Voyage-3（ボヤージュスリー）

import voyageai

client = voyageai.Client()  # VOYAGE_API_KEY 環境変数から自動読み込み

# 単一テキストの埋め込み
result = client.embed(
    ["自作PCにおけるCPUの選び方"],
    model="voyage-3",
    input_type="query"  # or "document"
)
print(result.embeddings[0])  # list[float], len=1024

# バッチ処理
docs = ["RTX 4090の性能", "Core i9-14900KSのベンチ", "DDR5メモリの選び方"]
result = client.embed(docs, model="voyage-3", input_type="document")

import anthropic
import voyageai
from qdrant_client import QdrantClient

voyage = voyageai.Client()
qdrant = QdrantClient(url="http://localhost:6333")
claude = anthropic.Anthropic()

def rag_answer(question: str) -> str:
    # 1. 質問をvoyage-3で埋め込み
    q_emb = voyage.embed([question], model="voyage-3", input_type="query").embeddings[0]
    
    # 2. Qdrantで類似文書検索
    hits = qdrant.search("my_collection", query_vector=q_emb, limit=5)
    context = "\n".join([h.payload["text"] for h in hits])
    
    # 3. Claudeで回答生成
    msg = claude.messages.create(
        model="claude-opus-4-8",
        max_tokens=1024,
        messages=[{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}"}]
    )
    return msg.content[0].text