Spaces:
Sleeping
Sleeping
| # Vector Databases for RAG | |
| ## What are Vector Databases? | |
| Vector databases are specialized databases designed to store and search high-dimensional vectors efficiently. They are essential for RAG systems because they enable semantic search over document embeddings. | |
| ## Why Use Vector Databases? | |
| Traditional databases use exact matching or keyword search. Vector databases use: | |
| - **Similarity Search**: Find semantically similar content | |
| - **Approximate Nearest Neighbors (ANN)**: Fast similarity search at scale | |
| - **Metadata Filtering**: Combine vector search with traditional filters | |
| ## Popular Vector Databases | |
| ### FAISS (Facebook AI Similarity Search) | |
| - **Type**: Library, not a full database | |
| - **Deployment**: In-memory or on-disk | |
| - **Best For**: Local development, research, small to medium datasets | |
| - **Pros**: Fast, free, runs locally | |
| - **Cons**: No built-in persistence, limited metadata support | |
| ### Pinecone | |
| - **Type**: Managed cloud service | |
| - **Deployment**: Cloud-hosted | |
| - **Best For**: Production applications, scalable systems | |
| - **Pros**: Fully managed, auto-scaling, metadata filtering | |
| - **Cons**: Paid service, cloud dependency | |
| ### Weaviate | |
| - **Type**: Open-source vector database | |
| - **Deployment**: Self-hosted or cloud | |
| - **Best For**: Production with control over infrastructure | |
| - **Pros**: GraphQL API, hybrid search, modular | |
| - **Cons**: More complex setup | |
| ### Chroma | |
| - **Type**: Open-source embedding database | |
| - **Deployment**: Local or server | |
| - **Best For**: Development and production | |
| - **Pros**: Simple API, built-in embeddings, free | |
| - **Cons**: Newer, smaller community | |
| ### Qdrant | |
| - **Type**: Open-source vector database | |
| - **Deployment**: Self-hosted or cloud | |
| - **Best For**: High-performance applications | |
| - **Pros**: Fast, filtered search, production-ready | |
| - **Cons**: Requires setup and management | |
| ## Key Concepts | |
| ### Embeddings | |
| Vector representations of text that capture semantic meaning: | |
| - 384 dimensions (MiniLM) | |
| - 768 dimensions (BERT) | |
| - 1536 dimensions (OpenAI ada-002) | |
| ### Distance Metrics | |
| Measure similarity between vectors: | |
| - **Cosine Similarity**: Angle between vectors (most common) | |
| - **Euclidean Distance**: Straight-line distance | |
| - **Dot Product**: Inner product of vectors | |
| ### Indexing Methods | |
| Optimize search performance: | |
| - **Flat Index**: Exact search, slower at scale | |
| - **IVF (Inverted File)**: Partition space into clusters | |
| - **HNSW**: Hierarchical graph structure (fast and accurate) | |
| - **LSH (Locality-Sensitive Hashing)**: Hash similar items together | |
| ## FAISS Examples | |
| ### Basic Usage | |
| ```python | |
| from langchain.vectorstores import FAISS | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| embeddings = HuggingFaceEmbeddings() | |
| vectorstore = FAISS.from_documents(documents, embeddings) | |
| # Search | |
| results = vectorstore.similarity_search("query", k=4) | |
| ``` | |
| ### Save and Load | |
| ```python | |
| # Save | |
| vectorstore.save_local("faiss_index") | |
| # Load | |
| vectorstore = FAISS.load_local("faiss_index", embeddings) | |
| ``` | |
| ### With Metadata Filtering | |
| ```python | |
| # Add metadata | |
| docs_with_metadata = [ | |
| Document(page_content="...", metadata={"source": "doc1.txt", "date": "2024-01-01"}) | |
| ] | |
| # Search with filter | |
| results = vectorstore.similarity_search( | |
| "query", | |
| k=4, | |
| filter={"source": "doc1.txt"} | |
| ) | |
| ``` | |
| ## Choosing a Vector Database | |
| ### For Development/Prototyping | |
| - **FAISS**: Fast local development | |
| - **Chroma**: Easy setup with persistence | |
| ### For Production | |
| - **Pinecone**: Minimal ops, quick to market | |
| - **Weaviate**: Control and flexibility | |
| - **Qdrant**: High performance needs | |
| ### Considerations | |
| 1. **Scale**: Expected data volume and query load | |
| 2. **Budget**: Managed vs self-hosted costs | |
| 3. **Latency**: Response time requirements | |
| 4. **Features**: Metadata filtering, hybrid search, etc. | |
| 5. **Maintenance**: Ops team capacity | |
| ## Performance Optimization | |
| ### 1. Chunk Size | |
| - Smaller chunks: More precise retrieval | |
| - Larger chunks: More context per result | |
| - Optimal: 500-800 tokens for most use cases | |
| ### 2. Index Configuration | |
| - Use HNSW for production (fast + accurate) | |
| - Tune ef_construction and M parameters | |
| - Consider quantization for large datasets | |
| ### 3. Batch Processing | |
| - Embed documents in batches | |
| - Use parallel processing for large datasets | |
| - Cache embeddings when possible | |
| ### 4. Query Optimization | |
| - Adjust k (number of results) based on use case | |
| - Use MMR (Maximum Marginal Relevance) for diversity | |
| - Implement result reranking for better quality | |