𝗟𝗟𝗠 𝗧𝗼𝗸𝗲𝗻 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
Stop paying too much for LLM APIs. You can lower your costs without losing search quality.
Traditional search looks for exact words. Vector search looks for meaning. This allows users to find "automotive repair" even if they search for "car trouble."
Vector search uses embeddings. These are lists of numbers that represent meaning. Similar meanings create similar numbers.
How to choose your embedding model:
- Quality: Test the model on your own data.
- Cost: Compare API fees against self-hosting costs.
- Dimensions: More dimensions mean higher accuracy but higher storage needs.
- Latency: Check how fast the model responds.
Common models:
- OpenAI text-embedding-3-small: Good for general use.
- OpenAI text-embedding-3-large: High accuracy.
- Sentence-Transformers: Great for cost-effective, open-source needs.
- Cohere: Best for multilingual tasks.
Picking the right similarity metric:
- Cosine Similarity: Best for text. It measures the angle between vectors.
- Euclidean Distance: Best for computer vision where magnitude matters.
- Dot Product: Best for speed when vectors are already normalized.
Scale requires ANN (Approximate Nearest Neighbor) algorithms. Searching every single document is too slow for large datasets.
- HNSW: Most popular for production. It uses a graph structure for fast queries.
- IVF: Uses clustering to group vectors. It saves memory.
- PQ: Compresses vectors to reduce memory usage by 10x to 20x.
Common mistakes to avoid:
- Using the wrong model: General models may fail on specific industry data.
- Poor chunking: Breaking text in the middle of a thought ruins context.
- Missing metadata: Always index metadata to allow users to filter results.
- Ignoring exact matches: Use hybrid search to combine vectors with keyword matching.
Build a complete pipeline. Chunk your text, create embeddings, store them in a vector database like Pinecone, Weaviate, or pgvector, and use a reranker to improve precision.
Optional learning community: https://t.me/GyaanSetuAi