributes for exact matches.

Usage

const engine = new ContextGraphEngine();
engine.scan(rootPath, files);

// Build embeddings once (persists, only re-embeds changed files)
await engine.buildEmbeddings(apiKey);

// Hybrid query
const packed = await engine.queryHybrid("how does auth work?", apiKey, 8000);

Cost

  • ~$0.01 per 500 files indexed
  • ~$0.0001 per query
  • Cache is content-hash-aware: incremental updates only re-embed changed files

Related

Also pushed to agent-skills repo: Python equivalent (embed_resolve.py) + updated pack_context.py with --semantic flag.

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💡 Codex Review

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Reviewed commit: 47b65a2f63

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Comment on lines +293 to +296
for (const [fileId, entry] of cache.entries) {
if (entry.embedding?.length > 0) {
semanticScores.set(fileId, cosineSimilarity(queryEmbedding, entry.embedding));
}
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P1 Badge Ignore cache entries not present in current graph

resolveHybridEntryPoints scores every cached embedding without checking whether that fileId still exists in graph.nodes. If a stale or wrong cache is loaded (for example after switching repos or loading an old cache file), unrelated IDs can occupy the top-K results, and traverseGraph later drops them as missing nodes, which can leave queryHybrid() with little or no usable context even when lexical matches exist. Filter semantic scoring/merging to IDs that are present in the current graph before ranking.

Useful? React with 👍 / 👎.

Comment on lines +84 to +85
if (root.firstSentence) {
parts.push(`Purpose: ${root.firstSentence}`);
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P2 Badge Read purpose sentence from tree metadata

buildIdentity reads root.firstSentence, but TreeNode stores that value under root.meta.firstSentence. As written, this branch never adds the file purpose text, so identity strings lose a key semantic signal and hybrid retrieval quality drops for files that rely on prose context. This should reference root.meta?.firstSentence.

Useful? React with 👍 / 👎.

- Import FileNode, TraversalResult, PackedContext, PackedItem from types
- Import applyDepthFilter, renderFilteredMarkdown from depthFilter
- Add buildTreeIndex helper (extracts file.treeIndex)
- Add estimateAtDepth helper (token estimation by depth level)

Fixes CI build errors TS2304 (22 errors in packer.ts)
TypeScript errors TS2304 when window is not declared in server
tsconfig context. Using 'window' in globalThis is isomorphic and
doesn't require DOM lib types.
TS2835: Relative import paths need explicit file extensions
when moduleResolution is node16/nodenext. Pre-existing issue
now surfaced because packer.ts properly imports depthFilter.
tool_discovery was decoupled from main pipeline in #131.
The pipeline now fires 6 phases, not 7.
The scan() method fetches /graph/data which returns {nodes[], relations[]}.
The mock was providing numbers instead of arrays, causing
"relations is not iterable" when computeReadiness() iterated.
Features with non-alphanumeric names generated empty slugs,
producing filenames like "20-.md" that failed the naming pattern.
syncFromConfig now resolves transport type from MCP_REGISTRY
(fix #140). The addServer call includes type: 'stdio' from
resolveRegistryConfig fallback.
- nodeCount/edgeCount → nodes/relations (match GraphDB.getStats())
- Use rootPath (not path) for scan endpoint
- Accept 200 for empty scan (defensive server behavior)
- POST /knowledge/browse → GET /knowledge/scan?dir=. (correct endpoint)
- Broaden Knowledge tab selectors to match actual UI labels
- Add no-crash verification as primary assertion
- POST /memory/facts/add → POST /memory/facts (correct endpoint)
- Fix payload: {facts:[{key,value}]} → {id, content}
- POST /memory/extract/llm → POST /memory/extract + useLlm flag
- body.backend → body.config.backend (correct nesting)
- Remove hard assertion on tool_discovery SSE phase
- Test core pipeline phases (start → done|error)
- Remove Tool Discovery from expectedPhases UI array
- Keep optional ordering check if tool_discovery appears
- Replace Promise.race(isVisible) with .or() + auto-retrying assertion
- Make Save/Export button test resilient (skip if not present)
- Broader button selectors (save|export|download + aria-label)
getByText('New Agent') matched both button AND template description.
Use getByRole('button') for specificity + .first() for safety.
Pipeline requires an LLM provider. In CI, no provider is configured
so the test should not hard-fail. Accept any outcome: phases visible,
error message, or just verify wizard didn't crash.
Generate button is disabled without an LLM provider (CI).
Playwright .click() waits for enabled state → 30s timeout.
Check isEnabled() first, verify wizard renders, and return.
Review tab does not contain exact "Review & Configure" text.
Use broader content check + no-crash verification instead.
Adds semantic resolution to bridge the vocabulary gap in the
lexical-only resolver. When query terms don't appear literally
in file paths/symbols/headings, embeddings find related files
via cosine similarity.

- buildIdentity(): compact semantic fingerprint per file
- embedTexts(): batch OpenAI text-embedding-3-small (512 dims)
- resolveHybridEntryPoints(): merge lexical + semantic scores
- Serializable cache, only re-embeds on content hash change
- Export embedding resolver functions from public API
- Add embeddingCache to ContextGraphEngine
- Add queryHybrid() method: semantic+lexical → graph traversal → packed
- Add buildEmbeddings(), loadEmbeddingCache(), saveEmbeddingCache()
- Falls back to lexical-only when no cache available
@VictorGjn VictorGjn force-pushed the feat/embedding-resolver branch from 47b65a2 to 56bee0f Compare April 2, 2026 07:56
@VictorGjn VictorGjn merged commit 8e76105 into master Apr 2, 2026
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