skills/redis-development/rules/vector-rag-pattern.md

title	impact	impactDescription	tags	description	alwaysApply
Implement RAG Pattern Correctly	HIGH	Proper RAG implementation improves LLM response quality	vector, rag, llm, embeddings, retrieval	Implement RAG Pattern Correctly	true

Implement RAG Pattern Correctly

Store documents with embeddings, retrieve relevant context, and pass to LLM.

Correct: Full RAG pipeline with RedisVL.

from redisvl.index import SearchIndex
from redisvl.query import VectorQuery

# 1. Store documents with embeddings
for doc in documents:
    embedding = embed_model.encode(doc["content"])
    index.load([{
        "content": doc["content"],
        "embedding": embedding.tolist(),
        "source": doc["source"]
    }])

# 2. Query with vector similarity
query_embedding = embed_model.encode(user_question)
results = index.search(VectorQuery(
    vector=query_embedding,
    vector_field_name="embedding",
    return_fields=["content", "source"],
    num_results=5
))

# 3. Pass context to LLM
context = "\n".join([r["content"] for r in results])
response = llm.generate(f"Context: {context}\n\nQuestion: {user_question}")

Best practices:

• Normalize vectors if using COSINE distance
• Batch inserts using index.load() with lists
• Set appropriate M and EF_CONSTRUCTION for HNSW based on dataset size
• Use filters to reduce the search space before vector comparison
• Consider chunking long documents for better retrieval

Reference: Redis RAG Quickstart