Vector Databases: Pinecone, Weaviate, and Chroma Compared

Mon, 22 Apr 2024 00:00:00 +0000

Vector databases store embeddings and perform similarity search-the retrieval layer in RAG and recommendation systems.

Comparison

	Pinecone	Weaviate	Chroma
Hosting	Managed cloud	Self-host or cloud	Embedded / local
Best for	Production scale	Hybrid search + GraphQL	Prototyping
Ops burden	Low	Medium	Low

pgvector Alternative

PostgreSQL with pgvector keeps vectors beside relational data-excellent when you already run Postgres and need ACID transactions.

Selection Criteria

Consider QPS, filtering (metadata predicates), hybrid keyword + vector search, cost, and data residency. Prototype on Chroma or pgvector; migrate to Pinecone or Weaviate at scale.

Building RAG Systems: Retrieval-Augmented Generation Explained

Thu, 18 Jan 2024 00:00:00 +0000

RAG grounds LLM responses in your private data by retrieving relevant documents before generation. It reduces hallucinations and keeps answers current without retraining models.

Pipeline Overview

Ingest - Load PDFs, wikis, tickets into chunks (500–1000 tokens).
Embed - Convert chunks to vectors with an embedding model.
Store - Save vectors in Pinecone, pgvector, or Chroma.
Retrieve - On query, embed the question and find top-k similar chunks.
Generate - Pass chunks as context to the LLM.

context = "

".join(retrieved_chunks)
prompt = f"Use only this context:
{context}

Question: {user_query}"

Chunking Strategy

Overlap chunks by 10–20% to avoid cutting sentences. Metadata (source, page) helps citations and debugging.