RAG lets LLMs answer questions using your documents. Embed chunks, store in pgvector or Qdrant, retrieve top-k with reranking, then pass to the LLM as context. Always cite sources in the response.
unstructured or langchain for PDFs. Chunk at 800 tokens with 100 overlap. const { data } = await ai.embeddings.create({
model: 'assisters-embed-v1',
input: chunks,
});
INSERT INTO documents (content, embedding) VALUES (...)CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops); const { results } = await ai.rerank.create({
query,
documents: candidates,
top_n: 5,
});
Answer using only the provided context. Cite sources with [n].| Tool | Purpose |
|---|---|
| pgvector | SQL + vectors in one DB |
| Qdrant | Dedicated vector DB |
| LangChain / LlamaIndex | Orchestration |
| Cohere Rerank | Reranking API |
| Unstructured | Document parsing |
Should I use pgvector or Qdrant? pgvector for < 10M docs and existing Postgres. Qdrant beyond.
Which embedding model is best? text-embedding-3-large or bge-m3 for multilingual.
How do I evaluate RAG quality? Use Ragas framework: faithfulness, answer relevancy, context precision.
Does RAG eliminate hallucinations? Reduces but doesn't eliminate. Citations + confidence scoring help.
Can I RAG over images? Yes — use CLIP embeddings for images, combine with text RAG.
How do I update the index? Incremental upserts. Delete old versions by doc_id.
RAG is the dominant pattern for domain-specific AI in 2026. Start with pgvector + Assisters, add reranking, always cite. Misar Dev builds full RAG stacks in minutes.
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