I'm building an AI search feature and need a vector database plus pipeline

Absolutely — for an AI search feature, you typically need two things:

1. A vector database to store embeddings and retrieve semantically similar content
2. An ingestion/search pipeline to generate embeddings, index documents, and serve queries

Good vector database options

Here are the most common choices:

Managed / production-friendly

• Pinecone — very easy to operate, strong managed service, good for production
• Weaviate Cloud — good hybrid search and schema support
• Qdrant Cloud — strong filtering, open-source core, simple API
• MongoDB Atlas Vector Search — useful if you already use MongoDB
• Elastic / OpenSearch — good if you want keyword + vector search together

Self-hosted / open source

• Qdrant
• Weaviate
• Milvus
• pgvector with PostgreSQL for smaller-scale or simpler setups

Typical pipeline architecture

A standard AI search pipeline looks like this:

1. Data ingestion

• Pull content from:
  • PDFs, docs, webpages, databases, tickets, knowledge base
• Clean and normalize text
• Deduplicate

2. Chunking

• Split documents into smaller pieces
• Common chunk size: 300–1,000 tokens
• Keep metadata:
  • doc_id
  • title
  • source
  • section
  • timestamp
  • access control fields

3. Embedding generation

• Convert each chunk into a vector using an embedding model
• Popular models:
  • OpenAI embeddings
  • Cohere embeddings
  • sentence-transformers
  • Jina embeddings

4. Indexing into vector DB

Store:

• vector
• chunk text
• metadata

5. Query pipeline

At search time:

• Embed the user query
• Retrieve top-k similar chunks from vector DB
• Optionally rerank results with a cross-encoder or LLM
• Return:
  • direct search results
  • cited snippets
  • answers via RAG

Recommended stack by use case

Fastest to ship

• Pinecone + OpenAI embeddings + LangChain or LlamaIndex
• Best when you want minimal infra work

Open-source / flexible

• Qdrant + sentence-transformers/OpenAI embeddings + LlamaIndex
• Good balance of simplicity and control

Enterprise hybrid search

• Elasticsearch/OpenSearch + embeddings
• Best if you need keyword, filters, and vector search together

PostgreSQL-based

• Postgres + pgvector
• Best for smaller systems or teams already on Postgres

Suggested MVP setup

If you want a practical default:

• Vector DB: Qdrant or Pinecone
• Embedding model: OpenAI text-embedding-3-small or 3-large
• Framework: LlamaIndex or LangChain
• Reranker: Cohere rerank or bge-reranker
• Backend: Python/FastAPI
• Storage: S3/GCS for raw documents
• Metadata store: PostgreSQL

Key design considerations

• Filters: tenant_id, doc_type, permissions
• Hybrid search: combine keyword + vector
• Reranking: improves result relevance a lot
• Chunk overlap: helps preserve context
• Latency: cache embeddings and frequent queries
• Freshness: incremental re-indexing for updates

Example architecture

Documents -> Cleaner -> Chunker -> Embeddings -> Vector DB
                                      |
User Query -> Embed -> Retrieve -> Rerank -> Results / RAG answer

If you want, I can also give you:

1. a recommended stack based on your scale
2. a sample architecture diagram
3. a FastAPI + Qdrant or Pinecone starter template
4. a step-by-step ingestion pipeline design

If you tell me your expected scale and data type, I can narrow it down quickly.