53 lines
1.6 KiB
Markdown
53 lines
1.6 KiB
Markdown
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---
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title: Implement RAG Pattern Correctly
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impact: HIGH
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impactDescription: Proper RAG implementation improves LLM response quality
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tags: vector, rag, llm, embeddings, retrieval
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description: Implement RAG Pattern Correctly
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alwaysApply: true
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---
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## Implement RAG Pattern Correctly
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Store documents with embeddings, retrieve relevant context, and pass to LLM.
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**Correct:** Full RAG pipeline with RedisVL.
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```python
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from redisvl.index import SearchIndex
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from redisvl.query import VectorQuery
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# 1. Store documents with embeddings
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records = []
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for doc in documents:
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records.append({
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"content": doc["content"],
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"embedding": embed_model.encode(doc["content"]).tolist(),
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"source": doc["source"]
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})
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index.load(records)
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# 2. Query with vector similarity
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query_embedding = embed_model.encode(user_question)
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results = index.query(VectorQuery(
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vector=query_embedding,
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vector_field_name="embedding",
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return_fields=["content", "source"],
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num_results=5
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))
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# 3. Pass context to LLM
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context = "\n".join([r["content"] for r in results])
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response = llm.generate(f"Context: {context}\n\nQuestion: {user_question}")
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```
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**Best practices:**
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- Match your distance metric to your embedding model; many modern text embeddings already work well with COSINE
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- Batch inserts using `index.load()` with lists
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- Set appropriate M and EF_CONSTRUCTION for HNSW based on dataset size
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- Use filters to reduce the search space before vector comparison
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- Consider chunking long documents for better retrieval
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Reference: [Redis RAG Quickstart](https://redis.io/docs/latest/develop/get-started/rag/)
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