𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗔𝗺𝗮𝘇𝗼𝗻 𝗔𝘂𝗿𝗼𝗿𝗮
Stop searching for exact words. Search for meaning. Vector databases make this possible.
Traditional databases look for exact matches. Vector databases look for similarity. They store data as arrays of numbers called vectors.
Use these for:
- RAG: Give LLMs internal knowledge.
- Semantic Search: Find documents by meaning.
- Recommendations: Suggest items based on preference.
- Image Search: Match similar visuals.
RAG is the most common use case. Here is the flow:
- You ask a question.
- An embedding model turns the text into a vector.
- The database finds the closest vectors.
- The LLM uses these results to answer.
Amazon Aurora PostgreSQL with pgvector is a great starting point. Use HNSW indexes for fast search. HNSW is the industry standard for speed and accuracy.
Want more speed? Add a semantic cache. Amazon MemoryDB works well here. It stores past answers for similar questions.
One test showed a 94 percent drop in response time. It skips the LLM call entirely when a similar question exists in the cache.
Optional learning community: https://t.me/GyaanSetuAi