When deploying Retrieval-Augmented Generation (RAG) at scale, the primary architectural bottleneck rarely lies in basic vector math—it surfaces in the proxy layer coordinating retrieval, LLM streaming, and client delivery. A naive reverse proxy simply relays Server-Sent Events (SSE) from the inference provider to the web browser. However, under high concurrency, slow mobile networks, and rapid user drop-offs, this approach rapidly induces memory spikes from unbounded token buffering and burns expensive GPU compute on prompts whose clients disconnected seconds ago. Building a hardened RAG gateway in Go allows us to enforce strict backpressure, propagate context cancellations, and optimize hybrid database queries simultaneously.
Tuning Hybrid pgvector Retrieval Under Load
Before tokens ever stream, your gateway must execute hybrid retrieval combining semantic vector search with lexical BM25/full-text indexing. In PostgreSQL using pgvector and native tsvector, running these queries sequentially per request quickly saturates your database connection pool. Utilizing Go's pgxpool, we execute HNSW similarity searches and full-text keyword scans concurrently across pooled connections, combining results via Reciprocal Rank Fusion (RRF). To prevent query pileups during traffic spikes, wrap the database interaction in a strict timeout context and size the pgxpool.Config{MaxConns: ...} according to Postgres worker saturation rather than HTTP request concurrency.
Enforcing Token Backpressure with Bounded Channels
Once the prompt is dispatched to an LLM provider (such as Anthropic or vLLM), tokens arrive in bursts. If a client is on a high-latency connection, writing these tokens directly to the http.ResponseWriter can cause internal buffers to balloon. Go's concurrency primitives solve this gracefully:
- Bounded Channels: Pipe upstream SSE tokens into a channel with a small, fixed buffer (e.g., 16–32 tokens).
- Writer Synchronization: The HTTP response loop reads from this channel and explicitly calls
http.Flusher.Flush()after writing each chunk. - Upstream Throttling: If the client cannot accept data quickly enough, the bounded channel fills, blocking the upstream LLM reader goroutine and naturally throttling network ingestion from the provider.
Propagating Client Cancellations Upstream
In streaming user interfaces, users frequently click "Stop generating" or navigate away mid-stream. If your proxy fails to listen to client teardowns, the LLM provider will continue generating tokens until completion, wasting billing credits and execution slots. By wiring the incoming r.Context() from the standard library HTTP request directly into both the pgvector query runner and the outgoing HTTP client streaming from the LLM API, any TCP reset or client disconnection instantly triggers ctx.Done(). This immediately tears down the upstream inference stream and releases database connections back to the pool.
By treating the RAG proxy as an active traffic regulator rather than a passive router, you insulate your backend infrastructure from client network instability. Combining Go's non-blocking runtime, deterministic backpressure channels, and coordinated context cancellation ensures your streaming infrastructure remains resilient and cost-effective under sustained production loads.
— Muntasir Ashif
