RAG Pipeline for Enterprise Search
AI Engineer
RAG LLM Vector Search Python FastAPI
Stack: Python, FastAPI, PostgreSQL, pgvector, OpenAI, Redis
Context
Business problem and user need — what was the pain point?
Existing solutions and their limitations — why weren’t they working?
Success criteria defined upfront — how would we know we succeeded?
Constraints
- Latency: < 500ms p95 for end-to-end response
- Scale: 10K+ queries/day, 100K+ document corpus
- Cost: Budget for embeddings and inference
- Privacy: PII handling and data residency requirements
Architecture
Document ingestion pipeline
Embedding strategy and model selection
Vector store choice and indexing approach
Retrieval ranking (hybrid search, reranking)
LLM integration and prompt design
Caching layer
Implementation Highlights
Chunking Strategy
Why the chunking approach mattered
Document Freshness
Handling updates and staleness
Fallback Behavior
What happens when retrieval fails
Cost Controls
Rate limiting and budget management
Evaluation
| Metric | Target | Achieved |
|---|---|---|
| Recall@10 | > 0.85 | TBD |
| MRR | > 0.7 | TBD |
| P95 Latency | < 500ms | TBD |
| User Satisfaction | > 4.0/5 | TBD |
Human evaluation approach
A/B test results if applicable
Outcomes
- Queries served per day
- Latency achieved
- Cost per query
- User satisfaction metrics
- Business impact (support tickets reduced, time saved)
Learnings
What Worked Well
Key successes
What I’d Do Differently
Retrospective insights
Unexpected Challenges
Surprises during implementation