53e0065e3e
配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
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1.6 KiB
title, impact, impactDescription, tags, description, alwaysApply
| 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
records = []
for doc in documents:
records.append({
"content": doc["content"],
"embedding": embed_model.encode(doc["content"]).tolist(),
"source": doc["source"]
})
index.load(records)
# 2. Query with vector similarity
query_embedding = embed_model.encode(user_question)
results = index.query(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:
- Match your distance metric to your embedding model; many modern text embeddings already work well with COSINE
- 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