Prometheus Chatbot
An assistant that knows when to understand content and when to query exact data — always grounded in website sources.
More than RAG
Native RAG is great at explaining content, but it should not guess “latest”, date ranges, filters, or counts. Prometheus AI adds a routing layer that selects the right mechanism for each question.
One question, four possible routes
Choose a sample question to see which route the Retrieval Planner selects — and how data moves before the chatbot answers.
“How does Prometheus Lab currently use RAG?”
LLM Query Planner
Reads intent, context, and safety boundaries
Select a route to run it
Planner selected: Semantic RAG
The question needs content understanding, not an exact numeric result.
Embed the question
Turn its meaning into a vector
Semantic search
Supabase pgvector • cosine similarity
Select Top 6 chunks
Keep relevant, authorized context
AI synthesis
Answer from context with sources
Grounded answer
Prometheus uses Routed RAG: the planner selects Semantic RAG for content questions, then the AI synthesizes only from retrieved passages.
The planner never answers directly. It only creates a retrieval plan; the server validates that plan before touching data.
What is the AI allowed to know?
Prometheus AI only retrieves indexed or authorized sources. It does not have blanket access to the entire database.
Blog Articles
Only published posts are queried. Content questions use the semantic index; lists and dates use live data.
Personal & Project Info
Public content from About Me and Projects supports answers about shared experience, skills, and projects.
Internal Resources (Assets)
Internal content is retrieved only when the account is authorized and the document is published or indexed.
How does AI learn about new content?
The two paths update differently: structured data is queried live, while semantic content needs a Knowledge Base refresh — never model retraining.
Publish new content
A Blog post is published, or a new Asset is published in Resources.
Blog • AssetChoose the update path
Query DSL reads live data immediately. For semantic questions, run npm run rag:index to refresh the vector index.
Database: live • RAG: re-indexRetrieve fresh data
The planner routes the next question to the updated source and responds in the selected mode.
Exact or semanticThe database sees records as soon as they are published. Semantic RAG needs an index refresh. Internal Assets remain role-protected.
How it works
From question to grounded answer: plan, validate, retrieve from the right source, then respond.
Receive question & context
The system receives the question, conversation history, current time, and user role to establish the correct access scope.
Plan retrieval
The planner classifies intent as Database, Semantic RAG, Clarify, or Unsupported instead of sending every question to vector search.
Validate the plan
Schemas and validators constrain entities, fields, operators, sorting, limits, and date formats. The model has no path to execute arbitrary SQL.
Retrieve from the right source
Exact questions query live data; content questions use vector search for relevant passages. Assets always pass authorization checks.
Build structured evidence
Time ranges, ordering, counts, limits, and source metadata are normalized before the answer layer may use the result.
Respond in the selected mode
Database results use deterministic formatting; semantic answers are synthesized from evidence and streamed with sources. Ambiguous requests are clarified.
Tech Stack
Technologies behind this routed retrieval system.
Next.js 16
API Routes & Server Components
OpenAI API
GPT-5.4 Mini & Embeddings
Supabase
pgvector & RPC Functions
Framer Motion
Smooth UI Animations
TypeScript
End-to-End Type Safety
Streaming
NDJSON Real-time Response
Ask in natural language
Ask about content, the latest post, a date range, a count, or Assets you are authorized to access.