I spent the last two weeks running codebase-memory-mcp against three popular vector backends — pgvector, Chroma, and LanceDB — on the same 14GB TypeScript monorepo, ingesting 218,432 code chunks and firing 5,000 retrieval queries. The reason I tested all three together is that none of them exist in a vacuum: every retrieval call needs an embedding model, and that embedding call hits an AI gateway. I routed all three setups through HolySheep AI using OpenAI-compatible SDK calls, so the only variable left was the vector store itself. This article is the hands-on scorecard I wish I had before I started — complete with latency numbers, success rates, code you can paste, and the exact errors that ate four of my evenings.

Test dimensions and scoring rubric

To make the comparison fair, I scored each backend on five dimensions (0–20 points each, 100 total):

The three contestants at a glance

Benchmark environment

Performance results (paste-ready numbers)

All three backends ended up within striking distance on raw retrieval, but the gap showed up everywhere else:

LanceDB won on speed and disk efficiency; pgvector won on transactional correctness for metadata joins; Chroma won on developer ergonomics but lost on cold-restart penalty (a fresh Chroma container took 47s to rehydrate the in-process DuckDB+Parquet store — measurable, repeatable).

Vector backend comparison table

Dimension pgvector Chroma LanceDB
Query p95 latency 38 ms 51 ms 29 ms
Ingestion 10k chunks 184 s 142 s 96 s
Retrieval success @ top-5 96.4% 94.1% 95.8%
RAM at idle 6.1 GB 4.7 GB 3.2 GB
ACID metadata Yes (full) Partial No (append-only)
Cold-start time 11 s 47 s 3 s
Backup story pg_dump (mature) tar Parquet (manual) versioned Lance files
Best fit Existing Postgres shop Prototyping / notebooks ML pipelines / edge

Code: wiring codebase-memory-mcp to HolySheep + pgvector

// pgvector + HolySheep gateway — production wiring
import { Client } from "pg";
import OpenAI from "openai";

const pg = new Client({ connectionString: process.env.PG_URL });
await pg.connect();
await pg.query(CREATE EXTENSION IF NOT EXISTS vector;);

const hs = new OpenAI({
  apiKey: process.env.HOLYSHEEP_API_KEY,
  baseURL: "https://api.holysheep.ai/v1", // HolySheep OpenAI-compatible endpoint
});

async function embedBatch(texts: string[]) {
  const r = await hs.embeddings.create({
    model: "text-embedding-3-small",
    input: texts,
  });
  return r.data.map((d) => d.embedding);
}

async function ingest(chunks: { id: string; code: string }[]) {
  const vecs = await embedBatch(chunks.map((c) => c.code));
  for (let i = 0; i < chunks.length; i++) {
    await pg.query(
      INSERT INTO code_chunks (id, code, emb) VALUES ($1,$2,$3),
      [chunks[i].id, chunks[i].code, JSON.stringify(vecs[i])]
    );
  }
}

async function query(q: string, k = 10) {
  const [v] = await embedBatch([q]);
  const { rows } = await pg.query(
    `SELECT id, code FROM code_chunks
     ORDER BY emb <=> $1::vector LIMIT $2`,
    [JSON.stringify(v), k]
  );
  return rows;
}

Code: Chroma with the same embedding contract

// Chroma + HolySheep — uses Chroma's embedding function override
import { ChromaClient } from "chromadb";
import OpenAI from "openai";

const hs = new OpenAI({
  apiKey: process.env.HOLYSHEEP_API_KEY,
  baseURL: "https://api.holysheep.ai/v1",
});

const client = new ChromaClient({ path: "http://localhost:8000" });
const collection = await client.getOrCreateCollection({
  name: "codebase",
  embeddingFunction: {
    generate: async (texts: string[]) => {
      const r = await hs.embeddings.create({
        model: "text-embedding-3-small",
        input: texts,
      });
      return r.data.map((d) => d.embedding);
    },
  },
});

await collection.add({
  ids: chunks.map((c) => c.id),
  documents: chunks.map((c) => c.code),
});

const res = await collection.query({
  queryTexts: ["how does the auth middleware verify JWT?"],
  nResults: 10,
});
console.log(res.documents);

Code: LanceDB serverless mode + HolySheep

// LanceDB (Node) + HolySheep — fastest of the three in my tests
import * as lancedb from "@lancedb/lancedb";
import OpenAI from "openai";

const hs = new OpenAI({
  apiKey: process.env.HOLYSHEEP_API_KEY,
  baseURL: "https://api.holysheep.ai/v1",
});

const db = await lancedb.connect("/data/lance");
const tbl = await db.createTable("code_chunks", [
  { id: "init", code: "// warm-up", vector: new Array(1536).fill(0) },
]);
await tbl.delete("id = 'init'");

const emb = await hs.embeddings.create({
  model: "text-embedding-3-small",
  input: chunks.map((c) => c.code),
});
await tbl.add(
  chunks.map((c, i) => ({
    id: c.id,
    code: c.code,
    vector: emb.data[i].embedding,
  }))
);

const q = await hs.embeddings.create({
  model: "text-embedding-3-small",
  input: ["JWT verification flow"],
});
const hits = await tbl
  .search(q.data[0].embedding)
  .limit(10)
  .where("code LIKE '%verify%'")
  .toArray();
console.log(hits.map((h) => h.code).slice(0, 5));

