What are the steps for setting up a vector search pipeline?

Here’s a structured explanation of setting up a vector search pipeline, incorporating practical details from the provided references:

1. Core Pipeline Components

A vector search pipeline involves three key phases: data ingestion, embedding generation/storage, and query execution. First, raw data (text, images, etc.) is collected, preprocessed, and split into manageable chunks. Next, an embedding model converts these chunks into vector representations stored in a specialized database. Finally, search queries are transformed into vectors and matched against stored embeddings using similarity metrics like cosine distance[1][2][6].

2. Implementation Steps

① Data Ingestion & Preprocessing

• Data collection: Pull data from APIs, databases, or files (e.g., CSV, PDF). For real-time use cases, tools like Kafka can stream data to a message queue[2].
• Chunking: Split large documents into smaller units (e.g., sentences or paragraphs) using text splitters. Elasticsearch’s ingest pipelines with script processors automate this step for scalability[6].
• Metadata enrichment: Attach context (timestamps, source URLs) to chunks for hybrid search[10].

② Embedding Generation & Storage

• Model selection: Use open-source models like BAAI/bge-small-en (via HuggingFace) or commercial APIs. For non-text data, custom preprocessing scripts are required[1][6].
• Vector indexing: Store embeddings with metadata in databases like Elasticsearch (k-NN search), Postgres (PGVector), or Upstash. Example using Postgres:

from llama_index.vector_stores.postgres import PGVectorStore
vector_store = PGVectorStore(host="localhost", database="vectordb")[1]

③ Query Execution

• Query embedding: Convert user input to a vector using the same model as ingested data.
• Hybrid search: Combine vector similarity (e.g., closeness(field, embedding)) with metadata filters. ClickHouse excels here by supporting SQL-based vector operations alongside traditional WHERE clauses[8].
• Reranking: Optional step to refine results using cross-encoders or LLM-based relevance scoring[10].

3. Toolchain Optimization

• Real-time pipelines: For news/article data, use Kafka producers to ingest content and Bytewax for parallel stream processing[2].
• Cost-performance balance:
• CPU-optimized models like all-MiniLM-L6-v2 reduce GPU dependency[6].
• Approximate Nearest Neighbor (ANN) indexes in Elasticsearch or ClickHouse improve speed at scale[8][10].
• Monitoring: Track latency (embedding generation time), recall rate, and chunk size impact on search accuracy.