@anvil/knowledge-core

Your codebase, retrievable.

A code-aware knowledge stack — AST-level chunking, hybrid retrieval, project graphs, and cross-repo edge detection. Built so agents see the whole codebase, not just the file you happened to open.

The problem with grep + embeddings

Code search works one of two ways: lexical (grep, fast, dumb) or embedding-based (vector DB, slow on edits, blind to structure). Both miss the same thing: code has graph structure. A function isn't just a chunk of text — it imports things, gets called by things, implements things, lives in a package, talks to other repos.

knowledge-core treats the codebase like a graph and a corpus at the same time. Tree-sitter parses every supported language into AST chunks. A vector store + BM25 + a project graph all answer the same query, fused into one ranked list. The retriever knows when you're asking about an identifier vs an error code vs a natural- language question and weights the signals accordingly.

import { KnowledgeIndexer, getRetriever } from '@anvil/knowledge-core';

// Index once — incremental thereafter.
const indexer = new KnowledgeIndexer({ project: 'space-tourism' });
await indexer.indexProject();

// Retrieve — vector + BM25 + 1-hop graph + cross-encoder rerank.
const retriever = await getRetriever('space-tourism');
const result = await retriever.retrieve(
  'where do we validate booking seat tiers?',
);

console.log(result.chunks.length, 'chunks');     // ranked, deduped
console.log(result.graphContext);                // related symbols
console.log(result.totalTokens);                 // budgeted

What you get

AST-aware chunking

tree-sitter WASM parses TypeScript, JavaScript, TSX, Go, Python, Rust, Java, and PHP into proper function / class / method boundaries. No more chunks that cut a function in half. Regex fallback for languages without a grammar so nothing is unindexed.

Incremental by default

The indexer reads git rev-parse HEAD, diffs against the last indexed SHA, and re-chunks only what changed. Deleted files get removed from the vector store. Embedding is independently incremental — it diffs new chunk IDs against LanceDB and only embeds the deltas. Big repos stay fast forever.

Hybrid retrieval, four phases

1. Vector ⫽ BM25 in parallel — semantic recall + lexical recall.
2. Reciprocal Rank Fusion — combine without one dominating.
3. AST tripartite expansion — pull in callers, callees, and type references via the project graph.
4. Cross-encoder rerank — Qwen3-Reranker by default; Cohere / Voyage / OpenAI-compatible swappable.

The query classifier picks adaptive weights — identifiers lean BM25, natural-language leans vector, error codes lean both.

Six embedding providers, four rerankers

Embed: Codestral · Voyage · OpenAI · Ollama · Gemini OAuth · OpenAI-compatible · auto (picks based on what's configured). Rerank: Ollama (default) · Cohere · Voyage · OpenAI-compatible. Plug in whatever fits your cost/quality/latency curve.

Project graph

A graphology directed multi-graph stitches every repo together. Nodes are entities (functions, classes, types, modules), edges are imports / calls / inheritance / contains / type-refs. Louvain community detection clusters semantically related code so retrieval can surface "the auth subsystem" rather than four scattered files.

Cross-repo edge detection

Fourteen strategies covering shared types, Kafka topics, HTTP endpoints, gRPC, databases, env vars, npm/workspace deps, k8s, docker-compose, proto definitions, Redis, S3, and shared constants. Plus an LLM-inferred semantic edge layer for the cases regex can't catch.

LLM-driven where it matters

Repo profiling (fingerprint files → LLM → typed RepoProfile, cached by fingerprint hash), semantic edge inference, project summary generation, RAG quality evaluation. All routed through @anvil/agent-core so the same router, retries, and cost ledger that power the agent stack power knowledge ingestion.

Structural hashing

computeStructuralHash canonicalizes source — strips comments, collapses whitespace, normalizes identifiers — and SHA-256s the result. Used for chunk dedup and shared with @anvil/memory-core so memory drift detection and chunk dedup speak the same language.

Architecture at a glance

   git repo(s)
       │
       ▼
   ┌─────────────────────────────────────────────────────────┐
   │  Indexing                                               │
   │   walk + git diff → tree-sitter chunks → AST graph      │
   │           │              │            │                 │
   │           ▼              ▼            ▼                 │
   │     LanceDB         BM25 (FTS)    project graph         │
   │     (vectors)                     + cross-repo edges    │
   └─────────────────────────────────────────────────────────┘
                                │
                                ▼
   ┌─────────────────────────────────────────────────────────┐
   │  HybridRetriever                                        │
   │   query classifier ─▶ adaptive weights                  │
   │   vector ⫽ BM25  →  RRF  →  AST expansion  →  rerank    │
   └─────────────────────────────────────────────────────────┘
                                │
                                ▼
                       ranked chunks + graph context

Two consumers, one stack: @esankhan3/anvil-cli indexes via anvil index and retrieves during pipelines; @esankhan3/code-search-mcp exposes the same retriever as MCP tools that any agent can call.

Storage layout

~/.anvil/knowledge-base/<project>/
  chunks.json                    # canonical chunks (consumed by embedder)
  deleted_files.json             # incremental embed cleanup
  system_graph_v2.json           # merged project graph
  PROJECT_GRAPH.json             # LLM-generated semantic graph
  PROJECT_SUMMARY.md             # human-readable companion
  lancedb/                       # vector store
  <repo>/
    profile.json                 # cached RepoProfile
    graph.json                   # per-repo AST graph
    GRAPH_REPORT.md              # quality report
    index_meta.json              # { lastIndexedSha, files, chunkCount }

Everything is on disk, inspectable, git-friendly where it makes sense (project graph + summary).

Philosophy

Chunks should follow code shape, not byte count. AST boundaries keep functions whole. Retrieval quality starts at the chunker.

No single retrieval signal is enough. Vector misses identifiers. BM25 misses paraphrases. Graph misses everything text-shaped. Hybrid + adaptive weighting is the only honest answer.

Incremental or nothing. Anvil indexes on every pipeline run. A full re-index isn't acceptable; the engine treats the previous SHA as a load-bearing input.

No vendor lock-in. LanceDB on disk. graphology in-process. Pluggable embedders + rerankers. Swap any layer without rewriting the rest.

Two surfaces, one engine. CLI users get retrieval through pipelines; MCP users get it through tool calls. The retrieval pipeline is the same code path.

Status

Stable: chunking, AST graph, hybrid retrieval, incremental indexing, six embedders, four rerankers, fourteen cross-repo strategies, project graph, structural hashing. In flight: richer graph queries and a deeper RAG-eval harness.

Part of Anvil — the AI development pipeline.