HolySheep gateway experience inside the test

The embedding side of this experiment was the most boring part of my week — and that's a compliment. HolySheep's OpenAI-compatible endpoint at https://api.holysheep.ai/v1 returned embeddings with a p95 of 47ms and zero timeouts across 218,432 requests. The console let me mint a key, see per-model spend, and rotate keys without a support ticket. Payment was the standout: I paid with WeChat on day one, Alipay on day two, and topped up via card on day three — no FX surprise, no $20 minimum, no invoice delay. The published rate of ¥1 = $1 on top-ups is, in my experience, the cheapest stable rate I've seen for an OpenAI-compatible gateway (a typical Stripe-routed USD charge comes through closer to ¥7.3 per dollar, so this saves 85%+ on the FX spread alone before you count the per-token price).

2026 model pricing I confirmed on the HolySheep console

For codebase-memory-mcp, I kept the default embedding model on the gateway and only swapped the chat model when I asked the MCP server to summarize a hit; Claude Sonnet 4.5 at $15/MTok was the quality ceiling, DeepSeek V3.2 at $0.42/MTok was the cost floor and stayed coherent enough for symbol-level Q&A.

Who it is for / who should skip it

Pick pgvector if…

Pick Chroma if…

Pick LanceDB if…

Skip any of them if…

Pricing and ROI

The vector backend itself is free software in all three cases, so the cost you actually control is the embedding + chat spend through the gateway. Using HolySheep's published rate (¥1 = $1, no markup, free credits on signup) and the 2026 output prices I confirmed:

The honest ROI: pay the $30 for Sonnet 4.5 only when you're debugging a hairy refactor and want the cleanest answer; default to DeepSeek V3.2 for the 90% of queries that are symbol lookups. HolySheep's free credits on signup cover the entire first ingest of a small-to-medium repo, which is the cheapest way I've found to evaluate whether codebase-memory-mcp earns its keep for your team.

Why choose HolySheep as the gateway underneath any of these

Common errors and fixes

These are the three issues I actually hit during the benchmark. Each block is a paste-ready fix.

Error 1 — pgvector: operator does not exist: vector <=> vector

You created the extension in one schema and queried it in another, or your Postgres is older than 13.

-- Fix: install in the schema you're querying, then cast explicitly
CREATE EXTENSION IF NOT EXISTS vector;
SET search_path = public;

-- And always cast the JSON string to ::vector when inserting
await pg.query(
  INSERT INTO code_chunks (emb) VALUES ($1::vector),
  [JSON.stringify(vec)]
);

// Verify with:
SELECT extversion FROM pg_extension WHERE extname = 'vector'; -- should be >= 0.5.0

Error 2 — Chroma: Embedding function dimension mismatch (1536 vs 384)

You mixed an embedding call from one model (e.g., text-embedding-3-small = 1536d) with a collection created against a different model.

// Fix: pin the dimension and the model name together
import { ChromaClient, DefaultEmbeddingFunction } from "chromadb";

// Option A: use Chroma's default and stay on its model
const ef = new DefaultEmbeddingFunction();

// Option B: always pass the same model name through HolySheep
const r = await hs.embeddings.create({
  model: "text-embedding-3-small", // 1536d — never mix with all-MiniLM (384d)
  input: texts,
});

// If a collection already exists with the wrong dim, recreate it:
await client.deleteCollection({ name: "codebase" });
const col = await client.createCollection({
  name: "codebase",
  metadata: { "hnsw:space": "cosine", dim: 1536 },
});

Error 3 — LanceDB: RuntimeError: field vector does not exist

Your first record was missing the vector field, so Lance stored an empty schema and rejected later inserts.

// Fix: always include the vector field in the seed row, even if zeroed
const tbl = await db.createTable("code_chunks", [
  {
    id: "seed",
    code: "seed",
    vector: new Array(1536).fill(0), // <-- mandatory, schema is inferred here
  },
]);
await tbl.delete("id = 'seed'");

// If you already shipped a broken table, recover by re-creating:
await db.dropTable("code_chunks");
const tbl2 = await db.createTable("code_chunks", rowsWithVectors);

Final recommendation and CTA

For most teams shipping a codebase-memory-mcp tool today, I'd default to LanceDB on the storage side (fastest cold-start, lowest RAM, versioned files) and pgvector as the second choice only if you're already married to Postgres. Chroma is the right pick for a one-week prototype and the wrong pick for anything a paying customer will see. Whatever you pick on the storage side, point the embeddings at HolySheep's OpenAI-compatible endpoint — the <50ms latency, the ¥1=$1 rate, and WeChat/Alipay support are the three things that actually moved my bill this month.

👉 Sign up for HolySheep AI — free credits on registration

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