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vector-databases

Integrate vector databases for semantic search and RAG pipelines.

Updated Mar 12, 2026
Search Tags:#semantic search#embeddings#vector database#LLM#RAG#AI#data retrieval
One-click install
npx skills add https://github.com/mefardales/skillbox --skill vector-databases-mefardales
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vector-databases
Source: https://github.com/mefardales/skillbox/tree/main/skills/data/vector-databases
Command: npx skills add https://github.com/mefardales/skillbox --skill vector-databases-mefardales

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, sentence-transformers, pinecone-client, pgvector, chromadb, weaviate-client, langchain, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables developers to build AI applications that understand and retrieve information based on semantic meaning, rather than just keywords, by leveraging various vector database technologies.

Core Features & Use Cases

  • Semantic Search & RAG: Implement advanced search capabilities and retrieval-augmented generation pipelines.
  • Database Integration: Supports Pinecone, pgvector, ChromaDB, and Weaviate for flexible deployment.
  • Embedding Generation: Includes strategies for generating embeddings using OpenAI and Sentence Transformers.
  • Use Case: Integrate this skill into a customer support chatbot to retrieve the most relevant knowledge base articles based on the user's natural language query, improving response accuracy.

Quick Start

Use the vector-databases skill to generate embeddings for a list of text documents.

Frequently Asked Questions about vector-databases

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG pipeline with a vector database for semantic search?▼

Build a RAG pipeline by generating text embeddings with OpenAI or Sentence Transformers, storing them in a vector database like Pinecone or ChromaDB, and retrieving semantically similar chunks to augment LLM generation.

What is the best way to implement chunking for embeddings in a retrieval system?▼

Implement chunking by splitting large text documents into smaller segments before generating embeddings, ensuring the vector database stores manageable context units for accurate semantic search and RAG retrieval.

Does pgvector support hybrid search for AI applications?▼

Yes, pgvector supports hybrid search by combining vector similarity search with traditional keyword filtering, allowing you to query embeddings stored in PostgreSQL alongside relational data for AI retrieval.

Can I use ChromaDB and Weaviate for local development of semantic search?▼

Yes, ChromaDB and Weaviate can be used for semantic search development, providing client interfaces to store and query embeddings locally before deploying RAG pipelines to production environments.

How do I generate embeddings for documents using Sentence Transformers?▼

Generate embeddings by passing text documents through Sentence Transformers models to create dense vector representations, which are then inserted into vector databases like Pinecone or pgvector for similarity search.

When should I use Pinecone versus pgvector for vector database storage?▼

Use Pinecone for managed, scalable cloud-native vector search, and use pgvector when you need to store embeddings directly within an existing PostgreSQL database alongside relational data for simplified architecture.

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