XHS Knowledge Retriever

Query the competitor-note RAG index for semantically relevant examples

Query the competitor-note RAG index and get back the most relevant chunks.

Run

From workspace-xhs-agent/products/xhs-note-learning-cycle:

bash

python3 skills/xhs-knowledge-retriever/scripts/retrieve.py --query "有娃家庭怎么选沙发"

With options:

bash

python3 skills/xhs-knowledge-retriever/scripts/retrieve.py \
  --query "有娃家庭怎么选沙发" \
  --top-k 5 \
  --output /tmp/retrieved.json

Check local readiness without running retrieval:

bash

python3 skills/xhs-knowledge-retriever/scripts/retrieve.py --check-only

Runtime Python packages:

• numpy
• sentence-transformers

The script does not require API credentials. If the standard workspace layout is not available, set XHS_KNOWLEDGE_ROOT to the local knowledge/xhs directory.

What it consumes

• Canonical Competitor RAG Storage (index.json)
• Canonical Competitor RAG Storage (embeddings.npy)
• Canonical Competitor RAG Storage (metadata.jsonl)

What it produces

• JSON with the query, model, and top-k results including:
  • score (cosine similarity)
  • chunk (the retrieved text)
  • metadata (competitor name, note title, note URL, title pattern, content signals, etc.)

Rules

1. Read only. It only queries the local index.
2. Planner/Writer should call this before making creative decisions.
3. Retrieved chunks are inspiration, not copy sources.
4. Do not publish competitor text verbatim.