RAG Assistant
A retrieval-augmented assistant designed to be useful and honest: reproducible indexing, clear guardrails, and conservative behavior when evidence is weak. Public-safe summary.
Core pipeline
Ingestion → embeddings → persistent index → retrieval → response generation
PythonFAISSEmbeddingsGuardrailsStreamlit
Reliability posture
- Deterministic indexing: reproducible builds of the knowledge index.
- Retrieval gating: conservative responses when evidence is weak.
- Provenance: links back to sources where possible.
Approach (high level)
- Normalize content into predictable chunks.
- Embed and store vectors with persistence across runs.
- Retrieve top matches and apply a confidence threshold.
- Generate responses only when retrieval evidence is sufficient; otherwise refuse or ask for clarification.
What I’d improve next
- Evaluation harness (retrieval precision/recall + end-to-end QA checks).
- Citations-by-default and stricter refusal policies for ambiguous prompts.
- Performance profiling for faster cold start and lower latency.
Note: the portfolio assistant on the main site is deliberately grounded and privacy-safe (local-only knowledge base, no logging).