vector-store

name	vector-store
description	Vector store schema protection and migration management

🗄️ Vector Store SKILL

Purpose

PROTECTED: Vector store schema management. Changes require pre-modification checklist validation.

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Supported Vector Stores

1. Pinecone (Cloud)

import pinecone
from langchain_pinecone import PineconeVectorStore

pinecone.init(api_key="your-key")

index = pinecone.Index("rag-demo")
vector_store = PineconeVectorStore(index, embeddings)

Schema: {dimension: 1536, metric: "cosine"}

---

2. Milvus (Self-hosted/Cloud)

from langchain_community.vectorstores import Milvus

vector_store = Milvus(
    embeddings,
    connection_args={"host": "localhost", "port": "19530"},
    collection_name="rag_demo",
    index_params={"metric_type": "IP", "index_type": "IVF_FLAT"}
)

---

3. Chroma (Local)

from langchain_community.vectorstores import Chroma

vector_store = Chroma(
    collection_name="rag_demo",
    embedding_function=embeddings,
    persist_directory="./chroma_db"
)

---

4. FAISS (Local)

from langchain_community.vectorstores import FAISS

vector_store = FAISS.from_documents(documents, embeddings)
vector_store.save_local("./faiss_index")

---

Schema Protection Rules

HALT before:

1. Dimension change → Breaks all vectors
2. Metric change → Affects rankings

CAUTION before: 3. Metadata schema change → Migration needed

See: templates/rag-checklist.md

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Migration Playbook

Scenario: Change Embedding Model

# 1. Backup
python scripts/export_vectors.py --output backup.json

# 2. Create new index
python scripts/create_index.py --dimension 3072

# 3. Re-embed documents
python scripts/reindex.py --model text-embedding-3-large

# 4. Switch app
export VECTOR_STORE_INDEX="rag-demo-v2"

# 5. Monitor
python scripts/monitor_quality.py

# 6. Delete old index (after 30 days)

Full guide: docs/protected-schemas.md

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Last Updated: 2025-12-04 Version: 1.0 Adapted from: WHRESUME blog-protection-SKILL.md