The Hybrid Orchestrator
Standard AI Agents either fetch from SQL or Vector DBs, but rarely fuse them gracefully. Our Custom Python Backend acts as a two-stage Hybrid Orchestrator using system prompt engineering.
Stage 1: Safe Text-to-SQL
Preventing SQL Injections
Instead of blindly asking an LLM to generate raw SELECT * FROM... queries (which exposes the backend to Prompt Injection and massive hallucination risks), we use a systemic Guardrail Prompt:
The Python backend then securely sanitizes this entity and uses standard ORM methods (ilike) against Supabase.
Stage 2: The Final Synthesis
Dynamic Context Injection
Once we have the raw SQL row (e.g., Apple's target price) AND the raw Semantic Chunks (e.g., geopolitical macro risks), we inject both into a strict synthesis prompt.
Dynamically Extracted Context: {context_text}
Critical Rules:
1. You MUST answer ONLY using the provided Extracted Context. Do not invent financial math...
2. If there is not enough information... firmly declare: "I do not have enough information"
3. YOUR FINAL RESPONSE MUST ALWAYS BE 100% IN ENGLISH.