RAGForge
Production RAG System
RAGForge is a production-grade RAG platform for document collections. It combines serious retrieval engineering, grounded generation with citations, ingestion infrastructure, and the safety and observability you need when the system has to hold up in production.
"RAG is an information retrieval problem first, an LLM problem second."
What RAGForge Covers
This is not a thin wrapper around embeddings. RAGForge covers the full system around retrieval-augmented generation: ingestion, retrieval, grounded responses, safety, evaluation, and admin visibility.
Multi-Stage Retrieval
BM25, dense retrieval, hybrid fusion, HyDE expansion, and cross-encoder reranking work together instead of betting on one retrieval method.
Grounded Responses
Answers are generated from retrieved evidence with inline citations and explicit abstention when the evidence is weak.
Ingestion Infrastructure
Connectors, chunking strategies, async processing, metadata preservation, and background status tracking for real document pipelines.
Operational Rigor
Security hardening, moderation, telemetry, online evaluation, admin analytics, and workspace isolation built into the product.
Query Intelligence
Queries are classified before retrieval so the system can choose the right path rather than treating every request the same. That covers direct lookup, semantic search, multi-hop questions, and explicit refusal when the request should not be answered.
lookup
Direct factual retrieval
semantic
Meaning-based search
multi-hop
Multi-step reasoning
refuse
Out-of-scope rejection
Evaluation Infrastructure
Evaluation is part of the system, not something bolted on for a demo. RAGForge measures retrieval quality, tracks response quality, and gives you a way to detect regressions before users do.
Retrieval Metrics
Recall, MRR, citation coverage, abstention behavior, and related retrieval metrics can be measured continuously instead of guessed at.
Failure Analysis
Failures can be separated into routing, retrieval, and synthesis problems so the fix lands on the right layer.
Online Evaluation
Faithfulness, relevance, and context precision are tracked during query processing to keep quality visible in production.
Observability
Tracing, telemetry correlation, and stage-level metrics make it possible to understand where latency or quality breaks down.
Document Ingestion
RAG systems fail upstream when ingestion is weak. RAGForge treats ingestion as first-class infrastructure with multiple connectors, chunking options, and an async pipeline that can keep collections fresh.
Connector Coverage
File upload, local folder, S3, GitHub, GitLab, SharePoint, Confluence, Google Drive, and Notion are supported for document intake.
Chunking Strategies
Recursive, structure-aware, sliding window, semantic, and auto chunking support different corpora and retrieval needs.
Async Processing
Documents move through parse, chunk, embed, and index stages with retry and status tracking rather than blocking the request path.
Upstream Safeguards
PII handling and document scanning for prompt injection, jailbreaks, and other hostile inputs help keep bad data out of the system.
Security and Operations
Safety Controls
Heuristic and optional LLM moderation, prompt-injection defenses, role stripping, unicode normalization, and retractable streaming responses.
Workspace Isolation
Multi-tenant workspaces, role-based access, and API key support make the system usable beyond a single-user demo.
Admin Visibility
Dashboards for usage, latency, cost, quality, audit logs, and content analytics help operators see what is happening.
Deployability
Designed for local development and containerized deployment, with Docker Compose support and optional monitoring and tracing services.
Stack
Built custom rather than leaning on orchestration frameworks. Model, embedding, and reranker providers are configurable, and the system is designed to be operated, not just shown.
Interested in RAGForge?
A deeper technical walkthrough is available on request.