Retrieval Pipeline

How the RAG system finds relevant content for chat queries.

Pipeline Overview

User Query → Query Processing → Hybrid Search → Ranking → Context → Response
                ↓                                    ↓
         Query Expansion                    Reciprocal Rank Fusion
                ↓                                    ↓
         Embed Generation                   Cross-Encoder Rerank

1. Query Processing

• Clean and normalize input
• Extract key terms for keyword search
• Generate query variations for better recall

2. Embedding Generation

• Model: Azure OpenAI text-embedding-3-small
• Dimensions: 1536

Caching Strategy

Redis (hot) → PostgreSQL (cold) → API
   1 hour         30 days

3. Hybrid Search

Vector Search (pgvector)

Semantic similarity using HNSW index.

Keyword Search (Postgres TSVECTOR)

Full-text search for exact matches.

Why Hybrid?

4. Score Fusion

Reciprocal Rank Fusion (RRF) combines rankings:

score = 1 / (k + rank)

5. Reranking

Cross-encoder reranking for better relevance:

• Model: BAAI/bge-reranker-base

6. Context Assembly

Token budget allocation:

Performance Targets