Tool Stack
RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
1 min read
/ quick answer
Ingest documents, embed, retrieve, and generate grounded answers. Minimum viable stack to ship a production RAG chatbot.
Minimum viable stack to ship a production RAG chatbot. Ingest documents, embed, retrieve, and generate grounded answers. The stack combines OpenAI / Lovable AI, pgvector (Postgres), LangChain or LlamaIndex, Next.js or TanStack Start. This tool stack node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Purpose
Ingest documents, embed, retrieve, and generate grounded answers.
Tools Included
- OpenAI / Lovable AI
- pgvector (Postgres)
- LangChain or LlamaIndex
- Next.js or TanStack Start
Workflow Supported
Alternatives
- Pinecone instead of pgvector
- Vercel AI SDK instead of LangChain
Use Cases
/ frequently asked
What is the RAG Starter Stack stack for?
Ingest documents, embed, retrieve, and generate grounded answers.
Which tools are in this stack?
OpenAI / Lovable AI, pgvector (Postgres), LangChain or LlamaIndex, Next.js or TanStack Start.
Are there alternatives to this stack?
Yes — Pinecone instead of pgvector, Vercel AI SDK instead of LangChain.
↳ connected nodes
Workflow↳ linked
Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
Workflow↳ linked
AI Customer Onboarding Flow
Walk every new user through activation with an AI guide.
Use Case↳ linked
SaaS Co. Cuts Support Tickets 40% With Internal Bot
A 50-person SaaS deployed a RAG bot over their help center and internal wiki.
Use Case↳ linked
E-commerce Brand Automates 70% of Support Tickets
A DTC brand deployed a RAG support agent over policies, FAQs, and order data.
Dictionary↳ linked
RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
Dictionary↳ linked
Vector Database
A database optimized for similarity search over embeddings.
Comparison↳ linked
RAG vs Fine-Tuning
When to retrieve, when to retrain.
Comparison↳ linked
Chatbot vs AI Agent
Conversational interface vs autonomous executor.
Prompt↳ linked
Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
Dictionary↳ linked
Semantic Search
Finding information by meaning rather than exact keyword match.
Workflow↳ linked
RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
Comparison↳ linked
Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
Use Case↳ linked
Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.
Dictionary↳ linked
Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
Dictionary↳ linked
Embedding
A numerical vector representation of text, image or audio that captures meaning for similarity search.
Dictionary↳ linked
Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
Dictionary↳ linked
Context Window
The maximum amount of text (in tokens) an LLM can consider in a single call.
Comparison↳ linked
Supabase vs Firebase
Open-source Postgres backend vs Google's managed app platform.
Dictionary↳ linked
Hallucination
When an LLM confidently generates false or fabricated information.
Dictionary↳ linked
Reranking
A second-pass model that reorders retrieved chunks by true relevance to the query.
Dictionary↳ linked
Chunking
Splitting documents into retrievable pieces before embedding them for RAG.
Comparison↳ linked
RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
Comparison↳ linked
LangChain vs LlamaIndex
Both are Python/JS frameworks for LLM apps. LangChain leans agents; LlamaIndex leans RAG.
Prompt↳ linked
RAG Answer With Strict Citations
Force the LLM to answer only from provided chunks and cite them by ID.
Dictionary↳ linked
Retrieval
Selecting the most relevant chunks for a query before generation.
Dictionary↳ linked
Reranker
A second-stage model that reorders retrieved chunks for precision.
Dictionary↳ linked
Hybrid Search
Combining keyword (BM25) and vector search for better recall.
Dictionary↳ linked
Chunk Size
The length of text segments stored in a vector index.
Dictionary↳ linked
Cosine Similarity
The dot-product-of-unit-vectors metric that ranks embeddings.
Dictionary↳ linked
Vector Index
A data structure that makes nearest-neighbor search fast.
Dictionary↳ linked
Embedding Dimensions
The vector length of an embedding model's output.
Workflow↳ linked
Build a Tier-1 Customer Support Agent
An agent that handles common tickets end-to-end and hands off the rest.
Comparison↳ linked
Chroma vs Qdrant vs Pinecone
Open-source local vs managed cloud vector databases.
Use Case↳ linked
SaaS Support Team Automates Tier-1, Focuses on Retention
Support org deflects 55% of tickets and re-invests the time into proactive retention.
Tool Stack↳ linked
LLM Context Management Stack
A technology stack for effectively managing and optimizing the context provided to large language models, ensuring efficient, relevant, and cost-effective operations.
Tool Stack↳ linked
AI Voice Agent Development Stack
This stack outlines essential technologies and tools for building and deploying AI voice agents, encompassing speech processing, natural language understanding, and conversational AI frameworks. It provides a foundation for creating intelligent voice interfaces.
Tool Stack↳ linked
AI Voice Assistant Stack
This stack outlines the core technologies for building personal or enterprise AI voice assistants, integrating components for speech recognition, natural language processing, and task execution. It supports intelligent, conversational interfaces for various applications.
Tool Stack↳ linked
Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.