86 lines
2.7 KiB
Markdown
86 lines
2.7 KiB
Markdown
# Vector Database Comparison and Configuration
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## Overview
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Vector databases store and efficiently retrieve document embeddings for semantic search in RAG systems.
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## Popular Vector Database Options
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### 1. Pinecone
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- **Type**: Managed cloud service
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- **Features**: Scalable, fast queries, managed infrastructure
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- **Use Case**: Production applications requiring high availability
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### 2. Weaviate
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- **Type**: Open-source, hybrid search
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- **Features**: Combines vector and keyword search, GraphQL API
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- **Use Case**: Applications needing both semantic and traditional search
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### 3. Milvus
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- **Type**: High performance, on-premise
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- **Features**: Distributed architecture, GPU acceleration
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- **Use Case**: Large-scale deployments with custom infrastructure
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### 4. Chroma
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- **Type**: Lightweight, easy to use
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- **Features**: Local deployment, simple API
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- **Use Case**: Development and small-scale applications
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### 5. Qdrant
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- **Type**: Fast, filtered search
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- **Features**: Advanced filtering, payload support
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- **Use Case**: Applications requiring complex metadata filtering
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### 6. FAISS
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- **Type**: Meta's library, local deployment
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- **Features**: High performance, CPU/GPU optimized
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- **Use Case**: Research and applications needing full control
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## Configuration Examples
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### Pinecone Setup
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```python
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import pinecone
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from langchain.vectorstores import Pinecone
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pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
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index = pinecone.Index("your-index-name")
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vectorstore = Pinecone(index, embeddings.embed_query, "text")
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```
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### Weaviate Setup
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```python
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import weaviate
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from langchain.vectorstores import Weaviate
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client = weaviate.Client("http://localhost:8080")
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vectorstore = Weaviate(client, "Document", "content", embeddings)
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```
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### Chroma Local Setup
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```python
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from langchain.vectorstores import Chroma
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vectorstore = Chroma(
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collection_name="my_collection",
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embedding_function=embeddings,
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persist_directory="./chroma_db"
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)
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```
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## Selection Criteria
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1. **Scale**: Number of documents and expected query volume
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2. **Performance**: Latency requirements and throughput needs
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3. **Deployment**: Cloud vs on-premise preferences
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4. **Features**: Filtering, hybrid search, metadata support
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5. **Cost**: Budget constraints and operational overhead
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6. **Maintenance**: Team expertise and available resources
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## Best Practices
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1. **Indexing Strategy**: Choose appropriate distance metrics (cosine, euclidean)
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2. **Sharding**: Distribute data for large-scale deployments
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3. **Monitoring**: Track query performance and system health
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4. **Backups**: Implement regular backup procedures
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5. **Security**: Secure access to sensitive data
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6. **Optimization**: Tune parameters for your specific use case |