Text Embedding and Similarity Search

Create a semantic text search system using embeddings.

System requirements:
- Document corpus: [SIZE AND TYPE]
- Query type: [QUESTIONS/KEYWORDS/DOCUMENTS]
- Language: [LANGUAGE(S)]
- Latency requirements: [MS]

Components needed:
1. Embedding model selection:
   - Sentence Transformers
   - OpenAI embeddings
   - Custom fine-tuned models
2. Embedding pipeline:
   - Text preprocessing
   - Chunking strategies
   - Batch encoding
3. Vector storage:
   - Faiss indexing
   - Pinecone/Weaviate/Qdrant
   - Hybrid search (BM25 + semantic)
4. Search functionality:
   - K-nearest neighbors
   - Approximate nearest neighbors
   - Filtering and metadata
5. Ranking:
   - Re-ranking models
   - Cross-encoder scoring
   - Diversity optimization
6. Evaluation:
   - Retrieval metrics (MRR, Recall@K)
   - Relevance annotation
7. Optimization:
   - Index compression
   - Query optimization
   - Caching strategies

Build RAG-ready retrieval system.