chore: import upstream snapshot with attribution

eval/runner/adapters/vector.ts

@@ -0,0 +1,108 @@
+import type { Adapter, RankedDoc, Session } from "../types.js";
+
+interface VectorState {
+  sessions: Session[];
+  embeddings: Float32Array[];
+}
+
+const OPENAI_URL = "https://api.openai.com/v1/embeddings";
+const MODEL = "text-embedding-3-small";
+const DIM = 1536;
+
+async function embed(text: string, apiKey: string): Promise<Float32Array> {
+  const res = await fetch(OPENAI_URL, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify({ input: text, model: MODEL }),
+  });
+  if (!res.ok) {
+    throw new Error(`OpenAI embed failed: ${res.status} ${await res.text()}`);
+  }
+  const data = (await res.json()) as { data: Array<{ embedding: number[] }> };
+  return Float32Array.from(data.data[0].embedding);
+}
+
+async function embedBatch(texts: string[], apiKey: string): Promise<Float32Array[]> {
+  const res = await fetch(OPENAI_URL, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify({ input: texts, model: MODEL }),
+  });
+  if (!res.ok) {
+    throw new Error(`OpenAI batch embed failed: ${res.status} ${await res.text()}`);
+  }
+  const data = (await res.json()) as { data: Array<{ embedding: number[]; index: number }> };
+  if (!Array.isArray(data.data) || data.data.length !== texts.length) {
+    throw new Error(
+      `OpenAI batch embed: expected ${texts.length} embeddings, got ${data.data?.length ?? 0}`,
+    );
+  }
+  const out = new Array<Float32Array>(texts.length);
+  for (const row of data.data) {
+    if (
+      !Number.isInteger(row.index) ||
+      row.index < 0 ||
+      row.index >= texts.length ||
+      out[row.index] !== undefined
+    ) {
+      throw new Error(`OpenAI batch embed: invalid or duplicate index ${row.index}`);
+    }
+    if (!Array.isArray(row.embedding) || row.embedding.length === 0) {
+      throw new Error(`OpenAI batch embed: empty embedding at index ${row.index}`);
+    }
+    out[row.index] = Float32Array.from(row.embedding);
+  }
+  return out;
+}
+
+function cosine(a: Float32Array, b: Float32Array): number {
+  let dot = 0;
+  let na = 0;
+  let nb = 0;
+  for (let i = 0; i < a.length; i++) {
+    dot += a[i] * b[i];
+    na += a[i] * a[i];
+    nb += b[i] * b[i];
+  }
+  const denom = Math.sqrt(na) * Math.sqrt(nb);
+  return denom === 0 ? 0 : dot / denom;
+}
+
+export const vectorAdapter: Adapter<VectorState> = {
+  name: "vector",
+  async init(sessions) {
+    const apiKey = process.env.OPENAI_API_KEY;
+    if (!apiKey) throw new Error("OPENAI_API_KEY required for vector adapter");
+    const embeddings: Float32Array[] = new Array(sessions.length);
+    const BATCH = 50;
+    for (let i = 0; i < sessions.length; i += BATCH) {
+      const batch = sessions.slice(i, i + BATCH);
+      const vecs = await embedBatch(
+        batch.map((s) => s.content.slice(0, 8000)),
+        apiKey,
+      );
+      for (let j = 0; j < vecs.length; j++) embeddings[i + j] = vecs[j];
+    }
+    if (embeddings.length > 0 && embeddings[0].length !== DIM) {
+      throw new Error(`unexpected embedding dim: ${embeddings[0].length}`);
+    }
+    return { sessions, embeddings };
+  },
+  async query(q, state, k) {
+    const apiKey = process.env.OPENAI_API_KEY;
+    if (!apiKey) throw new Error("OPENAI_API_KEY required for vector adapter");
+    const qvec = await embed(q, apiKey);
+    const scored: RankedDoc[] = state.sessions.map((s, i) => ({
+      sessionId: s.id,
+      score: cosine(qvec, state.embeddings[i]),
+    }));
+    scored.sort((a, b) => b.score - a.score);
+    return scored.slice(0, k);
+  },
+};